Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.
anxiety
cancer
depression
meta-analysis
risk
Journal
Cancer
ISSN: 1097-0142
Titre abrégé: Cancer
Pays: United States
ID NLM: 0374236
Informations de publication
Date de publication:
15 10 2023
15 10 2023
Historique:
revised:
16
03
2023
received:
12
12
2022
accepted:
07
04
2023
medline:
22
9
2023
pubmed:
7
8
2023
entrez:
7
8
2023
Statut:
ppublish
Résumé
Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.
Sections du résumé
BACKGROUND
Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium.
METHODS
The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2).
RESULTS
No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23).
CONCLUSIONS
Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.
Types de publication
Meta-Analysis
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3287-3299Subventions
Organisme : NIA NIH HHS
ID : R01 AG017644
Pays : United States
Organisme : Medical Research Council
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.
Références
Antoni MH, Lutgendorf SK, Cole SW, et al. The influence of bio-behavioural factors on tumour biology: pathways and mechanisms. Nat Rev Cancer. 2006;6(3):240-248. doi:10.1038/nrc1820
Chida Y, Hamer M, Wardle J, Steptoe A. Do stress-related psychosocial factors contribute to cancer incidence and survival? Nat Clin Pract Oncol. 2008;5(8):466-475. doi:10.1038/ncponc1134
Jia Y, Li F, Liu YF, Zhao JP, Leng MM, Chen L. Depression and cancer risk: a systematic review and meta-analysis. Public Health. 2017;149:138-148. doi:10.1016/j.puhe.2017.04.026
Wang YH, Li JQ, Shi JF, et al. Depression and anxiety in relation to cancer incidence and mortality: a systematic review and meta-analysis of cohort studies. Mol Psychiatr. 2020;25(7):1487-1499. doi:10.1038/s41380-019-0595-x
McGee R, Williams S, Elwood M. Depression and the development of cancer: a meta-analysis. Soc Sci Med. 1994;38(1):187-192. doi:10.1016/0277-9536(94)90314-x
Oerlemans ME, van den Akker M, Schuurman AG, Kellen E, Buntinx F. A meta-analysis on depression and subsequent cancer risk. Clin Pract Epidemiol Ment Health. 2007;3(1):29. doi:10.1186/1745-0179-3-29
Ahn HK, Bae JH, Ahn HY, Hwang IC. Risk of cancer among patients with depressive disorder: a meta-analysis and implications. Psychooncology. 2016;25(12):1393-1399. doi:10.1002/pon.4084
Stewart LA, Tierney JF. To IPD or not to IPD?: Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof. 2002;25(1):76-97. doi:10.1177/0163278702025001006
Tierney JF, Vale C, Riley R, et al. Individual participant data (IPD) meta-analyses of randomised controlled trials: guidance on their use. PLoS Med. 2015;12(7):e1001855. doi:10.1371/journal.pmed.1001855
van Tuijl LA, Voogd AC, de Graeff A, et al. Psychosocial Factors and Cancer Incidence (PSY-CA): protocol for individual participant data meta-analyses. Brain Behav. 2021;11(10):e2340. doi:10.1002/brb3.2340
Fortier I, Raina P, Van den Heuvel ER, et al. Maelstrom Research guidelines for rigorous retrospective data harmonization. Int J Epidemiol. 2017;46(1):103-105. doi:10.1093/ije/dyw075
IARC Monographs on the Identification of Carcinogenic Hazards to Humans and Handbooks of Cancer Prevention. International Agency for Research on Cancer. Published 2019. Accessed October 14, 2019. https://monographs.iarc.who.int/wp-content/uploads/2019/12/OrganSitePoster.PlusHandbooks.pdf
IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25. doi:10.1186/1471-2288-14-25
Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560. doi:10.1136/bmj.327.7414.557
Rücker G, Schwarzer G, Carpenter JR, Schumacher M. Undue reliance on I2 in assessing heterogeneity may mislead. BMC Med Res Methodol. 2008;8(1):79. doi:10.1186/1471-2288-8-79
Schwarzer G, Carpenter JR, Rücker G. Meta-Analysis with R. Springer International Publishing; 2015. doi:10.1007/978-3-319-21416-0
Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112-125. doi:10.1002/jrsm.11
Boyd A, Golding J, Macleod J, et al. Cohort profile: the ‘children of the 90s’-the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2013;42(1):111-127. doi:10.1093/ije/dys064
Fraser A, Macdonald-Wallis C, Tilling K, et al. Cohort profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42(1):97-110. doi:10.1093/ije/dys066
Borugian MJ, Robson P, Fortier I, et al. The Canadian Partnership for Tomorrow Project: building a pan-Canadian research platform for disease prevention. CMAJ. 2010;182(11):1197-1201. doi:10.1503/cmaj.091540
Sweeney E, Cui Y, DeClercq V, et al. Cohort profile: The Atlantic Partnership for Tomorrow’s Health (Atlantic PATH) study. Int J Epidemiol. 2017;46(6):1762-1763i. doi:10.1093/ije/dyx124
Dummer TJB, Awadalla P, Boileau C, et al. The Canadian Partnership for Tomorrow project: a pan-Canadian platform for research on chronic disease prevention. CMAJ. 2018;190(23):E710-E717. doi:10.1503/cmaj.170292
Awadalla P, Boileau C, Payette Y, et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int J Epidemiol. 2013;42(5):1285-1299. doi:10.1093/ije/dys160
Steptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: The English Longitudinal Study of Ageing. Int J Epidemiol. 2013;42(6):1640-1648. doi:10.1093/ije/dys168
Snijder MB, Galenkamp H, Prins M, et al. Cohort profile: the Healthy Life in an Urban Setting (HELIUS) study in Amsterdam, the Netherlands. BMJ Open. 2017;7(12):e017873. doi:10.1136/bmjopen-2017-017873
Stronks K, Snijder MB, Peters RJ, Prins M, Schene AH, Zwinderman AH. Unravelling the impact of ethnicity on health in Europe: the HELIUS study. BMC Public Health. 2013;13(1):402. doi:10.1186/1471-2458-13-402
Krokstad S, Langhammer A, Hveem K, et al. Cohort profile: The HUNT study, Norway. Int J Epidemiol. 2013;42(4):968-977. doi:10.1093/ije/dys095
Hoogendijk EO, Deeg DJH, Poppelaars J, et al. The Longitudinal Aging Study Amsterdam: cohort update 2016 and major findings. Eur J Epidemiol. 2016;31(9):927-945. doi:10.1007/s10654-016-0192-0
Huisman M, Poppelaars J, van der Horst M, et al. Cohort profile: The Longitudinal Aging Study Amsterdam. Int J Epidemiol. 2011;40(4):868-876. doi:10.1093/ije/dyq219
Scholtens S, Smidt N, Swertz MA, et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol. 2015;44(4):1172-1180. doi:10.1093/ije/dyu229
Penninx BWJH, Beekman ATF, Johannes HS, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 2008;17(3):121-140. doi:10.1002/mpr.256
Hofman A, Brusselle GGO, Murad SD, et al. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol. 2015;30(8):661-708. doi:10.1007/s10654-015-0082-x
Simons PCG, Algra A, van de Laak MF, Grobbee DE, van der Graaf Y. Second Manifestations of Arterial Disease (SMART) study: rationale and design. Eur J Epidemiol. 1999;15(9):773-781. doi:10.1023/a:1007621514757
Grobbee DE, Hoes AW, Verheij TJM, Schrijvers AJP, van Ameijden EJC, Numans ME. The Utrecht Health Project: optimization of routine healthcare data for research. Eur J Epidemiol. 2005;20(3):285-287. doi:10.1007/s10654-004-5689-2
Marmot M, Brunner E. Cohort profile: The Whitehall II study. Int J Epidemiol. 2005;34(2):251-256. doi:10.1093/ije/dyh372
Cho S, Shin A, Song D, et al. Validity of self-reported cancer history in the Health Examinees (HEXA) study: a comparison of self-report and cancer registry records. Cancer Epidemiol. 2017;50:16-21. doi:10.1016/j.canep.2017.07.010
Thiébaut ACM, Bénichou J. Choice of time-scale in Cox’s model analysis of epidemiologic cohort data: a simulation study. Stat Med. 2004;23(24):3803-3820. doi:10.1002/sim.2098
Dalton SO, Mellemkjaer L, Olsen JH, Mortensen PB, Johansen C. Depression and cancer risk: a register-based study of patients hospitalized with affective disorders, Denmark, 1969-1993. Am J Epidemiol. 2002;155(12):1088-1095. doi:10.1093/aje/155.12.1088
Knekt P, Raitasalo R, Heliövaara M, et al. Elevated lung cancer risk among persons with depressed mood. Am J Epidemiol. 1996;144(12):1096-1103. doi:10.1093/oxfordjournals.aje.a008887
Linkins RW, Comstock GW. Depressed mood and development of cancer. Am J Epidemiol. 1990;132(5):962-972. doi:10.1093/oxfordjournals.aje.a115739
Brewer JK. Behavioral genetics of the depression/cancer correlation: a look at the Ras oncogene family and the ‘cerebral diabetes paradigm.’ J Mol Neurosci. 2008;35(3):307-322. doi:10.1007/s12031-008-9078-2
Knol MJ, Janssen KJM, Donders ART, et al. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. J Clin Epidemiol. 2010;63(7):728-736. doi:10.1016/j.jclinepi.2009.08.028
Penninx BWJH, Guralnik JM, Pahor M, et al. Chronically depressed mood and cancer risk in older persons. J Natl Cancer Inst. 1998;90(24):1888-1893. doi:10.1093/jnci/90.24.1888