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Precipitation isoscapes have provided supporting data for numerous studies of water stable isotopes, alleviating the lack of observation data. However, the applicability of simulation data from global models to specific regional contexts remains a subject requiring further investigation, particularly concerning d-excess—an aspect often overlooked by prediction models. To bridge this gap, this study evaluates the performance of three mainstream precipitation isoscapes (OIPC3.2, RCWIP1, and RCWIP2) for the prediction of average annual δ2H, δ18O, and d-excess based on observations from the CHNIP database. The results show that while all three models can accurately reproduce δ2H and δ18O values, none are able to accurately match d-excess values. This disparity can be attributed to the absence of water-vapor source information in the models' input variables, a key determinant influencing d-excess outcomes. Additionally, it is noteworthy that OIPC3.2 stands out as the optimal choice for δ2H and δ18O estimations, while RCWIP2 exhibits progressive enhancements over RCWIP1 in d-excess estimations. This highlights the significance of selecting highly pluralistic information variables and recognizing the impact of error propagation in such models. As a result, the advancement of isoscapes in accurately and precisely depicting precipitation isotopes, particularly d-excess, necessitates further refinement. Future avenues for improvement might involve the incorporation of water-vapor source-clustering methodologies, the selection of information-rich variables, and the autonomous construction of a dedicated d-excess simulation. This research provides valuable insights for the further refining of isoscape modeling in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: The H9N2 avian influenza virus has spread to the whole world and become one of the dominant subtype influenza viruses in chickens in China. H9N2 virus could not only result in great economic losses by reducing egg production but also serve as a gene vector to provide its gene segments to other emerging severe influenza A viruses to cause higher mortality and more serious consequences. Thus, developing models to predict H9N2 status should be given priority. Our main aim was to use the machine learning method (XGBoost classification algorithm) and regular production data (laying rate and mortality) to predict the occurrence and development of H9N2 in laying hen houses and evaluate their performance in disease prediction. Additionally, we assessed the working ability of the framework with different time frames to predict H9N2 status in advance within a 3-day period. We found that this framework could work well in prediction with high accuracy and sensitivity, and with more information introduced into the model, more "don't care values" would be added into datasets to affect model performance by forming attribute noise. Besides, our study recommended efficient frameworks and models for H9N2 status prediction and provided practical potential uses in the livestock and poultry industry. The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. [ABSTRACT FROM AUTHOR]

Objective: To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province., Methods: The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables., Results: Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC ( t = 19.862, P < 0.05), TSS ( t = 10.140, P < 0.05) and Kappa values ( t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models., Conclusions: Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Purpose: We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. Patients and Methods: This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. Results: A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P< 0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). Conclusion: Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance. [ABSTRACT FROM AUTHOR]

Background: Recent studies have suggested that insulin resistance (IR) contributes to the development of cardiovascular diseases (CVD), and the estimated glucose disposal rate (eGDR) is considered to be a reliable surrogate marker of IR. However, most existing evidence stems from studies involving diabetic patients, potentially overstating the effects of eGDR on CVD. Therefore, the primary objective of this study is to examine the relationship of eGDR with incidence of CVD in non-diabetic participants., Method: The current analysis included individuals from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD and diabetes mellitus but had complete data on eGDR at baseline. The formula for calculating eGDR was as follows: eGDR (mg/kg/min) = 21.158 - (0.09 × WC) - (3.407 × hypertension) - (0.551 × HbA1c) [WC (cm), hypertension (yes = 1/no = 0), and HbA1c (%)]. The individuals were categorized into four subgroups according to the quartiles (Q) of eGDR. Crude incidence rate and hazard ratios (HRs) with 95% confidence intervals (CIs) were computed to investigate the association between eGDR and incident CVD, with the lowest quartile of eGDR (indicating the highest grade of insulin resistance) serving as the reference. Additionally, the multivariate adjusted restricted cubic spine (RCS) was employed to examine the dose-response relationship., Results: We included 5512 participants in this study, with a mean age of 58.2 ± 8.8 years, and 54.1% were female. Over a median follow-up duration of 79.4 months, 1213 incident CVD cases, including 927 heart disease and 391 stroke, were recorded. The RCS curves demonstrated a significant and linear relationship between eGDR and all outcomes (all P for non-linearity > 0.05). After multivariate adjustment, the lower eGDR levels were founded to be significantly associated with a higher risk of CVD. Compared with participants with Q1 of eGDR, the HRs (95% CIs) for those with Q2 - 4 were 0.88 (0.76 - 1.02), 0.69 (0.58 - 0.82), and 0.66 (0.56 - 0.79). When assessed as a continuous variable, per 1.0-SD increase in eGDR was associated a 17% (HR: 0.83, 95% CI: 0.78 - 0.89) lower risk of CVD, with the subgroup analyses indicating that smoking status modified the association (P for interaction = 0.012). Moreover, the mediation analysis revealed that obesity partly mediated the association. Additionally, incorporating eGDR into the basic model considerably improve the predictive ability for CVD., Conclusion: A lower level of eGDR was found to be associated with increased risk of incident CVD among non-diabetic participants. This suggests that eGDR may serve as a promising and preferable predictor and intervention target for CVD., (© 2024. The Author(s).)

Objective: Various population pharmacokinetic (PPK) models have been established to help determine the appropriate dosage of docetaxel, however, no clear consensus on optimal dosing has been achieved. The purpose of this study is to perform an external evaluation of published models in order to test their predictive performance, and to find an appropriate PPK model for Chinese breast cancer patients., Methods: A systematic literature search of docetaxel PPK models was performed using PubMed, Web of Science, China National Knowledge Infrastructure, and WanFang databases. The predictive performance of eleven identified models was evaluated using prediction-based and simulation-based diagnostics on an independent dataset (112 docetaxel concentrations from 56 breast cancer patients). The -2×log (likelihood) and Akaike information criterion were also calculated to evaluate model fit., Results: The median prediction error of eight of the eleven models was less than 10%. The model fitting results showed that the three-compartment model of Bruno et al. had the best prediction performance and that the three compartment model of Wang et al. had the best simulation effect. Furthermore, although the covariates that significantly affect PK parameters were different between them, seven models demonstrated that docetaxel PK parameters were influenced by liver function., Conclusions: Three compartment PPK models may be predictive of optimal docetaxel dosage for Chinese breast cancer patients. However, for patients with impaired liver function, the choice of which model to use to predict the blood concentration of docetaxel still requires great care., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions., Methods: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses., Results: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R t curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R t below 1.0, as well as reducing the number of peak cases and final affected population., Conclusions: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus., (© 2023. The Author(s).)

The accurate evaluation of human epidermal growth factor receptor 2 (HER2) is crucial for successful trastuzumab-based therapy in individuals with gastric cancer (GC). The present study, involving a retrospective cohort (N = 2865) from Wuhan Union Hospital and a prospective cohort (N = 392) from Renmin Hospital of Wuhan University, evaluated the benefits of clinical features using random forest and logistic regression models for the detection of HER2 status in patients with GC. Patients from the Union cohort were randomly assigned to either a training (N = 2005) or an internal validation (N = 860) group. Data processing and feature selection were done in Python, which was also used to build random forest and logistic regression models for the prediction of HER2 overexpression. The Renmin cohort (N = 392) was used as the external validation group. Ten features were closely correlated with HER2 overexpression, including age, albumin/globulin ratio, globulin, activated partial thromboplastin time, tumor stage, node stage, tumor node metastasis stage, tumor size, tumor differentiation, and neuron-specific enolase (NSE). Random forest and logistic regression had areas under the curve (AUC) of 0.9995 and 0.6653 in the training group and 0.923 and 0.667 in the internal validation group, respectively. When the two predictive models were validated using data from the Renmin cohort, random forest and logistic regression had AUCs of 0.9994 and 0.627, respectively. This is the first multicenter study to predict HER2 overexpression in individuals with GC, based on clinical variables. The random forest model significantly outperformed the logistic regression model., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The elderly patients admitted to cardiac intensive care unit (CICU) are at relatively high risk for developing delirium. A simple and reliable predictive model can benefit them from early recognition of delirium followed by timely and appropriate preventive strategies. To explore the role of frailty in delirium prediction and develop and validate a delirium predictive model including frailty for elderly patients in CICU. A prospective, observational cohort study. CICU at China–Japan Friendship Hospital from March 1, 2022 to August 25, 2022 (derivation cohort); CICU at Beijing Anzhen Hospital affiliated to Capital Medical University from March 14, 2023 to May 8, 2023 (external validation cohort). A total of 236 and 90 participants were enrolled in the derivation and external validation cohorts, respectively. Participants in the derivation cohort were assigned into either the delirium (n = 70) or non-delirium group (n = 166) based on the occurrence of delirium. The simplified Chinese version of the Confusion Assessment Method for the Diagnosis of Delirium in the Intensive Care Unit was used to assess delirium twice a day at 8:00–10:00 and 18:00–20:00 until the onset of delirium or discharge from the CICU. Frailty was assessed using the FRAIL scale during the first 24 h in the CICU. Other possible risk factors were collected prospectively through patient interviews and medical records review. After processing missing data via multiple imputations, univariate analysis and bootstrapped forward stepwise logistic regression were performed to select optimal predictors and develop the models. The models were internally validated using bootstrapping and evaluated comprehensively via discrimination, calibration, and clinical utility in both the derivation and external validation cohorts. The study developed D-FRAIL predictive model using FRAIL score, hearing impairment, Acute Physiology and Chronic Health Evaluation-II score, and fibrinogen. The area under the receiver operating characteristic curve (AUC) was 0.937 (95% confidence interval [CI]: 0.907–0.967) and 0.889 (95%CI: 0.840–0.938) even after bootstrapping in the derivation cohort. Inclusion of frailty was demonstrated to improve the model performance greatly with the AUC increased from 0.851 to 0.937 (p < 0.001). In the external validation cohort, the AUC of D-FRAIL model was 0.866 (95%CI: 0.782–0.907). Calibration plots and decision curve analysis suggested good calibration and clinical utility of the D-FRAIL model in both the derivation and external validation cohorts. For elderly patients in the CICU, FRAIL score is an independent delirium predictor and the D-FRAIL model demonstrates superior performance in predicting delirium. [ABSTRACT FROM AUTHOR]

Objectives: Appropriate dosage of vancomycin is critical for safety and efficacy in treating infectious diseases. Various population pharmacokinetic (PPK) models have been established. To lay a foundation for the clinical application, it is important to perform external validation of the published model. The aim of this study was to find the most suitable vancomycin PPK model for treatment of infection in China amongst all studied models developed for adults., Methods: A systematic literature search of published population vancomycin pharmacokinetic analyses was performed using the PubMed database. The identified models were evaluated in ten independent cohorts from China. Nonlinear mixed-effects modeling (NONMEM) was used for data analysis. The median prediction error (MPE), the distribution of prediction error (PE), and normalized prediction distribution errors (NPDE) were calculated and described. Predictive performance was evaluated by bias and accuracy., Results: A total of 449 vancomycin concentrations from 397 infected participants were used for external validation. The MPE of the three models from the Chinese population was less than 10%. Half of the models had an acceptable MPE. For patients with different site infections and different renal functions, each model differed in its predictive ability to some extent. The NPDE results showed that all models had an obvious bias., Conclusions: None of the models had good performance in both prediction- and simulation-based diagnostics. Carrying out sufficient external validation of the PPK model before clinical application is of utmost importance. In clinical practice, the model's population closest to the application population should be used., (Copyright © 2022. Published by Elsevier Ltd.)

Objective: To compare the predictive performance of five handgrip strengths for cardiovascular disease (CVD) risk factors., Methods: A total of 804 Chinese middle-aged community residents' health medical examinations were collected. The absolute handgrip strength was denoted as HGS. HGS/body weight (HGS/BW), HGS/body mass index (HGS/BMI), HGS/lean body mass (HGS/LBM), and HGS/muscle mass (HGS/MM) represented relative handgrip strength (RHGS). To assess predictive performance, receiver operating characteristic (ROC) curves and the area under the curve (AUC) were constructed., Results: HGS was not associated with most CVD risk biomarkers; however, RHGS showed a negative correlation trend after controlling for covariates (sex, age, smoking, and exercise). HGS/BMI and HGS/BW had better AUCs for predicting CVD risk factors than HGS/LBM or HGS/MM. HGS/BMI and HGS/BW can successfully predict all CVD risk factors in men with AUCs 0.55-0.65; similarly, women may effectively predict arteriosclerosis, hyperglycemia, hyperuricemia, and metabolic syndrome with AUCs 0.59-0.64, all p < 0.05. The optimal HGS/BW cut-off points for identifying different CVD risk factors were 0.59-0.61 in men and 0.41-0.45 in women, while the HGS/BMI were 1.75-1.79 in men and 1.11-1.15 in women., Conclusions: Almost all CVD risk biomarkers and CVD risk factors were unrelated to HGS. There is, however, a significant inverse relationship between RHGS and CVD risk factors. HGS/BMI or HGS/BW should be recommended to be the best choice for predicting the risk of CVD risk factors in five expressions of handgrip strength. We also acquired the recommended optimal cut-off points of HGS/BMI and HGS/BW for predicting CVD risk factors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gao, Huang, Ni, Feng, Yu, Huang, Luo, Jiang and Wang.)

Background: This study aims to validate the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria in Chinese patients with atrial fibrillation (AF) and acute coronary syndrome (ACS) or undergoing percutaneous coronary intervention (PCI) who received both oral anticoagulants (OAC) and antiplatelet therapy (APT)., Methods: 930 consecutive patients with AF and ACS or undergoing PCI receiving both OAC and APT were recruited and followed up for 1 year. The primary endpoint was BARC type 3 or 5 bleeding. The secondary endpoints included BARC type 2, 3, or 5 bleeding, TIMI major bleeding, TIMI major or minor bleeding, and major adverse cardiovascular events (a composite of all-cause death, stroke, non-central nervous system embolism, myocardial infarction, definite or probable stent thrombosis, and target vessel revascularization). Cox regressions were performed to evaluate the association between the ARC-HBR score and outcomes. Discrimination was evaluated through analysis of the receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI)., Results: Compared to patients with no HBR other than OAC, patients with HBR besides OAC tended to have more comorbidities and worse outcomes. The ARC-HBR score was significantly associated with the primary and secondary endpoints, both as a continuous variable and as a categorical variable. The ARC-HBR score performed better than the HAS-BLED score (c-statistic: 0.692 vs. 0.575, NRI = 0.313, IDI = 0.061) and the PRECISE-DAPT score (c-statistic: 0.692 vs. 0.616, NRI = 0.393, IDI = 0.049)., Conclusions: In patients with AF and ACS or undergoing PCI receiving both OAC and APT, the ARC-HBR score was a significant predictor of 1-year bleeding and ischemic endpoints. The ARC-HBR score performed better than the HAS-BLED score and the PRECISE-DAPT score in BARC type 3 or 5 bleeding prediction., (Copyright © 2021. Published by Elsevier Ltd.)

Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to determine whether genetic information can improve the prediction of incident hypertension using machine learning approaches in a prospective study. The study recruited 4592 subjects without hypertension at baseline from a cohort study conducted in rural China. A polygenic risk score (PGGRS) was calculated using 13 SNPs. According to a ratio of 7:3, subjects were randomly allocated to the train and test datasets. Models with and without the PGGRS were established using the train dataset with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) methods. The discrimination and reclassification of models were estimated using the test dataset. The PGGRS showed a significant association with the risk of incident hypertension (HR (95% CI), 1.046 (1.004, 1.090), P = 0.031) irrespective of baseline blood pressure. Models that did not include the PGGRS achieved AUCs (95% CI) of 0.785 (0.763, 0.807), 0.790 (0.768, 0.811), 0.838 (0.817, 0.857), and 0.854 (0.835, 0.873) for the Cox, ANN, RF, and GBM methods, respectively. The addition of the PGGRS led to the improvement of the AUC by 0.001, 0.008, 0.023, and 0.017; IDI by 1.39%, 2.86%, 4.73%, and 4.68%; and NRI by 25.05%, 13.01%, 44.87%, and 22.94%, respectively. Incident hypertension risk was better predicted by the traditional+PGGRS model, especially when machine learning approaches were used, suggesting that genetic information may have the potential to identify new hypertension cases using machine learning methods in resource-limited areas. CLINICAL TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375 ., (© 2021. The Author(s), under exclusive licence to The Japanese Society of Hypertension.)

Background: We investigated whether age influences the predictiveness of genetic risk score (GRS) for prostate cancer (PCa) in a Chinese hospital-based biopsy cohort., Methods: We included consecutive patients who underwent prostate biopsies in two tertiary centers between 2012 and 2014. GRS was calculated using 24 PCa-associated genetic variants and its predictiveness was assessed by area under curve (AUC)., Results: Of 1120 men tested, 724 with prostate-specific antigen (PSA) < 20 ng/ml were selected for further analysis. Patients were divided into 3 groups by age cutoffs at 60 and 70 years. GRS significantly predicted PCa for all patients (AUC: 0.561; 95% CI: 0.514-0.609) and was an independent predictor in multivariate analysis for the 60-70 year-olds (AUC: 0.612, 95% CI: 0.541-0.684), but not for patients aged < 60 years or ≥ 70 years. For PCa with Gleason score ≥ 7, GRS discriminative ability was 0.582 (95% CI=0.527-0.637) for all patients, and 0.647 (95% CI: 0.541-0.684) for the 60-70 year-old group., Conclusion: GRS significantly increased clinical prediction of PCa and high-grade disease in Chinese men aged 60-70 years, which implies that men in this age group would benefit most from genetic testing.