Yann C Klimentidis

Associate Professor, Public Health

Assistant Professor, Genetics - GIDP

Associate Professor, BIO5 Institute

Primary Department

Epidemiology and Biostatistics

Department Affiliations

Epidemiology and Biostatistics

BIO5 Institute

Contact

(520) 621-0147

yann@arizona.edu

Work Summary

I use human genetic data to find associations of genetic markers with complex traits and diseases, to shed light on disease pathophysiology, causal pathways, and health disparities, and to inform precision medicine.

Research Interest

Yann C. Klimentidis, PhD, is an Associate Professor in the Department of Epidemiology and Biostatistics in the Mel and Enid Zuckerman College of Public Health at the University of Arizona. His research centers on improving our understanding of the links between genetic variation, lifestyle factors, metabolic disease, and health disparities. In the past, he has used measures of genetic admixture and genomic tests of natural selection to understand the genetic basis of population differences in disease susceptibility. His most recent work examines the use various statistical approaches for the analysis of high-dimensional genetic data for improving prediction of genetic susceptibility to type-2 diabetes. In addition, his work examines gene-by-lifestyle interactions in type-2 diabetes, as well as understanding the causal links between metabolic traits such as dyslipidemia and type-2 diabetes. Keywords: Genetics, epidemiology, Cardiometabolic disease, Physical activity

Publications

Associations between ACE-inhibitors and angiotensin receptor blockers, and lean body mass in community dwelling older women

Bea, J. W., Smoller, S., Wertheim, B. C., Klimentidis, Y. C., Chen, Z., Zaslavsky, O., Manini, T., Womack, C., Kroenke, C., LaCroix, A., & Thomson, C. A. (2016). Associations between ACE-inhibitors and angiotensin receptor blockers, and lean body mass in community dwelling older women. Nutr, Metab and Cardio Vasc Diseases.

BIO5 Collaborators

Zhao Chen, Yann C Klimentidis

Association of physical activity with lower type 2 diabetes incidence is weaker among individuals at high genetic risk

Klimentidis, Y. C., Chen, Z., Arora, A., & Hsu, C. (2014). Association of physical activity with lower type 2 diabetes incidence is weaker among individuals at high genetic risk. Diabetologia, 57(12), 2530-4.

BIO5 Collaborators

Zhao Chen, Yann C Klimentidis

We examined whether or not the association of physical activity with type 2 diabetes incidence differs according to several types of genetic susceptibility.

Fractal complexity of daily physical activity patterns differs with age over the lifespan and predicts mortality in older adults

Raichlen, D. A., Klimentidis, Y., Hsu, C., & Alexander, G. E. (2017). Fractal complexity of daily physical activity patterns differs with age over the lifespan and predicts mortality in older adults. ..

BIO5 Collaborators

Gene E Alexander, Yann C Klimentidis

Triglyceride-Increasing Alleles Associated with Protection against Type-2 Diabetes

Klimentidis, Y. C., Chougule, A., Arora, A., Frazier-Wood, A. C., & Hsu, C. (2015). Triglyceride-Increasing Alleles Associated with Protection against Type-2 Diabetes. PLoS genetics, 11(5), e1005204.

Elevated plasma triglyceride (TG) levels are an established risk factor for type-2 diabetes (T2D). However, recent studies have hinted at the possibility that genetic risk for TG may paradoxically protect against T2D. In this study, we examined the association of genetic risk for TG with incident T2D, and the interaction of baseline TG with TG genetic risk on incident T2D in 13,247 European-Americans (EA) and 3,238 African-Americans (AA) from three prospective cohort studies. A TG genetic risk score (GRS) was calculated based on 31 validated single nucleotide polymorphisms (SNPs). We considered several baseline covariates, including body- mass index (BMI) and lipid traits. Among EA and AA, we find, as expected, that baseline levels of TG are strongly positively associated with incident T2D (p2 x 10-(10)). However, the TG GRS is negatively associated with T2D (p=0.013), upon adjusting for only race, in the full dataset. Upon additionally adjusting for age, sex, BMI, high-density lipoprotein cholesterol and TG, the TG GRS is significantly and negatively associated with T2D incidence (p=7.0 x 10(-8)), with similar trends among both EA and AA. No single SNP appears to be driving this association. We also find a significant statistical interaction of the TG GRS with TG (pi(nteraction) = 3.3 x 10-(4)), whereby the association of TG with incident T2D is strongest among those with low genetic risk for TG. Further research is needed to understand the likely pleiotropic mechanisms underlying these findings, and to clarify the causal relationship between T2D and TG.

Polymorphisms in stearoyl coa desaturase and sterol regulatory element binding protein interact with N-3 polyunsaturated fatty acid intake to modify associations with anthropometric variables and metabolic phenotypes in Yup'ik people

Lemas, D. J., Klimentidis, Y. C., Aslibekyan, S., Wiener, H. W., O'Brien, D. M., Hopkins, S. E., Stanhope, K. L., Havel, P. J., Allison, D. B., Fernandez, J. R., Tiwari, H. K., & Boyer, B. B. (2016). Polymorphisms in stearoyl coa desaturase and sterol regulatory element binding protein interact with N-3 polyunsaturated fatty acid intake to modify associations with anthropometric variables and metabolic phenotypes in Yup'ik people. Molecular nutrition & food research, 60(12), 2642-2653.

n-3 polyunsaturated fatty acid (n-3 PUFA) intake is associated with protection from obesity; however, the mechanisms of protection remain poorly characterized. The stearoyl CoA desaturase (SCD), insulin-sensitive glucose transporter (SLC2A4), and sterol regulatory element binding protein (SREBF1) genes are transcriptionally regulated by n-3 PUFA intake and harbor polymorphisms associated with obesity. The present study investigated how consumption of n-3 PUFA modifies associations between SCD, SLC2A4, and SREBF1 polymorphisms and anthropometric variables and metabolic phenotypes.

Yann C Klimentidis