What is MELODEM?

For the best viewing experience, make sure that you access MELODEM via this website (it should have “sites” in the URL):   https://sites.bu.edu/melodem/

MELODEM was originally organized around five areas that represent major challenges in dementia research:

• (1) SELECTION – Selection issues in dementia research arise from several sources: differential attrition of enrolled participants, differential survival of enrolled participants, and differential enrollment, either due to refusal to participate or differential survival up to the moment of study initiation. Each of these processes can bias effect estimates. Spurious associations between the putative risk factor and cognitive decline or dementia can occur when selection processes are related to cognitive status and the exposure of interest (or their determinants). The bias is not necessarily towards the null (i.e., which would tend to mask an association), and can sometimes reverse the direction of association (i.e., making harmful exposures appear protective or protective exposures appear harmful).
• (2) MEASUREMENT – Measurement challenges in dementia research result from the disjuncture between disease pathophysiology and clinical and research measures, due to imperfect validity and reliability. For example, performance on neuropsychological tests does not precisely reflect biological functioning and capacity of the brain. Similar measurement challenges pertain to measures of the consequences, severity, and progression of dementia, including functional dependency, neuropsychiatric symptoms and behavioral patterns. Validity refers to whether a measurement instrument assesses the phenomenon of interest. Gold standard measures can be used to assess the validity of alternative measurements, but there is often no clear gold standard in dementia research. Measures valid for one group of people may not be valid for another, leading to biased estimates of disparities and risk factor effects. Reliability is the proportion of variability in a measure explained by the construct of interest, as opposed to the proportion attributable to measurement error (random fluctuations in the measurement not reflecting changes in the underlying construct).
• (3) PHENOTYPIC HETEROGENEITY – This considers clinical issues linked to the heterogeneity in cognitive decline and Alzheimer’s Disease, and the associated methodological challenges. One particular question considers whether there may be subgroups of typical Alzheimer’s disease. There are many forms of clinical heterogeneity that have been identified (imaging, cognitive, fluid biomarkers, etc.). Are there patterns that may be used to sub-divide people with Alzheimer’s disease into groups that share similarities within the group and differ from members of other groups? In a precision medicine context, it may be also interesting to identify groups of individuals that are at high risk to develop AD, to start patient management earlier and improve individual health care. Finally, equally as important, how do we assess and validate the final classification ? What criteria should be considered to ensure that the clusters are clinically meaningful?
• (4) TIME-VARYING EXPOSURES – Pathological changes in the brain are evident at least two decades prior to clinical diagnosis of dementia. Thus the effect that an exposure exerts on a cognitive-related outcome may depend critically on that exposure’s timing, and the relevant timing likely differs based on whether the exposure influences pathogenesis, disease progression, and/or maintenance of function. Distinguishing the relevant etiologic period for an exposure is essential for guiding clinical decisions and preventive interventions.
• (5) HIGH-DIMENSIONAL DATA – Dementia research, as most health research fields, is facing new challenges and opportunities with growing sources of data and the emergence of high-dimensional data. These data include administrative databases, genomics data, brain imaging data (MRI, PET), biomarkers panels assessing gene expression and metabolic pathways, among many others. Challenges in the statistical analyses of high-dimensional data are numerous, even when the number of observations is much larger than typically available in research cohorts. Big data does not necessarily resolve the familiar internal and external validity challenges in epidemiological studies and may in some situations exacerbate challenges with measurement validity and selection bias.

When MELODEM was established in 2012, five working groups were created based on these topics. Now, these areas serve more as a framework in leading discussion and in emphasizing the types of questions that this group hopes to tackle.

MELODEM is open to new ideas and expanding beyond these 5 areas as the field grows and changes. We recently added an additional topic in a pharmacoepi subgroup interested in the challenges associated with evaluating the effects of pharmaceutical treatments on cognitive change and dementia risk.