Editing Epidemiology (section)

=== 21st century ===
Since the 2000s, [[Genome-wide association study|genome-wide association studies]] (GWAS) have been commonly performed to identify genetic risk factors for many diseases and health conditions.<ref>{{Cite web |date=17 August 2020 |title=Genome-Wide Association Studies Fact Sheet |url=https://www.genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet |access-date=17 June 2024 |website=National Human Genome Research Institute}}</ref>

While most molecular epidemiology studies are still using conventional disease [[diagnosis]] and classification systems, it is increasingly recognized that disease progression represents inherently heterogeneous processes differing from person to person. Conceptually, each individual has a unique disease process different from any other individual ("the unique disease principle"),<ref>{{cite journal |vauthors=Ogino S, Fuchs CS, Giovannucci E | year = 2012 | title = How many molecular subtypes? Implications of the unique tumor principle in personalized medicine | journal = Expert Rev Mol Diagn | volume = 12 | issue = 6| pages = 621–28 | doi=10.1586/erm.12.46 | pmid=22845482 | pmc=3492839}}</ref><ref>{{cite journal |vauthors=Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, Meyerhardt JA, Meissner A, Schernhammer ES, Fuchs CS, Giovannucci E | year = 2013 | title = Molecular pathological epidemiology of epigenetics: Emerging integrative science to analyze environment, host, and disease | journal = Mod Pathol | volume = 26 | issue = 4| pages = 465–84 | doi=10.1038/modpathol.2012.214 | pmid=23307060 | pmc=3637979}}</ref> considering uniqueness of the [[exposome]] (a totality of endogenous and exogenous / environmental exposures) and its unique influence on molecular pathologic process in each individual. Studies to examine the relationship between an exposure and molecular pathologic signature of disease (particularly [[cancer]]) became increasingly common throughout the 2000s. However, the use of [[molecular pathology]] in epidemiology posed unique challenges, including lack of research guidelines and standardized [[Statistics|statistical]] methodologies, and paucity of interdisciplinary experts and training programs.<ref>{{cite journal |vauthors=Ogino S, King EE, Beck AH, Sherman ME, Milner DA, Giovannucci E | year = 2012 | title = Interdisciplinary education to integrate pathology and epidemiology: Towards molecular and population-level health science | journal = Am J Epidemiol | volume = 176 | issue = 8| pages = 659–67 | doi=10.1093/aje/kws226| pmid = 22935517 | pmc = 3571252}}</ref> Furthermore, the concept of disease heterogeneity appears to conflict with the long-standing premise in epidemiology that individuals with the same disease name have similar etiologies and disease processes. To resolve these issues and advance population health science in the era of molecular [[precision medicine]], "molecular pathology" and "epidemiology" was integrated to create a new interdisciplinary field of "[[molecular pathological epidemiology]]" (MPE),<ref>{{cite journal |vauthors=Ogino S, Stampfer M | year = 2010 | title = Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology | journal = J Natl Cancer Inst | volume = 102 | issue = 6| pages = 365–67 | doi=10.1093/jnci/djq031 | pmid=20208016 | pmc=2841039}}</ref><ref>{{cite journal |vauthors=Ogino S, Chan AT, Fuchs CS, Giovannucci E | year = 2011 | title = Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field | journal = Gut | volume = 60 | issue = 3| pages = 397–411 | doi=10.1136/gut.2010.217182 | pmid=21036793 | pmc=3040598}}</ref> defined as "epidemiology of molecular pathology and heterogeneity of disease". In MPE, investigators analyze the relationships between (A) environmental, dietary, lifestyle and genetic factors; (B) alterations in cellular or extracellular molecules; and (C) evolution and progression of disease. A better understanding of heterogeneity of disease [[pathogenesis]] will further contribute to elucidate [[Etiology|etiologies]] of disease. The MPE approach can be applied to not only neoplastic diseases but also non-neoplastic diseases.<ref>{{cite journal |vauthors=Field AE, Camargo CA, Ogino S | year = 2013 | title = The merits of subtyping obesity: one size does not fit all | journal = JAMA | volume = 310 | issue = 20| pages = 2147–48 | doi=10.1001/jama.2013.281501| pmid = 24189835 }}</ref> The concept and paradigm of MPE have become widespread in the 2010s.<ref>{{cite journal |vauthors=Curtin K, Slattery ML, Samowitz WS | year = 2011 | title = CpG island methylation in colorectal cancer: past, present and future | journal = Pathology Research International | volume = 2011 | page = 902674 | doi = 10.4061/2011/902674 | pmid = 21559209 | pmc = 3090226 | doi-access = free }}</ref><ref>{{cite journal |vauthors=Hughes LA, Khalid-de Bakker CA, Smits KM, den Brandt PA, Jonkers D, Ahuja N, Herman JG, Weijenberg MP, van Engeland M|author-link6=Nita Ahuja|author-link7=James G. Herman | year = 2012 | title = The CpG island methylator phenotype in colorectal cancer: Progress and problems | journal = Biochim Biophys Acta | volume = 1825 | issue = 1| pages = 77–85 | doi=10.1016/j.bbcan.2011.10.005 | pmid=22056543|url=https://cris.maastrichtuniversity.nl/en/publications/64ca3af6-de2b-4150-b52e-0507ac49e51c}}</ref><ref>{{cite journal |vauthors=Ku CS, Cooper DN, Wu M, Roukos DH, Pawitan Y, Soong R, Iacopetta B | year = 2012 | title = Gene discovery in familial cancer syndromes by exome sequencing: prospects for the elucidation of familial colorectal cancer type X. | journal = Mod Pathol | volume = 25 | issue = 8| pages = 1055–68 | doi=10.1038/modpathol.2012.62 | pmid=22522846| doi-access = free }}</ref><ref>{{cite journal |vauthors=Chia WK, Ali R, Toh HC | year = 2012 | title = Aspirin as adjuvant therapy for colorectal cancer-reinterpreting paradigms | journal = Nat Rev Clin Oncol | volume = 9 | issue = 10| pages = 561–70 | doi=10.1038/nrclinonc.2012.137| pmid = 22910681 | s2cid = 7425809 }}</ref><ref>{{cite journal |vauthors=Spitz MR, Caporaso NE, Sellers TA | year = 2012 | title = Integrative cancer epidemiology – the next generation | journal = Cancer Discov | volume = 2 | issue = 12| pages = 1087–90 | doi=10.1158/2159-8290.cd-12-0424 | pmid=23230187 | pmc=3531829}}</ref><ref>{{cite journal |vauthors=Zaidi N, Lupien L, Kuemmerle NB, Kinlaw WB, Swinnen JV, Smans K | year = 2013 | title = Lipogenesis and lipolysis: The pathways exploited by the cancer cells to acquire fatty acids | journal = Prog Lipid Res | volume = 52 | issue = 4| pages = 585–89 | doi=10.1016/j.plipres.2013.08.005| pmc = 4002264 | pmid=24001676}}</ref><ref>{{cite journal |vauthors=Ikramuddin S, Livingston EH | year = 2013 | title = New Insights on Bariatric Surgery Outcomes | journal = JAMA | volume = 310 | issue = 22| pages = 2401–02 | doi=10.1001/jama.2013.280927| pmid = 24189645 }}</ref>{{excessive citations inline|date=March 2023}}

By 2012, it was recognized that many pathogens' [[evolution]] is rapid enough to be highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and [[molecular evolution]] to "inform control strategies, or even patient treatment."<ref>{{cite journal |vauthors=Little TJ, Allen JE, Babayan SA, Matthews KR, Colegrave N | year = 2012 | title = Harnessing evolutionary biology to combat infectious disease | journal = Nature Medicine | volume = 18| issue = 2| pages = 217–20 | doi=10.1038/nm.2572 | pmc=3712261 | pmid=22310693}}</ref><ref>{{cite journal |vauthors=Pybus OG, Fraser C, Rambaut A | year = 2013 | title = Evolutionary epidemiology: preparing for an age of genomic plenty | journal = Phil Trans R Soc B | volume = 368| issue = 1614| pages = 20120193 | doi=10.1098/rstb.2012.0193| pmid = 23382418 | pmc = 3678320}}</ref> Modern epidemiological studies can use advanced statistics and [[machine learning]] to create [[Predictive modelling|predictive models]] as well as to define treatment effects.<ref>{{cite journal|doi=10.1146/annurev-publhealth-040119-094437|doi-access=free|title=Machine Learning in Epidemiology and Health Outcomes Research|year=2020|last1=Wiemken|first1=Timothy L.|last2=Kelley|first2=Robert R.|journal=Annual Review of Public Health|volume=41|pages=21–36|pmid=31577910}}</ref><ref>{{cite journal|doi=10.1093/aje/kwz189|title=What is Machine Learning? A Primer for the Epidemiologist|year=2019|last1=Bi|first1=Qifang|last2=Goodman|first2=Katherine E.|last3=Kaminsky|first3=Joshua|last4=Lessler|first4=Justin|journal=American Journal of Epidemiology|volume=188|issue=12|pages=2222–2239|pmid=31509183}}</ref> There is increasing recognition that a wide range of modern data sources, many not originating from healthcare or epidemiology, can be used for epidemiological study. Such digital epidemiology can include data from internet searching, mobile phone records and retail sales of drugs.{{cn|date=November 2023}}