Alzheimer’s Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Abstract

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD.

A data-driven prospective study of dementia among older adults in the United States

“…Prior studies in which investigators used more contemporary statistical approaches to examine dementia have focused primarily on medical risk factors [8] and neuroimaging biomarkers [9] as well as resilience to genetic predispositions to dementia [10]. Although these characteristics are important to studying the onset of dementia, little work [e.g., 3,11,12] has combined genetic and life-course environmental risk factors in pursuit of a more comprehensive prediction model. A study by Casanova and colleagues [11] used data from the Health and Retirement Study (HRS) combined with a data-driven approach to predict cognitive impairment using sociodemographic, health, and genetic data.…”

“…Due to data limitations, this prior study also examined a smaller list of predictors, for example, using neighborhood socioeconomic status rather than a range of social and economic factors at the individual level. Sapkota and colleagues [12] used a data-driven approach to test the predictive importance of 19 characteristics from six risk factor domains (novel metabolomics biomarker panels; selected Alzheimer's disease genetic risk polymorphisms; functional health; lifestyle engagement; cognitive performance; and biodemographic factors) for mild cognitive impairment and Alzheimer's disease. The authors reported that characteristics from multiple risk factor domains were important for classifying mild cognitive impairment and Alzheimer's disease; however, their analysis was limited to a sample of fewer than 100 respondents.…”

Future Directions for Dementia Risk Reduction and Prevention Research: An International Research Network on Dementia Prevention Consensus

“…Rigorously designed trials that evaluate interactions between levels of risk factors are needed. Similarly, data from observational studies could be used to evaluate interactions or joint effects of risk factor combinations [ 31 ]. Another important knowledge gap relating to mechanisms is the understanding of effects of risk factor reversal.…”

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer’s Disease

“…Our aim was indeed to test whether or not there is a genetic signal associated with AD that could be apportioned to tissue specific gene-expression regulation rather than identify a prediction model. It is also known that genetics is just one of the component involved in AD susceptibility and therefore the use of multimodal data (e.g., imaging data, clinical features, metabolomic, and environmental factors) should be taken into account in order to build a reliable classifier in term of translational application (Sapkota et al, 2018). Despite that, our classification models were still capable of finding a signal between cases and controls (overall AUC > 0:5) suggesting that part of the genetic signal in AD related dementia can be associated with tissue-specific gene expression regulation.…”