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AlzRisk Methods
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1. Introduction

Alzheimer disease (AD) is a complex disorder estimated to affect over five million people in the United States alone (Alzheimer's Association). The public health impact of AD is substantial, and will continue to grow with increasing longevity and the aging of the Baby Boom generation, 10 million of whom are expected to develop AD in their lifetimes (Alzheimer's Association). However, consistent with other disorders of complex etiology, establishing risk factors for AD remains a challenge. While the precise etiologic mechanisms remain unknown, a variety of risk factors including age, family history, and the ε4-allele of the APOE gene have been identified. Multiple lines of evidence indicate that additional genetic factors play a role in the development of AD, and our sister project, AlzGene, (available at http://www.alzgene.org) aims to catalog findings regarding putative AD genes. However, much of that same body of evidence indicates that non-genetic risk factors have a role as well.

Cohort studies of aging populations have contributed valuable information on the impact of various risk factors on AD risk, and more recently, additional data have become available as a substantial number of well-established cohorts originally designed to characterize non-AD outcomes (e.g., cardiovascular disease) are collecting data on cognitive status. Collectively, these studies have a wealth of primary information collected many years before participants were at risk for AD. However, while some risk factors have been replicated across multiple studies (e.g., increased risk among those who have type 2 diabetes [Cukierman, 2005]), estimates of the magnitude of the effect of these factors have varied widely. And for other risk factors, studies are less consistent. Moreover, the volume of results already published from both types of cohort studies is substantial, and many more results are anticipated in the coming years, so tools to organize and summarize the relevant findings will be increasingly valuable.

2. Database Organization and Methods

Overview

The AlzRisk database aims to provide a comprehensive, unbiased, centralized, publicly available and regularly updated collection of epidemiologic reports that evaluate environmental (i.e., non-genetic) risk factors for Alzheimer disease (AD) in well-defined cohorts. Eligible publications are identified through contact with each cohort study supplemented by a systematic review of the literature. For each risk factor, we aim to provide a table or tables of all peer-reviewed studies published in English, and include data on the cohort, the distribution of the risk factor (the “exposure”), the analytic methods, and the effect size and statistical significance of the observed association. Many of the papers identified will provide results on multiple risk factors and/or multiple definitions of the same risk factor. Where the number of results for a given risk factor is substantial, we group them by the definition of the risk factor (e.g., diagnosis of hypertension vs. continuously measured systolic blood pressure). Within each table, where comparable data on the exposures (e.g., risk ratio for hypertensives vs. non-hypertensives, risk ratio per 10 mm increase in systolic blood pressure) is available (or can be derived) in at least four cohort samples, we present updated random-effects meta-analyses (see Meta-Analysis Methods). In some papers, the approach for categorizing or otherwise describing the exposure does not conform to the approach used by most other papers. However, we still list such papers and their results on the risk factor summary pages of the AlzRisk website.

To ensure the highest degree of objectivity regarding the posted information, only studies published or in press in peer-reviewed journals available in English are considered for inclusion into the database. Studies must have been conducted in well-defined cohorts, allowing for the inclusion of findings from both cohort studies and nested case-control studies. In addition, the results that we present on the site must be adjusted, at minimum, for age and sex (as appropriate). The information in the published report must be sufficient for us to discern fundamental details about the study and the results. Abstracts presented at scientific meetings or findings reported in non-peer-reviewed publications are not considered for inclusion. The database is currently in development, so data are available only for a limited selection of risk factors, with additional risk factors to be added in the coming year.

Meta-analysis Methods

For all risk factors for which comparable exposure data are available in four or more independent samples, hazard ratios (HRs) and 95 percent confidence intervals (CIs) are recorded or calculated from the reported data in the report. Summary HRs and 95 percent CIs are calculated using the DerSimonian and Laird (1986) random effects model, which utilizes weights that incorporate both within-study and between-study variance. This procedure is done including all studies irrespective of publication order (denoted by "All Studies" on the meta-analysis figures) and repeated after exclusion of the initial report ("All Excl Initial Report"). Overlapping samples (of which usually only the largest is included) and studies with missing data are indicated on the meta-analysis graphs. Occasionally, when findings are available from only three independent samples and we anticipate that findings from more samples are forthcoming, we will conduct meta-analyses of the existing three sets of findings. Please note that when only a few studies are included in the meta-analyses (i.e., less than ~10), the random effects model may yield summary HRs and confidence bounds that are slightly anti-conservative.

To allow a visual assessment of the presence of publication bias (or other sorts of reporting bias), we use a Begg modified funnel plot, which depicts the exposure level-specific HR (on a logarithmic scale) against its standard error for each report (Egger, 1997). Note that the power to detect deviations from a symmetrical distribution is limited, especially for analyses based on less than ~20 individual studies.

References

Alzheimer's Association. Alzheimer's Facts and Figures, available at www.alz.org/alzheimers_disease_facts_figures.asp. Accessed Aug 15 2008.

Cukierman T, Gerstein HC, Williamson JD. Cognitive decline and dementia in diabetes–systematic overview of prospective observational studies. Diabetologia 2005; 48: 2460–2469. Abstract

DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986 Sep;7(3):177-188. Abstract

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997 Sep 13;315(7109):629-634. Abstract

 
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