title: The "LEARn" (latent early-life associated regulation) model: an epigenetic pathway linking metabolic and cognitive disorders.
abstract: Diabetes, cardiovascular disease, hypertension, and other disorders have been unified within the metabolic syndrome. Recently, it has been proposed that Alzheimer's disease (AD) and other degenerative, age-related neurological disorders may also be etiologically linked to the metabolic syndrome in a metabolic-cognitive syndrome. We review current evidence in the field for this unification. In addition, we describe how the latent early-life associated regulation (LEARn) model provides specific mechanisms to predict genetic targets for both metabolic disorders, e.g., diabetes, and neurodegenerative disorders, e.g., AD. The LEARn model is based on environmental induction of latent epigenetic misregulation, which develops into disease upon suffering additional environmental insults. We review structural differences between gene sequences that are and are not susceptible to LEARn misregulation. In addition to suggesting research targets such as the IDE and SORCS1 genes, which are implicated in both AD and diabetes, LEARn suggests specific mechanisms for pre-disease remediation, based on nutritional adjustment of aberrant DNA methylation and oxidation. The possibility of a single metabolic-cognitive disorder opens up the possibility of unified preventative treatments that reduce monetary and social costs of disease. LEARn suggests specific, testable pathways within the large theory.
authors.affiliation: Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA. dlahiri@iupui.edu
authors.forename: Debomoy K
authors.initials: DK
authors.lastname: Lahiri
authors.forename: Bryan
authors.initials: B
authors.lastname: Maloney
mesh_terms: D003072:Cognition Disorders / Q000209:etiology* / Q000235:genetics*; D044127:Epigenesis, Genetic; D057890:Epigenomics; D006801:Humans; D008659:Metabolic Diseases / Q000209:etiology* / Q000235:genetics*; D008954:Models, Biological