*For the original research project summary see below*
Almost every one of us has a family member who suffers from cognitive impairment or dementia, and there are not many things that scare many of us more than the fear that we forget our memories, who we are, and who our loved ones are. Yet, dementia affects many people at older ages, with the most common form of dementia being Alzheimer’s Disease. There are currently almost 10 million people affected by dementia in Europe. Devastatingly, there is no medical cure available to halt or reverse the progress of the disease. There is now some first evidence available that multimodal interventions can to some extent delay cognitive decline for those at risk. Consequently, we need to know more about who is at risk, and which behavioral interventions work for whom.
For the better part of a decade, I have been working in the field of cognitive aging and dementia, both at the university as a researcher at the Institute for Research on Socio-Economic Inequality, but I also volunteer in a worldwide network of young professionals, the World Young Leaders in Dementia, and I have realized Dementia Awareness Workshops with teenage students at a secondary school in Luxembourg.
Two questions have been driving my research during the last years, the first, how does the immediate and more distant environment, such as parental household, schooling and job opportunities, shape to which extent cognitive potential can be fully realized? It is quite common knowledge that the conditions during the life course determine the amount of cognitive reserve we need to buffer brain pathology later in life. So it should be extremely important to live in a society where children can go to those schools that best enable them to reach their full potential (opposite to societies where parental status determines what children can or cannot achieve in life). Equally, both girls and boys should be able to reach their full potential: First in school, and later in vocational training or higher education, and, even later, as adults during their working life. However, there is little research systematically investigating the determinants of cognitive functioning across different environments. Especially unequal educational systems and gender inequalities in different domains such as the labor market should have an impact on the building up of cognitive reserve, and this impact should still be visible at older ages.
The second research question is a more statistical one. Often when I ran analyses with the state-of-the-art methods in my field to try to explain cognitive functioning and aging, I was dissatisfied with the reduction of complexity in the models. When you investigate how cognitive functioning in older people evolves over time, you often see amazingly different trajectories. Some people unfortunately decline at a very early old age already. Some people show stable cognitive performance over a very long time, and of those, many even gain quite substantially during the first couple of testings. Some people have sharp drops, others more complex declines in their performance. An additional complexity arises with the many exposures that influence cognitive decline and incidence of dementia: From genetic risk to social and behavioral factors such as having good friends, good health behaviors, and good sleep, from clinical conditions to mere age, there are many, many risk and protective factors at work. These complexities are very hard to capture in conjunction with traditional models, and as convincing proof to this, we have strong difficulties to predict at individual level who will decline and who will remain stable. However, knowing better about the exact individual risks, we could ideally help to reduce risk of decline and support people to remain cognitively stable.
In a nutshell, these research questions have been guiding my ERC project proposal. First, I want to understand better the differences in cognitive aging of middle-aged and older people across different countries by investigating how inequalities in education and by gender at different points in the life course may determine cognitive reserve and, consequently, cognitive aging in later life. Second, I employ new machine learning techniques to overcome some of the limitations of traditional statistical modeling to explain exactly, who is at risk of cognitive aging, and what works for whom at which point in time to delay cognitive aging. Here, it is important to know that for complex (social and behavioral) constructs like cognitive functioning, it is not well-advised to blindly use machine learning techniques that have been developed for more traditional big data applications. In many cases in the social sciences, we will be interested in questions of causality – what exactly is driving cognitive decline, or, why do some people decline in their cognitive performance and others don’t? (Judea Pearl has just published The Book of Why, which I can only recommend for all that are new to questions of causality!). So, machine learning methods should be in those cases combined with methods to establish causal inference. Ideally, the project will, through the acknowledging and further developing of knowledge of different disciplines, such as social epidemiology, sociology, psychology, computational and data science, gain new insights on the drivers of cognitive aging.
The University of Luxembourg will be host institution of my project CRISP – Cognitive Aging: From Educational Opportunities to Individual Risk Profiles. ERC Starting Grants are awarded to talented early-career scientists with a scientific track record showing great promise and an excellent research proposal. The grants may be awarded up to 1.5 million Euros for a period of five years. Feel free to contact me at anja.leist(at)uni.lu.
ERC StG 2018 Project CRISP. Cognitive Aging: From Educational Opportunities to Individual Risk Profiles
Cognitive impairment and dementia have dramatic individual and social consequences, and create high economic costs for societies. In order to delay cognitive aging of future generations as long as possible, we need evidence which contextual factors are most supportive for individuals to reach highest cognitive levels relative to their potential. At the same time, for current older generations, we need scalable methods to exactly identify individuals at risk of cognitive impairment. The project intends to apply recent methodological and statistical advancements to, firstly, comparatively assess contextual influences on cognitive aging, with a focus on inequalities related to educational opportunities, gender and economic inequalities, using longitudinal, population-representative, harmonized cross-national aging surveys that will be merged with contextual information. Secondly, the project will employ recent, robust statistical learning techniques to quantify the ability of individual characteristics, such as socioeconomic background, and behavior changes to predict cognitive aging and diagnosis of dementia. Applying statistical learning techniques in the field of cognitive reserve will open new research avenues for efficient handling of large amounts of data, among which most prominently the accurate prediction of health and disease outcomes. Quantifying the role of contextual inequalities related to education and gender will guide policymaking. Assessing risk profiles of individuals at risk of cognitive aging will support efficient and scalable risk screening of individuals. Identifying the value of behavioral interventions to delay cognitive impairment will guide treatment plans for individuals affected by dementia.