To support innovation in Aging research, CPHA offers members and affiliates seed grants for developing promising ideas into projects that compete successfully for NIH R01 awards or similar extramural funding. Pilot Awards have also been used to test ideas that are time-sensitive to either research or funding opportunities.
Competition for Pilot Awards is held in the fall every year and each application will be reviewed by a committee chaired by the CPHA Director. Applications are open to all CPHA affiliates; preference will be given to projects involving multiple CPHA investigators, investigations that support collaboration between UNC and Duke, or junior faculty.
In addition, because population science increasingly relies on approaches that integrate the social, health, and biological sciences, collaboration with researchers in allied fields has the potential to enrich the research environment for all involved. Thus we also give priority to projects that pursue outreach to other disciplines.
As a policy the program will not fund faculty time; instead, typical requests will be to fund small pilot data collection projects or experiments where participant compensation is the norm, costs associated with processing of biological data, data acquisition using special data archives, research assistant time, and travel to sites or travel for collaborators to Duke for research.
2015-2016 Pilot Projects
Advancing translation of molecular signatures of biological aging
The aging process is a gradual and progressive deterioration of the body’s systems that is the leading cause of disease and disability. 1 To extend healthspan (i.e., years of life lived free of disease and disability), interventions are needed that can slow the aging process itself.2 My own work shows the aging process is already underway in young adult humans in their 20s-30s. 3 This suggests that the optimal timing for interventions to slow aging may be early in adulthood, in time to prevent age-related disease. Animals studies are producing therapies for healthspan extension in humans.4 To test the effectiveness of these therapies, we need to be able to measure “biological aging.” Several methods to quantify biological aging in humans have been proposed. So far, they have been studied in isolation from one another, and in different samples. I propose the first integrative analysis of 7 prominent methods that use DNA methylation,5,6 gene expression,7,8 telomeres9 and composites of non-genomic biomarkers10,11 to measure biological aging. I will evaluate these measures in the same sample of pre-senescent humans. My proposed integrative analysis will test proof-of-concept for translating biological aging measures into surrogate endpoints for interventions to extend healthspan. I will test if the different biological aging measures appear to measure the same underlying aging process; if they show evidence of being able to quantify change over time within individuals, such as would be caused by an intervention; and if they can quantify early-emerging signs of diminished healthspan. My long-term goal is to advance translation of biological aging measures for use in population studies and clinical trials of interventions to extend healthspan. The overall objective of this application is to establish whether any of several existing measures are promising for such translation. The setting for my proposal is the Dunedin Study, a prospective longitudinal study of a 1972-3 birth cohort (N=1,037, 95% retention as of age-38 follow-up in 2012). The Dunedin Study is the only human study with whole-genome data on DNA methylation and RNA expression, telomeres, and repeated measures of many non-genomic biomarkers, together with indicators of healthspan. Dunedin Study data are stored at Duke University and I have a track record of productivity working and publishing with the Study.
Assessing Stress, Well-Being & Connectedness across Three Generations using Mobile Technologies
This 2-year pilot project seeks to address the following specific aims: Pilot a field-based trial to evaluate the use of mobile technologies to monitor stress, wellbeing and social connectedness among older parents and their adult children and grandchildren. To address this aim, we will: (a) evaluate the feasibility of using mobile technologies to collect ecological momentary assessments (EMA) of daily stressors, interpersonal exchanges and stress-related biomarkers among older parents, their adult children and grandchildren, (b) field test commercially available wearable devices that continuously capture heart rate, skin temperature, movement, and sleep duration and quality among study participants, (c) test whether social connectedness and daily exchanges between family members buffers the negative effects of daily stressors on the health and wellbeing of older adults, and (d) conduct semi-structured exit interviews to optimize the EMA protocol delivery, study compliance and quality of assessments within each of the targeted age groups.
Computer-based Cognitive Testing for Adults after a Disaster
Natural disasters that have killed tens of thousands of people have struck in 6 countries across the globe in just the last decade. Large scale disasters are potentially devastating for older adults whose health is typically in decline, for whom loss of assets and family often means loss of current and future economic support and who have less time to rebuild their lives than their younger counterparts. Studies have examined the impact of disasters on subsequent mortality, morbidity and economic security. Relatively little research examines the longer-term consequences of large-scale disasters on a key marker of the human capital and well-being of older adults: cognitive functioning. The aim of this pilot study is to develop, field and evaluate neuroscience-based assessments of cognitive functioning that measure specific domains of cognitive functioning and emotional control. The assessment will be implemented on touch-screen laptops in order to provide fine-grained and precise measures of accuracy, reactivity and control for each respondent. The pilot will be conducted on a sample of older males and females whose lives were differently devastated by the 2004 Indian Ocean tsunami. The project will provide critical information with respect to the feasibility of implementing touch-screen based assessments for older adults and also provide preliminary evidence on whether exposure to a devastating disaster results in premature cognitive aging. The work will complement on-going research on the longer-term health consequences of the tsunami. The pilot will lay the groundwork for an application to NIH to implement the assessments on a larger sample in order to test hypothesis about resilience and recovery among older adults after a disaster.
Duke Alzheimer’s Progression Index: Development of Software for Clinical Applications
Recent research on human aging, health, and longevity by us and our colleagues using longitudinal panel study data has progresses to the point of identifying precise phenotypic developmental patterns. The specific topic of the present study is the rate of progression of Alzheiner’s disease (AD) among individuals with AD diagnoses. Findings from application of Grade of Membership (GoM) and Sullivan Life Table (SLT) models to panel data have produced prototypical trajectories of progression for four AD subtypes that can be used to estimate personalized expected trajectories of survival times and development of impaired functioning together with upper and lower bounds of uncertainty. The objective of this project is to write a computer software program based on the estimated GoM and SLT models that uses input data from tests conducted in clinical offices to make personalized estimates for individual patients.
Studying Immigration through Network Sampling and Memory
Immigration will be the driving force in the future growth of the US population, political debates surrounding the subject are intensifying, and the immigration system needs reform — all of which underscore the need for better understanding of the foreign-born population. The study of immigration to the United States is complicated by the difficulty and costs of obtaining analytical samples for inference of small groups and hidden segments (undocumented immigrants) of the population for which no sampling frame exists. This leads to sampling frames with incomplete coverage and to partial inclusion of immigrants in official data (i.e., CPS or ACS).
Using conventional survey methods to collect data on specific immigrant groups is difficult because each group represents a small portion of the overall population. a large number of screening interviews are necessary to recruit a sufficient sample (McKenzie and Mistaien, 2009). In addition, undocumented migrants may constitute a "hidden" or stigmatized population (Kalton 2009) that is reluctant to respond to conventional surveys. The goals of this pilot are: 1) to field-test a novel sampling approach, network sampling with memory (NSM); to recruit an analytical sample of Chinese immigrants in a circumscribed area of North Carolina, the Raleigh-Durham area; and to evaluate how closely it represents the target population through comparison with ACS fata; 2) to reduce the costs of implementing NSM on a large geographic scale and in transnational networks by assessing a dual data collection mode: a conventional version of NSM implemented through personal referrals and in person interviews and a Web version that recruits respondents through online ties; and 3) to use network data collected through NSM to analyze immigrants' network-related social processes.
With respect to Aim 1, while a principal advantage of network sampling is to draw in hidden populations and since ACS may undercount these, we pick a population for testing where this is less likely to be an issue, because comparatively few Chinese immigrants are believed to be undocumented (Hoefer et al., 2011). With respect to Aim 2, we plan to develop a mobile-enabled survey Web site that would allow for the survey to be accesses on a browser or smartphone platform for which the digital divide is smaller (Rainie 2013). With respect to Aim 3, data collected through the pilot will begin to shed light on important issues related to immigrant social networks, for example the role of co-ethnic social networks in job search; the effect of social networks on the social incorporation of recent immigrants; the role fo peer effects in health behaviors and health-related knowledge transfers; and the differential impacts of immigration on gender relations.
This project supports DPRC's theme of social connectedness by using network sampling methods to assemble representative samples and experimenting to extend these cost-effective Web surveys and network analyses, with a focus on using network relationships to understand immigrant settlement and assimilation.
In recent years, an explosion in the field of microbiome research has highlighted how the intimate relationship between the human body and its microbial inhabitants shapes our health. Microbes train our immune systems, help resist pathogens, and contribute substantially to energy acquisition. Already, targeted experimental manipulations of the microbiome have been shown to produce dramatic effects on host phenotypes, including obesity, autoimmune disease susceptibility, and, in some animal models, behavior itself. These findings strongly motivate discovery of the factors that predict microbiome structure across the life course—especially given emerging evidence that it may constitute a novel route through which social relationships influence health. For example, I recently led a study that showed that social networks predict gut microbiome composition in wild baboons, and more so for specific types of bacterial taxa (Tung et al 2015, eLife).
Understanding the relationship between social interactions, microbiome composition, and its potential health-related outcomes requires repeated, longitudinally collected samples over the lifetime of known individuals. Such samples will take decades to collect in humans, but are already available for the baboon population I study in Kenya, an important model for human social behavior, demography, and evolution. Specifically, as a result of banked samples originally collected for other purposes, we have over 20,000 samples available for 613 individuals—a sample size that vastly outstrips all published work on the microbiome to date (Figure 1). Detailed data on diet, space use, social interactions, social status, early life adversity, and changes with age are available for the same animals, making this system ideal for understanding the causes and consequences of natural variation in gut microbiome composition. Specifically, we are interested in whether (i) variation in the gut microbiome predicts fertility and survival; and (ii) the extent to which early life conditions and social interactions throughout life contribute to long-term variation in microbiome composition.
Our unique sample set has already attracted substantial interest, including from the Earth Microbiome Project—one of the largest consortia focused on microbiome studies assembled to date. With the EMP, we are currently in the process of profiling all 20,000 samples, in a large-scale effort that involves collaborations between multiple PIs and multiple institutions (myself, Dr. Elizabeth Archie at University of Notre Dame, Dr. Ran Blekhman at University of Minnesota, Dr. Luis Barreiro at University of Montreal, and the EMP, led by Dr. Jack Gilbert at University of Chicago and Dr. Rob Knight at UCSD). The total cost of data generation itself, excluding the original costs of sample preparation, is expected to be >$150,000, of which I will contribute approximately $25,000. Given the unique value of the resulting data to understanding how early life conditions influence this key component of human health, and to how the microbiome itself changes with age as a result of social and demographic conditions, investing pilot/seed grant funding from DUPRI into this project would be ideal, allowing me to reallocate the funds earmarked for this project (from my start-up) for a grad student or post-doc dedicated to analyzing the resulting data.
Sustainable Development and Population: Making the Most of Spatial Data for Demographic Studies
Economic development and change in exposures to environmental toxins go hand in hand. High-frequency data from satellite pictures provide unique opportunities to pinpoint location and timing of exposure events. The combination of these data with information on production techniques and the availability of administrative records on births, death, and scholastic achievement allow social scientists to study the impact of economic development for the population directly exposed to its costs and benefits.
This project proposes to examine the health effects of traditional sugarcane harvesting techniques in Brazil. Ethanol from sugarcane is increasingly seen as a viable, renewable energy source that can help meet rising global energy demand, but traditional farming practices involve pre-harvest field burning, which elevates air pollution levels around producing regions. This study will quantify the consequences for infants exposed to smoke before and after birth. In addition to contributing to the burgeoning economics literature on the negative effects of air pollution on infant health (Currie, Graff, Mullins, and Neidell, 2014), the study will respond to debates about pollution released in the production of many energy sources, not just ethanol. In the United States, such debates have recently focused on the natural gas extraction process of hydraulic fracturing; longer-standing controversy surrounds nuclear power, coal, and oil. In most cases, these production externalities pose distributional questions, with nonproducing areas benefiting from cheaper fuels or reduced emissions even as producing areas suffer. Distributional questions are especially pertinent in Brazil, where automobile fuel must be at least one-fourth ethanol, but sugarcane fields cover less than one percent of the country’s land mass.
This project aims to combine data from satellite imagery with administrative and survey data to measure economic development and its environmental challenges to population and demographic processes, including fertility, mortality, birth outcomes, human capital accumulation, and migration flows. Data are employed from the Fire Information for Resource Management System (FIRMS) of NASA’s Earth Observing System Data and Information System to track the use of fires in agricultural activities in Brazil. These are combined with weather data from geographically disperse weather stations and administrative data on birth outcomes, death records, hospitalizations, and school performance to examine the impact of modernization of agricultural production (as well its expansion) on various dimensions of welfare.
The innovations of this project center on the extensive usage of satellite imagery information combined with geocoded administrative records, which allow us to infer causal relationships and to detect heterogeneity in effects. Satellite imagery provides an important tool for population research and social policy and offers a wealth of scientific possibilities when the granularity of such data (both in terms of time and location) is fully explored but its use remains modest among population scientists. The use of “big data” that capture variations in exposure and outcomes over time and space can provide opportunities for detailed analysis of policies.
The results will also speak to policy debates around the benefits of changing technology in order to reduce small but repeated health insults that stem from a production process many see as crucial for enabling increased global reliance on sustainable energy sources. Both in Brazil and elsewhere, the sugarcane industry is gradually adopting mechanized harvesting methods that do not require fires. We will quantify the benefits of limiting emissions of particulate matter, the primary byproduct of field fires.
This project supports two DPRC research themes: 1) biodemography, with its focus on the developmental consequences of exposure to various environmental hazards; and 2) estimation of causal impacts, with its assembling of unique data that combines satellite-imaging data on the environment with Brazilian administrative records that will measure health outcomes and indicators of populations, especially children, by geography.