title: The application of genomic and proteomic technologies in predictive, preventive and personalized medicine.
abstract: The long asymptomatic period before the onset of chronic diseases offers good opportunities for disease prevention. Indeed, many chronic diseases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction is vital for disease prevention so that therapy can be given to those individuals who are most likely to develop the disease. The utility of predictive markers is dependent on three parameters, which must be carefully assessed: sensitivity, specificity and positive predictive value. Specificity is important if a biomarker is to be used to identify individuals either for counseling or for preventive therapy. However, a reciprocal relationship exists between sensitivity and specificity. Thus, successful biomarkers will be highly specific without sacrificing sensitivity. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find for many diseases. One potential solution is to use the combinatorial power of a large number of biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer a great opportunity for biomarker discovery. The newly identified biomarkers have the potential to bring increased accuracy in disease diagnosis and classification, as well as therapeutic monitoring. In this review, we will use type 1 diabetes (T1D) as an example, when appropriate, to discuss pertinent issues related to high throughput biomarker discovery.
authors.affiliation: Center for Biotechnology and Genomic Medicine, Medical College of Georgia, 1120 15th Street, CA4124, Augusta, GA 30912-2400, United States.
authors.forename: C D
authors.initials: CD
authors.lastname: Collins
authors.forename: S
authors.initials: S
authors.lastname: Purohit
authors.forename: R H
authors.initials: RH
authors.lastname: Podolsky
authors.forename: H S
authors.initials: HS
authors.lastname: Zhao
authors.forename: D
authors.initials: D
authors.lastname: Schatz
authors.forename: S E
authors.initials: SE
authors.lastname: Eckenrode
authors.forename: P
authors.initials: P
authors.lastname: Yang
authors.forename: D
authors.initials: D
authors.lastname: Hopkins
authors.forename: A
authors.initials: A
authors.lastname: Muir
authors.forename: M
authors.initials: M
authors.lastname: Hoffman
authors.forename: R A
authors.initials: RA
authors.lastname: McIndoe
authors.forename: M
authors.initials: M
authors.lastname: Rewers
authors.forename: J X
authors.initials: JX
authors.lastname: She
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D002908:Chronic Disease; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism* / Q000517:prevention & control; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D040081:Protein Array Analysis; D011506:Proteins / Q000378:metabolism; D040901:Proteomics / Q000379:methods; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D011506:Proteins