The Sampling and Data Management core will conduct random sampling using the selected database. It will assume multiple functions pursuant to its central task of assuring effective and valid collection, processing, and utilization of study data. It will ensure consistent study methodology and proper execution across sites. It will also develop and service the communications network of the study, involving the online discussion bulletin board and the central database containing all of the analytic values derived from the study subjects, from both the questionnaires and laboratory data. This central database will feature a user-friendly, secure, web-based interface for study scientists, who will be able to enter their own data, view their own data as well as data generated by others, and to perform basic statistical analyses, including correlational analysis across domains, in real time. The Sampling and Data Management Core will provide support to the Recruitment and Clinical Testing Core in the initial phases of subject recruitment. The Sampling and Data Management Core will take responsibility for security issues involving the study subjects. It will also consult with and service all of the participating scientists to enhance the speed and quality of the data analyses and publications from the study. It will provide statistical and epidemiologic analytic support, will consult with all investigators in the early stages of the study, will support preparation of manuscripts from the study, and ensure that methods appropriate for the study design and database are used. It will also provide data verification of all tables prior to publication. Additionally, the Sampling and Data Management Core will enhance the quality of its epidemiologic procedures by evaluating how representative the use of the Medicare Beneficiary Enrollment file and Louisiana Voters Registration file is compared to the Drivers License/Personal Identification Card file. It will also conduct data verification of self-reported age in this study.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
7P01AG022064-05
Application #
7496542
Study Section
Special Emphasis Panel (ZAG1)
Project Start
Project End
Budget Start
2007-09-15
Budget End
2008-06-30
Support Year
5
Fiscal Year
2007
Total Cost
$269,872
Indirect Cost
Name
Tulane University
Department
Type
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118
Jazwinski, S Michal; Jiang, James C; Kim, Sangkyu (2018) Adaptation to metabolic dysfunction during aging: Making the best of a bad situation. Exp Gerontol 107:87-90
Kim, Sangkyu; Jazwinski, S Michal (2018) The Gut Microbiota and Healthy Aging: A Mini-Review. Gerontology 64:513-520
Maffei, Vincent J; Kim, Sangkyu; Blanchard 4th, Eugene et al. (2017) Biological Aging and the Human Gut Microbiota. J Gerontol A Biol Sci Med Sci 72:1474-1482
Kim, Sangkyu; Myers, Leann; Wyckoff, Jennifer et al. (2017) The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age. Geroscience 39:83-92
Cherry, Katie E; Brown, Jennifer Silva; Kim, Sangkyu et al. (2016) Social Factors and Healthy Aging: Findings from the Louisiana Healthy Aging Study (LHAS). Kinesiol Rev (Champaign) 5:50-56
Kim, Sangkyu; Myers, Leann; Ravussin, Eric et al. (2016) Single nucleotide polymorphisms linked to mitochondrial uncoupling protein genes UCP2 and UCP3 affect mitochondrial metabolism and healthy aging in female nonagenarians. Biogerontology 17:725-36
Kim, Sangkyu; Simon, Eric; Myers, Leann et al. (2016) Programmed Cell Death Genes Are Linked to Elevated Creatine Kinase Levels in Unhealthy Male Nonagenarians. Gerontology 62:519-29
Kim, Sangkyu; Welsh, David A; Myers, Leann et al. (2015) Non-coding genomic regions possessing enhancer and silencer potential are associated with healthy aging and exceptional survival. Oncotarget 6:3600-12
Stanko, Katie E; Cherry, Katie E; Ryker, Kyle S et al. (2015) Looking for the Silver Lining: Benefit Finding after Hurricanes Katrina and Rita in Middle-Aged, Older, and Oldest-Old Adults. Curr Psychol 34:564-575
Kim, Sangkyu; Jazwinski, S Michal (2015) Quantitative measures of healthy aging and biological age. Healthy Aging Res 4:

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