Epigenetic Biomarkers of AdiposityTechnology #rad001
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Epigenetic Biomarkers of Adiposity
The inventions relate to the use of epigenetic markers (RXRA + others listed), in perinatal tissues, as a means for predicting the propensity for the occurrence of a phenotype in an individual. In particular, for example, the invention relates to the prediction of a propensity for altered body composition, and altered bone mineral content occurring in an individual.
The increased incidence of non-communicable diseases in both industrialised and developing countries together with an ageing population will require the development of new cost-efficient and effective diagnostic approaches to minimise the healthcare costs. Molecular diagnostics, particularly DNA-based diagnostics, is considered the cornerstone of personalised medicine and is expected to fuel the growth of personalised medicine market. However, it is clear that variation in DNA sequence alone can only predict a small percentage of the variation in disease risk and that epigenetic processes are also important determinants of disease risk. Moreover as inter-individual DNA methylation is specified by the interaction of both genotype and environment, differential DNA methylation may provide more powerful prognostic biomarkers than either genotypic or lifestyle factors alone. DNA methylation, although a stable modification, is reversible and potentially modifiable by therapeutic interventions. This raises the possibility of sensitive real-time biomarkers of disease status to track intervention efficacy.
We found that the methylation of a CpG site in the promoter region of the nuclear receptor RXRA was strongly related to childhood adiposity in both boys and girls in two independent cohorts, explaining >25% of the variance in childhood fat mass. Methylation of specific CpG loci in the promoter of PGC1α, at 5 years of age predicted adiposity year-on-year from 8-14 years. We found associations between the methylation of CDKN2A (involved in cell senescence and in regulating adipocyte number and function) and SLC6A4 (a serotonin transporter linked with appetite) at birth with later adiposity. We have replicated these associations in three independent cohorts from culturally diverse populations, at different ages, and across tissue types including blood and adipose tissue.
We have found developmentally induced epigenetic marks which are strongly associated with later phenotype, providing substantial support for a role for epigenetics in mediating the long-term consequences of the early life environment on health. The identification of perinatal epigenetic marks that are predictive of later disease risk represents an opportunity to identify those individuals who are at greater risk of subsequent disease in early life and a means to monitor the effectiveness of preventive interventions.
We are combining our knowledge of modifiable risk factors with changes in biomarker status to identify infant or childhood exposures that alter biomarker status and disease risk, aiding the design of new intervention strategies. For example, our SWS data show that late gestation faltering of fetal growth is associated with lower CDKN2A methylation, and that there is a true statistical interaction between late gestation growth faltering and early childhood diet in relation to late childhood adiposity. Potential applications include human diagnostics, nutraceuticals, pharmaceutical targets and development and monitoring of interventions.
· Identification and validation of nutraceuticals that programme childhood obesity through epigenetic modification.
· Develop an integrated genetic and candidate epigenetic biomarker signature of childhood obesity, combining this with clinical data, to produce a highly predictive perinatal algorithm and companion diagnostic that enables segmented postnatal intervention.
· To identify modifiable influences during infancy and childhood that interact with perinatal methylation resulting in childhood obesity, that will enable targeted postnatal intervention.
Phenotype Prediction (EP 2194143) and Predictive use of CpG Methylation (EP 2391730)