Metabolic health phenotypes, accelerated biological aging and cancer risk.
Principal Investigator
Name
Prasoona Karra
Degrees
Ph.D.
Institution
Geisel School of Medicine at Dartmouth College
Position Title
Postdoctoral Fellow
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1028
Initial CDAS Request Approval
Aug 29, 2022
Title
Metabolic health phenotypes, accelerated biological aging and cancer risk.
Summary
Metabolic dysregulation underlying type 2 diabetes, cardiovascular disease, and cancer includes accumulated cellular damage and abrogated resilience mechanisms also characteristic of accelerated biological aging. Accelerated aging has not been evaluated with respect to metabolic dysregulation, and no studies have explored the causal association of metabolic dysregulation and accelerated aging with cancer risk and mortality. We propose to leverage existing blood-based DNAm data from nested breast and kidney cancer case-control studies within The Prostate, Lung, Colorectal, and Ovarian cancer study (PLCO) and the European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts in a test/validation design. Within PLCO, DNAm data are available on 2,086 individuals (N=583 breast and N=233 kidney cancer cases). For our analysis, participants with DNAm data, components of metabolic syndrome (fasting glucose, triglycerides, waist circumference, blood pressure, HDL-cholesterol) and no cancer at baseline will be included.
Exposures of interest will include metabolic phenotype and accelerated biological aging and the outcome of interest will be breast or kidney (renal-cell) cancer diagnosis. First, in PLCO, we will: 1) Measure the association of metabolic phenotype with accelerated aging using logistic regression to calculate an odds ratio (OR) and 95% CI (α<0.05). 2) Measure the association of metabolic phenotype with the risk of cancer by calculating OR and 95% CI using conditional logistic regression, adjusting for potential confounders. 3) If the associations are statistically significant, we will test whether accelerated biological age mediates the relationship between metabolic phenotype and cancer risk using mediation analysis. 4) The results from PLCO will be validated in EPIC.
Exposures of interest will include metabolic phenotype and accelerated biological aging and the outcome of interest will be breast or kidney (renal-cell) cancer diagnosis. First, in PLCO, we will: 1) Measure the association of metabolic phenotype with accelerated aging using logistic regression to calculate an odds ratio (OR) and 95% CI (α<0.05). 2) Measure the association of metabolic phenotype with the risk of cancer by calculating OR and 95% CI using conditional logistic regression, adjusting for potential confounders. 3) If the associations are statistically significant, we will test whether accelerated biological age mediates the relationship between metabolic phenotype and cancer risk using mediation analysis. 4) The results from PLCO will be validated in EPIC.
Aims
Aim: Measure the extent to which accelerated biological age explains the association of metabolic health phenotype with breast and kidney cancer risk. We hypothesize that accelerated biological age mediates the association of metabolic health phenotype and breast and kidney cancer risk.
Collaborators
Lucas Salas, Geisel School of Medicine at Dartmouth
Brock Christensen, Geisel School of Medicine at Dartmouth
Mary C. Playdon, Huntsman Cancer Institute, University of Utah
Sheetal Hardikar, Huntsman Cancer Institute, University of Utah