Comparison of Cognitive Intervention Strategies for Older Adults With Mild to Moderate Alzheimer's Disease: A Bayesian Meta-analytic Review

Objectives

We aimed to identify the best form of cognitive therapy among 3 main cognitive interventions of Alzheimer's disease (AD) including cognitive training (CT), cognitive stimulation (CS), and cognitive rehabilitation (CR).

Trees Assembling Mann‐Whitney Approach for Detecting Genome‐Wide Joint Association Among Low‐Marginal‐Effect Loci

Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LMEs) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high‐dimensional data (e.g., genome‐wide data) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann‐Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood ratio‐based Mann‐Whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions. For instance, in a simulation with 20 interacting LME loci, TAMW attained a higher power (power = 0.931) than both MDR (power = 0.599) and LRMW (power = 0.704). In an empirical study of 29 known Crohn's disease (CD) loci, TAMW also identified a stronger joint association with CD than those detected by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct a genome‐wide analysis. The analysis of 459K single nucleotide polymorphisms was completed in 40 hrs using parallel computing, and revealed a joint association predisposing to CD (P‐value = 2.763 × 10^?19). Further analysis of the newly discovered association suggested that 13 genes, such as ATG16L1 and LACC1, may play an important role in CD pathophysiological and etiological processes.     

Willingness-to-pay for predictive tests with no immediate treatment implications: a survey of US residents

We assessed how much, if anything, people would pay for a laboratory test that predicted their future disease status. A questionnaire was administered via an internet-based survey to a random sample of adult US respondents. Each respondent answered questions about two different scenarios, each of which specified: one of four randomly selected diseases (Alzheimer's, arthritis, breast cancer, or prostate cancer); an ex ante risk of developing the disease (randomly designated 10 or 25%); and test accuracy (randomly designated perfect or 'not perfectly accurate'). Willingness-to-pay (WTP) was elicited with a double-bounded, dichotomous-choice approach. Of 1463 respondents who completed the survey, most (70-88%, depending on the scenario) were inclined to take the test. Inclination to take the test was lower for Alzheimer's and higher for prostate cancer compared with arthritis, and rose somewhat with disease prevalence and for the perfect versus imperfect test [Correction made here after initial online publication.]. Median WTP varied from $109 for the imperfect arthritis test to $263 for the perfect prostate cancer test. Respondents' preferences for predictive testing, even in the absence of direct treatment consequences, reflected health and non-health related factors, and suggests that conventional cost-effectiveness analyses may underestimate the value of testing.     

Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression

To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome-wide association studies with the case-control design. Due to linkage disequilibrium (LD), multi-marker association tests can gain power over single-marker tests with a Bonferroni multiple testing adjustment. Among many existing multi-marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance-based regression (GDBR) approach aims to detect some high-order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time-consuming permutations for deriving P-values and covariates, including gene-gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P-value combining method can boost statistical power by incorporating information from allele frequencies, Hardy-Weinberg disequilibrium, LD patterns, and other higher-order interactions among multi-markers as captured by GDBR.     

Antihypertensive Medication Classes and the Risk of Dementia: A Systematic Review and Network Meta-Analysis

ObjectivesTo systematically review and synthesize the evidence on differential associations between antihypertensive medication (AHM) classes and the risk of incident dementia.DesignSystematic review and random effects frequentist network meta-analysis. Embase, MEDLINE, and the Cochrane library were searched from origin to December 2019.Setting and participantsRandomized controlled trials (RCTs) and prospective cohort studies that compared associations of different AHM classes with incident all-cause dementia and/or Alzheimer's disease over at least 1 year of follow-up.MeasuresAll cause dementia and/or Alzheimer's disease.ResultsFifteen observational studies and 7 RCTs were included. Data on AHM classes were available for 649,790 participants and dementia occurred in 19,600 (3.02%). Network meta-analysis showed that in observational studies, treatment with either calcium channel blockers (CCBs) or angiotensin II receptor blockers (ARBs) was associated with lower dementia risks than treatment with other antihypertensives: CCBs vs angiotensin converting enzyme inhibitors (ACE inhibitors) (HR=0.84, 95% CI 0.74-0.95), beta blockers (HR=0.83, 95% CI 0.73-0.95) and diuretics (HR=0.89, 95% CI 0.78-1.01) and ARBs vs ACE inhibitors (HR=0.88, 95% CI 0.81-0.97), beta blockers (HR=0.87, 95% CI 0.77-0.99), and diuretics (HR=0.93, 95% CI 0.83-1.05). There were insufficient RCTs to create a robust network based on randomized data alone.Conclusions and ImplicationsRecommending CCBs or ARBs as preferred first-line antihypertensive treatment may significantly reduce the risk of dementia. If corroborated in a randomized setting, these findings reflect a low-cost and scalable opportunity to reduce dementia incidence worldwide.     

Confronting complexity in late-onset Alzheimer disease: application of two-stage analysis approach addressing heterogeneity and epistasis

Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene-gene interactions in the second stage. We applied this two-stage analysis strategy to late-onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in leucine-rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in three genes--urokinase-type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)--were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.