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Observational studies in Alzheimer disease: bridging preclinical studies and clinical trials

Abstract

Recent high-profile failures of Alzheimer disease treatments at the clinical trial stage have led to renewed efforts to identify and test novel interventions for Alzheimer disease and related dementias (ADRD). In this Perspective, we highlight the importance of including well-designed observational studies as part of these efforts. Observational research is an important cornerstone for gathering evidence on risk factors and causes of ADRD; this evidence can then be combined with data from preclinical studies and randomized controlled trials to inform the development of effective interventions. Observational study designs can be particularly beneficial for hypothesis generation, posing questions that are unethical or impractical for a trial setting, studying life-course associations, research in populations typically not included in trials, and public health surveillance. Here, we discuss each of these situations in the specific context of ADRD research. We also highlight novel approaches to enhance causal inference and provide a timely discussion on how observational epidemiological studies help provide a bridge between preclinical studies and successful interventions for ADRD.

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Fig. 1: Natural history of Alzheimer disease and related dementias and timing of interventions.
Fig. 2: Life-course risk factors for ADRD.
Fig. 3: Causal models for randomized and quasi-experimental studies.

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W.D.B. researched data for the article. All authors contributed substantially to discussion of the content. All authors wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Kristine Yaffe.

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The research of W.D.B. and K.Y. is supported by the National Institutes of Health (NIH)/National Institute on Aging (NIA) grants NIA K01AG062722 (W.D.B) and R35AG071916 (K.Y.). The authors declare no other competing interests.

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Glossary

Amyloid cascade hypothesis

The hypothesis that amyloid-β is the main pathological agent that causes Alzheimer disease.

Big data

Large-scale data comprising many observations and/or many traits.

Causal inference

Inferring the independent effect of one factor on an outcome, typically from data of observations.

Pleiotropic effects

When one gene influences two or more phenotypic traits.

Pragmatic trials

Clinical trials developed after drug approval to test the effectiveness of a drug in a real-world setting.

Real-world data

Observational data that represent real-world settings, for example, health-care records in a large health system.

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Brenowitz, W.D., Yaffe, K. Observational studies in Alzheimer disease: bridging preclinical studies and clinical trials. Nat Rev Neurol 18, 747–757 (2022). https://doi.org/10.1038/s41582-022-00733-7

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