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Levels of Disease Biomarkers Influenced by Genetics, Lifestyle Factors

NEW YORK (GenomeWeb) – The plasma levels of a number of disease biomarkers are influenced by genetic and lifestyle factors, a new study in Nature Communications reported today.

A team of researchers from Uppsala University in Sweden studied more than 90 protein biomarkers in a thousand healthy people. They further examined whether the plasma levels of those disease biomarkers were influenced by environmental and lifestyle factors like age, blood pressure, or smoking status, and whether the levels of the biomarkers in plasma were heritable.

Some three quarters of the biomarkers the researchers studied were swayed by such factors.

"These results are important, as they show which variables are significant for variations in the measurable values," said first author Stefan Enroth, a researcher in the Department of Immunology, Genetics, and Pathology at Uppsala University, in a statement. "If these factors are known, we have a greater possibility of seeing variations and we get clearer breakpoints between elevated values and normal values. By extension this may lead to the possibility of using more biomarkers clinically."

Ideally, Enroth and his colleagues noted, biomarkers should only be present or at elevated levels in disease and not be influenced by confounding factors. As most biomarkers have a role in normal cells, they are not unique to the disease state and are influenced by outside factors. But knowing how biomarkers can be affected by genetics and lifestyle can help identify individual clinical cutoff levels, the researchers said.

Using both proximity extension assays and qPCR, the researchers gauged the abundance of 92 proteins that are, or are suspected, biomarkers for cancer and inflammation in the blood plasma of 1,005 people from the Northern Sweden Population Health Study. Some 77 proteins could be detected.
At the same time, the researchers collected data on 158 phenotypes ranging from age, blood type, and BMI to medication and tobacco use. Through a multiple linear regression model, they found that 18 phenotypic covariates had significant effect on one or more of the proteins studied.

Age and weight, they noted, affected a range of proteins, while smoking status affected two proteins, and ABO blood group influenced three proteins. Additionally, hypertension and asthma medications also appeared to affect the levels of some biomarkers.

Not all of the factors are independent, Enroth and his colleagues noted. For instance, age and the use of blood pressure medication are themselves linked.

Additionally, they noted a high correlation between some of the biomarkers, with many of the linked ones sharing functions. For instance, CASP-3 was correlated with ERF and that, in turn, was linked with CD69 levels.

The researchers also calculated that the plasma levels of 75 percent of the proteins were influenced by heredity.

Through an association analysis examining nearly 5 million SNPs and indels, the researched homed in on 14 biomarkers whose levels had significant genetic associations. They further reported that more than a quarter of the phenotypic variation seen could be due to a single marker, after controlling for significant clinical and lifestyle factors. That is, for many of the biomarkers, genetic effects could significantly change protein levels.

While some biomarkers are affected by genetic factors, others are influenced by environmental ones.

For instance, IL-6RA levels were strongly linked to genotype, while lifestyle factors had little effect — BMI explained some 1 percent of the variance. At the same time, HGF levels didn't show significant heritability, though some 17 other factors like weight, blood pressure, and age affected its levels, each to a modest degree.

Such information on the variables that influence each biomarker, the researchers said, could inform how such biomarkers are used in individual cases.

"Our results imply that using biomarker-specific covariate profiles will make it possible to determine more precise, individualized, clinical cutoff levels," Enroth and his colleagues said. "This in term could lead to a more efficient use of protein biomarkers for early detection of abnormal levels and for increased sensitivity and specificity in disease diagnosis."

The researchers cautioned, though, that their sample size was small.