The TOMMORROW study: Design of an Alzheimer's disease delay-of-onset clinical trial.

INTRODUCTION: Alzheimer's disease (AD) is a continuum with neuropathologies manifesting years before clinical symptoms; thus, AD research is attempting to identify more disease-modifying approaches to test treatments administered before full disease expression. Designing such trials in cognitively normal elderly individuals poses unique challenges. METHODS: The TOMMORROW study was a phase 3 double-blind, parallel-group study designed to support qualification of a novel genetic biomarker risk assignment algorithm (BRAA) and to assess efficacy and safety of low-dose pioglitazone to delay onset of mild cognitive impairment due to AD. Eligible participants were stratified based on the BRAA (using TOMM40 rs 10524523 genotype, Apolipoprotein E genotype, and age), with high-risk individuals receiving low-dose pioglitazone or placebo and low-risk individuals receiving placebo. The primary endpoint was time to the event of mild cognitive impairment due to AD. The primary objectives were to compare the primary endpoint between high- and low-risk placebo groups (for BRAA qualification) and between high-risk pioglitazone and high-risk placebo groups (for pioglitazone efficacy). Approximately 300 individuals were also asked to participate in a volumetric magnetic resonance imaging substudy at selected sites. RESULTS: The focus of this paper is on the design of the study; study results will be presented in a separate paper. DISCUSSION: The design of the TOMMORROW study addressed many key challenges to conducting a dual-objective phase 3 pivotal AD clinical trial in presymptomatic individuals. Experiences from planning and executing the TOMMORROW study may benefit future AD prevention/delay-of-onset trials.

Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease.

OBJECTIVE: To identify single-nucleotide polymorphisms (SNPs) associated with risk and age at onset of Alzheimer disease (AD) in a genomewide association study of 469 438 SNPs. DESIGN: Case-control study with replication. SETTING: Memory referral clinics in Canada and the United Kingdom. PARTICIPANTS: The hypothesis-generating data set consisted of 753 individuals with AD by National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association criteria recruited from 9 memory referral clinics in Canada and 736 ethnically matched control subjects; control subjects were recruited from nonbiological relatives, friends, or spouses of the patients and did not exhibit cognitive impairment by history or cognitive testing. The follow-up data set consisted of 418 AD cases and 249 nondemented control cases from the United Kingdom Medical Research Council Genetic Resource for Late-Onset AD recruited from clinics at Cardiff University, Cardiff, Wales, and King's College London, London, England. MAIN OUTCOME MEASURES: Odds ratios and 95% confidence intervals for association of SNPs with AD by logistic regression adjusted for age, sex, education, study site, and French Canadian ancestry (for the Canadian data set). Hazard ratios and 95% confidence intervals from Cox proportional hazards regression for age at onset with similar covariate adjustments. RESULTS: Unadjusted, SNP RS4420638 within APOC1 was strongly associated with AD due entirely to linkage disequilibrium with APOE. In the multivariable adjusted analyses, 3 SNPs within the top 120 by P value in the logistic analysis and 1 in the Cox analysis of the Canadian data set provided additional evidence for association at P< .05 within the United Kingdom Medical Research Council data set: RS7019241 (GOLPH2), RS10868366 (GOLPH2), RS9886784 (chromosome 9), and RS10519262 (intergenic between ATP8B4 and SLC27A2). CONCLUSIONS: Our genomewide association analysis again identified the APOE linkage disequilibrium region as the strongest genetic risk factor for AD. This could be a consequence of the coevolution of more than 1 susceptibility allele, such as APOC1, in this region. We also provide new evidence for additional candidate genetic risk factors for AD that can be tested in further studies.

A single-nucleotide polymorphism tagging set for human drug metabolism and transport.

Interindividual variability in drug response, ranging from no therapeutic benefit to life-threatening adverse reactions, is influenced by variation in genes that control the absorption, distribution, metabolism and excretion of drugs. We genotyped 904 single-nucleotide polymorphisms (SNPs) from 55 such genes in two population samples (European and Japanese) and identified a set of tagging SNPs that represents the common variation in these genes, both known and unknown. Extensive empirical evaluations, including a direct assessment of association with candidate functional SNPs in a new, larger population sample, validated the performance of these tagging SNPs and confirmed their utility for linkage-disequilibrium mapping in pharmacogenetics. The analyses also suggest that rare variation is not amenable to tagging strategies.

Case-control single-marker and haplotypic association analysis of pedigree data.

Related individuals collected for use in linkage studies may be used in case-control linkage disequilibrium analysis, provided one takes into account correlations between individuals due to identity-by-descent (IBD) sharing. We account for these correlations by calculating a weight for each individual. The weights are used in constructing a composite likelihood, which is maximized iteratively to form likelihood ratio tests for single-marker and haplotypic associations. The method scales well with increasing pedigree size and complexity, and is applicable to both autosomal and X chromosomes. We apply the approach to an analysis of association between type 2 diabetes and single-nucleotide polymorphism markers in the PPAR-gamma gene. Simulated data are used to check validity of the test and examine power. Analysis of related cases has better power than analysis of population-based cases because of the increased frequencies of disease-susceptibility alleles in pedigrees with multiple cases compared to the frequencies of these alleles in population-based cases. Also, utilizing all cases in a pedigree rather than just one per pedigree improves power by increasing the effective sample size. We demonstrate that our method has power at least as great as that of several competing methods, while offering advantages in the ability to handle missing data and perform haplotypic analysis.

Linkage analysis using single nucleotide polymorphisms.

We performed multipoint linkage analysis using 83 markers from the SNP Consortium (TSC) SNP linkage map in 3 regions covering 190 cM previously scanned with microsatellite markers and found to be linked to type 2 diabetes. Since the average linkage disequilibrium present in the TSC SNP marker clusters is relatively low, we assumed the intracluster genetic distances were a reasonable small nonzero distance (0.03 cM) and performed linkage analysis using GENEHUNTER PLUS and ASM linkage analysis software. We found that for the pedigree structures and missing data patterns in our samples the average information content in all three regions and the LOD score curves in two regions obtained from the TSC SNP markers were similar to results obtained from microsatellite marker maps with 10 cM average spacing. We also give an algorithm which extends the Lander-Green algorithm to permit multipoint linkage analysis of clusters of tightly linked markers with arbitrarily high levels of intracluster linkage disequilibrium.