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Novel Tools for Early Childhood Predisposition to Obesity

Final Report Summary - SELECTIONPREDISPOSED (Novel Tools for Early Childhood Predisposition to Obesity)

The SELECTionPREDISPOSED ERC Advanced Grant-supported project has made it possible to identify novel obesity-and diabetes-risk genes as tools for detection of early childhood obesity, making possible a future, selective prevention program in predisposed children.

We used records and blood samples from 114,000 children, and their parents in the Norwegian Mother and Child Cohort Study. Thanks to the ERC AdG, we were able to establish a dataset of approximately 20,000 children and their parents. The dataset consists of microarray data (9 million SNPs covering the whole genome) as well as a wealth of questionnaire data from birth, at age 6 weeks, at 3, 6, and 8 months, and at 1, 1.5 2, 3, 5, 7, and 8 years. This genotyping effort is the largest ever done in Norway. Moreover, our cohort is the largest, single cohort of children from one population ever genotyped world-wide. We cross-linked this database to records from the Norwegian Birth Registry. We had permission to use these cross-correlated databases for genetic research. Based on the discovery of several diabetes genes linked to birth weight, we hypothesized that children who are large at birth and exhibit enhanced infantile growth may be predisposed to obesity by genetic factors. In this and collaborative projects, we found that genes linked to obesity and diabetes include a mix of variants associated with glucose, insulin and fat metabolism, as well as beta-cell development. We integrated cutting-edge functional genomics-related methods, available through functional genomics and computational biology platforms with these unique biological data sets.

Until this project was initiated, the state-of the-art approach had been to identify diabetes- or obesity-associated genes in subjects that have already presented with disease. Our approach was to investigate subsets of children with high and low birth weights, and high and low BMIs at multiple timepoints from birth till eight years. Using cutting-edge genetic techniques like genome-wide association studies in our unique biological data sets, we correlated the genetic information with clinical data in our large national end-point registries by a case-control design and subsequent replication in a second set of our large data set. By cross-correlating the data to our national birth registry we controlled the analyses for environmental confounders.

We wanted to change the field by working with the predisposed children, rather than individuals who had already presented with a disease, and influence the outcome of those that may develop obesity and diabetes, and those that may not. Hence, our latest data show that a set of genes linked to the leptin receptor are involved in a dynamic role of common genetic variation in infant and early childhood growth. Our findings demonstrate a previously unknown and dynamic role of common genetic variants in genes implicated in the leptin-leptin receptor axis, influencing BMI during fetal, neonatal, and infant growth. This finding offers a potential drug target for interventions aiming at changed weight gain in infant care.