Gleb Haynatzki, PhD: Developing Statistical Models for Disease to Prevent, Treat, and Improve Quality of Life at the Individual and Population Level

Gleb Haynatzki, PhD

Gleb Haynatzki, PhD

Spotlight on Research at COPH – Dr. Haynatzki’s research focuses on developing statistical models for disease to prevent, treat, and improve the quality of life in individuals and whole populations. Statistical modeling is currently the most accepted approach to modeling random phenomena that are studied in the biomedical and population health sciences. Dr. Haynatzki’s past research was focused on osteoporosis, bone biology, violence prevention, and glaucoma screening. He currently works with researchers on the UNMC campus on the design and analysis of research studies, both designed and observational. These studies range from explaining the causes of health disparities, peripheral arterial disease, and pancreatic cancer to Alzheimer’s disease treatment.

His statistical methodology work is developing methods for modeling hereditary and sporadic carcinogenesis, carcinogenesis, and genetic anticipation. For example, genetic anticipation can involve an earlier age at onset, greater disease severity, and/or a higher number of affected individuals in successive generations in a family with a familial disorder. Established anticipation provides clues to the nature of the disease and facilitates prediction of age of disease onset. It is important to detect true anticipation and not artifacts (which may be due to ascertainment bias, difference in length of follow-up time between generations, and effects of secular/nongenetic trends). In this type of statistical model building for time-to-event data, it is also important to control for the family-clustered structures in the dataset. There are different statistical methods to analyze this type of data, which are divided into two large classes: semiparametric and nonparametric. Dr. Haynatzki’s work focused on the comparison between these two approaches, and the conclusion was that the current nonparametric methods, as a whole, are the better approach.

Another research project developed by Dr. Haynatzki focused on the association of meat consumption, preparation, and meat-derived carcinogens with the risk of sporadic pancreatic cancer. The objective of this hospital-based study was to identify dietary meat and preparation type factors as well as meat-derived mutagens that are associated with the risk of pancreatic cancer. Data collected on 99 case-control pairs from Italy matched by age, gender, and region, and enrolled in the international Pancreatic Cancer Collaborative Registry, were analyzed. It was discovered that pancreatic cancer was associated in a nonlinear fashion with dietary intake of processed meat as well as increased intake of a certain mutagen (MeIQX) and, to a lesser extent, was associated in a nonlinear fashion with frying and increased dietary intake of the mutagen BaP.

Gleb Haynatzki, PhD, is a professor and graduate program director in the UNMC COPH Department of Biostatistics.