https://agingcenters.org/news/detail/2922
Announcement
Erin Bakshis Ware, Kenneth Langa, Steven Heeringa, Jennifer Smith, Sharon Kardia, Jessica Faul, and Kelly Marie Bakulski examine joint genetic-environmental disparities in late-onset Alzheimer's disease prevalence by sex, race/ethnicity, education, and residence.
OCTOBER 18, 2019
By Erin Ware
SRC researcher Margaret T. Hicken and collaborators recently published an analysis using Cox proportional hazards and linear mixed regression models to investigate residential neighborhood context and its relationship to chronic kidney disease (CKD), over time. The group published their findings in the American Journal of Kidney Diseases – the official journal of the National Kidney Foundation. Within five months of the publication of the article, news outlets have mentioned the article five times and social media has mentioned the article over three dozen times.
Context is an important factor in identifying risk for disease – specifically residential neighborhood. While lower neighborhood socioeconomic status and low access to healthy foods and spaces for physical activity have been associated with higher rates of hypertension, diabetes, and obesity, these factors have not been fully studied with respect to CKD risk. Furthermore, there are known disparities across race/ethnicity in prevalence of CKD and distribution of neighborhood contexts, with blacks experiencing higher levels of CKD, more neighborhood problems and worse neighborhood cohesion. Being able to identify features of the neighborhood beyond socioeconomic factors could help public health practitioners and clinicians identify important sources of stress and resilience may influence CKD-related health behaviors and thus help reduce these disparities.
These researchers capture aspects of residential neighborhood by creating scores based on participant-reported aspect of their neighborhoods at study entry for neighborhood problems (adequacy of food sources, availability of parks/playgrounds, noise, sidewalks, traffic, trash and litter, and violence) and social cohesion (attributes of people in their neighborhood including close knit, get along, willing to help neighbors, trustworthy, share values). They also examine kidney function in terms of an estimated glomerular filtration rate (eGFR; creatinine-cystatin C equation) and an indicator of eGFR decline (>30% across ten years and four exams).
The authors used baseline characteristics of participants in the Multi-Ethnic Study of Atherosclerosis (MESA) across quartiles of neighborhood problems and social cohesions scores. Next, they examined unadjusted cross-sectional associations between the neighborhood dimensions and eGFR at baseline. To examine the relationship between neighborhood dimensions and eGFR decline over time, they used linear mixed models with random intercepts, accounting for the correlation within participants. In addition to their unadjusted model, they build a second set of epidemiological models by including individual-level sociodemographic factors and MESA study site as confounders and then a third model including potential mediators that may link neighborhood context to eGFR decline in a fully specified model.
The authors report no association between the neighborhood scores and eGFR in the baseline models within any of the race/ethnic groups. In the longitudinal analyses, the hazard of an eGFR decline >30% from baseline was not statistically different for those in the highest quartile of neighborhood problems to the lowest quartile, though the decline in eGFR for those with greater neighborhood problems was greater than for those in the lowest quartile of problems.
At the conclusion, the authors state their null findings may be interpreted in several ways. It is possible that perceived social environment does not play an important role in very early stages of CKD. Null findings could have been a result of measurement error, as neighborhood social context is difficult to measure without error. Social context may be interrelated with neighborhood sociodemographic characteristics and disentangling the two may be difficult. They also note that individual-level factors (e.g. individual-level SES) may be important with eGFR decline in a healthy cohort – though literature on this topic is unclear.
Despite the null findings of this study, neighborhood SES has been a consistently associated with known risk factors for CKD including incident hypertension, cardiovascular disease, diabetes, and smoking prevalence, inflammatory markers, depressive symptoms, and alcohol use. With the large disparities in CKD and in neighborhood context between blacks and whites, combined with the null findings in these race/ethnic groups between CKD and neighborhood context, the authors suggest that future work should examine the role of racial residential segregation.
In order to uncover the origins of these pronounced health disparities (e.g. CKD); we need to have a clear picture of what is, and what is not. I feel it is extremely important to publish these null findings, and appreciate that the authors, and the journal, have taken the time to carefully lay out their analysis and publish the results – even as null results.
Hicken, Margaret T., Ronit Katz, Carmen A. Peralta, Deidra C. Crews, and Holly J. Kramer. 2019. “Neighborhood Social Context and Kidney Function Over Time: The Multi-Ethnic Study of Atherosclerosis” American Journal of Kidney Diseases 73 (5): 585–95.
We’re excited to introduce the world-class faculty who will be teaching in the Master of Applied Data Science program this fall.
Paul Resnick is the Michael D. Cohen Collegiate Professor of Information, the Associate Dean for Research and Faculty Affairs, and Director of the Center for Social Media Responsibility at UMSI. He received his PhD in computer science from MIT. Paul is a pioneer in the field of recommender systems, and his areas of research include crowdsourcing rumor tracking and fact-correction on the internet, and social media monitoring.
Paul is teaching Being a Data Scientist in September.
Chris Brooks holds a PhD in computer science with a concentration in human-computer interaction and artificial intelligence from the University of Saskatchewan. He is an assistant professor at UMSI and Director of Learning Analytics & Research at the Center for Academic Innovation at the University of Michigan. His research focuses on understanding how learning analytics can be applied to human-computer interaction through educational data mining, machine learning and information visualization.
Chris is teaching Data Manipulation in September and Visual Exploration of Data in November.
Eytan Adar is an associate professor of Information and Computer Science at the University of Michigan. He earned his PhD in computer science at the University of Washington. His work is at the boundary of HCI and data mining. Specific research projects range from modeling behaviors at a very large-scale and applying those models to constructing better HCI systems.
Eytan is teaching Information Visualization I in October.
Erin Ware holds a PhD in epidemiological sciences from the University of Michigan with a concentration in genetic epidemiology. Her research includes integrating genomics and social science. She has previously been an instructor of statistics in the UMSI MSI residential program and epidemiology and statistical programming in the School of Medicine at Wayne State University. She is extremely excited to be back teaching in the School of Information in the new online Master’s program.
Erin Ware is teaching Math Methods for Data Science in October.
Yan Chen is the Daniel Kahneman Collegiate Professor in the School of Information. She also holds an appointment as a research professor with the U-M Institute for Social Research. She holds a PhD from California Institute of Technology in Social Science with a concentration in economics. Her research interests are in behavioral and experimental economics, market and mechanism design, and public economics.
Yan is teaching Experiment Design and Analysis in November.
Qiaozhu Mei is the founding director of the Master of Applied Data Science program and a professor in the School of Information and the College of Engineering. He received his PhD in computer science from the University of Illinois at Urbana-Champaign. His research focuses on information retrieval and text mining, with applications in web, social media, scientific literature, bioinformatics, and health informatics.
Qiaozhu is teaching Data Mining I in December.
May 11, 2016
NIH/National Institute on Aging
Researchers have identified 74 areas of the human genome associated with educational attainment. It is well known that social and other environmental factors influence education, but these findings suggest that large genetics analyses may be able to help discover biological pathways as well.
https://www.sciencedaily.com/releases/2016/05/160511134721.htm
September 21, 2016
American Heart Association
Smoking leaves its "footprint" on the human genome in the form of DNA methylation, a process by which cells control gene activity, according to new research. Even after someone stops smoking, the effects of smoking remain in their DNA.
https://www.sciencedaily.com/releases/2016/09/160921215106.htm