Increasing evidence from human epidemiologic studies suggests that metabolic abnormalities are associated with age-related cognitive decline and impairment. Because obesity and sedentary lifestyle might be on rise in the United States, it is critical to elucidate the role of these modifiable behaviors on risk for cognitive decline. Alzheimer's disease (AD) is the most common progressive neurodegenerative illness that leads to severe cognitive, functional and behavioral impairments on individuals. Identifying factors that enhance one's vulnerability to AD can provide a basis for its prevention, treatment and management. A clustering of several commonly occurring disorders, including abdominal obesity, hypertriglyceridemia, low high-density lipoprotein level, hypertension and hyperglycemia define the metabolic syndrome profile. The extant literature suggests that a major vulnerability factor to AD is genetic predisposition, accounting for 60-80% of the attributable risk. A number of recent genetic and genomic studies demonstrated evidence for genetic predisposition to the metabolic syndrome profile. The goal of this proposal is to apply advanced statistical techniques to study the independent, cumulative and interactive effects of genetic and metabolic syndrome risk factors in AD. The proposed study has the potential to uncover new genetic risk factors and to better characterize role of the metabolic syndrome risk factors for AD. Another innovative feature of this project is that this methodology is flexible and can be applied to other complex diseases, such as Parkinson's, substance abuse or Schizophrenia where other large genetic and clinical datasets exist.
The proposed advanced analysis has the potential to improve understanding of genetic predisposition and environmental risk factors of Alzheimer's disease through their joint analysis. The approach is flexible and can be adapted for the analysis of other age-related diseases.
| Lobach, Iryna; Sampson, Joshua; Alekseyenko, Alexander et al. (2018) Case-control studies of gene-environment interactions. When a case might not be the case. PLoS One 13:e0201140 |
| Lobach, Iryna (2018) Bias in parameter estimates due to omitting gene-environment interaction terms in case-control studies. Genet Epidemiol 42:838-845 |
