Document text
Principal Investigator: Linda Valeri
Organization: COLUMBIA UNIVERSITY HEALTH SCIENCES
Fiscal Year: 2024
Award: $532,149
Funding agency: National Institute on Aging
SUMMARY
To develop multi-faceted interventions for Alzheimer’s Disease and Related Disorders (ADRD) prevention, it
is key to quantify joint effects of environmental exposures throughout the life-span as well as the mechanisms
through which the exposures operate, which involve dynamic disease processes. These causal investigations
in observational studies face methodological challenges. This application, in response to PAR-22-093, will
accomplish the following goals: (1) develop robust Bayesian machine learning methods for causal mediation
analyses in life-course observational studies of ADRD; (2) apply these approaches in the analysis of two 30+
year cohort studies of Native Americans (Strong Heart Study) and of the Greater Boston area (Normative
Aging Study) to determine whether and to what extent the onset and severity of hypertension and
cardiovascular disease mediate the harmful effects of air pollution and heavy metals on ADRD; (3) develop
and disseminate computationally efficient and user-friendly software for widespread application of the methods.
The proposed work will address methodologic gaps in the causal investigation of health effects. First, no
mediation analyses approaches are available that simultaneously allow for multiple exposures and multiple
time-to-event and longitudinal mediators. Investigators can currently only build models that do not reflect real-
life conditions, considering a single exposure, or a single mediator producing segmented results, limited in
informing prevention. Second, most of observational studies in ADRD research are plagued by selection bias.
Participants may die before an ADRD diagnosis (attrition due to death) or may drop-out due to cognitive
impairment. Third, multicollinearity, skewness of exposures, complex exposure-response relationships and
time dependent confounding challenge valid estimation and inference. Fourth, no statistical approaches are
available to evaluate the generalizability of findings on determinants of and mechanisms leading to ADRD. We
propose to fill these gaps by developing and applying Bayesian machine learning approaches for quantifying
the total, direct and indirect effects of environmental and health factors on ADRD outcomes under the
counterfactual framework. We will develop and apply the new methods to estimate complex exposure-
response relationships of pollutants with time-to-event or longitudinal outcome potentially mediated by a single
time-to-event or longitudinal mediator (Aim 1), and mechanisms through multiple longitudinal and time-to-event
mediators (Aim 2). Furthermore, we will develop Bayesian data fusion algorithms to evaluate unmeasured
confounding bias and to generalize evidence from the study sample to the target population (Aim 3). In the
SHS and NAS we will investigate the role of hypertension and CVD trajectory, onset, and severity in mid and
late life as mediators of the neurotoxic effects of AP and metals. We will develop user-friendly and efficient R
packages that implement the proposed new methods. Our work has great potential to have broad impact on
life-course epidemiology research and prevention for ADRD and other chronic health outcomes.
Terms: <AD prevention><AD related dementia><ADRD><Accounting><Address><Air Pollutants><Air Pollution><Algorithms><Alzheimer disease prevention><Alzheimer prevention><Alzheimer risk factor><Alzheimer's and related dementias><Alzheimer's disease and related dementia><Alzheimer's disease and related disorders><Alzheimer's disease or a related dementia><Alzheimer's disease or a related disorder><Alzheimer's disease or related dementia><Alzheimer's disease related dementia><Alzheimer's disease risk><Amentia><Area><Arsenic><Bayesian learning><Bayesian machine learning><Blood Pressure><Boston><Cadmium><Carbon Black><Cardiovascular><Cardiovascular Body System><Cardiovascular Diseases><Cardiovascular Organ System><Cardiovascular system><Causality><Cd element><Cessation of life><Chronic><Cognitive><Cognitive Disturbance><Cognitive Impairment><Cognitive decline><Cognitive function abnormal><Cohort Studies><Complex><Concurrent Studies><Data><Death><Dementia><Diagnosis><Disease><Disorder><Disturbance in cognition><Dropout><Dysfunction><Ensure><Environmental Exposure><Environmental Policy><Epidemiologic Research><Epidemiologic Studies><Epidemiological Studies><Epidemiology><Epidemiology Research><Etiology><Event><Face><Functional disorder><Goals><Health><Health Care Costs><Health Costs><Healthcare Costs><Heart><Heart Vascular><Heavy Metals><Hypertension><Impaired cognition><Incidence><Individual><Intervention><Intervention Strategies><Investigation><Investigators><Joints><Life><Life Cycle><Life Cycle Stages><Life course epidemiology><Lifecourse epidemiology><Link><Longitudinal Studies><Measurement><Mediating><Mediation><Mediator><Metals><Methodology><Methods><Modeling><Native Americans><Negotiating><Negotiation><Observation research><Observation study><Observational Study><Observational research><Outcome><Participant><Physiopathology><Population Heterogeneity><Prevention><Process><Public Health><Reproducibility><Research><Research Personnel><Researchers><Risk Factors><Role><Sampling Studies><Science><Selection Bias><Severities><Statistical Methods><Target Populations><Testing><Time><Vascular Hypertensive Disease><Vascular Hypertensive Disorder><Work><alzheimer risk><arsenics><cardiovascular disorder><cardiovascular health><causation><circulatory system><cognitive dysfunction><cognitive function><cognitive loss><cohort><data fusion><disease causation><disease prevention><disorder prevention><diverse populations><epidemiologic><epidemiologic investigation><epidemiological><epidemiology study><faces><facial><heterogeneous population><high blood pressure><hyperpiesia><hyperpiesis><hypertension prevention><hypertensive disease><hypertensive disorder><interventional strategy><late life><life course><life span><lifespan><long-term study><longitudinal outcome studies><longterm study><machine learning based method><machine learning method><machine learning methodologies><malleable risk><mid life><mid-life><middle age><middle aged><midlife><model building><modifiable risk><multi-component intervention><multi-faceted intervention><multi-modal intervention><multicomponent intervention><multifaceted intervention><multimodal intervention><natural aging><neurotoxic><new approaches><normal aging><normative aging><novel><novel approaches><novel strategies><novel strategy><pathophysiology><pollutant><population diversity><prevent><preventing><resilience><resilient><response><simulation><social role><statistic methods><statistical learning><tool><translational study><usability><user friendly computer software><user friendly software><user-friendly>