TIME-AD

TIME-AD

Here are our wonderful investigators!

Administrative Core

The Administrative Core will administer and lead the P01, including the other four Cores and the four Projects. The Administrative Core is led by TIME-AD MPIs, with additional support from an Executive Committee comprised of co-Investigators from across TIME-AD institutions.

Jacqueline Torres, PhD, MPH, MA

profile: Jacqueline Torres | UCSF Profiles

Maria Glymour, SD, SM/ScM

profile: Maria Glymour | Profiles RNS

Analytics Core

The Analytics Core will support all four research components of the TIME-AD program, ensuring coordinated statistical approaches and principled decisions on common challenges. The Analytics Core will (1) work with the Cognition, Exposure, and Covariates Core to ensure construction of analytic data sets is consistent with each proposed study design for each research project; (2) partner with research project leads to develop and implement the analytic strategy for each research project, develop template analysis code for each method, and lead program-wide methodologic decisions; (3) guide investigative teams in each research project on systematic evidence triangulation across study designs; (4) and provide ad hoc guidance on statistical problems that arise in each research project.

Elizabeth Rose Mayeda, PhD, MPH

Profile: Elizabeth Rose Mayeda | UCLA Fielding

Equity and Dissemination Core

The purpose of the Equity and Dissemination (E&D) Core is to maximize the impact of the Improving causal inference in AD projects for prevention research on modifiable risk factors: the Triangulation of Innovative Methods to End AD (TIME-AD) project on improving health equity and population health. The E&D Core will accomplish this foremost by continually focusing on health equity while collaborating with projects and cores throughout the analytic and dissemination stages to produce unbiased, culturally-relevant scientific findings.

Yvette Cozier, DSc, MPH

profile: Yvette Cozier | SPH

Yulin Hswen, ScD, MPH

profile: Yulin Hswen | UCSF Profiles

Cognition, Exposure, and Covariates Core

The CEC will provide analytic data sets to each of the four projects, taking advantage of shared resources for the intensive data cleaning, wrangling, and data set construction efforts required, bringing together diverse expertise to guide decisions about how to code AD/ADRD-related outcomes, and minimizing the need for each research team to revisit decisions about coding outcomes or covariates.

Jacqueline Torres, PhD, MPH, MA

profile: Jacqueline Torres | UCSF Profiles

Kaitlin Casaletto, PhD

profile: Kaitlin Casaletto, PhD | Memory and Aging Center

Genetic and Policy Data Core

The GPD Core addresses the unique data access and statistical modeling issues for quasi-experimental methods, whereas the Analytics Core focuses on methods that rely on control of confounders. We will use genetic and policy data primarily as instrumental variables (IVs). The GPD Core will identify genetic and policy data needed for IV analyses in each Project, develop code to create genetic and policy IVs and estimate effects, implement over-identification and other tests to evaluate the validity of each instrument, and provide documentation on the construction, motivation, and quality of each instrument.

Thomas Hoffman, PhD, AM

profile: Thomas Hoffmann | UCSF Profiles

Maria Glymour, SD, SM/ScM

profile: Maria Glymour | Profiles RNS

Project 1

The effects of alcohol use on Alzheimer’s Disease and Alzheimer’s Disease Related Disorders (AD/ADRD) remain uncertain. Long hypothesized to have a J-shaped relationship, with the best cognitive outcomes among light drinkers, growing evidence suggests that benefits of even ”low-risk” drinking are unlikely. Yet, there is not clear quantification of how low-risk or moderate alcohol consumption (considered unhealthy but not constituting alcohol use disorder) affects AD/ADRD risk. Drinking is extremely common and modifiable via clinical and policy interventions. Estimates of the adverse effects of heavy drinking on AD/ADRD also vary widely. The recent Lancet Review estimation of the population attributable fraction (PAF) for AD/ADRD associated with alcohol use was based on a small number of studies that may not be generalizable. The population impact of alcohol use may be substantially underestimated. Individual studies of alcohol use are potentially biased due to confounding, reverse causation, or measurement error. We propose coordinated analyses across ten diverse clinical and population cohorts. Working with the TIME-AD Cores, we will triangulate evidence from doubly- robust g-methods, genetic instrumental variables (IVs), and policy IV analyses to derive the best possible estimates on the effects of alcohol use on AD/ADRD risk.

Stacy Sterling, DrPH, MPH, MSW

profile: Sterling, Stacy - Kaiser Permanente Division of Research

Maria Glymour, SD, SM/ScM

profile: Maria Glymour | Profiles RNS

Project 2

Depression is a highly prevalent and modifiable risk factor for Alzheimer’s Disease and Alzheimer’s Disease Related Disorders (AD/ADRD). Severe and untreated depression is associated with substantially higher risk of AD/ADRD. Older individuals with comorbidities and individuals from marginalized populations are more likely to be undertreated for depression, possibly increasing disparities in AD/ADRD risk in these groups. Yet the long- term effects of pharmacological and non-pharmacological treatments of depression on AD/ADRD risk are currently unknown. Chronic pain is extremely prevalent and commonly co-occurs and exacerbates depression. Evidence suggests chronic pain is a risk factor for AD/ADRD highlighting the need for a rigorous assessment of independent and joint effects of depression and chronic pain on AD/ADRD risk. We propose a systematic approach to interrogating potential biases by comparing patterns across populations, study designs, and analytic approaches to derive the best possible estimates of the effects of depression, treatment of depression, and chronic pain on AD/ADRD risk. We will leverage complementary sources of data including large, diverse cohorts with electronic health record (EHR) databases enriched with survey and genetic information, as well as diverse, national cohorts with repeated measures of cognition and depressive symptoms.

Ulrike Muench, PhD, MSN

profile: Ulrike Muench | UCSF Profiles

Project 3

Our overarching goal is to rigorously evaluate the influence of sensory impairment and treatment of sensory impairments on AD/ADRD risk using modern causal inference methods and a data triangulation framework, which is a process of integrating evidence from multiple study designs and settings. Our secondary objective is to leverage multiple data sources to evaluate heterogeneity of effects in diverse populations and whether sensory impairments contribute to social inequities in AD/ADRD incidence.

Project 4

Our primary objective is to identify the effects of social isolation and hypothetical interventions which reduce social isolation on ADRD risk by carefully triangulating evidence across multiple data sources using a range of methods meant to address core methodological challenges in this research. We will leverage a framework to emulate “target” intervention trials in observational cohorts, which can approximate the impact of hypothetical social isolation interventions on ADRD risk, while addressing bias in observational studies such as reverse causation and time-dependent confounding. Our secondary objectives are to identify the extent to which social isolation contributes to well- characterized ADRDs inequities.

Ashwin Kotwal, MD, MS

profile: Ashwin Kotwal | UCSF Profiles

Jacqueline Torres, PhD, MPH, MA

profile: Jacqueline Torres | UCSF Profiles