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Amid the increasing prevalence of Alzheimer’s disease and related dementias (ADRD), a team led by researchers from Harvard Medical School and Harvard T.H. Chan School of Public Health has conducted what they describe as the most extensive systematic evaluation to date of associations between ADRD and nearly 200 environmental exposures — from pollution to diet to social connections.
The researchers also produced a first-of-its-kind exposomic risk score that can help predict people’s risk of these diseases and could therefore contribute to prevention strategies.
The study, supported in part by federal funding, was published Sept. 29 in Alzheimer’s & Dementia.
Co-senior authors Chirag Patel, associate professor of biomedical informatics in the Blavatnik Institute at HMS, and Peng Gao, assistant professor of environmental health and exposomics at Harvard Chan School, study the exposome — all the environmental exposures people face across their lifespan — and how these exposures impact health.
Although genetic factors are involved in ADRD, most cases cannot be attributed to genetics alone, and previous research has suggested that environmental exposures also play a significant role.
The study analyzed data from the UK Biobank — a database that collects health, genetic, and lifestyle data over time — collected from nearly 500,000 participants for a median of 15.3 years. Patel, Gao, and colleagues conducted an exposome-wide association study examining 193 exposures across five domains: chemical, such as air pollutants and medications; physical, such as noise; lifestyle, such as diet, physical activity, and sleep; social, such as education and socioeconomic status; and ecosystems, such as access to green space.
An association with ADRD doesn’t necessarily mean that these factors are causes of ADRD. For example, the study found that use of multiple medications was associated with higher ADRD risk, which the authors said likely reflects underlying health complexity rather than the direct effects of the drugs. But evaluating these multiple, overlapping exposures simultaneously may help identify high-risk populations that could be targeted for public health interventions.
The study identified 76 exposures significantly linked with ADRD and 34 linked with Alzheimer’s disease specifically. In the social domain, unemployment, financial difficulties, lower educational attainment, and poor housing conditions, such as renting rather than owning a home, were associated with increased ARDR risk. Lifestyle risk associations included never eating eggs, current smoking, improper alcohol consumption, frequent daytime dozing, and sleeping more or less than the optimal amount. Chemical exposures, particularly nitrogen dioxide air pollution, were also linked with higher ADRD risk.
Additionally, the study found several factors associated with lower ADRD risk, including physical activities such as walking for pleasure, other exercise, and heavy do-it-yourself activities like yard work; eating cheese and poultry; computer and mobile phone use; living with a partner; and higher household income. As with factors linked to higher risk, the authors cautioned that these are associations from an observational study and that further research is needed to rule out reverse causation and confounding.
The researchers also developed a novel exposome-based ADRD risk prediction model and an exposomic risk score to help stratify distinct risk groups in a population.
Adding environmental exposures to a model based on demographics and APOE genotype — the best-known genetic risk factor for Alzheimer’s — improved the model’s ability to predict who would develop ADRD. People with risk scores in the highest quarter were nearly three times as likely to develop ADRD during follow-up as those in the lowest quarter. The authors noted that the score still needs to be validated in other, more diverse populations.
The findings could support the development of multi-component intervention strategies targeting modifiable exposures for ADRD prevention — an effort, the authors wrote, “which may prove essential for reducing the growing burden of dementia.”
Adapted from a Harvard Chan School news story.
Authorship, funding, disclosures
Chenshuang Li is the first author of this study. Additional authors include Chenxu Zhao, Yiqun Lan, Randall J. Ellis, Shakson Isaac, Sai Zhang, Gary W. Miller, Yi Sun, Shalini Jain, and Hariom Yadav.
This work was supported by the Harvard Data Science Initiative Faculty Special Projects Fund and the National Institutes of Health (grants R01AG074372, U24ES036819, and R01ES032470).
