Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center.

PURPOSE: mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy. METHODS: Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information. RESULTS: Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa. CONCLUSION: mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.

Pancreas Transplantation Outcome Predictions-PTOP: A Risk Prediction Tool for Pancreas and Pancreas-Kidney Transplants Based on a European Cohort.

Background: For patients with complicated type 1 diabetes having, for example, hypoglycemia unawareness and end-stage renal disease because of diabetic nephropathy, combined pancreas and kidney transplantation (PKT) is the therapy of choice. However, the shortage of available grafts and complex impact of risk factors call for individualized, impartial predictions of PKT and pancreas transplantation (PT) outcomes to support physicians in graft acceptance decisions. Methods: Based on a large European cohort with 3060 PKT and PT performed between 2006 and 2021, the 3 primary patient outcomes time to patient mortality, pancreas graft loss, and kidney graft loss were visualized using Kaplan-Meier survival curves. Multivariable Cox proportional hazards models were developed for 5- and 10-y prediction of outcomes based on 26 risk factors. Results: Risk factors associated with increased mortality included previous kidney transplants, rescue allocations, longer waiting times, and simultaneous transplants of other organs. Increased pancreas graft loss was positively associated with higher recipient body mass index and donor age and negatively associated with simultaneous transplants of kidneys and other organs. Donor age was also associated with increased kidney graft losses. The multivariable Cox models reported median C-index values were 63% for patient mortality, 62% for pancreas loss, and 55% for kidney loss. Conclusions: This study provides an online risk tool at https://riskcalc.org/ptop for individual 5- and 10-y post-PKT and PT patient outcomes based on parameters available at the time of graft offer to support critical organ acceptance decisions and encourage external validation in independent populations.

Prevalence, lifestyle, and risk factors of erectile dysfunction, premature ejaculation, and low libido in middle-aged men: first results of the Bavarian Men's Health-Study.

BACKGROUND: Erectile dysfunction (ED), premature ejaculation (PE), and low libido (LL) are reported as the most common male sexual dysfunctions. OBJECTIVE: To evaluate the prevalence of ED, PE, and LL and associations with lifestyle risk factors and comorbidities in middle-aged men. MATERIALS AND METHODS: This study included a population-based random sample of 2500 50-year-old men who completed validated questionnaires, including the International Index of Erectile Function, the Erection Hardness Score, the Sexual Complaints Screener, and further questionnaires. Multiple logistic regression of outcomes ED, PE, and LL was used to model the association with explanatory factors. RESULTS: The prevalence of at least one sexual dysfunction was 30%. 21%, 5.2%, and 7.2% of men had ED, PE, and LL, respectively. The risk of ED increased with PE (odds ratio [OR]: 1.94, 95% confidence interval [95%CI]: 1.22-3.08), LL (OR: 2.04, 95%CI: 1.26-3.29), higher waist circumference (OR: 2.23, 95%CI: 1.67-2.96), and lower urinary tract symptoms (LUTS) (OR: 1.88, 95%CI: 1.39-2.55), partnership was associated with a lower risk (OR: 0.57, 95%CI: 0.39-0.85). The risk of PE increased with ED (OR: 1.94, 95%CI: 1.23-3.07), partnership (OR:5.42, 95%CI: 1.30-22.60), depression (OR: 2.37, 95%CI: 1.09-5.14), and LUTS (OR: 2.42, 95%CI: 1.52-3.87), and decreased with physical activity (OR: 0.44, 95%CI: 0.21-0.93). The risk of LL increased with ED (OR: 2.09, 95%CI: 1.31-3.34) and poorer self-rated health (OR: 2.97, 95%CI: 1.54-5.71). DISCUSSION AND CONCLUSIONS: Roughly one in three 50-year-old men experience some form of sexual dysfunction and risk factors identified in this study underline the multifactorial nature of ED, PE, and LL. Many risk factors are modifiable which underlines the role of patient education. Modifiable risk factors should be addressed in patient education and men should take active measures to remove the risk posed by these factors.

Diabetes, Obesity, and Pathological Upstaging in Renal Cell Carcinoma: Results From a Large Multi-institutional Consortium.

PURPOSE: We sought to determine whether clinical risk factors and morphometric features on preoperative imaging can be utilized to identify those patients with cT1 tumors who are at higher risk of upstaging (pT3a). MATERIALS AND METHODS: We performed a retrospective international case-control study of consecutive patients treated surgically with radical or partial nephrectomy for nonmetastatic renal cell carcinoma (cT1 N0) conducted between January 2010 and December 2018. Multivariable logistic regression models were used to study associations of preoperative risk factors on pT3a pathological upstaging among all patients, as well as subsets with those with preoperative tumors ≤4 cm, renal nephrometry scores, tumors ≤4 cm with nephrometry scores, and clear cell histology. We also examined association with pT3a subsets (renal vein, sinus fat, perinephric fat). RESULTS: Among the 4,092 partial nephrectomy and 2,056 radical nephrectomy patients, pathological upstaging occurred in 4.9% and 23.3%, respectively. Among each group independent factors associated with pT3a upstaging were increasing preoperative tumor size, increasing age, and the presence of diabetes. Specifically, among partial nephrectomy subjects diabetes (OR=1.65; 95% CI 1.17, 2.29), male sex (OR=1.62; 95% CI 1.14, 2.33), and increasing BMI (OR=1.03; 95% CI 1.00, 1.05 per 1 unit BMI) were statistically associated with upstaging. Subset analyses identified hilar tumors as more likely to be upstaged (partial nephrectomy OR=1.91; 95% CI 1.12, 3.16; radical nephrectomy OR=2.16; 95% CI 1.44, 3.25). CONCLUSIONS: Diabetes and higher BMI were associated with pathological upstaging, as were preoperative tumor size, increased age, and male sex. Similarly, hilar tumors were frequently upstaged.

Prevalence and determinants of decision regret in long-term prostate cancer survivors following radical prostatectomy.

BACKGROUND: Patients with localized prostate cancer (PC) are faced with a wide spectrum of therapeutic options at initial diagnosis. Following radical prostatectomy (RP), PC patients may experience regret regarding their initial choice of treatment, especially when oncological and functional outcomes are poor. Impacts of psychosocial factors on decision regret, especially after long-term follow-up, are not well understood. This study aimed to investigate the prevalence and determinants of decision regret in long-term PC survivors following RP. METHODS: 3408 PC survivors (mean age 78.8 years, SD = 6.5) from the multicenter German Familial PC Database returned questionnaires after an average of 16.5 (SD = 3.8) years following RP. The outcome of decision regret concerning the initial choice of RP was assessed with one item from the Decision Regret Scale. Health-related quality of life (HRQoL), PC-anxiety, PSA-anxiety, as well as anxiety and depressive symptoms were considered for independent association with decision regret via multivariable logistic regression. RESULTS: 10.9% (373/3408) of PC survivors reported decision regret. Organ-confined disease at RP (OR 1.39, 95%CI 1.02-1.91), biochemical recurrence (OR 1.34, 1.00-1.80), low HRQoL (OR 1.69,1.28-2.24), depressive symptoms (OR 2.32, 1.52-3.53), and prevalent PSA anxiety (OR 1.88,1.17-3.01) were significantly associated with increased risk of decision regret. Shared decision-making reduced the odds of decision regret by 40% (OR 0.59, 0.41-0.86). CONCLUSIONS: PC survivors may experience decision regret even after 16 years following RP. Promoting shared decision-making in light of both established and novel, potentially less invasive treatments at initial diagnosis may help mitigate long-term regret. Awareness regarding patients showing depressive symptoms or PSA anxiety should be encouraged to identify patients at risk of decision regret in need of additional psychological support.

Kidney Transplantation Outcome Predictions (KTOP): A Risk Prediction Tool for Kidney Transplants from Brain-dead Deceased Donors Based on a Large European Cohort.

BACKGROUND: European kidney donation shortages mandate efficient organ allocation by optimizing the prediction of success for individual recipients. OBJECTIVE: To develop the first European online risk tool for kidney transplant outcomes on the basis of recipient-only and recipient plus donor characteristics. DESIGN, SETTING, AND PARTICIPANTS: We used individual recipient and donor risk factors and three outcomes (death, death with functioning graft [DWFG], and graft loss) for 32 958 transplants within the Eurotransplant kidney allocation system and the Eurotransplant senior program between January 2006 and May 2018 in eight European countries to develop and validate a risk tool. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Cox proportional-hazards models were used to analyze the association of risk factors with overall patient mortality, and proportional subdistribution hazard regression models for their association with graft loss and DWFG. Prediction models were developed with recipient-only and recipient-donor risk factors. Sensitivity analyses based on time-specific area under the receiver operating characteristic curve (AUC) with leave-one-country-out validation were performed and calibration plots were generated. RESULTS AND LIMITATIONS: The 10-yr cumulative incidence rate was 37% for mortality, 12% for DWFG, and 41% for graft loss. In recipient-donor models the leading risk factors for mortality were recipient diabetes (hazard ratio [HR] 10.73), retransplantation (HR 3.08 per transplant), and recipient age (HR 1.08). Effects were similar for DWFG. For graft loss, diabetes (subdistributional HR [SHR] 1.32), increased donor age (SHR 1.02), and prolonged cold ischemia time (SHR 1.02) had increased SHRs. All p values were <0.001. CONCLUSIONS: Previously identified risk factors for outcomes following kidney transplants allow for outcome prediction with 10-yr AUC values of up to 0.81. PATIENT SUMMARY: Using European data, we estimated individual risks to predict the success of kidney transplants and support physicians in decision-making. An online tool is now available (https://riskcalc.org/ktop/) for predicting kidney transplant outcomes both before and after a donor has been identified.

Treatment decision regret in long-term survivors after radical prostatectomy: a longitudinal study.

OBJECTIVES: To investigate prevalence, course, and predictors of longitudinal decision regret in long-term prostate cancer (PCa) survivors treated by radical prostatectomy (RP). PATIENTS AND METHODS: A total of 1003 PCa survivors from the multicentre German Familial PCa Database completed questionnaires on average 7 years after RP in 2007 and at follow-up 13 years later in 2020. Patients completed standardised patient-reported outcome measures on decision regret, decision-making, health-related quality of life, and psychosocial factors. Hierarchical multivariable logistic regression was used to assess predictors of longitudinal decision regret. RESULTS: Decision regret increased significantly over time (9.0% after 6.9 years in 2007 and 12% after 19 years in 2020; P = 0.009). Favourable localised PCa (odds ratio [OR] 1.97, 95% confidence interval [CI] 1.05-3.68), decision regret in 2007 (OR 6.38, 95% CI 3.55-11.47), and a higher depression score (OR 1.37, 95% CI 1.03-1.83) were associated with decision regret in 2020. Shared decision-making (OR 0.55, 95% CI 0.33-0.93) was associated with less decision regret. CONCLUSION: The findings of the present study underline the perseverance of decision regret in long-term PCa survivors and the definitive need for involving patients in the decision-making process to mitigate regret over the long term.

Prostate cancer-related anxiety among long-term survivors after radical prostatectomy: A longitudinal study.

BACKGROUND: Prostate cancer (PC)-related anxiety is associated with clinically significant declines in health-related quality of life (HRQoL) and psychological well-being. This longitudinal study investigates course and predictors of PC-related anxiety in long-term PC survivors treated by radical prostatectomy (RP). METHODS: Two thousand nine hundred and three survivors from the multicenter German Familial PC Database completed the Memorial Anxiety Scale for PC on average 11 years after RP at the initial assessment in 2015 and then 5 years later. Hierarchical multiple linear regression was used to assess predictors of PC-related anxiety at follow-up. RESULTS: PC-related anxiety remained stable over the 5 years. In hierarchical multiple linear regression, longitudinal predictors of PC-related anxiety 5 years later included a lower level of education (beta: -0.035, p = 0.019), biochemical recurrence (BCR; beta: 0.054, p = 0.002), late BCR (beta: 0.054, p < 0.001), PC anxiety at initial assessment (beta: 0.556, p < 0.001), HRQoL (beta: -0.076, p < 0.001), depression and anxiety symptoms (beta: 0.072, p = 0.001; beta: 0.165, p < 0.001). Predictors of prostate-specific antigen (PSA) anxiety 5 years later included late BCR (beta: 0.044, p = 0.019), PSA anxiety at initial assessment (beta: 0.339, p < 0.001), depression and anxiety symptoms (beta: 0.074, p = 0.008; beta: 0.191, p < 0.001), and treatment decision regret (beta: 0.052, p = 0.006). CONCLUSION: PC-related anxiety remains a burden to survivors many years after diagnosis and treatment. The respective disease-specific anxiety was the strongest predictor of this anxiety 5 years later, which emphasizes the need of screening and monitoring in a timely manner for PC-related anxiety. Treating urologists should screen, identify, and monitor patients at risk for targeted referrals to psychosocial services.

Accommodating population differences when validating risk prediction models.

Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross-validation in the data used for model development. However, several differences between the populations that gave rise to the training and the validation data can lead to seemingly poor performance of a risk model. In this paper we formalize the notions of "similarity" or "relatedness" of the training and validation data, and define reproducibility and transportability. We address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. When individual level information from both the training and validation data sets is available, we propose and study weighted versions of the validation metrics that adjust for differences in the risk factor distributions and in outcome verification between the training and validation data to provide a more comprehensive assessment of model performance. We provide conditions on the risk model and the populations that gave rise to the training and validation data that ensure a model's reproducibility or transportability, and show how to check these conditions using weighted and unweighted performance measures. We illustrate the method by developing and validating a model that predicts the risk of developing prostate cancer using data from two large prostate cancer screening trials.

Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders.

Background: The focus of many studies is to estimate the effect of risk factors on outcomes, yet results may be dependent on the choice of other risk factors or potential confounders to include in a statistical model. For complex and unexplored systems, such as the COVID-19 spreading process, where a priori knowledge of potential confounders is lacking, data-driven empirical variable selection methods may be primarily utilized. Published studies often lack a sensitivity analysis as to how results depend on the choice of confounders in the model. This study showed variability in associations of short-term air pollution with COVID-19 mortality in Germany under multiple approaches accounting for confounders in statistical models. Methods: Associations between air pollution variables PM2.5, PM10, CO, NO, NO2, and O3 and cumulative COVID-19 deaths in 400 German districts were assessed via negative binomial models for two time periods, March 2020-February 2021 and March 2021-February 2022. Prevalent methods for adjustment of confounders were identified after a literature search, including change-in-estimate and information criteria approaches. The methods were compared to assess the impact on the association estimates of air pollution and COVID-19 mortality considering 37 potential confounders. Results: Univariate analyses showed significant negative associations with COVID-19 mortality for CO, NO, and NO2, and positive associations, at least for the first time period, for O3 and PM2.5. However, these associations became non-significant when other risk factors were accounted for in the model, in particular after adjustment for mobility, political orientation, and age. Model estimates from most selection methods were similar to models including all risk factors. Conclusion: Results highlight the importance of adequately accounting for high-impact confounders when analyzing associations of air pollution with COVID-19 and show that it can be of help to compare multiple selection approaches. This study showed how model selection processes can be performed using different methods in the context of high-dimensional and correlated covariates, when important confounders are not known a priori. Apparent associations between air pollution and COVID-19 mortality failed to reach significance when leading selection methods were used. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-022-00657-5.

External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort.

Background: External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed. Objective: To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC). Design setting and participants: We used data from the prospective, population-based STHLM3 screening study, performed in 2012-2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included. Outcome measurements and statistical analysis: Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models. Results and limitations: Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1-7.1%) and 20% (IQR 15-28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman's r = 0.55). Using the RPCRC's recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72-0.76) and the PBCG-RC of 0.70 (95% CI 0.68-0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03-1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68-0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs decreased. Conclusions: Assessment of calibration is essential to ensure the clinical value of risk prediction tools. The PBCG-RC provided clinical benefit in its current version online. On the contrary, the RPCRC cannot be recommended in this setting. Patient summary: Predicting the probability of finding prostate cancer on biopsy differed between two assessed risk calculators. After recalibration, the agreement of the models improved, and both were shown to be clinically useful.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials.

BACKGROUND: A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort. METHODS: A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator. RESULTS: The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa. CONCLUSIONS: A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

Angpt2/Tie2 autostimulatory loop controls tumorigenesis.

Invasive nonfunctioning (NF) pituitary neuroendocrine tumors (PitNETs) are non-resectable neoplasms associated with frequent relapses and significant comorbidities. As the current therapies of NF-PitNETs often fail, new therapeutic targets are needed. The observation that circulating angiopoietin-2 (ANGPT2) is elevated in patients with NF-PitNET and correlates with tumor aggressiveness prompted us to investigate the ANGPT2/TIE2 axis in NF-PitNETs in the GH3 PitNET cell line, primary human NF-PitNET cells, xenografts in zebrafish and mice, and in MENX rats, the only autochthonous NF-PitNET model. We show that PitNET cells express a functional TIE2 receptor and secrete bioactive ANGPT2, which promotes, besides angiogenesis, tumor cell growth in an autocrine and paracrine fashion. ANGPT2 stimulation of TIE2 in tumor cells activates downstream cell proliferation signals, as previously demonstrated in endothelial cells (ECs). Tie2 gene deletion blunts PitNETs growth in xenograft models, and pharmacological inhibition of Angpt2/Tie2 signaling antagonizes PitNETs in primary cell cultures, tumor xenografts in mice, and in MENX rats. Thus, the ANGPT2/TIE2 axis provides an exploitable therapeutic target in NF-PitNETs and possibly in other tumors expressing ANGPT2/TIE2. The ability of tumor cells to coopt angiogenic signals classically viewed as EC-specific expands our view on the microenvironmental cues that are essential for tumor progression.

Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma.

Growing evidence has indicated that pyroptosis functions in the development of cancer. Nonetheless, specific roles of pyroptosis-related genes in tumor progression, immune response, prognosis, and immunotherapy have not been thoroughly elucidated. After a comprehensive evaluation of pyroptosis genes, unsupervised clustering was performed to generate three distinct clusters from hepatocellular carcinoma (HCC) samples. Three distinct pyroptosis-related molecular subtypes comprising three gene clusters that had differential prognostic effects on patient survival were then identified. Immune characteristics analyses revealed diversified immune cell infiltration among the subtypes. Two clusters served as immune-hot phenotypes associated with significantly poorer survival compared to a remaining third immune-cold cluster. Among these, the immune-hot clusters were characterized by abundant adaptive immune cell infiltration, active CD4+ and CD8+ T cells, high total leukocyte counts and tumor growth status, and lower Th17 cell and M2 macrophage densities. Then, risk scores indicated that low-risk patients were more sensitive to anti-tumor therapy. Subsequently, we found a significant correlation between pyroptosis and prognosis in HCC and that pyroptosis genes drive the heterogeneity of the tumor microenvironment. The risk scoring system, based on pyroptosis-related differentially expressed genes, was established to evaluate the individual outcomes and contribute to new insights into the molecular characterization of pyroptosis-related subtypes.

Defining the Impact of Family History on Detection of High-grade Prostate Cancer in a Large Multi-institutional Cohort.

BACKGROUND: The risk of high-grade prostate cancer, given a family history of cancer, has been described in the general population, but not among men selected for prostate biopsy in an international cohort. OBJECTIVE: To estimate the risk of high-grade prostate cancer on biopsy based on a family history of cancer. DESIGN, SETTING, AND PARTICIPANTS: This is a multicenter study of men undergoing prostate biopsy from 2006 to 2019, including 12 sites in North America and Europe. All sites recorded first-degree prostate cancer family histories; four included more detailed data on the number of affected relatives, second-degree relatives with prostate cancer, and breast cancer family history. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable logistic regressions evaluated odds of high-grade (Gleason grade group ≥2) prostate cancer. Separate models were fit for family history definitions, including first- and second-degree prostate cancer and breast cancer family histories. RESULTS AND LIMITATIONS: A first-degree prostate cancer family history was available for 15 799 men, with a more detailed family history for 4617 (median age 65 yr, both cohorts). Adjusted odds of high-grade prostate cancer were 1.77 times greater (95% confidence interval [CI] 1.57-2.00, p < 0.001, risk ratio [RR] = 1.40) with first-degree prostate cancer, 1.38 (95% CI 1.07-1.77, p = 0.011, RR = 1.22) for second-degree prostate cancer, and 1.30 (95% CI 1.01-1.67, p = 0.040, RR = 1.18) for first-degree breast cancer family histories. Interaction terms revealed that the effect of a family history did not differ based on prostate-specific antigen but differed based on age. This study is limited by missing data on race and prior negative biopsy. CONCLUSIONS: Men with indications for biopsy and a family history of prostate or breast cancer can be counseled that they have a moderately increased risk of high-grade prostate cancer, independent of other risk factors. PATIENT SUMMARY: In a large international series of men selected for prostate biopsy, finding a high-grade prostate cancer was more likely in men with a family history of prostate or breast cancer.

Kidney Transplantation After Rescue Allocation-the Eurotransplant Experience: A Retrospective Multicenter Outcome Analysis.

BACKGROUND: At Eurotransplant (ET), kidneys are transferred to "rescue allocation" (RA), whenever the standard allocation (SA) algorithms Eurotransplant Kidney Allocation System (ETKAS) and Eurotransplant Senior Program (ESP) fail. We analyzed the outcome of RA. METHODS: Retrospective patient clinical and demographic characteristics association analyses were performed with graft outcomes for 2422 recipients of a deceased donor renal transplantation (DDRT) after RA versus 25 481 after SA from 71 centers across all ET countries from 2006 to 2018. RESULTS: Numbers of DDRTs after RA increased over the time, especially in Germany. RA played a minor role in ESP versus ETKAS (2.7% versus 10.4%). RA recipients and donors were older compared with SA recipients and donors, cold ischemia times were longer, waiting times were shorter, and the incidence of primary nonfunction was comparable. Among ETKAS recipients, HLA matching was more favorable in SA (mean 3.7 versus 2.5). In multivariate modeling, the incidence of graft loss in ETKAS recipients was reduced in RA compared with SA (subdistribution hazard ratio, 0.80; 95% confidence interval [0.70-0.91], P < 0.001), whereas other outcomes (mortality, death with functioning graft (DwFG)) were not significantly different. None of the 3 outcomes were significantly different when comparing RA with SA within the ESP program. CONCLUSIONS: Facing increased waiting times and mortality on dialysis due to donor shortage, this study reveals encouragingly positive DDRT outcomes following RA. This supports the extension of RA to more patients and as an alternative tool to enable transplantation in patients in countries with prohibitively long waiting times or at risk of deterioration.

Universal thermal climate index associations with mortality, hospital admissions, and road accidents in Bavaria.

When meteorological conditions deviate from the optimal range for human well-being, the risks of illness, injury, and death increase, and such impacts are feared in particular with more frequent and intense extreme weather conditions resulting from climate change. Thermal indices, such as the universal thermal climate index (UTCI), can better assess human weather-related stresses by integrating multiple weather components. This paper quantifies and compares the seasonal and spatial association of UTCI with mortality, morbidity, and road accidents in the federal state of Bavaria, Germany. Linear regression was applied to seasonally associate daily 56 million hospital admissions and 2.5 million death counts (1995-2015) as well as approximately 930,000 road accidents and 1.7 million people injured (2002-2015) with spatially interpolated same day- and lagged- (up to 14 days) average UTCI values. Additional linear regressions were performed stratifying by age, gender, region, and district. UTCI effects were clear in all three health outcomes studied: Increased UTCI resulted in immediate (1-2 days) rises in morbidity and even more strongly in mortality in summer, and lagged (up to 14 days) decreases in fall, winter, and spring. The strongest UTCI effects were found for road accidents where increasing UTCI led to immediate decreases in daily road accidents in winter but pronounced increases in all other seasons. Differences in UTCI effects were observed e.g. between in warmer north-western regions (Franconia, more districts with heat stress-related mortality, but hospital admissions for lung, heart and external reasons decreasing with summer heat stress), the touristic alpine regions in the south (immediate effect of increasing UTCI on road accidents in summer), and the colder south-eastern regions (increasing hospital admissions for lung, heart and external reasons in winter with UTCI). Districts with high percentages of elderly suffered from higher morbidity and mortality, particularly in winter. The influences of UTCI as well as the spatial and temporal patterns of this influence call for improved infrastructure planning and resource allocation in the health sector.