Confounding clues

Lifestyle differences make even healthy genes vary in people   

By Sumiti Jain
Last Updated: Saturday 04 July 2015

With the cracking of the human genome in 2003 emerged the “Omics” era. Genomics and Proteomics turned into fields of study dedicated entirely to genes, proteins and their complex reign over the human body. Terms like “biomarkers” were coined. These are proteins formed in the presence of a disease and are used to predict the disease or determine its progression.

Although for some diseases one biomarker can suffice, in complex cases like cancer several genes may be implicated. The most convenient and non-invasive human sample used for such tests is blood. It can reflect short-term changes in gene expression such as insulin levels that vary through the day or long-term influences from our environment such as chronic inflammation proteins in rheumatoid arthritis cases. The caveat in this is that blood can be affected by factors other than the disease leading to over- or under-evaluation of results.

The Norwegian women and cancer (nowac) study investigated the effect of gene variation in healthy women between 1991 and 2006. Researchers led by Eiliv Lund from the Institute of Community Medicine from the University of Tromso in Norway borrowed blood samples from the nowac study and tested them for the expression of various genes in the blood. The genes were those found in all healthy people and, therefore, not expected to show much variation. Further, they were looking at which specific conditions could possibly influence changes in these genes. The study found inter-individual and lifestyle differences such as age, sex, diet, long-term smoking or medication use can make healthy genes vary in function between people. Such variations usually go undetected during gene testing which the researchers concluded must be rectified. The study was published on March 12 in PloS Genetics.

“This find could alter the way one screens for biomarkers in the future. Confounding genes must be identified for disease prediction at a larger scale,” said Xiao-Dan Yao, molecular biologist at McMaster University in Canada.

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