Genetic Link Between Alzheimer’s and Heart Disease

Researchers at the Center for Precision Medicine at Edith Cowan University (ECU) have uncovered a significant genetic link between Alzheimer’s disease (AD) and several coronary heart disease (CHD)-related disorders and lipid classes, offering opportunities to improve health outcomes for two of the leading causes of death in Australia. The new study found that several factors related to heart disease, such as angina, atherosclerosis, ischemic heart disease, myocardial infarction and coronary heart disease, and lipids such as cholesterol, triglycerides and high- and low-density lipoproteins (HDL and LDL), may have similar biological causes to Alzheimer’s disease.

Coronary Heart Disease Linked to Cognitive Impairment and Risk of Dementia

This means that some of the same genes play a role in or are associated with these diseases. “There is a lot of evidence from observational and other studies showing an association between these diseases, but the complex biological mechanisms of Alzheimer’s disease are poorly understood and its link to lipids and CHD traits remains unclear,” said lead researcher and doctoral student at the Center for Precision Health, Artika Kirby.The study used a genetic approach to examine the complex relationships of these co-morbid conditions and provided new insights into their shared biological underpinnings.

According to Professor Simon Laws, Director of the Centre for Precision Health and co-supervisor of the study, the Center for Precision Health’s use of advanced statistical genetics approaches is significantly contributing to our understanding of the links between many of today’s most important health issues – this study highlights the strength of this approach. Dementia, of which Alzheimer’s disease is the leading cause, and coronary heart disease are the two leading causes of death for Australians. Researchers say there may be more to the link between these conditions than their association with poor health outcomes. There is growing evidence that coronary heart disease is associated with cognitive impairment and risk of dementia. Research suggests that individuals with CHD experience accelerated cognitive decline after diagnosis, and that CHD patients have a 26% higher relative risk of dementia. However, the nature of the relationship and the underlying mechanisms for the association between CHD and AD and cognitive impairment remain unclear. The association between CHD and AD may be partly due to common risk factors such as dyslipidemia and inflammation. Dyslipidemia and CHD have significant effects on human health and are considered to be major risk factors for AD, as well as being reported to be associated with AD.

There is also the possibility that all these factors share a common genetic predisposition. By applying genetic approaches to gain a deeper understanding of the relationship between Alzheimer’s and coronary artery disease – the two leading causes of death in Australia – researchers have gained new insights into the underlying mechanisms linking these diseases. These insights could lead to improvements in patient care and outcomes for these two leading health issues – not just in Australia, but worldwide.

17 Genes Involved in Coronary Heart Disease

But how does coronary heart disease develop? Using an advanced artificial intelligence tool, researchers at the Icahn School of Medicine at Mount Sinai have identified rare coding variants in 17 genes that provide insights into the molecular basis of coronary heart disease (CHD), the leading cause of morbidity and mortality worldwide. The discoveries, detailed in Nature Genetics, highlight genetic factors that impact heart disease and open new avenues for targeted treatments and personalized approaches to cardiovascular care.

The researchers used a in silico, or computational, coronary artery disease (CAD) score, ISCAD, that holistically maps CAD, as described in an earlier paper by the team in The Lancet. The ISCAD score takes into account hundreds of different clinical characteristics from the electronic health record, including vital signs, lab test results, medications, symptoms, and diagnoses. To create the score, they trained machine learning models on the electronic health records of 604,914 individuals from the UK Biobank, the All of Us Research Program, and the BioMe Biobank in this comprehensive meta-analysis. The score was then tested for an association with rare and very rare coding variants found in the exome sequences of these individuals. In addition, the research team conducted further research on the discovered genes to examine their role in causal risk factors for CAD, clinical manifestations of CAD, and their associations with CAD status in traditional large-scale genome-wide association studies, among other factors.

“Our results help us understand how these 17 genes are involved in coronary artery disease. Some of these genes are already known to influence the development of heart disease, while others have never been associated with it before,” says Ron Do, PhD, senior study author and Charles Bronfman Professor of Personalized Medicine at Icahn Mount Sinai. The study demonstrates how machine learning tools can uncover genetic insights that might be missed by conventional case-control comparisons. This could lead to new ways of identifying biological mechanisms of heart disease or gene targets for treatment.

Improving Patient Treatment Outcomes

Because they occur in only a small percentage of individuals, rare coding variants can have a significant impact on disease risk or susceptibility when present. Therefore, studying these variants is essential for understanding the genetic basis of disease and may provide insights into therapeutic targets. The study was driven by the challenges of identifying rare coding variants associated with CHD using conventional case-control methods over the past decade. The limitations of diagnostic codes in capturing the complexity of CHD pushed the researchers to explore new avenues of investigation.

Next, the researchers plan to further investigate the role of the identified genes in CAD biology and explore potential applications of machine learning in the genetic study of other complex diseases. This is part of their ongoing effort to advance the understanding of disease mechanisms, discover new treatments, and improve patient outcomes.