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Revista médica de Chile

Print version ISSN 0034-9887

Rev. méd. Chile vol.128 n.11 Santiago Nov. 2000

http://dx.doi.org/10.4067/S0034-98872000001100012 

Molecular epidemiology: The
impact of molecular biology in
epidemiology research

Epidemiología molecular: El impacto
de la biología molecular en la
investigación epidemiológica

Janice S Dorman, Ph.D

Progress in molecular biology and genetics is changing the practice of medicine and public health through the development of molecular diagnostics and targeted interventions for susceptible individuals. The ethical, legal and social issues that are becoming apparent as these important discoveries are introduced into practice will have an enormous impact on society. The accurate translation of this new genetic information from the laboratory to the community is an urgent need. Molecular epidemiology is at the foundation of this important link, and represents the scientific basis of public health for the 21st Century. (Rev Méd Chile 2000; 128: 1261-68).

(Key Words: Epidemiologic methods; Epidemiology, molecular; Molecular biology).

Los avances en la biología y genética moleculares, a través del desarrollo del diagnóstico molecular y de acciones médicas dirigidas a individuos genéticamente susceptibles, están cambiando aceleradamente la práctica de la medicina y de la salud pública. Las consecuencias éticas, legales y sociales están llegando a ser evidentes mientras estos descubrimientos se introducen en la práctica clínica. La apropiada traducción de esta nueva información desde el laboratorio a la comunidad es una necesidad urgente. La aplicación de herramientas moleculares a la epidemiología es considerada parte fundamental en el cumplimiento de esa tarea. El presente artículo describe el concepto de epidemiología molecular que representa la base científica de la salud pública para el Siglo XXI.

Manuscrito preparado por invitación de los editores. Recibido el 27 de junio del 2000.
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh,
Pittsburgh, PA 15261, USA.

The revolution in molecular biology, which began several decades ago, has led to many incredible scientific advancements, particularly the identification of genes known to contribute to the occurrence of human disease. The recent availability of genetic maps of highly polymorphic loci that span the human genome, as well as the evolution of statistical methods and computer technology, have provided important new tools for studying the genetics of chronic diseases. Once a gene can be assigned to a specific chromosomal region, however, it becomes necessary to isolate and characterize its product, and to identify relevant mutations before molecular studies can be conducted in populations. After these goals have been accomplished, the implementation of molecular epidemiology research will be essential to determine the contribution of susceptibility genes, and their interaction with environmental risk factors, to the occurrence of disease. Molecular epidemiology is, therefore, the necessary link for translating genetic advances from the laboratory to the community.

GENETIC EPIDEMIOLOGY OR MOLECULAR EPIDEMIOLOGY?

In 1978, Morton and Chung defined genetic epidemiology as "a science that deals with the etiology, distribution and control of disease in groups of relatives and with inherited causes of disease in populations"1. Since then, other definitions of the field have been proposed2. Rao noted that genetic epidemiology "differs from epidemiology by its explicit consideration of genetic factors and family resemblance; it differs from population genetics by its focus on disease; it also differs from medical genetics by its emphasis on population aspects"3. Recent attention to the interaction between susceptibility genes and environmental risk factors in the occurrence of disease has broadened the scope of genetic epidemiology2.

Although there is now a more global perspective of the types of investigations encompassed by genetic epidemiology, these definitions do not distinguish between the hypotheses and methodologies of studies designed to identify susceptibility genes, from those that quantify disease risk for susceptible and/or exposed individuals across populations. The former represents one of the most active areas of genetic epidemiologic research, but is not directly related to the principles and practice of general epidemiology. In contrast, the latter group of investigations is entirely epidemiologic in nature, with an emphasis on genetic markers and gene-environment interactions as risk factors. Because of these conceptual and practical differences, a formal distinction between genetic epidemiology and molecular epidemiology was made4.

Molecular epidemiology is related directly to genetic epidemiology, but had its foundation in cancer and infectious disease epidemiology4. It was initially described as "an approach in which advanced laboratory methods are used in combination with analytical epidemiology to identify, at the biochemical or molecular level, specific exogenous agents and / or host factors that play a role in human cancer causation"5. This approach represents the incorporation of biochemistry and molecular biology in traditional epidemiologic research for the purpose of assessing biologic markers of potential carcinogenic exposure6,7. From this perspective, molecular and genetic epidemiology are obviously distinct4.

The development of molecular epidemiology has also been apparent in infectious disease research, where the polymerase chain reaction (PCR) and other molecular techniques are now being used to more accurately identify organisms that cause communicable diseases8,9. DNA probes are now available for various species of parasites, bacteria, and viruses to diagnose infectious disorders directly from finger-stick blood samples. This technology allows large numbers of specimens to be processed quickly, permitting rapid detection of such organisms at a much more sensitive and specific level than traditional serological methods. In population studies, molecular epidemiology has led to better evaluations of the distribution of infectious diseases, as well as to new possibilities for early diagnosis and treatment of these disorders.

Molecular epidemiology is now considered an excellent approach for evaluating the causes of many acute and chronic diseases7. Thus, molecular epidemiology has been redefined as "a science that focuses on the contribution of potential genetic and environmental risk factors, identified at the molecular and biochemical level, to the etiology, distribution and prevention of disease within families and across populations"4. As such, molecular epidemiology represents an interface between human genetics, advanced biotechnology and epidemiology. The objectives of molecular epidemiology are quite broad and include: 1) descriptive and analytic studies to evaluate host/environment interactions in disease; 2) the development of strategies for the control of bacterial, parasitic, and viral disorders through molecular diagnosis; and 3) the prevention of noncommunicable diseases and genetic disorders by assessing risk and identifying susceptible individuals through genetic testing.

CHALLENGES FOR MOLECULAR EPIDEMIOLOGY

Molecular epidemiology draws from basic science, medicine and public health, and is, therefore, a collaborative discipline10. While this collaboration represents the major strength of molecular epidemiology, it also poses its primary challenge. Epidemiologists, biomedical scientists, health professionals and biostatisticians have different backgrounds, training, experience and goals. Indeed, they have discipline-specific scientific views and speak different languages. Such differences tend to inhibit collaboration. However, with sufficient attention to the development of a common vocabulary and perspective, successful partnerships can be achieved10-12.

To ensure their development and continuation, collaborators with diverse background, training, and experience must first acquire an understanding of the objectives, methods, and nomenclature of their colleagues’ areas of expertise10. New opportunities for training in human genetics and molecular biology for epidemiologists and health practitioners (and in epidemiology and public health for basic scientists and human geneticists) are required to provide the necessary framework for the development of molecular epidemiology.

A second major challenge for molecular epidemiology is reflected by the need to foster stronger community links among epidemiologists, health practitioners, policy makers, and members of the general population4,10,13. An ongoing dialogue across these different groups is essential if members of society are to have access to the information necessary to make appropriate decisions regarding genetic testing. This will also provide a foundation for discussions regarding the societal implications of basic research, which will become increasingly important as molecular approaches for predicting and preventing diseases are developed and implemented into practice. Consider the following two examples:

Example 1: Molecular Epidemiology and Breast Cancer.

Breast cancer is one of the leading causes of morbidity and mortality among women in the U.S., and incidence rates are increasing worldwide14,15. The cumulative lifetime risk for Caucasian women in North America and Europe approximates 10%. Population-based epidemiologic research has revealed that age at menarche, diet, reproductive history, and a positive family history of breast cancer are among the major risk factors for the disease. Accurate epidemiologic data regarding the magnitude of these associations were of great importance during the recent controversy concerning mammography and breast cancer screening for the general population16. This illustrates the significant contribution of epidemiology to the development of practice guidelines and public health policy for noncommunicable diseases, such as breast cancer.

There has been enormous interest in the genetics of breast cancer during the past decade. Recent advances from the Human Genome Project has led to the identification of several susceptibility genes, including BRCA1 on chromosome 7q21, which is linked to early-onset breast and ovarian cancer17. Ever since this initial discovery, more than 200 distinct BRCA1 mutations have been identified, none of which appear to be very common18-20. Therefore, a mutation segregating in one family with early-onset breast cancer is likely to be different from that found in another affected family. Despite the genetic heterogeneity of BRCA1, unaffected relatives who carry the same mutation as do family members with breast cancer appear to be at very high risk for developing the disease (i.e., approximately 80%)21,22.

These statistics received considerable attention in both the literature and the press, and obviously cause great concern among women with a positive history of breast cancer10. However, the estimates quoted were generated from linkage studies, which require large extended families with many affected individuals. The families selected for such analyses were chosen specifically because of their unusually high prevalence of breast and/or ovarian cancer. By definition, they were not representative of most families with early-onset breast cancer. As a result, the risk estimates from these cohorts were inflated and not appropriate for the general population. However, this point was not typically recognized by practitioners or the general public.

Only recently have populations-based molecular epidemiology data for BRCA1 and breast cancer become available23-27. This information is based on studies of breast cancer cases who were not selected because of their family history. However, the heterogeneous nature of BRCA1 has complicated the technical aspects of genetic screening. Current molecular tests are still limited because of the likelihood of false negatives. Despite these difficulties, recent molecular epidemiologic studies of BRCA1 have revealed that a very small proportion of women with early-onset breast cancer carry known BRCA1 mutations (i.e., approximately 3%). Most women with the disease are negative for BRCA1 mutations. Obviously, the implications of these results are considerably different from the findings of linkage studies. Still, positive test results must be interpreted cautiously because the disease penetrance for BRCA1 has not yet been accurately established10.

In addition, genetic counseling for BRCA1 carriers is currently limited to risk-factor modification, which is difficult because many of the potential disease determinants relate to reproductive history (eg., age at menarche, parity, etc.)10. Dietary intervention may also be an option, particularly if molecular epidemiologic studies reveal significant gene - environment interactions. More invasive procedures, such as hormone therapy and prophylactic mastectomy have also recently been considered.

The American Society of Human Genetics (ASHG) has discouraged the use of genetic testing for BRCA128. The rationale for this decision was based on the low prevalence of BRCA1 carriers in the general population, and the potential inaccuracies of current molecular tests10. Despite the limited usefulness of genetic screening for BRCA1, the health service industry has not followed the recommendations of the ASHG because the potential profits are great. Thus, BRCA1 testing is now available to any women in the U.S. who wishes to be tested.

The breast cancer story illustrates the critical need for population-based molecular epidemiologic research to obtain accurate risk estimates for genetic testing10. In addition, the translation of these data to practitioners, health administrators, industry representatives, educators, and members of the general public is essential. This must be an active, not passive, process. After research projects have been conducted, molecular epidemiologists must not only emphasize the importance of population-based data, but also promote their use for decision making from an individual, clinical and public health perspective. Considerable progress has already been made in the area of type 1 diabetes.

Example 2: Molecular Epidemiology and Type 1 Diabetes.

Type 1 diabetes is one of the most common chronic diseases of childhood, with prevalence rates for Caucasians in the U.S. approximating 2 per 1,000. Moreover, incidence rates appear to be increasing worldwide29. Significant temporal trends have recently been reported in the U.S., Europe, Asia, the reasons for which are not known. Although it has been established that type 1 diabetes is an autoimmune disease, the etiology of the disorder remains unclear. Following disease onset, individuals with type 1 diabetes often experience acute complications, such as hypoglycemia, ketoacidosis, and cerebral edema; and after a decade with the disease, their risk of developing long-term diabetes complications becomes significant30. These conditions contribute to the high rates of morbidity, mortality, and disability that are commonly observed in persons with type 1 diabetes.

Because of the seriousness of type 1 diabetes, the World Health Organization (WHO) recently supported the Multinational Project for Childhood Diabetes (also known as the WHO DiaMond Project). This investigation has been based on the establishment of standardized incidence registries for type 1 diabetes in more than 70 countries worldwide31. The project began in 1990 and has attracted considerable attention to the epidemiology of the disease. Indeed, the WHO DiaMond Project is the largest known international collaborative study of a noncommunicable disease.

Analyses of the vast amount of data generated revealed dramatic geographic differences in the incidence of type 1 diabetes32. Rates are extremely high in Finland, Sardinia, and the Scandinavian countries (>20/100,000 per year), but extraordinarily low in Asian and Native American populations (<3/100,000 per year). Although the reasons for the worldwide patterns of type 1 diabetes have not been established, the availability of standardized incidence registries facilitated the development of a collaborative population-based study of the molecular epidemiology of the disease33.

Immunogenetic analyses from a variety of populations revealed that of the various HLA-DQB1 alleles (which code for the HLA-DQß chain), those that contain DNA sequences for an amino acid other than aspartic acid in position 57 (non-Asp-57; ND) were highly associated with disease susceptibility34,35. These relationships were even more striking among individuals who also carried DQA1 alleles (which code for the HLA-DQa chain) containing DNA sequences for arginine in position 52 (non-Arg-52; R)36.

The identification of the strong associations between diabetes and the HLA-DQ alleles, as well as the availability of reliable, valid and inexpensive molecular tests, and the documentation of worldwide patterns of type 1 diabetes incidence from standardized registries provided the rationale for the WHO DiaMond Molecular Epidemiology Project37. This multinational study, which began in 1994, was designed to test the hypothesis that the geographic differences in type 1 diabetes risk reflect population variation in the frequencies of DQA1*R-DQB1*ND haplotypes.

Prior to initiating this project, epidemiologists and immunogeneticists had numerous discussions about the methods required to test this hypothesis10. However, epidemiologists had considerable difficulty explaining to their colleagues the importance of large sample sizes, and the need to develop standard inclusion/ exclusion criteria for selecting cases and controls. At the same time, immunogeneticists found it hard to convince epidemiologists that their large studies would place a burden on research laboratories in terms of staff time and available equipment. Thus, considerable effort was required to develop a common understanding of the basic principles and methods of these diverse scientific fields. This ‘transition’ occurred during a period of approximately a year, and was necessary before active collaboration could begin.

Table 1 presents some of the initial results from populations representing areas with high, moderate and low type 1 diabetes incidence rates38. These data revealed that relative to individuals who carried zero DQA1*R-DQB1*ND haplotypes, those with two DQA1*R-DQB1*ND haplotypes had a significantly higher risk of developing type 1 diabetes in all countries except Japan. In general, the magnitude of these associations was greater in the moderate-high than low incidence populations. Individuals with one DQA1*R-DQB1*ND haplotype were at moderately increased risk for developing the disease.


Because the descriptive epidemiology of type 1 diabetes was established by each participating center, it was possible to estimate genotype-specific incidence rates for individuals with two, one and zero DQA1*R-DQB1*ND haplotypes38. These rates can be determined by expressing the overall population incidence as an average of the genotype-specific rates, weighted by the proportion of the general population who carry susceptibility and/or protective haplotypes34. As illustrated, in the moderate-high incidence areas, risk estimates for individuals with two high risk haplotypes were markedly increased, and approximated those typically observed for first-degree relatives of type 1 diabetics (3%-6% through age 40 years). Similar results were apparent in the low incidence areas. However, the magnitude of these risk estimates was lower than that seen in moderate-high incidence areas.

The WHO DiaMond Molecular Epidemiology Project illustrates the need for population-based data for establishing genotype-specific risks for evaluating the etiology of the disease33,38. However, it also emphasizes the importance of these estimates for developing primary and secondary prevention strategies. At the present time, there is no cure for type 1 diabetes. Lifelong insulin therapy is the only available treatment. Moreover, it is not possible to stop the onset of the disease once beta-cell destruction has occurred. As a result, there are currently several major clinical trials focusing on potential primary and secondary intervention strategies10,39.

One of these trials is being conducted in Finland, the country with the highest incidence in the world40. This investigation is testing the hypothesis that avoidance of cow’s milk formula for the first six months of life will reduce the incidence of type 1 diabetes in high risk family members. Previous epidemiologic studies have shown that children exposed to cow’s milk protein early in life were more likely to develop type 1 diabetes than children who were exclusively breast-fed. Early cow’s milk exposure may be particularly problematic for those who carry high risk DQA1 and DQB1 alleles41. Thus, the cow’s milk trial began by offering newborn genetic screening to eligible first degree relatives. Those with the highest risk genotypes were randomized to either a control group (standard cow’s milk formula) or a treatment group (hydrolyzed protein formula) at weaning. Infants with moderate or low-risk haplotypes were not eligible for the trial. Those who enrolled received frequent medical evaluations during the first 2 years of life.

Several ongoing population-based natural history studies also utilize genetic screening42,43. For these investigations, newborns from the general population are tested at birth for susceptible HLA-DQ haplotypes. Those who are at high genetic risk for type 1 diabetes are eligible for the study, which is based on an extensive series of follow-up exams during infancy and early childhood. The endpoints of interest in both the prevention trials and natural history studies include the presence of high titres of beta cell antibodies, as well as the development of type 1 diabetes.

A major concern of limiting these investigations to susceptible newborns is that children at high genetic risk represent only a fraction of those who eventually develop the disease10. Population-based data from the WHO DiaMond Molecular Epidemiology Project revealed that only about half of the type 1 diabetics in most areas carried two susceptibility haplotypes38. Moreover, these cases were more likely to be female, had a younger age at diagnosis, and a higher prevalence of a positive family history of type 1 diabetes, compared to cases with one or zero DQA1*R-DQB1*ND haplotypes. Therefore, the exclusion of moderate and low-risk infants, among whom approximately half of the future cases of type 1 diabetes will occur, seriously limits the generalizability of data generated from these expensive and labor-intensive clinical trials. It also provides a false assumption to parents that children at moderate or low genetic risk are ‘immune’ to type 1 diabetes10. This impression could have important clinical implications, as parents of these children may be less likely to detect early symptoms, assuming their child is unlikely to develop type 1 diabetes. After more extensive beta cell damage has occurred, children are at much greater risk for the serious acute complications of type 1 diabetes, such as ketoacidosis, coma and death at diabetes onset than are those who are diagnosed early.

Genetic screening and subsequent intervention, even in high-incidence countries, is unlikely to be a reasonable approach to the prevention of type 1 diabetes. Therefore, ongoing clinical trials and natural history studies, that utilize genetic testing should provide genetic counseling and education to all potential participants. Accurate risk information can be generated from population-based molecular epidemiological research, such as the WHO DiaMond Project. These data can be made readily available to family members and individuals from the general population through websites and other consumer-oriented education materials. Molecular epidemiology research will, therefore, be instrumental in translating research findings from studies of type 1 diabetes for the prediction and prevention of the disease.

Address correspondence to: Janice S Dorman, Ph.D. A548 Crabtree Hall, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261. Phone: 412-383-1286. Fax: 412-383-1022. Email: Jansdorman@aol.com.

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Acknowledgements

This research was supported by NIH Grants: R01-DK42316, R01-DK49588 and R01-DK44590.