Roc, the G-domain of the Parkinson's disease-associated protein LRRK2.

Mutation in leucine-rich repeat (LRR) kinase 2 (LRRK2) is a common cause of Parkinson's disease (PD). Aberrant LRRK2 kinase activity is associated with disease pathogenesis and thus it is an attractive drug target for combating PD. Intense efforts in the past nearly two decades have focused on the development of small-molecule inhibitors of the kinase domain of LRRK2 and have identified potent kinase inhibitors. However, most LRRK2 kinase inhibitors have shown adverse effects; therefore, alternative-mechanism-based strategies are desperately needed. In this review, we discuss the new insights gleaned from recent cryoelectron microscope (cryo-EM) structures of LRRK2 towards understanding the mechanisms of actions of LRRK2 and explore the potential new therapeutic avenues.

Predicting myosin heavy chain isoform from postdissection fiber length in human skeletal muscle fibers.

Experimental techniques in single human skeletal muscle cells require manual dissection. Unlike other mammalian species, human skeletal muscle is characterized by a heterogeneous mixture of myosin heavy chain (MHC) isoforms, typically used to define "fiber type," which profoundly influences cellular function. Therefore, it is beneficial to predict MHC isoform at the time of dissection, facilitating a more balanced fiber-type distribution from a potentially imbalanced sample. Although researchers performing single fiber dissection report predicting fiber-type based on mechanical properties of fibers upon dissection, a rigorous examination of this approach has not been performed. Therefore, we measured normalized fiber length (expressed as a % of the length of the bundle from which the fiber was dissected) in single fibers immediately following dissection. Six hundred sixty-eight individual fibers were dissected from muscle tissue samples from healthy, young adults to assess whether this characteristic could differentiate fibers containing MHC I ("slow" fiber type) or not ("fast" fiber type). Using receiver operator characteristic (ROC) curves, we found that differences in normalized fiber length (114 ± 13%, MHC I; 124 ± 17%, MHC IIA, P < 0.01) could be used to predict fiber type with excellent reliability (area under the curve = 0.72). We extended these analyses to include older adults (2 females, 1 male) to demonstrate the durability of this approach in fibers with likely different morphology and mechanical characteristics. We report that MHC isoform expression in human skeletal muscle fibers can be predicted at the time of dissection, regardless of origin.NEW & NOTEWORTHY A priori estimation of myosin heavy chain (MHC) isoform in individual muscle fibers may bias the relative abundance of fiber types in subsequent assessment. Until now, no standardized assessment approach has been proposed to characterize fibers at the time of dissection. We demonstrate an approach based on normalized fiber length that may dramatically bias a sample toward slow twitch (MHC I) or fast twitch (not MHC I) fiber populations.

Multiomics Analysis Revealed Colorectal Cancer Pathogenesis.

Gut microbiota-derived microbial compounds may link to the pathogenesis of colorectal cancer (CRC). However, the role of the host-microbiome in the incidence and progression of CRC remains elusive. We performed 16S rRNA sequencing, metabolomics, and proteomic studies on samples from 85 CRC patients who underwent colonoscopy examination and found two distinct changed patterns of microbiome in CRC patients. The relative abundances of Catabacter and Mogibacterium continuously increased from intramucosal carcinoma to advanced stages, whereas Clostridium, Anaerostipes, Vibrio, Flavonifractor, Holdemanella, and Hungatella were significantly altered only in intermediate lesions. Fecal metabolomics analysis exhibited consistent increases in bile acids, indoles, and urobilin as well as a decrease in heme. Serum metabolomics uncovered the highest levels of bilin, glycerides, and nucleosides together with the lowest levels of bile acids and amino acids in the stage of intermediate lesions. Three fecal and one serum dipeptides were elevated in the intermediate lesions. Proteomics analysis of colorectal tissues showed that oxidation and autophagy through the PI3K/Akt-mTOR signaling pathway contribute to the development of CRC. Diagnostic analysis showed multiomics features have good predictive capability, with AUC greater than 0.85. Our overall findings revealed new candidate biomarkers for CRC, with potentially significant diagnostic and prognostic capabilities.

Integrative analysis of an endoplasmic reticulum stress-related signature in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS-related signature of MM and assess its diagnostic value. METHODS: We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS-related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein-protein interaction network, a transcription factor-mRNA network, a miRNA-mRNA network and a drug-mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs. RESULTS: We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein-protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples. CONCLUSION: The nine ERS-related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma.

BACKGROUND: The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm. METHODS: Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software. RESULTS: We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis. CONCLUSIONS: In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Comparison of triglyceride glucose index and modified triglyceride glucose indices in prediction of cardiovascular diseases in middle aged and older Chinese adults.

BACKGROUND: Triglyceride and glucose (TyG) index, a surrogate marker of insulin resistance, has been validated as a predictor of cardiovascular disease. However, effects of TyG-related indices combined with obesity markers on cardiovascular diseases remained unknown. We aimed to investigate the associations between TyG index and modified TyG indices with new-onset cardiovascular disease and the time-dependent predictive capacity using a national representative cohort. METHODS: This study is a retrospective observational cohort study using data from China Health and Retirement Longitudinal Study (CHARLS) of 7 115 participants. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. The modified TyG indices were developed combining TyG with body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR). We used adjusted Cox proportional hazards regression to analyze the association and predictive capacity based on hazard ratio (HR) and Harrell's C-index. RESULTS: Over a 7-year follow-up period, 2136 participants developed cardiovascular disease, including 1633 cases of coronary heart disease and 719 cases of stroke. Compared with the lowest tertile group, the adjusted HR (95% CI) for new-onset cardiovascular disease in the highest tertile for TyG, TyG-BMI, TyG-WC, and TyG-WHtR were 1.215 (1.088-1.356), 1.073 (0.967-1.191), 1.078 (0.970-1.198), and 1.112 (1.002-1.235), respectively. The C-indices of TyG index for cardiovascular disease onset were higher than other modified TyG indices. Similar results were observed for coronary heart disease and stroke. CONCLUSION: TyG and TyG-WhtR were significantly associated with new-onset cardiovascular diseases, and TyG outperformed the modified TyG indices to identify individuals at risk of incident cardiovascular event.

Association of serum trefoil factor 3 and leptin levels with obesity: A case-control study.

BACKGROUND: Obesity has a detrimental impact on individuals, communities, and healthcare systems. Trefoil factor 3 is a secretory protein involved in metabolic processes related to weight regulation. However, its relation with obesity is not fully understood. OBJECTIVE: We aimed to assess the serum trefoil factor 3 level and to immunohistochemical detect the leptin in obese patients to evaluate their relation to obesity pathogenesis. METHODS: As a case-control study, we enrolled 83 non-obese persons as a control group with a BMI (18.5-24.9) and 83 obese persons as a patient group with a BMI > 30. All the study volunteers are subjected to anthropometric measurements, glucose, and lipid profile analysis by colorimetric methods. Serum trefoil factor 3 level was estimated by ELISA and leptin hormone was detected immunohistochemically in the blood using cell block technique. RESULTS: ROC curve analysis for TFF3 showed a good relation with obesity with an AUC of 0.891 and a cut-off value of > 96 ng/ml. There was a significant positive correlation between TFF3 and fasting blood sugar, total cholesterol, and triglycerides. The logistic regression analysis showed that TFF3 is a good risk factor for obesity incidence [p = 0.008; OR = 1.117; (95 % CI): 1.029-1.213]. This was confirmed by multiple linear regression that gave an equation for the possibility of predicting BMI using several factors including TFF3 [BMI = 0.821 + 0.051 × TFF3 + 0.044 × FBS + 0.85 × TC]. The more surprising was the ability of the immunohistochemistry cell block technique to detect leptin antigens associated with an obese person blood not only adipose tissue or serum. CONCLUSION: Leptin hormone and TFF3 could be good indicators for obesity incidence. Further research with a larger sample size and in different populations could completely approve our results.

Investigating the expression level of miR-17-3p, miR-101-3p, miR-335-3p, and miR-296-3p in the peripheral blood of patients with acute myocardial infarction.

The role of inflammation has been proven in acute myocardial infarction (AMI) pathogenesis. Due to the effect of NLRP3 gene expression in the inflammation process of MI, we aimed to explore the expression changes and diagnostic power of four inflammation-related miRNAs including miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p and their potential target, NLRP3, in ST-segment elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) patients as two major classes of AMI. The expression level of these genes were evaluated in 300 participants equally divided into three groups of STEMI, NSTEMI, and control using quantitative real-time PCR. The expression level of NLRP3 was upregulated in STEMI and NSTEMI patients compared to control subjects. Besides, the expression levels of miR-17-3p, miR-101-3p, and miR-296-3p were significantly downregulated in STEMI and NSTEMI patients compared to controls. The increased expression of NLRP3 had a very strong inverse correlation with miR-17-3p in patients with STEMI and with miR-101-3p in the STEMI and NSTEMI patients. ROC curve analysis showed that the expression level of miR-17-3p had the highest diagnostic power for discrimination between STEMI patients and controls. Remarkably, the combination of all markers resulted in a higher AUC. In summary, there is a significant association between the expression levels of miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p, and NLRP3 and the incidence of AMI. Although the miR-17-3p expression level has the highest diagnostic power to distinguish between STEMI patients and control subjects, the combination of these miRNAs and NLRP3 could serve as a novel potential diagnostic biomarker of STEMI.

ISTH bleeding assessment tool and platelet function analyzer in children with mild inherited platelet function disorders.

OBJECTIVES: To evaluate the diagnostic performance of platelet function analyzer (PFA) and The International Society on Thrombosis and Hemostasis bleeding-assessment-tool (ISTH-BAT) in detecting mild inherited platelet function disorders (IPFDs) in children with suspected bleeding disorders. METHODS: Prospective single-center diagnostic study including consecutive patients <18 years with suspected bleeding disorder and performing a standardized workup for platelet function defects including ISTH-BAT, PFA, platelet aggregation testing, blood smear-based immunofluorescence, and next-generation sequencing-based genetic screening for IPFDs. RESULTS: We studied 97 patients, of which 34 von Willebrand disease (VWD, 22 type-1, 11 type-2), 29 IPFDs (including delta-/alpha-storage pool disease, Glanzmann thrombasthenia, Hermansky-Pudlak syndrome) and 34 with no diagnosis. In a model combining PFA-adenosine diphosphate (ADP), PFA-epinephrine (EPI), and ISTH-BAT overall performance to diagnose IPFDs was low with area under the curves of 0.56 (95% CI 0.44, 0.69) compared with 0.84 (95% CI 0.76, 0.92) for VWD. Correlation of PFA-EPI/-ADP and ISTH-BAT was low with 0.25/0.39 Spearman's correlation coefficients. PFA were significantly prolonged in patients with VWD and Glanzmann thrombasthenia. ISTH-BAT-scores were only positive in severe bleeding disorders, but not in children with mild IPFDs or VWD. CONCLUSION: Neither ISTH-BAT nor PFA or the combination of both help diagnosing mild IPFDs in children. PFA is suited to exclude severe IPFDs or VWD and is in this regard superior to ISTH-BAT in children.

Hypothesis tests in ordinal predictive models with optimal accuracy.

In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$-statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings.

Tuberculosis (TB) studies often involve four different states under consideration, namely, "healthy," "latent infection," "pulmonary active disease," and "extra-pulmonary active disease." While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so-called "tree" or "umbrella" orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non-parametric kernel-based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.

Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm.

Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his or her profiles. A standard grid search algorithm can be used to estimate the optimal linear PDR that maximizes the area under the receiver operating characteristics curve (AUC) among all the linear PDRs, but it is time-consuming especially when the number of variables is large. Alternatively, we developed an efficient grid rotation algorithm that provides a nearly suboptimal solution and studied its variation to find the optimal solution. We implemented the cross-validated forward variable selection method to find a subset of useful variables while avoid overfitting. Extensive simulations show that our proposed method reduces bias and variance. Analysis of a gastric cancer biomarker study and censored survival outcome data illustrates the practical utility of our proposed method. The proposed method is implemented in the open-source R package persDx.

Evaluating joint confidence region of hypervolume under ROC manifold and generalized Youden index.

In biomarker evaluation/diagnostic studies, the hypervolume under the receiver operating characteristic manifold ( HUM K $$ {\mathrm{HUM}}_K $$ ) and the generalized Youden index ( J K $$ {J}_K $$ ) are the most popular measures for assessing classification accuracy under multiple classes. While HUM K $$ {\mathrm{HUM}}_K $$ is frequently used to evaluate the overall accuracy, J K $$ {J}_K $$ provides direct measure of accuracy at the optimal cut-points. Simultaneous evaluation of HUM K $$ {\mathrm{HUM}}_K $$ and J K $$ {J}_K $$ provides a comprehensive picture about the classification accuracy of the biomarker/diagnostic test under consideration. This article studies both parametric and non-parametric approaches for estimating the confidence region of HUM K $$ {\mathrm{HUM}}_K $$ and J K $$ {J}_K $$ for a single biomarker. The performances of the proposed methods are investigated by an extensive simulation study and are applied to a real data set from the Alzheimer's Disease Neuroimaging Initiative.

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Investigating the diagnostic potential of IL-1ß, IL-10, and IL-36γ in gingival crevicular fluid in patients with different periodontal conditions.

BACKGROUND: The study aimed to analyze cytokine levels, including interleukin (IL)-1ß, IL-10, and IL-36γ, to investigate the link between pro- and anti-inflammatory responses in periodontal conditions and assess their potential as diagnostic biomarkers for distinguishing between different types of periodontal conditions. METHODS: 80 systemically healthy non-smokers (25 periodontally healthy, 25 with gingivitis, 30 with periodontitis) were included. Clinical periodontal parameters were recorded, and gingival crevicular fluid (GCF) samples were obtained. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic value of cytokines. RESULTS: IL-36γ had the highest sensitivity for diagnosing periodontitis, although its specificity for identifying those without periodontitis was relatively low. The combination of IL-1ß and IL-36γ was the most effective in differentiating periodontitis from periodontal health. IL-10 was found to be an acceptable discriminator for distinguishing gingivitis from healthy conditions. However, its sensitivity and specificity for identifying gingivitis were lower. The combination of the three cytokines showed the highest ability to distinguish between periodontitis and gingivitis. CONCLUSION: The levels of IL-1ß, IL-10, and IL-36γ in GCF may provide insights into periodontal health and disease status. Further studies are needed to validate these results and explore the potential of these cytokines in periodontal disease management.

All three of these cytokines exhibit exceptional diagnostic accuracy, particularly in distinguishing between chronic periodontitis and periodontal health.Moreover, the combination of IL-1ß and IL-36γ stands out as the most accurate diagnostic indicator for periodontitis. This combination could serve as a robust biomarker panel for the early detection and monitoring of periodontal disease, potentially allowing for timely interventions to prevent disease progression.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM.

BACKGROUND: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.

Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review.

INTRODUCTION: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. METHODS: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. RESULTS: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. CONCLUSION: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Gender differences in risk factors for ischemic stroke: a longitudinal cohort study in East China.

OBJECTIVES: Epidemiological studies of stroke and its risk factors can help develop strategies to prevent stroke. We aimed to explore the current gender-specific prevalence of stroke and associated risk factors. METHODS: Data were collected using a structured precoded questionnaire designed by the Stroke Screening and Prevention Programme of the National Health and Wellness Commission Stroke Prevention and Control Project Committee, between June 2020 and November 2021. A total of 7394 residents took part in the study, 187 of whom had a stroke. The baseline information of each participant was obtained and included in this study. The chi-square test and Kruskal-Wallis tests were used to examine the relationship between these indicators and stroke, and then multivariate logistic regression was used to construct the prediction scale between different genders. RESULTS: of 7394 participants,4571 (61.82%) were female. The overall prevalence of stroke patients in the study population was 2.53%, Multivariate analysis found that residence status (OR = 0.43, p = 0.002) 、HCY (OR = 0.962, p = 0.000)、Previous TIA (OR = 0.200, p = 0.002) 、Hypertension (OR = 0.33, p = 0.000) and Dyslipidemia (OR = 0.668, p = 0.028) were significant predictors of stroke. there are gender differences in the traditional risk factors for stroke, and women have more risk factors. ROC analysis confirmed the accuracy of the stroke risk model, and the AUC of the stroke risk model for the general population was 0.79 with p < 0.05. In the gender model, the female AUC was 0.796 (p < 0.05). and the male AUC was 0.786 with p < 0.05. CONCLUSION: The prevalence of stroke in adults aged 40 years and above is high in eastern China were high. management of risk factors can effectively prevent the occurrence of most strokes. more attention should be paid to gender differences associated with stroke.