Metabolic regulation is essential for homeostasis and for resilience in the face of external threats. Hormones play key roles in metabolic regulation, and, therefore, in maintaining homeostatic resilience. Although it is clear that many hormones decline with increasing age at the population level, analysis of individual trajectories involving multiple timepoints has never been performed. Therefore, the question of the predictive value of individual trajectories remains unanswered. IGF-1 and DHEAS represent excellent candidates for trajectory analysis: each corresponds to a distinct hormonal pathway; both have diverse effects on physiologic systems critical for functional aging (e.g., cardiovascular, musculoskeletal, and neurologic); and both, at the population level, decrease with age. Our central hypothesis is that trajectories of change inIGF-1 and DHEAS individually and jointly predict health, function, and survival in old age. We propose to evaluate this hypothesis using data from the Cardiovascular Health Study (CHS), an established, well-characterized, prospective, NIH-sponsored longitudinal study of community-dwelling men and women over the age of 65. Existing data collected over a 16-year follow-up period, with banked blood specimens at eight timepoints over this time span, are available for the proposed analyses.We hypothesize that those who have no decline in their hormonal levels over time will have greater survival and retain higher levels of function with aging than those whose trajectory demonstrates an overall decline and those who have extreme variability in hormonal levels overtime. This study proposes the following research aims: 1) to define the prevalence of the predominant patterns of trajectories of IGF-1 and DHEAS and to determine the association of each trajectory pattern with health and survival, 2) to determine differences between men and women in their trajectory patterns, 3) to establish whether the baseline values alone, the independent trajectories alone, or the independent trajectories plus the baseline values are the best predictors of health and survival, 4) to define the impact of adverse events and exercise on the trajectory, and 5) to determine the impact of joint abnormalities in trajectory patterns of IGF-1and DHEAS. This research is a novel application of endocrine models of homeostasis and trajectory analysis to studying the biology of aging at the population level. Our work will directly guide the selection of the appropriate population for growth hormone analogues and DHEA.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG027058-03
Application #
7367152
Study Section
Special Emphasis Panel (ZRG1-HOP-R (50))
Program Officer
Sherman, Sherry
Project Start
2006-02-15
Project End
2010-01-31
Budget Start
2008-04-01
Budget End
2009-01-31
Support Year
3
Fiscal Year
2008
Total Cost
$277,397
Indirect Cost
Name
University of Pennsylvania
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Yoneyama, S; Yao, J; Guo, X et al. (2017) Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations. Int J Obes (Lond) 41:324-331
Saber, Hamidreza; Yakoob, Mohammad Yawar; Shi, Peilin et al. (2017) Omega-3 Fatty Acids and Incident Ischemic Stroke and Its Atherothrombotic and Cardioembolic Subtypes in 3 US Cohorts. Stroke 48:2678-2685
Zillikens, M Carola; Demissie, Serkalem; Hsu, Yi-Hsiang et al. (2017) Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 8:80
Kaplan, Robert C; Strizich, Garrett; Aneke-Nash, Chino et al. (2017) Insulinlike Growth Factor Binding Protein-1 and Ghrelin Predict Health Outcomes Among Older Adults: Cardiovascular Health Study Cohort. J Clin Endocrinol Metab 102:267-278
Steenstrup, Troels; Kark, Jeremy D; Verhulst, Simon et al. (2017) Telomeres and the natural lifespan limit in humans. Aging (Albany NY) 9:1130-1142
Holzinger, Emily R; Verma, Shefali S; Moore, Carrie B et al. (2017) Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min 10:25
Lin, Honghuang; Mueller-Nurasyid, Martina; Smith, Albert V et al. (2016) Gene-gene Interaction Analyses for Atrial Fibrillation. Sci Rep 6:35371
Chouraki, Vincent; Reitz, Christiane; Maury, Fleur et al. (2016) Evaluation of a Genetic Risk Score to Improve Risk Prediction for Alzheimer's Disease. J Alzheimers Dis 53:921-32
Ibrahim-Verbaas, C A; Bressler, J; Debette, S et al. (2016) GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol Psychiatry 21:189-197
Jun, G; Ibrahim-Verbaas, C A; Vronskaya, M et al. (2016) A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry 21:108-17

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