Literature DB >> 16341118

Epidemiology informing clinical practice: from bills of mortality to population laboratories.

John D Potter1.   

Abstract

The earliest observations on population patterns of disease and how they might inform medical practice probably occurred during the 17th century, and they continue to the present day, with increasing relevance to nutritional and infectious diseases, and cancer and other chronic diseases. Chronic-disease methods grew out of infectious-disease epidemiology, in which both field and laboratory methods are used. In diseases where intermediate biology was not initially observable (particularly cancer), record-based and interview-based epidemiology revealed some key exposures (e.g. smoking and radiation). With measurable intermediates (e.g. blood lipids), cardiovascular epidemiology also yielded inferences on causal pathways. Important changes that are remaking the field of epidemiology and will ultimately influence all aspects of medical practice include the following: high-throughput genotyping, allowing genetic and gene-environment causes of disease to be identified; high-throughput proteomics, which should allow the development of early-detection methods; new tools for the measurement of exposures; and a molecular basis for disease taxonomy. These new methods will allow a much better understanding of both the etiology and the intermediate stages of disease; however, new methods do not obviate the necessity for good study design, especially the need to be clear on the difference between observation and experiment. The greatest opportunities to inform medical practice come from the application of new methods to large-scale human observational studies, which include genetics, environment, early-detection markers, molecular classification of outcome, and treatment data. Improved molecular classification of disease will allow smaller, focused clinical trials to be undertaken and, ultimately, the tailoring of treatment to the biological profile of patient and disease.

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Mesh:

Year:  2005        PMID: 16341118     DOI: 10.1038/ncponc0359

Source DB:  PubMed          Journal:  Nat Clin Pract Oncol        ISSN: 1743-4254


  8 in total

1.  Transcriptional output in a prospective design conditionally on follow-up and exposure: the multistage model of cancer.

Authors:  Eiliv Lund; Sandra Plancade
Journal:  Int J Mol Epidemiol Genet       Date:  2012-05-10

2.  The Cancer Epidemiology Descriptive Cohort Database: A Tool to Support Population-Based Interdisciplinary Research.

Authors:  Amy E Kennedy; Muin J Khoury; John P A Ioannidis; Michelle Brotzman; Amy Miller; Crystal Lane; Gabriel Y Lai; Scott D Rogers; Chinonye Harvey; Joanne W Elena; Daniela Seminara
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2016-07-20       Impact factor: 4.254

3.  A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer.

Authors:  Claire S Zhu; Paul F Pinsky; Daniel W Cramer; David F Ransohoff; Patricia Hartge; Ruth M Pfeiffer; Nicole Urban; Gil Mor; Robert C Bast; Lee E Moore; Anna E Lokshin; Martin W McIntosh; Steven J Skates; Allison Vitonis; Zhen Zhang; David C Ward; James T Symanowski; Aleksey Lomakin; Eric T Fung; Patrick M Sluss; Nathalie Scholler; Karen H Lu; Adele M Marrangoni; Christos Patriotis; Sudhir Srivastava; Saundra S Buys; Christine D Berg
Journal:  Cancer Prev Res (Phila)       Date:  2011-03

4.  Towards a more functional concept of causality in cancer research.

Authors:  Eiliv Lund; Vanessa Dumeaux
Journal:  Int J Mol Epidemiol Genet       Date:  2010-03-15

5.  The Canadian Partnership for Tomorrow Project: a pan-Canadian platform for research on chronic disease prevention.

Authors:  Trevor J B Dummer; Philip Awadalla; Catherine Boileau; Camille Craig; Isabel Fortier; Vivek Goel; Jason M T Hicks; Sébastien Jacquemont; Bartha Maria Knoppers; Nhu Le; Treena McDonald; John McLaughlin; Anne-Marie Mes-Masson; Anne-Monique Nuyt; Lyle J Palmer; Louise Parker; Mark Purdue; Paula J Robson; John J Spinelli; David Thompson; Jennifer Vena; Ma'n Zawati
Journal:  CMAJ       Date:  2018-06-11       Impact factor: 8.262

6.  Sources of bias in specimens for research about molecular markers for cancer.

Authors:  David F Ransohoff; Margaret L Gourlay
Journal:  J Clin Oncol       Date:  2009-12-28       Impact factor: 44.544

Review 7.  Asia Cohort Consortium: challenges for collaborative research.

Authors:  Minkyo Song; Betsy Rolland; John D Potter; Daehee Kang
Journal:  J Epidemiol       Date:  2012-05-10       Impact factor: 3.211

8.  Ensuring long-term sustainability of existing cohorts remains the highest priority to inform cancer prevention and control.

Authors:  Graham A Colditz
Journal:  Cancer Causes Control       Date:  2010-01-09       Impact factor: 2.506

  8 in total

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