Incorporating published univariable associations in diagnostic and prognostic modeling

Background: Diagnostic and prognostic literature is overwhelmed with studies reporting univariable predictor-outcome associations. Currently, methods to incorporate such information in the construction of a prediction model are underdeveloped and unfamiliar to many researchers. Methods. This article aims to improve upon an adaptation method originally proposed by Greenland (1987) and Steyerberg (2000) to incorporate previously published univariable associations in the construction of a novel prediction model. The proposed method improves upon the variance estimation component by reconfiguring the adaptation process in established theory and making it more robust. Different variants of the proposed method were tested in a simulation study, where performance was measured by comparing estimated associations with their predefined values according to the Mean Squared Error and coverage of the 90% confidence intervals. Results: Results demonstrate that performance of estimated multivariable associations considerably improves for small datasets where external evidence is included. Although the error of estimated associations decreases with increasing amount of individual participant data, it does not disappear completely, even in very large datasets. Conclusions: The proposed method to aggregate previously published univariable associations with individual participant data in the construction of a novel prediction models outperforms established approaches and is especially worthwhile when relatively limited individual participant data are available

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multitemporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI–temperature)/increasing (EVI–precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intraseasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations

Insoles Treated with Bacteria-Killing Nanotechnology Bio-Kil Reduce Bacterial Burden in Diabetic Patients and Healthy Controls

Our study investigated the effectiveness of bacteria-killing nanotechnology Bio-Kil socks on bacterial burden reduction in diabetic patients and healthy individuals. Four strains of S. aureus and four strains of E. coli were cultured and dropped on Bio-Kil socks and control socks for 0 h, 8 h, and 48 h of incubation. Diluted samples were inoculated and bacterial counts were recorded. Additionally, 31 patients with type 2 diabetes and 31 healthy controls were assigned to wear one Bio-Kil sock on one foot and a control sock on the other for four hours, and then they were told to exchange socks from one foot to the other for four hours. The socks were sampled and diluted and then inoculated to record bacterial counts. Bacterial counts were reduced in Bio-Kil socks compared with control socks in all S. aureus strains after 0 h, 8 h, and 48 h of incubation. In E. coli strains, bacterial counts declined in Bio-Kil socks comparing with control socks in most of the experiments with ESBL-negative E. coli and ATCC35218 at 0 h and 48 h of incubation. In all participants, the mean bacterial counts significantly decreased in Bio-Kil socks in comparison with control socks both at 0 h and at 40 h of incubation (p=0.003 at 0 h and p=0.006 at 40 h). Bio-Kil socks from diabetic patients showed significantly lessened bacterial count at 40 h of incubation (p=0.003). In healthy individuals, Bio-Kil socks reflected a significantly smaller mean bacterial count than control socks (p=0.016). Socks using Bio-Kil nanotechnology efficiently reduce bacterial counts in both diabetic patients and healthy individuals and might exert stronger efficacy in Gram-positive bacteria

Rs46522 in the Ubiquitin-Conjugating Enzyme E2Z Gene Is Associated with the Risk of Coronary Artery Disease in Individuals of Chinese Han Population with Type 2 Diabetes

Aims. We investigated the association between ubiquitin-conjugating enzyme E2Z (UBE2Z) gene SNP rs46522 and the risk of CAD in a Chinese Han population with type 2 diabetes and explored a possible interactive effect with environmental risk factors of CAD. Methods. 665 patients with T2D were enrolled; 390 were CAD patients and 275 were non-CAD patients. Genotype analysis of rs46522 (T>C) was performed using PCR-RFLP. Results. The SNP rs47522 was associated with the risk of CAD supposing recessive inheritance model (TT versus CC+CT, OR′=1.277, 95%CI′ 1.039–1.570, p′=0.020) and codominant model (TT versus CT, OR′=1.673, 95%CI′ 1.088–2.570, p′=0.019) after adjustment for confounders of CAD. A synergistic effect of rs46522 and BMI was discovered (β=0.012, p for interreaction = 0.028). In subgroup analysis, minor allele T was significantly associated with CAD in overweight and obesity subgroup (p=0.034), and the association was also proved in recessive model (OR=1.537, 95%CI 1.075–2.196, p=0.018). Smokers with genotype TT had threefold risk of CAD in comparison to nonsmokers with genotype TC or CC (p<0.001). Conclusions. The SNP rs46522 in UBE2Z gene is associated with the risk of CAD in the individuals of Chinese Han descent with type 2 diabetes and is of synergistic effect with BMI

Genetic Variability of the Glucose-Dependent Insulinotropic Peptide Gene Is Involved in the Premature Coronary Artery Disease in a Chinese Population with Type 2 Diabetes

Background. Glucose-dependent insulinotropic polypeptide (GIP) is closely related to diabetes and obesity, both of which are confirmed to increase the risk of coronary artery disease (CAD). Our study aimed to investigate whether the polymorphisms in GIP genes could affect the risk of cardiovascular disease in type 2 diabetic patients in the Chinese Han population. Methods. We selected and genotyped two haplotype-tagging single nucleotide polymorphisms (tag-SNPs) (rs2291725 C>T, rs8078510 G>A) of GIP gene based on CHB data in HapMap Phase II database (r2<0.8). The case-control study of Chinese Han population involved 390 diabetic patients with CAD as positive group and 276 diabetic patients without CAD as control group. Allele and genotype frequencies were compared between the two groups. Results. In dominant inheritance model, the carriers of T/T or T/C had a lower risk of CAD (OR = 0.635, 95% CI = 0.463–0.872, p=0.005), even after adjustment other CAD risk factors (gender, age, BMI, smoking status, dyslipidemia, hypertension history, and diabetic duration) (OR′ = 0.769, 95% CI′ = 0.626–0.945, p′=0.013). The allele A at rs8078510 was associated with decreased risk of CAD (OR = 0.732, p=0.039). p=0.018 in subgroup analysis, individuals with higher BMI (≥24 kg/m2) had increased risk for CAD when carrying C/C at rs2291725 (OR′ = 1.291, 95% CI′ = 1.017–1.639, p′=0.036). In age < 55 men and age < 65 women, the carriers of allele C at rs2291725 had a higher risk of CAD than noncarriers (OR = 1.627, p=0.015). Carriers of allele G in rs8078510 had higher susceptibility to CAD (OR = 2.049, 95% = CI 1.213–3.463, p=0.007). p=0.004; in addition, allele G in rs8078510 would bring higher CAD risk to the carriers who ever smoked (OR = 1.695, 95% CI = 1.080–2.660, p=0.021). Conclusion. The genetic variability of GIP gene is associated with CAD and it may play a role in the premature CAD in the Chinese Han population with type 2 diabetes

Association between STK11 Gene Polymorphisms and Coronary Artery Disease in Type 2 Diabetes in Han Population in China

Background. Recent studies indicated that the Serine threonine kinase 11 (STK11), which is a key regulator of the AMP-activated protein kinase (AMPK), plays a crucial role in cardiovascular system. This study aimed to investigate whether genetic variations in the STK11 gene affect the risk of coronary artery disease (CAD) in Chinese type 2 diabetics. Methods. 5 haplotype-tagging single nucleotide polymorphisms (SNPs) were selected, and 288 CAD-positive cases and 159 CAD-negative controls with type 2 diabetes were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. Results. The carriers of minor allele A at rs12977689 had a higher risk of CAD compared to the homozygotes of CC (OR = 1.572, 95% CI = 1.039–2.376, p=0.035), and the difference was still significant after adjustment for the other known CAD risk factors (OR′ = 1.184, 95%  CI′ = 1.036–1.353, p′=0.013). Conclusion. Genetic variability at STK11 locus is associated with CAD risk in type 2 diabetes in the Chinese population

LGL1 binds to Integrin β1 and inhibits downstream signaling to promote epithelial branching in the mammary gland.

Branching morphogenesis is a fundamental process by which organs in invertebrates and vertebrates form branches to expand their surface areas. The current dogma holds that directional cell migration determines where a new branch forms and thus patterns branching. Here, we asked whether mouse Lgl1, a homolog of the Drosophila tumor suppressor Lgl, regulates epithelial polarity in the mammary gland. Surprisingly, mammary glands lacking Lgl1 have normal epithelial polarity, but they form fewer branches. Moreover, we find that Lgl1 null epithelium is unable to directionally migrate, suggesting that migration is not essential for mammary epithelial branching as expected. We show that LGL1 binds to Integrin β1 and inhibits its downstream signaling, and Integrin β1 overexpression blocks epithelial migration, thus recapitulating the Lgl1 null phenotype. Altogether, we demonstrate that Lgl1 modulation of Integrin β1 signaling is essential for directional migration and that epithelial branching in invertebrates and the mammary gland is fundamentally distinct