RBP4 variants are significantly associated with plasma RBP4 levels and hypertriglyceridemia risk in Chinese Hans

We previously found that plasma RBP4 levels were strongly associated with metabolic syndrome components. This study aimed to determine whether RBP4 variants are associated with the metabolic syndrome components and plasma RBP4 levels, and to investigate whether the associations between plasma RBP4 and the metabolic syndrome components are causal. Five tagSNPs were tested for their associations with plasma RBP4 levels and metabolic syndrome components in a population-based sample of 3,210 Chinese Hans. A possible causal relationship between plasma RBP4 levels and hypertriglyceridemia was explored by Mendelian randomization. Plasma RBP4 levels were significantly associated with rs10882273 (βz −0.10SD[−0.17, −0.03], P = 0.0050), rs3758538 (βz −0.13SD[−0.24, −0.02], P = 0.0249) in all participants, and with rs17108993 in Shanghai participants (βz −0.19SD[−0.32, −0.05], P = 0.0061). The single nucleotide polymorphism (SNP) rs3758538 was significantly associated with hypertriglyceridemia (OR 0.62[0.45–0.85], P = 0.0026) and triglycerides (βz −0.19SD[−0.30, −0.07], P = 0.001) in all participants. In Mendelian randomization analysis, the observed effect size of association between rs3758538 and hypertriglyceridemia was different from the expected effect size (P = 0.0213). This is the first study to show that the RBP4 variants are significantly associated with plasma RBP4 levels and hypertriglyceridemia risk in Chinese Hans. However, results of Mendelian randomization do not support the hypothesis that RBP4 levels are causally related to hypertriglyceridemia risk.     

Randomization and Additivity in the Two-Period Crossover Clinicial Trial

In certain areas of medical research, the two-period crossover design is a frequent choice for comparing treatments A and B in a randomized clinical trial. Earlier work by Grizzle and by Brown was based upon a parametric theory linear model. Recently, the present authors employed D. R. Cox's additive randomization models and, for the case of zero residual effect, found a discrepancy between it and the parametric model with respect to the precision of period effects. In the present note, this divergence is accounted for by allowing for the possibility of non-additivity through the use of a completely general randomization model. It is concluded that the structure of the crossover design is such that use of the parametric theory linear model is required if a single, consistent model is desired.     

Circulating serum vitamin D levels and total body bone mineral density: A Mendelian randomization study

Until recently, randomized controlled trials have not demonstrated convincing evidence that vitamin D, or vitamin D in combination with calcium supplementation could improve bone mineral density (BMD), osteoporosis and fracture. It remains unclear whether vitamin D levels are causally associated with total body BMD. Here, we performed a Mendelian randomization study to investigate the association of vitamin D levels with total body BMD using a large‐scale vitamin D genome‐wide association study (GWAS) dataset (including 79 366 individuals) and a large‐scale total body BMD GWAS dataset (including 66,628 individuals). We selected three Mendelian randomization methods including inverse‐variance weighted meta‐analysis (IVW), weighted median regression and MR‐Egger regression. All these three methods did not show statistically significant association of genetically increased vitamin D levels with total body BMD. Importantly, our findings are consistent with recent randomized clinical trials and Mendelian randomization study. In summary, we provide genetic evidence that increased vitamin D levels could not improve BMD in the general population. Hence, vitamin D supplementation alone may not be associated with reduced fracture incidence among community‐dwelling adults without known vitamin D deficiency, osteoporosis, or prior fracture.     

An artificial neural network approach for studying phytoplankton succession

Artificial neural networks are used to model phytoplankton succession and gain insight into the relative strengths of bottom-up and top-down forces shaping seasonal patterns in phytoplankton biomass and community composition. Model comparisons indicate that patterns in chlorophyll aconcentrations response instantaneously to patterns in nutrient concentrations (phosphorous (P), nitrite and nitrate (NO₂/NO₃–N) and ammonium (NH₄–H) concentrations) and zooplankton biomass (daphnid cladocera and copepoda biomass); whereas lagged responses in an index of algal community composition are evident. A randomization approach to neural networks is employed to reveal individual and interacting contributions of nutrient concentrations and zooplankton biomass to predictions of phytoplankton biomass and community composition. The results show that patterns in chlorophyll aconcentrations are directly associated with P, NO₂/NO₃–N and daphnid cladocera biomass, as well as related to interactions between daphnid cladocera biomass, and NO₂/NO₃–N and P. Similarly, patterns in phytoplankton community composition are associated with NO₂/NO₃–N and daphnid cladocera biomass; however show contrasting patterns in nutrient– zooplankton and zooplankton–zooplankton interactions. Together, the results provide correlative evidence for the importance of nutrient limitation, zooplankton grazing and nutrient regeneration in shaping phytoplankton community dynamics. This study shows that artificial neural networks can provide a powerful tool for studying phytoplankton succession by aiding in the quantification and interpretation of the individual and interacting contributions of nutrient limitation and zooplankton herbivory on phytoplankton biomass and community composition under natural conditions.     

Genetically predicted selenium is negatively associated with serum TC,LDL-C and positively associated with HbA1C levels

Background & AimsPervious epidemiological evidence on the associations of selenium, zinc with lipid profile and glycemic indices was contradictory. The aim of this study was to investigate whether selenium and zinc were casually associated with lipid profile and glycemic indices using mendelian randomization (MR) analysis.MethodA two-sample MR was used to evaluate the causal-effect estimations. Summary statistics for selenium, zinc, lipids and glycemic indices were retrieved from previous large-scale genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) that independently and strongly associated with the selenium and zinc were selected as the instrumental variables. The casual estimates were calculated using inverse variance weighted method (IVW), with weighted median, MR-Egger, and MR-PRESSO test as sensitivity analysis, respectively.ResultsIn the standard IVW analysis, per SD increment in selenium was associated with an 0.077 mmol/L decrease of TC (95 %CI: −0.102,−0.052) and 0.074 mmol/L of LDL-C (95 %CI: −0.1,−0.048). Suggestive casual associations were found between selenium and insulin or HbA1c. With IVW method, per SD increase in selenium was associated with an 0.023 mmol/L increase of insulin (95 %CI: 0.001,0.045), and an 0.013 mmol/L increase of HbA1c (95 %CI: 0.003,0.023). The results were robust in the sensitivity analysis. Zinc was not casually associated with any of lipid and glycemic markers.ConclusionOur MR analysis provides evidence of the potential causal effect of Se on beneficial lipid profile, including decreased TC and LDL-C. Furthermore, suggestive casual evidence was suggested between Se and increased serum HbA1c levels. Careful consideration is required for the protective effects of Se supplementation. No casual-effect association was found between Zn and any indices of the lipid and glucose parameters.     

循环亚油酸水平与动脉粥样硬化风险的因果关联：一项两样本孟德尔随机化研究

摘要 目的：本文拟探讨遗传预测的循环亚油酸水平与不同部位动脉粥样硬化的因果关联。方法：采用两样本孟德尔随机化（Mendelian randomization, MR）研究方法,选择与亚油酸相关联的单核苷酸多态性位点（single nucleotide polymorphism, SNPs）作为工具变量（Instrument Variables, IVs）,评估遗传预测的循环亚油酸水平与不同部位动脉粥样硬化的因果关联。结果：逆方差加权法（Inverse Variance Weighted, IVW）分析结果显示,遗传预测的循环亚油酸水平与冠状动脉粥样硬化风险存在显著正相关（OR=1.32, 95% CI: 1.09-1.61, P=0.005）;循环亚油酸水平与脑动脉粥样硬化风险之间无因果关联 （OR=1.18, 95% CI: 0.63-2.23, P=0.602）。循环亚油酸水平与外周动脉粥样硬化风险存在显著负相关（OR= 0.55, 95% CI: 0.39-0.77, P=0.001）。循环亚油酸水平与其他动脉粥样硬化（不包括脑、冠状动脉和外周动脉）之间无显著的因果关联（OR=0.99, 95% CI: 0.81-1.21, P=0.916）。结论：遗传预测的循环亚油酸水平与冠状动脉粥样硬化及外周动脉硬化存在因果关联,亚油酸在动脉粥样硬化防治中的作用值得重视及进一步研究。     

Effects of Transect Selection and Seasonality on Lemur Density Estimates in Southeastern Madagascar

I investigated how transect type (trails vs. cut transects) and seasonality influenced density estimates for 5 lemur taxa (Avahi laniger, Cheirogaleus major, Eulemur rubriventer, Hapalemur griseus griseus, and Microcebus rufus) in the Vohibola III Classified Forest in SE Madagascar. I surveyed tree height and diameter and lemur populations from June 1 to December 28, 2004 along 2 1250-m trails local people used and 2 1250-m transects cut parallel to the trails in primary rain forest. Despite dendrometric variations within and between trails and transects, only density estimates of Hapalemur griseus griseus differed significantly by transect type. The spatial variation may be a result of removal by local people of giant bamboo, which is the main food for Hapalemur griseus griseus, along trails. Conversely, seasonality influenced density estimates for Cheirogaleus major, Eulemur rubriventer, Hapalemur griseus griseus, and Microcebus rufus. The temporal variations may be related to seasonal torpor for Cheirogaleus major and increased detection probabilities during periods of fruit exploitation for Eulemur rubriventer, Microcebus rufus, and Hapalemur griseus griseus. Transect type and seasonality did not affect density estimates for Avahi laniger, which may be related to the highly folivorous and low-energy diet of the nocturnal lemur. Researchers surveying lemurs along line transects should be aware that transect selection may influence density estimates for Hapalemur griseus griseus and that seasonality may influence density estimates for Cheirogaleus major, Eulemur rubriventer, Hapalemur griseus griseus, and Microcebus rufus.     

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.     

Be careful with your principal components

Principal components analysis (PCA) is a common method to summarize a larger set of correlated variables into a smaller and more easily interpretable axes of variation. However, the different components need to be distinct from each other to be interpretable otherwise they only represent random directions. This is a fundamental assumption of PCA and, thus, needs to be tested every time. Sample correlation matrices will always result in a pattern of decreasing eigenvalues even if there is no structure. Tests are, therefore, needed to discern real patterns from illusionary ones. Furthermore, the loadings of the vectors need to be larger than expected by random data to be useful in the calculation of PC‐scores. PC‐scores calculated from nondistinct PC's have very large standard errors and cannot be used for biological interpretations. I give a number of examples to illustrate the potential problems with PCA. Robustness of the PC's increases with increasing sample size but not with the number of traits. I review a few simple test statistics appropriate for testing PC's and use a real‐world example to illustrate how this can be done using randomization tests. PCA can be very useful but great care is needed to avoid spurious results.     

A combinatorial mutagenesis approach for functional epitope mapping on phage-displayed target antigen

Although multiple different procedures to characterize the epitopes recognized by antibodies have been developed, site-directed mutagenesis remains the method of choice to define the energetic contribution of antigen residues to binding. These studies are useful to identify critical residues and to delineate functional maps of the epitopes. However, they tend to underestimate the roles of residues that are not critical for binding on their own, but contribute to the formation of the target epitope in an additive, or even cooperative, way. Mapping antigenic determinants with a diffuse energetic landscape, which establish multiple individually weak interactions with the antibody paratope, resulting in high affinity and specificity recognition of the epitope as a whole, is thus technically challenging. The current work was aimed at developing a combinatorial strategy to overcome the limitations of site-directed mutagenesis, relying on comprehensive randomization of discrete antigenic regions within phage-displayed antigen libraries. Two model antibodies recognizing epidermal growth factor were used to validate the mapping platform. Abrogation of antibody recognition due to the introduction of simultaneous replacements was able to show the involvement of particular amino acid clusters in epitope formation. The abundance of some of the original residues (or functionally equivalent amino acids sharing their physicochemical properties) among the set of mutated antigen variants selected on a given antibody highlighted their contributions and allowed delineation of a detailed functional map of the corresponding epitope. The use of the combinatorial approach could be expanded to map the interactions between other antigens/antibodies.     

Testing for latitudinal and other body-size gradients

Testing for a relationship between the body size of animals and a gradient such as latitude is complicated by the fact that typically there is a single average size for each species, and each species occurs at several sample stations over the gradient. This results in standard tests for statistical significance being invalid. This problem can be overcome by using a randomization test. A more difficult problem, however, is determining whether the relationship between size and latitude is the same for two subfamilies of species. In this paper a general method for relating body size to latitude and subfamily differences is proposed, with the significance of effects determined by randomization. A simulation study suggests that this procedure has good properties. This approach to data analysis has promise both for the particular situation considered and for other related problems in biogeography.     

Evaluation of community-intervention trials via generalized linear mixed models

In community-intervention trials, communities, rather than individuals, are randomized to experimental arms. Generalized linear mixed models offer a flexible parametric framework for the evaluation of community-intervention trials, incorporating both systematic and random variations at the community and individual levels. We propose here a simple two-stage inference method for generalized linear mixed models, specifically tailored to the analysis of community-intervention trials. In the first stage, community-specific random effects are estimated from individual-level data, adjusting for the effects of individual-level covariates. This reduces the model approximately to a linear mixed model with the unit of analysis being community. Because the number of communities is typically small in community-intervention studies, we apply the small-sample inference method of Kenward and Roger (1997, Biometrics53, 983-997) to the linear mixed model of second stage. We show by simulation that, under typical settings of community-intervention studies, the proposed approach improves the inference on the intervention-effect parameter uniformly over both the linearized mixed-effect approach and the adaptive Gaussian quadrature approach for generalized linear mixed models. This work is motivated by a series of large randomized trials that test community interventions for promoting cancer preventive lifestyles and behaviors.     

A combinatorial mutagenesis approach for functional epitope mapping on phage-displayed target antigen: Application to antibodies against epidermal growth factor

Although multiple different procedures to characterize the epitopes recognized by antibodies have been developed, site-directed mutagenesis remains the method of choice to define the energetic contribution of antigen residues to binding. These studies are useful to identify critical residues and to delineate functional maps of the epitopes. However, they tend to underestimate the roles of residues that are not critical for binding on their own, but contribute to the formation of the target epitope in an additive, or even cooperative, way. Mapping antigenic determinants with a diffuse energetic landscape, which establish multiple individually weak interactions with the antibody paratope, resulting in high affinity and specificity recognition of the epitope as a whole, is thus technically challenging. The current work was aimed at developing a combinatorial strategy to overcome the limitations of site-directed mutagenesis, relying on comprehensive randomization of discrete antigenic regions within phage-displayed antigen libraries. Two model antibodies recognizing epidermal growth factor were used to validate the mapping platform. Abrogation of antibody recognition due to the introduction of simultaneous replacements was able to show the involvement of particular amino acid clusters in epitope formation. The abundance of some of the original residues (or functionally equivalent amino acids sharing their physicochemical properties) among the set of mutated antigen variants selected on a given antibody highlighted their contributions and allowed delineation of a detailed functional map of the corresponding epitope. The use of the combinatorial approach could be expanded to map the interactions between other antigens/antibodies.     

Sample size and power for the weighted log-rank test and Kaplan-Meier based tests with allowance for nonproportional hazards

Asymptotic distributions under alternative hypotheses and their corresponding sample size and power equations are derived for nonparametric test statistics commonly used to compare two survival curves. Test statistics include the weighted log-rank test and the Wald test for difference in (or ratio of) Kaplan-Meier survival probability, percentile survival, and restricted mean survival time. Accrual, survival, and loss to follow-up are allowed to follow any arbitrary continuous distribution. We show that Schoenfeld's equation—often used by practitioners to calculate the required number of events for the unweighted log-rank test—can be inaccurate even when the proportional hazards (PH) assumption holds. In fact, it can mislead one to believe that 1:1 is the optimal randomization ratio (RR), when actually power can be gained by assigning more patients to the active arm. Meaningful improvements to Schoenfeld's equation are made. The present theory should be useful in designing clinical trials, particularly in immuno-oncology where nonproportional hazards are frequently encountered. We illustrate the application of our theory with an example exploring optimal RR under PH and a second example examining the impact of delayed treatment effect. A companion R package npsurvSS is available for download on CRAN.     

Simultaneous confidence intervals from randomization tests with application in testing bioequivalence with multiple endpoints

We propose a method to construct simultaneous confidence intervals for a parameter vector from inverting a series of randomization tests (RT). The randomization tests are facilitated by an efficient multivariate Robbins–Monro procedure that takes the correlation information of all components into account. The estimation method does not require any distributional assumption of the population other than the existence of the second moments. The resulting simultaneous confidence intervals are not necessarily symmetric about the point estimate of the parameter vector but possess the property of equal tails in all dimensions. In particular, we present the constructing the mean vector of one population and the difference between two mean vectors of two populations. Extensive simulation is conducted to show numerical comparison with four methods. We illustrate the application of the proposed method to test bioequivalence with multiple endpoints on some real data.