Abdominal lean muscle is associated with lower mortality among kidney waitlist candidates

Morphometric assessments, such as muscle density and body fat distribution, have emerged as strong predictors of cardiovascular risk and postoperative morbidity and mortality. To date, no study has examined morphometric mortality risk prediction among kidney transplant (KT) candidates. KT candidates, waitlisted 2008‐2009, were identified (n=96) and followed to the earliest of transplant, death, or administrative end of study. Morphometric measures, including abdominal adipose tissue, paraspinous and psoas muscle composition, and aortic calcification, were measured from CTs. Risk of waitlist mortality was examined using Cox proportional hazard regression. On adjusted analyses, radiologic measures remained independently and significantly associated with lower waitlist mortality; the addition of radiologic measures significantly improved model predictive ability over models containing traditional risk factors alone (net reclassification index: 0.56, 95% CI: 0.31‐0.75). Higher psoas muscle attenuation (indicative of leaner muscle) was associated with decreased risk of death (aHR: 0.93, 95% CI: 0.91‐0.96, P<.001), and for each unit increase in lean paraspinous volume, there was an associated 2% decreased risk for death (aHR: 0.98, 95% CI: 0.96‐0.99, P=.03). Radiologic measures of lean muscle mass, such as psoas muscle attenuation and paraspinous lean volume, may improve waitlist mortality risk prediction and candidate selection.     

降钙素原及C反应蛋白联合检测在急性重度胰腺炎发生风险中的预测价值

目的探讨早期降钙素原(PCT)及C反应蛋白(CRP)水平在急性重度胰腺炎发生风险中的预测价值。方法收集2013年1月至2015年12月在我院治疗的68例急性胰腺炎患者为研究对象进行回顾性研究。所有病例依照2013版中国急性胰腺炎诊疗指南将患者划分为轻度胰腺炎组(对照组),以及重度胰腺炎组(观察组)。通过t检验、Logistic回归以及ROC曲线分析起病24小时内的PCT及CRP水平与重度胰腺炎发生的相关性。结果观察组中PCT、CRP平均水平高于对照组(P0.001),Logistic回归分析得到PCT、CRP的OR值分别为1.41、1.125,两者95%置信区间均1,PCT、CRP均为重度胰腺炎的危险因素,可联合对重度胰腺炎发生风险进行预测。而ROC曲线提示PCT对重度胰腺炎预测敏感性及准确性要优于CRP。结论通过早期检测血清PCT和CRP水平能够对急性胰腺炎患者的病情严重程度提供帮助,而对重度胰腺炎早期综合治疗具有一定的临床指导价值。     

Comparisons of risk prediction methods using nested case‐control data

Using both simulated and real datasets, we compared two approaches for estimating absolute risk from nested case‐control (NCC) data and demonstrated the feasibility of using the NCC design for estimating absolute risk. In contrast to previously published results, we successfully demonstrated not only that data from a matched NCC study can be used to unbiasedly estimate absolute risk but also that matched studies give better statistical efficiency and classify subjects into more appropriate risk categories. Our result has implications for studies that aim to develop or validate risk prediction models. In addition to the traditional full cohort study and case‐cohort study, researchers designing these studies now have the option of performing a NCC study with huge potential savings in cost and resources. Detailed explanations on how to obtain the absolute risk estimates under the proposed approach are given. Copyright © 2016 John Wiley & Sons, Ltd.     

The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk‐set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline‐only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Targeted use of growth mixture modeling: a learning perspective

From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we utilize the benefits of the conventional use of GMM for the purpose of generating potential candidate models based on empirical model fitting, which can be viewed as unsupervised learning. We then evaluate candidate GMM models on the basis of a direct measure of success; how well the trajectory types are predicted by clinically and demographically relevant baseline features, which can be viewed as supervised learning. We examine the proposed approach focusing on a particular utility of latent trajectory classes, as outcomes that can be used as valid prediction targets in clinical prognostic models. Our approach is illustrated using data from the Longitudinal Assessment of Manic Symptoms study. Copyright © 2016 John Wiley & Sons, Ltd.     

Parametric multistate survival models: Flexible modelling allowing transition‐specific distributions with application to estimating clinically useful measures of effect differences

Multistate models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this article, we concentrate on parametric multistate models, both Markov and semi‐Markov, and develop a flexible framework where each transition can be specified by a variety of parametric models including exponential, Weibull, Gompertz, Royston‐Parmar proportional hazards models or log‐logistic, log‐normal, generalised gamma accelerated failure time models, possibly sharing parameters across transitions. We also extend the framework to allow time‐dependent effects. We then use an efficient and generalisable simulation method to calculate transition probabilities from any fitted multistate model, and show how it facilitates the simple calculation of clinically useful measures, such as expected length of stay in each state, and differences and ratios of proportion within each state as a function of time, for specific covariate patterns. We illustrate our methods using a dataset of patients with primary breast cancer. User‐friendly Stata software is provided.     

在肺腺癌组织中低表达的STING可通过抑制内质网应激促进肺腺癌进展

目的：分析干扰素基因刺激因子（STING）在肺腺癌中的表达及其与肺腺癌患者临床特征间的关系,探讨STING与内质网应激的相关性及其在调控肺腺癌进展中的作用机制。方法：利用TIMER数据库分析STING基因在泛癌水平的表达情况,利用UALCAN和HPA数据库分析STING在肺腺癌组织中的表达及其与肺腺癌患者临床特征间的关系,利用Kaplan-Meier生存函数分析STING表达与肺腺癌患者OS率间的关系。利用LinkedOmics数据库对肺腺癌表达谱数据进行STING基因共表达分析,对STING相关差异表达基因（DEG）进行GO功能与KEGG通路富集分析,通过GSEA筛选STING调控肺腺癌的潜在通路。使用STING激动剂diABZI及内质网应激抑制剂TUDCA对肺腺癌A549与H460细胞进行处理,通过qPCR、WB法检测STING及内质网应激相关分子的表达,通过CCK-8法检测细胞增殖活力。结果：肺腺癌组织和细胞中STING的表达水平均显著低于正常肺组织（均P<0.01）,STING高表达肺腺癌患者5年OS率显著高于低表达患者（P<0.01）,STING的表达与肺腺癌患者...     

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (N_eff = 40k) and Latino training data in small sample size (N_eff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R² = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (N_eff = 40k) and South Asian (N_eff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non‐European target populations.     

基于偏最小二乘的Kriging代理模型在加氢裂化建模中的应用

提出了一种改进的代理模型方法 （Kriging with Partial Least Squares,KPLS）。该方法在Kriging模型的基础上引入偏最小二乘的思想,利用偏最小二乘方法构建新的Kriging模型的高斯核函数。将该模型应用于加氢裂化过程建模,有效地提高了航煤、柴油质量收率的预测精度。采用 GLAMP（Global and local search strategy）优化算法对建立的KPLS模型进行优化,仿真结果显示航煤、柴油质量收率得到了显著提升。