In a recent study published in the JAMA Network Open, researchers performed a genetic association study using a Mendelian Randomization (MR) framework to assess associations between genetically determined modifiable risk factors, such as high-density lipoprotein (HDL) cholesterol and systolic blood pressure (SBP) and Alzheimer's disease (AD).
They worked with a large genomic dataset, the European Alzheimer & Dementia Biobank (EADB), encompassing 39,106 participants with clinically diagnosed AD and 401,577 control participants without AD.
Study: Genetic Associations Between Modifiable Risk Factors and Alzheimer Disease. Image Credit: LightFieldStudios/Shutterstock.com
Per recent estimates, the prevalence of dementia might triple by 2050. According to other scientific reports, up to 40% of dementia cases are preventable simply by modulating 12 risk factors.
Thus, knowledge of the genomic basis of associations between modifiable risk factors and dementia could inform preventive and therapeutic strategies for dementia, including AD.
Both observational studies and randomized clinical trials (RCTs) have identified associations between risk factors and dementia; however, their findings have remained limited and were not equivalent to causality.
On the contrary, MR design uses genetic variants to find potential causal relationships between modifiable risk factors and outcomes of dementia.
The MR approach could also guide whether a comprehensive RCT is meaningful or needed.
Large-scale genome-wide association studies (GWAS) have been done only for AD, the most common type of dementia. These studies have found inconclusive evidence of the association between blood pressure (BP), body mass index (BMI), smoking, and alcohol consumption, like risk factors and AD.
Since many biases and study designs might have impacted the findings of these studies, there is a need for more powerful MR studies to test genomic associations between AD and modifiable risk factors.
About the study
In the present study, researchers used a two-sample univariable and multivariable MR framework to perform a genetic association study to extensively delineate potential causes of modifiable risk factors for AD to inform the development of new drugs for AD prevention.
They selected independent genetic variants related to these factors as instrumental variables to fetch primary outcome data for AD per EADB as odds ratios (ORs) and 95% confidence intervals (CIs).
To align estimates of associations between genetic variants, risk factors, and AD on the same allele, the team harmonized the estimates and removed ambiguous variants.
For primary study analysis, they used the inverse-variance weighted (IVW) method, which combines single nucleotide variants (SNV)-specific estimates computed using Wald ratios.
This method works on the assumption that key MR assumptions are not violated, thus, and constraining intercepts to zero.
Conversely, the MR-Egger method is statistically less efficient, i.e., it yields wider CIs but provides a causal estimate accounting for horizontal pleiotropy.
Likewise, they performed multivariable MR for estimating correlated risk factors, e.g., apoA1 and apoB, which are correlated with HDL and LDL cholesterol, respectively. In this way, they fetched the direct effect of exposure to each factor in the model with no confounding.
The team performed four additional sensitivity analyses; notably, they performed all the study analyses between April 12 and October 27, 2022.
In the EADB-diagnosed cohort, 39,106 and 4015,77 participants had clinically diagnosed AD and no AD, respectively. The mean age of participants with AD ranged between 72 and 83 years, and 54% to 75% of participants were female.
Likewise, the mean age of control participants ranged between 51 to 80 years, and 48% to 60% of them were females.
Genetically ascertained HDL cholesterol levels were associated with increased odds of AD; accordingly, the authors noted OR per 1-SD increase of 1.10 [95% CI]. Genetically determined high systolic BP was associated with an increased risk of AD after accounting for diastolic BP, and the OR per 10–mm Hg increase was 1.22 [95% CI].
In a second analysis, the authors excluded the entire UK Biobank from the EADB consortium to minimize bias due to overlapping samples. Yet, the odds for developing AD remained comparable for HDL cholesterol and systolic BP, with OR per 1-SD unit increases of 1.08 and OR per 10–mm Hg increase of 1.23, respectively.
Overall, genetically identified high HDL cholesterol levels and higher SBP increased the likelihood of developing AD.
According to the authors, this is the first study to identify an association between high HDL cholesterol levels and higher AD risk in an extensive range of complementing analyses.
The genetic instruments for HDL cholesterol used in this study, e.g., cholesteryl ester transfer protein, and hepatic lipase, to name a few, further strengthened the validity of the study findings.
Since the identified genetic associations are novel, the study results might inspire the development of new drug treatments to prevent dementia, including AD, and diagnosis of dementia at an early stage.
Luo, J. et al. (2023) "Genetic Associations Between Modifiable Risk Factors and Alzheimer Disease", JAMA Network Open, 6(5), p. e2313734. doi: 10.1001/jamanetworkopen.2023.13734. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2805006
Posted in: Medical Science News | Medical Research News | Medical Condition News | Healthcare News
Tags: Alcohol, Allele, Alzheimer's Disease, Blood, Blood Pressure, Body Mass Index, Cholesterol, Dementia, Drugs, Genetic, Genome, Genomic, Lipase, Lipoprotein, Nucleotide, Protein, Smoking, UK Biobank
Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.
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