Disease and treatment

Inconclusive research may end up benefitting people with dementia

Fifty million people worldwide suffer from the devastating effects of dementia on memory and behaviour. Alzheimer’s and vascular dementia are the two most common types; although the symptoms are similar, the treatment differs. Researchers tried to develop a new tool to distinguish these two types of dementia. The results were disappointing. Now it turns out that this is probably because many people have both types, so what looked like a failure may prove to be a breakthrough in treating people with dementia.

Dementia is rarely an isolated disease. People whose brain is incapacitated by dementia often have numerous comorbidities – but studies do not agree on which ones are more or less prevalent among people with dementia. Researchers therefore searched Denmark’s health registries to see whether the comorbid diseases among people with different types of dementia differ. The goal was to determine whether the type of dementia is associated with comorbidity.

“Since the different types of dementia require very different treatment, we hoped that the comorbidities might be able to distinguish between types of dementia. However, this only succeeded for people who developed dementia at an early age. When we examined this more closely, we found that the reason we did not find a difference could be that many people have multiple types of dementia, so they should actually be treated for these multiple types simultaneously,” explains Søren Brunak, Professor and Group Leader, Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen.

Only found what was already known

The diagnostic process to evaluate the underlying cause of dementia can be challenging and inefficient, posing difficulty in differentiating between Alzheimer’s disease and vascular dementia, which was the original purpose of the project. About 60% of people with dementia have Alzheimer’s disease, characterized by an abnormal accumulation of amyloid beta protein, resulting in senile plaques that can disrupt signals between neurons and cause inflammation. In contrast, vascular dementia, which affects 20% of the people with dementia, is caused by reduced blood supply to the brain due to deteriorating blood vessels.

“Although the symptoms are similar, the causes and treatment differ widely, but doctors are often unsure of the correct diagnosis. We hoped by collecting data from a large number of patients that we could distinguish between the two types by statistically assessing the other diseases people with dementia have,” explains Isabella Friis Jørgensen, Postdoctoral Fellow, Novo Nordisk Foundation Center for Protein Research.

The researchers collected and compared data from 1994 to 2016 from Denmark’s health registries on the hospital encounters of more than 73,000 people with dementia to determine whether dementia is associated with the presence of other diseases. Unfortunately, the results were disappointing.

“Overall, we only got results we already knew. Our analysis could only identify a link to comorbidities for people with early onset dementia such as Down syndrome, alcohol-related diagnoses and cardiovascular disease that we already know are risk factors,” says Isabella Friis Jørgensen.

When the researchers examined the literature further, however, they were surprised but found a really good explanation for the frustrating results: mixed dementia.

“We had initially eliminated the people who had shown signs of both types of dementia from our analysis, but as we read through the literature based on brain scans and autopsies of patients, we realized that mixed dementia is far more common,” says Isabella Friis Jørgensen.

Potentially serendipitous

Whether one type of dementia leads to another is not known, but the researchers found that one comorbid condition of Alzheimer’s dementia turns out to be vascular dementia – and vice versa.

“New studies will naturally be needed to substantiate this result, and we are already well underway with these, but the more we analyse the data, the more convinced we become, especially because it all just suddenly makes really good sense,” explains Søren Brunak, and continues “Mixed etiologies – different mechanisms leading to highly similar outcomes – is a general problem in diagnosing diseases, in which several routes can lead to the same condition. Unravelling these mixtures is the primary goal of precision medicine”.

In addition to the challenge of making the right diagnosis, one major challenge of treating people with dementia has also been the difficulties in creating effective treatment.

“If you give a person with mixed dementia medicine for Alzheimer’s, it may well be effective against Alzheimer’s, but if they also have vascular dementia, they still show symptoms of dementia, which incorrectly indicates that the medication is not effective,” says Søren Brunak.

The researchers hope that they can return to the original study once everyone with mixed dementia is identified.

“Maybe we will find some comorbidity patterns among people with only one type of dementia. We cannot see the patterns now, but if our theory is valid, this may be serendipitous, so that we can both improve diagnosis and optimize the treatment of people with dementia,” concludes Søren Brunak.

Age‐stratified longitudinal study of Alzheimer’s and vascular dementia patients” has been published in Alzheimer’s & Dementia. The authors are employed at the Novo Nordisk Foundation Center for Protein Research, University of Copenhagen.

Søren Brunak
Research director, professor
Søren Brunak is a leading pioneer in the biomedical sciences through invention and introduction of new computational strategies for analysis of biomedical data for use in molecular biology, medicine and biotechnology. His main achievements can be divided into two categories: 1) new, advanced bioinformatics and systems biology techniques, and 2) discovery of biological mechanisms, revealed by the use of these methods in a wide range of biological systems. Søren Brunak has been working within bioinformatics and computational biology since mid-1980s. In the early data-poor period Søren Brunak pioneered the introduction of new computational strategies for analysis of biological data of relevance in molecular biology, medicine and biotechnology – in particular machine learning techniques. In 1993 Søren Brunak became the founding Director of the Center for Biological Sequence Analysis (CBS) at the Technical University of Denmark (DTU), heading a multi-disciplinary research group of molecular biologists, biochemists, medical doctors, physicists, and computer scientists. In 2007 Søren Brunak became one of the founding research directors at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen. His program for Disease Systems Biology combines molecular level systems biology and the analysis of phenotypic data from the healthcare sector. In 2011 Søren Brunak was also one of the founders of the Novo Nordisk Foundation Center for Biosustainability at DTU, where he led the Section for Metagenomic Systems Biology until 2013. He continues to be affiliated professor at DTU (DTU Bioinformatics) as well as at Copenhagen University Hospital (Rigshospitalet). The impact of Brunak’s research is in particular a consequence of his ability to combine scientific disciplines in novel ways, including computer technology (hardware and software), physics, biology, biomedical and biotechnological insights. His multi-disciplinary approaches, where concepts from different areas have been combined, have led to advances in the understanding of the function of biological systems, and thereby fundamentally improved the possibilities for control of disease via novel intervention strategies, and enhancement of health in general. Søren Brunak has been a member of the Royal Swedish Academy of Sciences since 2016, a member of the Royal Danish Academy of Sciences and Letters since 2004 and a member of the European Molecular Biology Organization since 2009. Søren Brunak has published close to 300 papers in international peer reviewed scientific journals (excluding proceedings), co-authored four books, three proceedings and edited books.
Isabella Friis Jørgensen
The Brunak Group aims for understanding multi-morbidity disease progression patterns and their relation to treatment events. The group integrates heterogeneous life science data from the molecular and clinical domains and is also engaged in methodology of translational utility, such as techniques of relevance within precision medicine. Research focus The Brunak Group has specific interest in genes and proteins, which play a role in several diseases, genes that may rationalize clinically observed patterns of multi-morbidity, or be of interest in relation to treatment strategies in the domain of chronic pathology. The group aims for discriminating between treatment-related disease correlations and other comorbidities, stratifying patients not only from their genotype but also on phenotypic data from resources such as clinical descriptions in electronic medical records. “Together with our secure supercomputing infrastructure, that is designed to handle population-wide data from Denmark and other countries, our goal is to complement classical epidemiology towards disease-spectrum wide analyses in a lifelong perspective, that can take events separated by long time periods into account,” says Professor and Group Leader Søren Brunak. The human genome, proteome variation and personalized medicine are themes with a strong focus in the group. In particular the ranking of treatment options and the reduction of patient-specific adverse drug reactions. Data integration and machine learning methods development in the big biomedical data domain is a major theme, as is the design of supercomputing infrastructure and private cloud solutions needed for person-sensitive data integrity. Main findings From Danish population-wide health data, we have developed a disease trajectory concept that can stratify patients according to longitudinal patterns rather than conventional subgroups of a single disease. The concept can, for example, estimate the mortality risk from a long-term prehistory of a single diagnosis. The approach can aggregate long and short timescales, for example combining 20-40 years of disease history with high-frequency data from short timescales from a single admission.