GBIPM is a medical model that employs individual’s genetic information in determining measures to prevent or treat a particular illness. It emerged as a result of Genome Project evaluating the genetic sequence by identifying genes that cause various illnesses. People usually have different genetic make up. For this reason, GBIPM is rapidly developing to improve personal health care. One and most significant aspect how this model accomplishes its purpose is by influencing decision making concerning individualized heath care. This is attributed to the fact that its employs genetic information of each individual. As mentioned above that individuals have different genetic profiles, therefore, they experience different health problems. Despite other contributing factors such as environment, genetics play a major role in individuals’ health issues. In this respect, prevention and treatment measures should be precisely tailored for each and every individual. This has achieved through several dimensions which if emphasized will continue to shape the direction of public health (Willard, and Ginsburg, 2009).
To begin with is identification and development of predictive and preventive medicine. Predictive medicine deals with prediction about the likelihood of a disease whereas preventive medicine is concerned about preventing the disease. Genomic profiles can completely be prepared in advance by individuals undergoing specialized tests to provide their specific genetic information. For instance, if the genomic profile of an individual predicts likelihood of a particular disease happening like the heart disease, medical practitioner is in position to initiate preventive measures to prevent or reduce the severity of the disease respectively (Willard, and Ginsburg, 2009).
Another significant dimension GBIPM can be or have been used is in selection of treatment and medication for individuals. This can be/has been successful in determining of treatments and medications for various diseases more so cancer. Likewise in prevention, prediction is necessary in treatment of a disease. It can help to determine the disease cause and thus assist in selecting the appropriate treatment and medication for the disease. For example, when one has cancer, genetic information can be of vital importance in determining of medication. Based on genetic variation, cancer medication responds different. In addition, genetic variation can also be used to predict whether diseases like cancer will spread in other body parts. This information can directly influence how to treat the disease as well as improve the patient outcomes (Willard, and Ginsburg, 2009).
As earlier mentioned, apart from genetic factor s alone, other factors contribute in causing health issues among individuals. However, these factors particularly environment interact with the genetic factors in one way or another. Gene to gene and gene to environment interaction has taken a dominant position in epidemiology studies regarding disease causes and outcomes. Research has proved that occurrence of any disease is attributed to genetic factors and other factors at the same time. Molecular and Genetic epidemiology has sought to evaluate ways in which genetic factors interact with various factors in contributing to human carcinogens that trigger heath issues within individuals. In the first way, it establishes the correlation between the genes and the disease/disorder. Secondly, it establishes the magnitude of genetic impact with respect to other risk factors to the disorder. Finally, it identifies the genes that make up the genetic component. Molecular epidemiology measures exposures related to specific substances and evaluate characteristics of the genotypes using specific markers to identify disease categories. On the other hand, genetic epidemiology is similar to molecular epidemiology; however, it concentrates on the causes attributed to inheritance. It incorporates molecular biology into genetic research which has enabled medical practitioners to understand the nature and risk factors of inherited diseases. In return, this has ensured development of effective medical prevention and treatments methods (Friis, 2010).
Measurement of these carcinogens or biomarkers which include proteins, chemicals, DNA, and hormones provide relevant measures to human exposure. Exposure to certain environmental factors causes mutation of genes. Presence of carcinogens contributes much in gene mutation. Epidemiology knowledge would therefore help to identify the targeted genes and predict ways in which others factors cause diseases. In the instances where carcinogens trigger gene mutation, detecting of the mutation pattern can be instrumental in identifying the risk factors. Additionally, this would help to initiate early prevention and diagnostic measures of the disease (Friis, 2010).
Besides the advances of epidemiology in genetics, its absence would lead to certain implications. Without use of molecular and genetic epidemiology focus of GBIPM would mostly concentrate more on prevention of diseases rather than their treatment and outcomes. This will result to more challenges in treatment of diseases. This is because GBIPM rely only in gene to gene interactions to determine preventive and treatment measures of diseases. Next, epidemiology helps to identifying the role other factors play where genetics factors have minimal or no influence in a certain disease. The interaction of these factors is crucial in understanding a well as interpreting genes contribution to diseases. Failure to incorporate the role of other risk factors would mean decreased sensitivity in determining the relation of the gene to the disease. Thereby, this would lead to inconsistent results pertaining to genes association to diseases hence poor prevention and treatment measures (Friis, 2010).
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