Improving the sensitivity of cluster‐based statistics for functional magnetic resonance imaging data

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false‐alarm rate, (b) that the sensitivity profiles of cluster‐based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.     

An Easily Accessible Web-Based Minimization Random Allocation System for Clinical Trials

Background

Minimization as an adaptive allocation technique has been recommended in the literature for use in randomized clinical trials. However, it remains uncommonly used due in part to a lack of easily accessible implementation tools.

Objective

To provide clinical trialists with a robust, flexible, and readily accessible tool for implementing covariate-adaptive biased-coin randomization.

Methods

We developed a Web-based random allocation system, MinimRan, that applies Pocock–Simon (for trials with 2 or more arms) and 2-way (currently limited to 2-arm trials) minimization methods for trials using only categorical prognostic factors or the symmetric Kullback–Leibler divergence minimization method for trials (currently limited to 2-arm trials) using continuous prognostic factors with or without categorical factors, in covariate-adaptive biased-coin randomization.

Results

In this paper, we describe the system’s essential statistical and computer programming features and provide as an example the randomization results generated by it in a recently completed trial. The system can be used in single- and double-blind trials as well as single-center and multicenter trials.

Conclusions

We expect the system to facilitate the translation of the 3 validated random allocation methods into broad, efficient clinical research practice.     

Kidney stone disease and cardiovascular events: a study on bidirectional causality based on mendelian randomization

BackgroundKidney stone disease (KSD) has been reported to be associated with several cardiovascular diseases. However, the causality between the conditions remains unknown. In the study, we performed a study on bidirectional causality by two-sample Mendelian randomization (MR) to investigate the causality between KSD and cardiovascular diseases including coronary atherosclerosis, hypertension, and cardiomyopathy.MethodsIn the recent study, we performed a bidirectional two-sample MR study using available genome-wide association summary data from the online database MRBASE. We identified genetic variants associated with KSD in one European population from UK Biobank (version 2, n=462,933). Two phenotypes of samples were chosen from the population to define our genetic instrumental variables: (I) samples with the phenotype of kidney stone/ureter stone/bladder stone (ukb-b-8297), and (II) samples with the phenotype of kidney stone surgery/lithotripsy (ukb-b-13537). For cardiovascular diseases, we picked up another independent European population from FinnGen Biobank (n=93,421). We selected the exposure and outcome SNPs and then performed the two-sample MR using R package.ResultsAfter bidirectional causality by two-sample MR, we verified that genetic predisposition to KSD could increase the risk of coronary atherosclerosis (OR: 4.45×1037; SE=±7.80×10¹⁴, P for MR-Egger =0.024) and cardiomyopathy (OR: 5.35×10¹³; SE=±7.18×10⁶, P for IVW=0.045 for finn-a-I9_CARDMYO, and OR: 3.60×10²⁵; SE=±3.26×10¹², P for IVW=0.041 for finn-a-I9_CARDMYOOTH) when we used ukb-b-13537 as exposure group. Furthermore, hypertension could increase the risk of KSD (OR: 1.001; SE=±1.00, P for IVW=0.003) when we used ukb-b-8297 as exposure group, without detected pleiotropy bias (P>0.05).ConclusionsWe confirmed KSD may trigger causal pathological processes including coronary atherosclerosis and cardiomyopathy. Furthermore, hypertension may causally affect KSD.     

Genetics of coronary heart disease: towards causal mechanisms,novel drug targets and more personalized prevention

Coronary heart disease (CHD) is an archetypical multifactorial disorder that is influenced by genetic susceptibility as well as both modifiable and nonmodifiable risk factors, and their interactions. Advances during recent years in the field of multifactorial genetics, in particular genomewide association studies (GWASs) and their meta‐analyses, have provided the statistical power to identify and replicate genetic variants in more than 50 risk loci for CHD and in several hundreds of loci for cardiometabolic risk factors for CHD such as blood lipids and lipoproteins. Although for a great majority of these loci both the causal variants and mechanisms remain unknown, progress in identifying the causal variants and underlying mechanisms has already been made for several genetic loci. Furthermore, identification of rare loss‐of‐function variants in genes such as PCSK9, NPC1L1, APOC3 and APOA5, which cause a markedly decreased risk of CHD and no adverse side effects, illustrates the power of translating genetic findings into novel mechanistic information and provides some optimism for the future of developing novel drugs, given the many genes associated with CHD in GWASs. Finally, Mendelian randomization can be used to reveal or exclude causal relationships between heritable biomarkers and CHD, and such approaches have already provided evidence of causal relationships between CHD and LDL cholesterol, triglycerides/remnant particles and lipoprotein(a), and indicated a lack of causality for HDL cholesterol, C‐reactive protein and lipoprotein‐associated phospholipase A2. Together, these genetic findings are beginning to lead to promising new drug targets and novel interventional strategies and thus have great potential to improve prevention, prediction and therapy of CHD.     

Assessing the Causal Effects of Adipokines on Uric Acid and Gout: A Two-Sample Mendelian Randomization Study

Previous observational studies have highlighted associations between adipokines and hyperuricemia, as well as gout, but the causality and direction of these associations are not clear. Therefore, we attempted to assess whether there are causal effects of specific adipokines (such as adiponectin (ADP) and soluble leptin receptors (sOB-R)) on uric acid (UA) or gout in a two-sample Mendelian randomization (MR) analysis, based on summary statistics from large genome-wide association studies. The inverse-variance weighted (IVW) method was performed as the primary analysis. Sensitivity analyses (including MR-Egger regression, weighted median, penalized weighted median, and MR pleiotropy residual sum and outlier methods) were also performed, to ensure reliable results. In the IVW models, no causal effect was found for sOB-R (odds ratios (OR), 1.002; 95% confidence intervals (CI), 0.999–1.004; p = 0.274) on UA, or ADP (OR, 1.198; 95% CI, 0.865–1.659; p = 0.277) or sOB-R (OR, 0.988; 95% CI, 0.940–1.037; p = 0.616) on gout. The results were confirmed in sensitivity analyses. There was no notable directional pleiotropy or heterogeneity. This study suggests that these specific adipokines may not play causal roles in UA or gout development.     

Dietary-Derived Essential Nutrients and Amyotrophic Lateral Sclerosis: A Two-Sample Mendelian Randomization Study

Previous studies have suggested a close but inconsistent relationship between essential nutrients and the risk of amyotrophic lateral sclerosis (ALS), and whether this association is causal remains unknown. We aimed to investigate the potential causal relation between essential nutrients (essential amino acids, essential fatty acids, essential minerals, and essential vitamins) and the risk of ALS using Mendelian randomization (MR) analysis. Large-scale European-based genome-wide association studies’ (GWASs) summary data related to ALS (assembling 27,205 ALS patients and 110,881 controls) and essential nutrient concentrations were separately obtained. MR analysis was performed using the inverse variance–weighted (IVW) method, and sensitivity analysis was conducted by the weighted median method, simple median method, MR–Egger method and MR–PRESSO method. We found a causal association between genetically predicted linoleic acid (LA) and the risk of ALS (OR: 1.066; 95% CI: 1.011–1.125; p = 0.019). An inverse association with ALS risk was noted for vitamin D (OR: 0.899; 95% CI: 0.819–0.987; p = 0.025) and for vitamin E (OR: 0.461; 95% CI: 0.340–0.626; p = 6.25 × 10⁻⁷). The sensitivity analyses illustrated similar trends. No causal effect was observed between essential amino acids and minerals on ALS. Our study profiled the effects of diet-derived circulating nutrients on the risk of ALS and demonstrated that vitamin D and vitamin E are protective against the risk of ALS, and LA is a suggested risk factor for ALS.     

Obesity as a Causal Risk Factor for Aortic Valve Stenosis

BackgroundCausal risk factors for aortic valve stenosis are poorly understood, limiting the possibility of preventing the most common heart valve disease.ObjectivesThe hypothesis was tested that genetically based obesity measured by body mass index is causally associated with risk of aortic valve stenosis and replacement.MethodsThe authors included 108,211 individuals from the Copenhagen General Population Study. Participants had measurements of body mass index, waist-hip ratio, and waist circumference, and information on 5 genetic variants associated with obesity. A Mendelian randomization design was used to investigate genetic and observational associations of obesity with incident aortic valve stenosis (n = 1,215) and replacement (n = 467) for a median follow-up time of 8.7 years.ResultsGenetically increased body mass index was causally associated with increased risk of aortic valve stenosis. Compared with an unweighted allele score of 0 to 3, individuals with an allele score 7 to 10 had a mean increase in body mass index of 0.87 kg/m², and the age and sex–adjusted hazard ratio for aortic valve stenosis was 1.3 (95% confidence interval [CI]: 1.0 to 1.7) for allele score 4, 1.4 (95% CI: 1.1 to 1.8) for allele score 5 to 6, and 1.6 (95% CI: 1.3 to 2.1) for allele score 7 to 10 (p for trend: 9 × 10⁻⁵). A 1-kg/m² increase in body mass index was associated with causal risk ratios for aortic valve stenosis and replacement, respectively, of 1.52 (95% CI: 1.23 to 1.87) and 1.49 (95% CI: 1.07 to 2.08) genetically, and with corresponding hazard ratios of 1.06 (95% CI: 1.05 to 1.08) and 1.06 (95% CI: 1.03 to 1.08) observationally.ConclusionsObesity from human genetics was causally associated with higher risk of aortic valve stenosis and replacement.     

中央随机网络系统的应用探讨

文章通过对中央随机网络系统受试者信息登记、随机化分配、药品管理、在线访视、紧急揭盲等方面的介绍,说明中央随机网络系统可以保证随机化的准确实施,降低偏倚、减少浪费、简化管理环节、提高临床科研效率和质量,探讨目前中央随机网络系统在临床科研应用中存在问题。