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Study Suggests Non-Genetic Factors are Better Type 2 Diabetes Risk Predictors than Genetics

NEW YORK (GenomeWeb News) – Genetic factors offer little additional information about type 2 diabetes risk over models based on non-genetic factors, according to a recent paper in the British Medical Journal.

As part of the larger Whitehall II prospective cohort study, researchers from the University College London followed thousands of individuals in London's Whitehall district over roughly 20 years. When they looked specifically at factors affecting type 2 diabetes risk, they found that two non-genetic risk models were better predictors of diabetes risk than a genetic model based on nearly two dozen risk alleles.

The researchers also found little evidence that existing genetic information improves type 2 diabetes risk prediction, senior author Steve Humphries, a cardiovascular genetics researcher at the University College London, told GenomeWeb Daily News, since existing non-genetic approaches are already quite good at finding those at increased risk.

Rather, he argued, the utility of existing type 2 diabetes-associated alleles seems to be their potential for providing insights into the biology of the disease as well as possibly pointing the way to new treatments.

Humphries and his co-workers followed 5,135 participants — civil servants recruited from the Whitehall district of central London — for a median of 11.7 years, starting between 1985 and 1988. Of these, 302 developed type 2 diabetes.

When the researchers assessed the so-called Cambridge and Framingham type 2 diabetes risk models, which are based on non-genetic factors such as age, sex, family history, waist circumference, body mass index, smoking behavior, cholesterol levels and so on, they found that both predicted risk of the disease better than a genetic risk model based on 20 common, independently inherited risk SNPs.

The Cambridge model had 19.7 percent sensitivity for detecting type 2 diabetes cases in the Whitehall cohort based on a five percent false positive rate, while the Framingham model had 30.6 percent sensitivity. The gene count score, meanwhile, detected 6.5 percent of cases at a five percent false positive rate and 9.9 percent of cases at a 10 percent false positive rate.

In addition, the team noted, adding genetic risk information did not significantly improve the ability to identify individuals at risk of type 2 diabetes over either non-genetic risk model alone.

Based on the results using information from the 20 risk alleles, Humphries said, it seems unlikely that companies testing one or a few risk alleles are providing risk information that could not be determined by more straightforward, non-genetic measures.

Even so, while they say the study does not support the use of genetic testing for predicting type 2 diabetes risk, the researchers emphasized that risk alleles for the disease hold promise for identifying type 2 diabetes related pathways that may aid in coming up with new treatments.

"The fact that the SNPs don't help you in diagnosis doesn't mean these can't be useful therapeutically," Humphries said.

In addition, he added, it might be interesting to explore whether individuals are more motivated by genetic test results to make lifestyle changes than they are by non-genetic risk information.

"For the time being I don't believe that purchasing genetic tests to predict your susceptibility to type 2 diabetes or heart disease is a good investment and the [British Heart Foundation] does not endorse the use of genetic home screening tests," British Heart Foundation Medical Director Peter Weissberg said in a statement. Weissberg was not directly involved in the study, though the British Heart Foundation helped fund the research.

Weissberg emphasized the importance of diet, exercise, and weight control as a means of reducing the risk of type 2 diabetes and heart disease, noting that '[c]onventional' risk factors such as obesity, smoking, cholesterol and blood sugar levels remain the cornerstone of risk prediction."

Joanna Mountain, senior director of research for direct-to-consumer genetics firm 23andMe, agreed that non-genetic factors are important contributors to type 2 diabetes risk. But she says there is still a place for genetic testing — particularly for a subset of individuals with a relatively high genetic risk for the disease.

"One point that the authors don't make is that a small fraction of individuals learn from genetic data that their type 2 diabetes risk is very high," Mountain told GWDN by e-mail. "The results for these individuals don't influence the statistic that is used heavily in this study, but for those individuals, the genetic information can be very valuable."

Mountain also noted that genetic testing allows for earlier risk assessment than non-genetic factors. And, she said, in contrast to weight, body mass index, and other non-genetic factors, genetic profiles do not change over time.

23andMe currently evaluates eight of the 20 alleles tested in the study. Mountain said the company will continue adding genetic markers for type 2 diabetes risk as these alleles meet their scientific criteria.

In addition, Mountain emphasized that her company "lists the relevant non-genetic factors in its type 2 diabetes report, and emphasizes that their contribution to variation in risk is higher than that of the genetic factors."

Similarly, Vance Vanier, chief medical officer of DTC genomics firm Navigenics, noted that, in the case of type 2 diabetes, preventative genomics may help motivate lifestyle changes prior to the onset of obesity, though he agreed that obesity itself is an important predictor of the disease.

"Diabetes is unique in that the risk factor of obesity is one of the most powerful environmental risk factors known in medicine," Vanier told GWDN by e-mail. "Obesity increases risk for diabetes more than 20 fold, and any other risk factor — be it smoking or genetics — will be modestly incremental."

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