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Kalkbrenner AE, Zheng C, Yu J, Jenson TE, Kuhlwein T, Ladd-Acosta C, Grove J, Schendel D. Method for Testing Etiologic Heterogeneity Among Noncompeting Diagnoses, Applied to Impact of Perinatal Exposures on Autism and Attention Deficit Hyperactivity Disorder. Epidemiology 2024; 35:689-700. [PMID: 39024025 PMCID: PMC11309336 DOI: 10.1097/ede.0000000000001760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/27/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method. METHODS We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors. RESULTS Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups. CONCLUSION Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.
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Affiliation(s)
- Amy E. Kalkbrenner
- From the Joseph J. Zilber College of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE
| | - Justin Yu
- From the Joseph J. Zilber College of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI
| | - Tara E. Jenson
- From the Joseph J. Zilber College of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI
| | - Thomas Kuhlwein
- Lundbeck Foundation Initiative for Psychiatric Research (iPSYCH), Aarhus, Denmark
- National Centre for Register-based Research, Department of Economics and Business, Aarhus University, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jakob Grove
- Lundbeck Foundation Initiative for Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus C, Denmark
| | - Diana Schendel
- Lundbeck Foundation Initiative for Psychiatric Research (iPSYCH), Aarhus, Denmark
- National Centre for Register-based Research, Department of Economics and Business, Aarhus University, Aarhus, Denmark
- AJ Drexel Autism Institute, Drexel University, Philadelphia, PA
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Hurwitz LM, Townsend MK, Jordan SJ, Patel AV, Teras LR, Lacey JV, Doherty JA, Harris HR, Goodman MT, Shvetsov YB, Modugno F, Moysich KB, Robien K, Prizment A, Schildkraut JM, Berchuck A, Fortner RT, Chan AT, Wentzensen N, Hartge P, Sandler DP, O'Brien KM, Anton-Culver H, Ziogas A, Menon U, Ramus SJ, Pearce CL, Wu AH, White E, Peters U, Webb PM, Tworoger SS, Trabert B. Modification of the Association Between Frequent Aspirin Use and Ovarian Cancer Risk: A Meta-Analysis Using Individual-Level Data From Two Ovarian Cancer Consortia. J Clin Oncol 2022; 40:4207-4217. [PMID: 35867953 PMCID: PMC9916035 DOI: 10.1200/jco.21.01900] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 03/31/2022] [Accepted: 06/17/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Frequent aspirin use has been associated with reduced ovarian cancer risk, but no study has comprehensively assessed for effect modification. We leveraged harmonized, individual-level data from 17 studies to examine the association between frequent aspirin use and ovarian cancer risk, overall and across subgroups of women with other ovarian cancer risk factors. METHODS Nine cohort studies from the Ovarian Cancer Cohort Consortium (n = 2,600 cases) and eight case-control studies from the Ovarian Cancer Association Consortium (n = 5,726 cases) were included. We used Cox regression and logistic regression to assess study-specific associations between frequent aspirin use (≥ 6 days/week) and ovarian cancer risk and combined study-specific estimates using random-effects meta-analysis. We conducted analyses within subgroups defined by individual ovarian cancer risk factors (endometriosis, obesity, family history of breast/ovarian cancer, nulliparity, oral contraceptive use, and tubal ligation) and by number of risk factors (0, 1, and ≥ 2). RESULTS Overall, frequent aspirin use was associated with a 13% reduction in ovarian cancer risk (95% CI, 6 to 20), with no significant heterogeneity by study design (P = .48) or histotype (P = .60). Although no association was observed among women with endometriosis, consistent risk reductions were observed among all other subgroups defined by ovarian cancer risk factors (relative risks ranging from 0.79 to 0.93, all P-heterogeneity > .05), including women with ≥ 2 risk factors (relative risk, 0.81; 95% CI, 0.73 to 0.90). CONCLUSION This study, the largest to-date on aspirin use and ovarian cancer, provides evidence that frequent aspirin use is associated with lower ovarian cancer risk regardless of the presence of most other ovarian cancer risk factors. Risk reductions were also observed among women with multiple risk factors, providing proof of principle that chemoprevention programs with frequent aspirin use could target higher-risk subgroups.
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Affiliation(s)
- Lauren M. Hurwitz
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Mary K. Townsend
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Susan J. Jordan
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Alpa V. Patel
- Department of Population Science, American Cancer Society, Atlanta, GA
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, GA
| | - James V. Lacey
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA
| | - Jennifer A. Doherty
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Holly R. Harris
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Marc T. Goodman
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Yurii B. Shvetsov
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI
| | - Francesmary Modugno
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Kirsten B. Moysich
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - Kim Robien
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Anna Prizment
- Division of Hematology, Oncology and Transplantation, University of Minnesota Masonic Cancer Center, University of Minnesota, Minneapolis, MN
| | - Joellen M. Schildkraut
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Andrew Berchuck
- Division of Gynecologic Oncology, Duke University Medical Center, Durham, NC
| | - Renée T. Fortner
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andrew T. Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Patricia Hartge
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Dale P. Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC
| | - Katie M. O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC
| | - Hoda Anton-Culver
- Department of Epidemiology, University of California Irvine, Irvine, CA
| | - Argyrios Ziogas
- Department of Epidemiology, University of California Irvine, Irvine, CA
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Susan J. Ramus
- School of Women's and Children's Health, Faculty of Medicine, University of NSW Sydney, Sydney, Australia
- Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney, Sydney, Australia
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI
| | - Anna H. Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Emily White
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ulrike Peters
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Penelope M. Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Shelley S. Tworoger
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Britton Trabert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
- Department of Obstetrics and Gynecology, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT
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Zheng J, Dong X, Newton CC, Hsu L. A Generalized Integration Approach to Association Analysis with Multi-category Outcome: An Application to a Tumor Sequencing Study of Colorectal Cancer and Smoking. J Am Stat Assoc 2022; 118:29-42. [PMID: 37193510 PMCID: PMC10168026 DOI: 10.1080/01621459.2022.2105703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 07/06/2022] [Accepted: 07/14/2022] [Indexed: 10/16/2022]
Abstract
Cancer is a heterogeneous disease, and rapid progress in sequencing and -omics technologies has enabled researchers to characterize tumors comprehensively. This has stimulated an intensive interest in studying how risk factors are associated with various tumor heterogeneous features. The Cancer Prevention Study-II (CPS-II) cohort is one of the largest prospective studies, particularly valuable for elucidating associations between cancer and risk factors. In this paper, we investigate the association of smoking with novel colorectal tumor markers obtained from targeted sequencing. However, due to cost and logistic difficulties, only a limited number of tumors can be assayed, which limits our capability for studying these associations. Meanwhile, there are extensive studies for assessing the association of smoking with overall cancer risk and established colorectal tumor markers. Importantly, such summary information is readily available from the literature. By linking this summary information to parameters of interest with proper constraints, we develop a generalized integration approach for polytomous logistic regression model with outcome characterized by tumor features. The proposed approach gains the efficiency through maximizing the joint likelihood of individual-level tumor data and external summary information under the constraints that narrow the parameter searching space. We apply the proposed method to the CPS-II data and identify the association of smoking with colorectal cancer risk differing by the mutational status of APC and RNF43 genes, neither of which is identified by the conventional analysis of CPS-II individual data only. These results help better understand the role of smoking in the etiology of colorectal cancer.
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Affiliation(s)
- Jiayin Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Xinyuan Dong
- Department of Biostatistics, University of Washington, Seattle, WA
| | | | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Biostatistics, University of Washington, Seattle, WA
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4
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Nevo D, Ogino S, Wang M. Reflection on modern methods: causal inference considerations for heterogeneous disease etiology. Int J Epidemiol 2021; 50:1030-1037. [PMID: 33484125 DOI: 10.1093/ije/dyaa278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 12/19/2020] [Indexed: 11/12/2022] Open
Abstract
Molecular pathological epidemiology research provides information about pathogenic mechanisms. A common study goal is to evaluate whether the effects of risk factors on disease incidence vary between different disease subtypes. A popular approach to carrying out this type of research is to implement a multinomial regression in which each of the non-zero values corresponds to a bona fide disease subtype. Then, heterogeneity in the exposure effects across subtypes is examined by comparing the coefficients of the exposure between the different subtypes. In this paper, we explain why this common method potentially cannot recover causal effects, even when all confounders are measured, due to a particular type of selection bias. This bias can be explained by recognizing that the multinomial regression is equivalent to a series of logistic regressions; each compares cases of a certain subtype to the controls. We further explain how this bias arises using directed acyclic graphs and we demonstrate the potential magnitude of the bias by analysis of a hypothetical data set and by a simulation study.
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Affiliation(s)
- Daniel Nevo
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Departments of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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5
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Zabor EC, Seshan VE, Wang S, Begg CB. Validity of a method for identifying disease subtypes that are etiologically heterogeneous. Stat Methods Med Res 2021; 30:2045-2056. [PMID: 34319833 DOI: 10.1177/09622802211032704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A focus of cancer epidemiologic research has become the identification of risk factors that influence specific subtypes of disease, a phenomenon known as etiologic heterogeneity. In previous work we developed a novel strategy to cluster tumor markers and identify disease subtypes that differ maximally with respect to known risk factors for use in the context of case-control studies. The method relies on the premise that unsupervised k-means clustering will find candidate solutions that are closely aligned with the sought-after etiologically distinct clusters, which may not be true in the presence of clusters of tumor markers that are not related to risk of disease. In this article, we investigate in detail the ability of the method to identify the "true" clusters in the presence of clusters that are unrelated to risk factors, what we term "counterfeit" clusters. We find that our method works when the strength of structure is larger in the clusters that truly represent etiologic heterogeneity than in the counterfeit clusters, but when this condition is not met, or when there are many tumor markers that simply represent noise, the method will not find the correct solution without first performing variable selection to identify the tumor markers most strongly related to the risk factors. We illustrate the results using data from a breast cancer case-control study.
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Affiliation(s)
- Emily C Zabor
- Department of Quantitative Health Sciences & Taussig Cancer Institute, 2569Cleveland Clinic, Cleveland Clinic, Cleveland, OH, USA
| | - Venkatraman E Seshan
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Colin B Begg
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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6
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Laskar R, Ferreiro-Iglesias A, Bishop DT, Iles MM, Kanetsky PA, Armstrong BK, Law MH, Goldstein AM, Aitken JF, Giles GG, Cust AE. Risk factors for melanoma by anatomical site: an evaluation of aetiological heterogeneity. Br J Dermatol 2021; 184:1085-1093. [PMID: 33270213 PMCID: PMC9969114 DOI: 10.1111/bjd.19705] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Melanoma aetiology has been proposed to have two pathways, which are determined by naevi and type of sun exposure and related to the anatomical site where melanoma develops. OBJECTIVES We examined associations with melanoma by anatomical site for a comprehensive set of risk factors including pigmentary and naevus phenotypes, ultraviolet radiation exposure and polygenic risk. METHODS We analysed harmonized data from 2617 people with incident first invasive melanoma and 975 healthy controls recruited through two population-based case-control studies in Australia and the UK. Questionnaire data were collected by interview using a single protocol, and pathway-specific polygenic risk scores were derived from DNA samples. We estimated adjusted odds ratios using unconditional logistic regression that compared melanoma cases at each anatomical site with all controls. RESULTS When cases were compared with control participants, there were stronger associations for many naevi vs. no naevi for melanomas on the trunk, and upper and lower limbs than on the head and neck (P-heterogeneity < 0·001). Very fair skin (vs. olive/brown skin) was more weakly related to melanoma on the trunk than to melanomas at other sites (P-heterogeneity = 0·04). There was no significant difference by anatomical site for polygenic risk. Increased weekday sun exposure was positively associated with melanoma on the head and neck but not on other sites. CONCLUSIONS We found evidence of aetiological heterogeneity for melanoma, supporting the dual pathway hypothesis. These findings enhance understanding of risk factors for melanoma and can guide prevention and skin examination education and practices.
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Affiliation(s)
- Ruhina Laskar
- International Agency for Research on Cancer, Lyon, France
| | | | - D Timothy Bishop
- Leeds Institute of Haematology and Immunology, University of Leeds, Leeds, UK
| | - Mark M Iles
- Division of Haematology and Immunology, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
| | - Peter A Kanetsky
- Cancer Epidemiology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Bruce K Armstrong
- Cancer Epidemiology and Prevention Research Group, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Queensland University of Technology (QUT), Brisbane, Australia
| | - Alisa M Goldstein
- Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Joanne F Aitken
- Viertel Centre for Research in Cancer Control, the Cancer Council Queensland, Brisbane, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | | | | | | | - Anne E Cust
- International Agency for Research on Cancer, Lyon, France
- Cancer Epidemiology and Prevention Research Group, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
- The Melanoma Institute Australia, The University of Sydney, Sydney, Australia
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7
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Zhang H, Zhao N, Ahearn TU, Wheeler W, García-Closas M, Chatterjee N. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics 2020; 22:772-788. [PMID: 32112086 DOI: 10.1093/biostatistics/kxz065] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 12/17/2019] [Accepted: 12/20/2019] [Indexed: 01/05/2023] Open
Abstract
Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible subtypes defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).
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Affiliation(s)
- Haoyu Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA and Division of Cancer Epidemiology and Genetics, National Cancer Institute, Shady Grove, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - Ni Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Shady Grove, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - William Wheeler
- National Cancer Institute, Information Management Service, Inc. 11730 Plaza America Dr, Reston, VA 20190, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Shady Grove, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA; Department of Oncology, Johns Hopkins University School of Medicine SPH, 733 N Broadway, Baltimore, MD 21205, USA and Department of Epidemiology, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA
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8
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Tefft BC. Acute sleep deprivation and culpable motor vehicle crash involvement. Sleep 2019; 41:5067408. [PMID: 30239905 DOI: 10.1093/sleep/zsy144] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/17/2018] [Indexed: 11/12/2022] Open
Abstract
Study Objectives To quantify the relationship between acute sleep deprivation and culpable involvement in motor vehicle crashes. Methods Participants were 6845 drivers involved in a representative sample of crashes investigated by the US Department of Transportation in years 2005-2007. A modified case-control study design was used to compare self-reported hours of sleep in the 24 hr before crashing between drivers deemed culpable versus nonculpable. Analyses controlled for fatigue-related, driver-related, and environmental factors. Specific errors that led to crashes were also examined. Results Drivers who reported having slept for 6, 5, 4, and less than 4 hr in the 24 hr before crashing had 1.3 (95% confidence interval [CI] = 1.04 to 1.7), 1.9 (1.1 to 3.2), 2.9 (1.4 to 6.2), and 15.1 (4.2 to 54.4) times the odds, respectively, of having been culpable for their crashes, compared with drivers who reported 7-9 hr of sleep. Drivers who had slept less than 4 hr had 3.4 (95% CI = 2.1 to 5.6) times the increase in odds of culpable involvement in single-vehicle crashes compared with multiple-vehicle crashes. Recent change in sleep schedule, typically feeling drowsy upon waking, and driving for 3+ hr were also associated with culpability (all p ≤ 0.013). Assuming nonculpable drivers comprised a representative sample of all drivers present where crashes occurred, these odds ratios approximate incidence rate ratios for culpable crash involvement per unit of time driving. Conclusions Driving after having slept less than 7 hr in a 24 hr period is associated with elevated risk of culpable crash involvement. Risk is greatest for drivers who have slept less than 4 hr and is manifested disproportionately in single-vehicle crashes.
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Hamada T, Nowak JA, Milner DA, Song M, Ogino S. Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms. J Pathol 2019; 247:615-628. [PMID: 30632609 PMCID: PMC6509405 DOI: 10.1002/path.5236] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/24/2018] [Accepted: 01/06/2019] [Indexed: 02/06/2023]
Abstract
Molecular pathological epidemiology (MPE) is an integrative transdisciplinary field that addresses heterogeneous effects of exogenous and endogenous factors (collectively termed 'exposures'), including microorganisms, on disease occurrence and consequences, utilising molecular pathological signatures of the disease. In parallel with the paradigm of precision medicine, findings from MPE research can provide aetiological insights into tailored strategies of disease prevention and treatment. Due to the availability of molecular pathological tests on tumours, the MPE approach has been utilised predominantly in research on cancers including breast, lung, prostate, and colorectal carcinomas. Mounting evidence indicates that the microbiome (inclusive of viruses, bacteria, fungi, and parasites) plays an important role in a variety of human diseases including neoplasms. An alteration of the microbiome may be not only a cause of neoplasia but also an informative biomarker that indicates or mediates the association of an epidemiological exposure with health conditions and outcomes. To adequately educate and train investigators in this emerging area, we herein propose the integration of microbiology into the MPE model (termed 'microbiology-MPE'), which could improve our understanding of the complex interactions of environment, tumour cells, the immune system, and microbes in the tumour microenvironment during the carcinogenic process. Using this approach, we can examine how lifestyle factors, dietary patterns, medications, environmental exposures, and germline genetics influence cancer development and progression through impacting the microbial communities in the human body. Further integration of other disciplines (e.g. pharmacology, immunology, nutrition) into microbiology-MPE would expand this developing research frontier. With the advent of high-throughput next-generation sequencing technologies, researchers now have increasing access to large-scale metagenomics as well as other omics data (e.g. genomics, epigenomics, proteomics, and metabolomics) in population-based research. The integrative field of microbiology-MPE will open new opportunities for personalised medicine and public health. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Tsuyoshi Hamada
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jonathan A Nowak
- Department of Pathology Program in MPE Molecular Pathological Epidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Danny A Milner
- American Society for Clinical Pathology, Chicago, Illinois, USA
| | - Mingyang Song
- Departments of Epidemiology and Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Shuji Ogino
- Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology Program in MPE Molecular Pathological Epidemiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
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10
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Ogino S, Nowak JA, Hamada T, Phipps AI, Peters U, Milner DA, Giovannucci EL, Nishihara R, Giannakis M, Garrett WS, Song M. Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine. Gut 2018; 67:1168-1180. [PMID: 29437869 PMCID: PMC5943183 DOI: 10.1136/gutjnl-2017-315537] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/02/2018] [Accepted: 01/05/2018] [Indexed: 12/14/2022]
Abstract
Immunotherapy strategies targeting immune checkpoints such as the CTLA4 and CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) T-cell coreceptor pathways are revolutionising oncology. The approval of pembrolizumab use for solid tumours with high-level microsatellite instability or mismatch repair deficiency by the US Food and Drug Administration highlights promise of precision immuno-oncology. However, despite evidence indicating influences of exogenous and endogenous factors such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures and microbiome on tumour-immune interactions, integrative analyses of those factors and immunity lag behind. Immune cell analyses in the tumour microenvironment have not adequately been integrated into large-scale studies. Addressing this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) offers research frameworks to integrate tumour immunology into population health sciences, and link the exposures and germline genetics (eg, HLA genotypes) to tumour and immune characteristics. Multilevel research using bioinformatics, in vivo pathology and omics (genomics, epigenomics, transcriptomics, proteomics and metabolomics) technologies is possible with use of tissue, peripheral blood circulating cells, cell-free plasma, stool, sputum, urine and other body fluids. This immunology-MPE model can synergise with experimental immunology, microbiology and systems biology. GI neoplasms represent exemplary diseases for the immunology-MPE model, given rich microbiota and immune tissues of intestines, and the well-established carcinogenic role of intestinal inflammation. Proof-of-principle studies on colorectal cancer provided insights into immunomodulating effects of aspirin, vitamin D, inflammatory diets and omega-3 polyunsaturated fatty acids. The integrated immunology-MPE model can contribute to better understanding of environment-tumour-immune interactions, and effective immunoprevention and immunotherapy strategies for precision medicine.
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Affiliation(s)
- Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA,Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tsuyoshi Hamada
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Danny A Milner
- American Society for Clinical Pathology, Chicago, Illinois, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Reiko Nishihara
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Marios Giannakis
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Wendy S Garrett
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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11
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Mazumdar M, Moshier EL, Özbek U, Parsons R. Ten Essential Practices for Developing or Reforming a Biostatistics Core for a NCI Designated Cancer Center. JNCI Cancer Spectr 2018; 2:pky010. [PMID: 31360841 PMCID: PMC6649702 DOI: 10.1093/jncics/pky010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/11/2018] [Accepted: 03/06/2018] [Indexed: 01/17/2023] Open
Abstract
There are 69 National Cancer Institute (NCI) designated Cancer Centers (CCs) in the United States. Biostatistical collaboration is pivotal in cancer research, and support for a cancer biostatistics shared resource facility (C-BSRF) is included in the award. Although the services and staff needed in a C-BSRF have been outlined in general terms and best practices for biostatistical consultations and collaboration in an academic health center have been agreed upon, implementing these practices in the demanding setting of cancer centers interested in pursuing or maintaining NCI designation remains challenging. We surveyed all C-BSRF websites to assess their organizational charts, governance, size, services provided, and financial models and have identified 10 essential practices for the development of a successful C-BSRF. Here, we share our success with, and barriers to, implementation of these practices. Showcasing development plans for these essential practices resulted in an NCI score of "Excellent to Outstanding" for our C-BSRF in 2015, and performance metrics in 2016-2017 demonstrated notable improvement since our original Cancer Center Support Grant (CCSG) application in 2014. We believe that the essential practices described here can be adapted and adjusted, as needed, for CCs of various sizes and with different types of cancer research programs.
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Affiliation(s)
- Madhu Mazumdar
- Institute for Healthcare Delivery Science, Mount Sinai Health System, New York, NY
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Erin L Moshier
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Umut Özbek
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ramon Parsons
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
- Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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12
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Ogino S, Jhun I, Mata DA, Soong TR, Hamada T, Liu L, Nishihara R, Giannakis M, Cao Y, Manson JE, Nowak JA, Chan AT. Integration of pharmacology, molecular pathology, and population data science to support precision gastrointestinal oncology. NPJ Precis Oncol 2017; 1. [PMID: 29552640 PMCID: PMC5856171 DOI: 10.1038/s41698-017-0042-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Precision medicine has a goal of customizing disease prevention and treatment strategies. Under the precision medicine paradigm, each patient has unique pathologic processes resulting from cellular genomic, epigenomic, proteomic, and metabolomic alterations, which are influenced by pharmacological, environmental, microbial, dietary, and lifestyle factors. Hence, to realize the promise of precision medicine, multi-level research methods that can comprehensively analyze many of these variables are needed. In order to address this gap, the integrative field of molecular pathology and population data science (i.e., molecular pathological epidemiology) has been developed to enable such multi-level analyses, especially in gastrointestinal cancer research. Further integration of pharmacology can improve our understanding of drug effects, and inform decision-making of drug use at both the individual and population levels. Such integrative research demonstrated potential benefits of aspirin in colorectal carcinoma with PIK3CA mutations, providing the basis for new clinical trials. Evidence also suggests that HPGD (15-PDGH) expression levels in normal colon and the germline rs6983267 polymorphism that relates to tumor CTNNB1 (β-catenin)/WNT signaling status may predict the efficacy of aspirin for cancer chemoprevention. As immune checkpoint blockade targeting the CD274 (PD-L1)/PDCD1 (PD-1) pathway for microsatellite instability-high (or mismatch repair-deficient) metastatic gastrointestinal or other tumors has become standard of care, potential modifying effects of dietary, lifestyle, microbial, and environmental factors on immunotherapy need to be studied to further optimize treatment strategies. With its broad applicability, our integrative approach can provide insights into the interactive role of medications, exposures, and molecular pathology, and guide the development of precision medicine.
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Affiliation(s)
- Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Iny Jhun
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas A Mata
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Thing Rinda Soong
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tsuyoshi Hamada
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Li Liu
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Reiko Nishihara
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marios Giannakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yin Cao
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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13
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Duffy A, Vandeleur C, Heffer N, Preisig M. The clinical trajectory of emerging bipolar disorder among the high-risk offspring of bipolar parents: current understanding and future considerations. Int J Bipolar Disord 2017; 5:37. [PMID: 29164495 PMCID: PMC5698240 DOI: 10.1186/s40345-017-0106-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 09/25/2017] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Relatively little is known about the onset of bipolar disorder, yet the early illness course is already associated with significant morbidity and mortality. Therefore, characterizing the bipolar illness trajectory is key to risk prediction and early intervention advancement. MAIN BODY In this narrative review, we discuss key findings from prospective longitudinal studies of the high-risk offspring of bipolar parents and related meta-analyses that inform us about the clinical trajectory of emerging bipolar disorder. Challenges such as phenotypic and etiologic heterogeneity and the non-specificity of early symptoms and syndromes are highlighted. Implications of the findings for both research and clinical practice are discussed. CONCLUSION Bipolar disorder in young people at familial risk does not typically onset with a hypomanic or manic episode. Rather the first activated episode is often preceded by years of impairing psychopathological states that vary over development and across emerging bipolar subtype. Taking heterogeneity into account and adopting a more comprehensive approach to diagnosis seems necessary to advance earlier identification and our understanding of the onset of bipolar disorder.
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Affiliation(s)
- A Duffy
- Student Wellness Centre, Department of Psychiatry, Queen's University, 146 Stuart Street, Kingston, ON, K7L3N6, Canada.
| | - C Vandeleur
- Department of Psychiatry Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - N Heffer
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - M Preisig
- Department of Psychiatry Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
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