31 October 2013

The era of genomic preventive medicine: prospects and expectations

Vyacheslav Kipen, "Biomolecule"

Currently, there is not a single example of the use of genetic screening to study the risk of disease at the population level. Predictive genetics, as well as any other new technology, should be evaluated in relation to each disease and in each population. The safety and effectiveness of the use of genetic approaches require the creation of a clear policy on population screening.

XXI century – the beginning of the era of genomic preventive medicineThe ideal method of genetic screening of diseases should have at least three advantages over "traditional" diagnostic tests:

  1. The results of such screening should more accurately indicate the risk of developing the disease than phenotypic and (or) biochemical screening.
  2. There must be an economically justified and highly effective way to prevent this disease in "suspected" patients.
  3. The recommendations received as a result of genetic testing should be carried out by patients without question.

So far, all these advantages are purely speculative, which means that the expediency of using mass genetic screening remains controversial to this day.

At the end of the last century, many researchers had high hopes for the results of mass screening programs aimed at identifying predisposition to the development of oncopathology and diseases of the cardiovascular system, which are the cause of almost two thirds of all deaths in the world [1]. It was assumed that all people could be divided according to the degree of genetic susceptibility and give appropriate recommendations for lifestyle changes or provide timely medication correction to reduce the risk of developing these diseases [2]. Genome-wide studies that started at the beginning of the XXI century and are still ongoing have not led to the identification of the main predisposition alleles for those most common diseases [3, 4]. Very rarely identified predisposing alleles had a high predictive ability to assess the risk of the disease. The predisposition genes identified at the moment are able to explain only a small percentage of the genetic risk of diseases, and the relationship between these genetic variations and environmental risk factors has not yet been sufficiently studied [5].

However, despite everything, many researchers who have devoted their lives to the study of the genetic foundations of heredity continue to adhere to the line of using the results of genetic testing to predict the risk of developing diseases. And there is no need to talk about the commercialization of this direction at all – after all, these are multimillion-dollar projects, and the income, according to some analysts, can be comparable to the situation in the computer industry at the end of the last century [6]. Let's try to figure out whether the bright prospects expressed at the end of the last century regarding genetic screening at the population level are still valid?

A rosy beginning is an ambiguous endA number of studies have shown that information about numerous genetic variants (single nucleotide polymorphisms; SNP) does not say anything new compared to already identified risk factors, such as family history or environmental factors.

In theory, individual genetic variants could predict the risk of developing a particular disease, but only if all the potential effects of all predisposing alleles were determined. Unfortunately, it is almost impossible to implement this approach.

Here are some examples of how genetic screening has been tried to predict diseases in large samples. In one study, researchers used 18 polymorphic genetic markers to identify predisposition to type 2 diabetes [7]. It has been shown that the combined use of these signs, each of which predicts the likelihood of type 2 diabetes to one degree or another, does not greatly increase the accuracy of separating the test sample of patients and the control group of healthy people. In another study, scientists tried to assess the feasibility of assessing the risk of cardiovascular diseases using genetic markers, but it was shown that genetic information is less effective in predicting these diseases than data on age, blood pressure, smoking, diabetes and cholesterol and triglyceride levels in the blood.

However, there are diseases (in particular, breast cancer) in which genetic information can be extremely useful for diagnosis [8].

These meager successes in analyzing a large amount of empirical genetic data can be explained by differences between populations – both ethnic and age, geographical, ecological and behavioral. Thus, the predictive ability and the possibility of clinical use of genetic tests strongly depend on the specific population and disease, which means that such tests should be developed with mandatory consideration of such information.

The effectiveness of predictive medicineWhen people talk about the effectiveness of using genetic testing information, it is assumed that the results obtained really predict the risks of diseases.

But even in this case, population screening will be justified only if effective and economically feasible ways of preventing diseases in patients with an increased risk of their development are created. In the case of the most common oncopathologies (for example, breast cancer), regular monitoring and treatment at an early stage helps to drastically reduce the number of deaths. The presence in the family history (in relatives of the first-second degree of kinship) of breast cancer in combination with the data of molecular genetic testing (mutations in high-percentage genes in breast cancer – BRCA1,2) will reduce the risk of the disease with the help of timely medical intervention. Think of Angelina Jolie's recent radical mastectomy surgery.

Thus, even if there is available prevention, a large number of clinical studies are necessary. Only their results will allow us to assess whether prescribing these preventive procedures to people with no symptoms of the disease, but with an increased genetic risk, is more effective than simply treating all people with physiological risk factors (for example, in cases of high blood pressure or high cholesterol for cardiovascular diseases).

The impact of genetic risk information on behaviorSome adherents of medical ethics claim that providing information about genetic risks to patients will lead to changes in their behavior [9, 10].

Information about the genetic propensity to diseases in most cases causes a sense of doom, and in such cases, erroneous data can undermine the patient's faith in the ability to change anything. Incorrect explanation of the results of genetic analysis may prompt the patient to think about completely unnecessary pharmacological intervention. For example, when patients with hypercholesterolemia were informed of the results of genetic tests, they tried to start taking medications that reduce lipid levels, instead of changing their diet and starting to lead a mobile lifestyle, which is really required in this situation.

Another proof in favor of the above is the work in which it was demonstrated that smokers who were told that they had a genetic predisposition to nicotine addiction were much easier to resort to medications than to rely on their willpower [11]. It was also shown that the found genetic predisposition to lung cancer increased the number of quitting smoking (or reduced the number of cigarettes smoked), but neither the first nor the second changes in behavior lasted more than six months.

Competing strategies in healthcarePredictive genomic medicine tells patients at high risk of developing the disease "how to live on."

Epidemiologists claim that the unprocessed standards of genetic testing can violate the already existing practice of combating addictions and diseases – for example, alcoholism, smoking, hypertension, physical inactivity, etc.

Strategies based on controlling the consumption of tobacco products by the population (such as, for example, increasing taxes on cigarettes) have helped to reduce tobacco smoking in Australia and the United States by half over the past 35 years. As practice has shown, this approach has proved to be more effective than strategies related to the study of an increased risk of diseases, since much less resources are required than in the case of a total survey of the entire population. There are similar arguments about the effectiveness of similar strategies to reduce the risk of alcoholism, obesity and diabetes, cancer.

The need for technology assessmentThe main task in population screening is to clearly formulate a policy of use and ensure that the benefits of genomic medicine to combat common diseases do not contradict existing health initiatives.

It is assumed that in the medical context, the use of wellness programs for the population should be separated from genetic awareness. Studies of genetic predisposition can help identify rare genetic variants that have a strong prognostic effect, which can be used in the preparation of a program for the treatment of patients in clinical settings. Such rare variants can also also contribute to the emergence of new drugs aimed at treating common diseases.

Although the first studies did not provide real evidence of the predictive power of the combined use of genetic and environmental factors, the use of the Mendelian randomization method (correlation between the presence of gene markers of risk factors and the development of the disease – VM) may allow epidemiologists to assess the influence of environmental factors on common diseases. If the relationship between genetic factors and environmental factors is described in more detail, then the use of tests for a general population health survey will be justified only if phenotypic information and information about environmental factors are combined. And it would be advisable to use these tests only among those categories of the population who already have other indicators of increased risk (for example, the presence of diseases in close relatives).

But it is absolutely clear that a genetic examination of the entire population cannot lead to a change in preventive medicine in the direction predicted at the beginning of the XXI century. The introduction of a system for predicting individual genetic risk into preventive strategies for preserving public health will require indisputable evidence that such measures will be effective compared to those already available. Predictive genetics, as well as any other new technology, should be evaluated in relation to each disease and in each population. Its benefits will depend not only on the price of genetic examination (which has been declining quite rapidly lately), but also on its effectiveness in determining the increased risk and the number of cases of disease and mortality that these interventions can help to avoid.


  1. Collins F.S. (1999). Shattuck lecture–medical and societal consequences of the Human Genome Project. N. Engl. J. Med. 341, 28–37;
  2. van Ommen G.J., Bakker E., den Dunnen J.T. (1999). The human genome project and the future of diagnostics, treatment, and prevention. Lancet 354, Suppl. 5–10;
  3. biomolecule: "The code of life: to read does not mean to understand";
  4. biomolecule: "Human genome: how it was and how it will be";
  5. Donnelly P. (2008). Progress and challenges in genome-wide association studies in humans. Nature 456, 728–731;
  6. Biomolecule: "Over a thousand: the third phase of human genomics";
  7. Lango H., Palmer C.N., Morris A.D., Zeggini E., Hattersley A.T., et al. (2008). Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 57, 3129–3135;
  8. Pharoah P.D., Antoniou A., Bobrow M., Zimmern R.L., Easton D.F., et al. (2002). Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31, 33–36;
  9. biomolecule: "A word about the genetics of behavior";
  10. Biomolecule: "Thank you, dear Ministry of Health, for warning me!";
  11. Wright A.J., Weinman J., Marteau T.M. (2003). The impact of learning of a genetic predisposition to nicotine dependence: an analogue study. Tob. Control 12, 227–230.

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