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Albers FEM, Lou MWC, Dashti SG, Swain CTV, Rinaldi S, Viallon V, Karahalios A, Brown KA, Gunter MJ, Milne RL, English DR, Lynch BM. Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis. Cancer Causes Control 2024; 35:921-933. [PMID: 38363402 DOI: 10.1007/s10552-024-01856-6] [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: 10/03/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways. METHODS This analysis included 1208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex-hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders. RESULTS Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI 1.03 to 1.44), androstenedione (RR 1.20, 95% CI 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI 0.98 to 1.28), estrone (RR 1.21, 95% CI 0.99 to 1.48), total estradiol (RR 1.19, 95% CI 1.02 to 1.39) and free estradiol (RR 1.22, 95% CI 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR 0.83, 95% CI 0.66 to 1.05). CONCLUSION Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex-hormone-driven nature of postmenopausal breast cancer.
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Affiliation(s)
- Frances E M Albers
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - Makayla W C Lou
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Christopher T V Swain
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Amalia Karahalios
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Kristy A Brown
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- Cancer Epidemiology and Prevention Research Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia.
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.
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Li DL, Hodge AM, Southey MC, Giles GG, Milne RL, Dugué PA. Self-rated health, epigenetic ageing, and long-term mortality in older Australians. GeroScience 2024:10.1007/s11357-024-01211-2. [PMID: 38795183 DOI: 10.1007/s11357-024-01211-2] [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: 04/05/2024] [Accepted: 05/16/2024] [Indexed: 05/27/2024] Open
Abstract
Self-rated health (SRH) is a subjective indicator of overall health based on a single questionnaire item. Previous evidence found that it is a strong predictor of mortality, although the underlying mechanism is poorly understood. Epigenetic age is an objective, emerging biomarker of health, estimated using DNA methylation data at hundreds of sites across the genome. This study aimed to assess the overlap and interaction between SRH and epigenetic ageing in predicting mortality risk. We used DNA methylation data from 1059 participants in the Melbourne Collaborative Cohort Study (mean age: 69 years) to calculate three age-adjusted measures of epigenetic ageing: GrimAge, PhenoAge, and DunedinPACE. SRH was assessed using a five-category questionnaire item ("excellent, very good, good, fair, poor"). Cox models were used to assess the associations of SRH, epigenetic ageing, and their interaction, with all-cause mortality over up to 17 years of follow-up (Ndeaths = 345). The association of SRH with mortality per category increase was HR = 1.29; 95%CI: 1.14-1.46. The association was slightly attenuated after adjusting for all three epigenetic ageing measures (HR = 1.25, 95%CI: 1.10-1.41). A strong gradient was observed in the association of GrimAge (Pinteraction = 0.006) and DunedinPACE (Pinteraction = 0.002) with mortality across worsening SRH strata. For example, the association between DunedinPACE and mortality in participants with "excellent" SRH was HR = 1.02, 95%CI: 0.73-1.43 and for "fair/poor" HR = 1.72, 95%CI: 1.35-2.20. SRH and epigenetic ageing were synergistic risk factors of mortality in our study. These findings suggest that consideration of subjective and objective factors may improve general health assessment, which has implications for the ongoing development of molecular markers of ageing.
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Affiliation(s)
- Danmeng Lily Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.
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Li S, Dite GS, MacInnis RJ, Bui M, Nguyen TL, Esser VFC, Ye Z, Dowty JG, Makalic E, Sung J, Giles GG, Southey MC, Hopper JL. Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses. Genet Epidemiol 2024. [PMID: 38472646 DOI: 10.1002/gepi.22556] [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: 07/31/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024]
Abstract
A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
- Genomic Medicine Institute, Seoul National University, Euigwahakgwan #402, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, 1st GwanakRo, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
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Joo JE, Chu YL, Georgeson P, Walker R, Mahmood K, Clendenning M, Meyers AL, Como J, Joseland S, Preston SG, Diepenhorst N, Toner J, Ingle DJ, Sherry NL, Metz A, Lynch BM, Milne RL, Southey MC, Hopper JL, Win AK, Macrae FA, Winship IM, Rosty C, Jenkins MA, Buchanan DD. Intratumoral presence of the genotoxic gut bacteria pks + E. coli, Enterotoxigenic Bacteroides fragilis, and Fusobacterium nucleatum and their association with clinicopathological and molecular features of colorectal cancer. Br J Cancer 2024; 130:728-740. [PMID: 38200234 PMCID: PMC10912205 DOI: 10.1038/s41416-023-02554-x] [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: 07/04/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND This study aimed to investigate clinicopathological and molecular tumour features associated with intratumoral pks+ Escherichia coli (pks+E.coli+), pks+E.coli- (non-E.coli bacteria harbouring the pks island), Enterotoxigenic Bacteroides fragilis (ETBF) and Fusobacterium nucleatum (F. nucleatum). METHODS We screened 1697 tumour-derived DNA samples from the Australasian Colorectal Cancer Family Registry, Melbourne Collaborative Cohort Study and the ANGELS study using targeted PCR. RESULTS Pks+E.coli+ was associated with male sex (P < 0.01) and APC:c.835-8 A > G somatic mutation (P = 0.03). The association between pks+E.coli+ and APC:c.835-8 A > G was specific to early-onset CRCs (diagnosed<45years, P = 0.02). The APC:c.835-A > G was not associated with pks+E.coli- (P = 0.36). F. nucleatum was associated with DNA mismatch repair deficiency (MMRd), BRAF:c.1799T>A p.V600E mutation, CpG island methylator phenotype, proximal tumour location, and high levels of tumour infiltrating lymphocytes (Ps < 0.01). In the stratified analysis by MMRd subgroups, F. nucleatum was associated with Lynch syndrome, MLH1 methylated and double MMR somatic mutated MMRd subgroups (Ps < 0.01). CONCLUSION Intratumoral pks+E.coli+ but not pks+E.coli- are associated with CRCs harbouring the APC:c.835-8 A > G somatic mutation, suggesting that this mutation is specifically related to DNA damage from colibactin-producing E.coli exposures. F. nucleatum was associated with both hereditary and sporadic MMRd subtypes, suggesting the MMRd tumour microenvironment is important for F. nucleatum colonisation irrespective of its cause.
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Affiliation(s)
- Jihoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Yen Lin Chu
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Romy Walker
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
- Melbourne Bioinformatics, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark Clendenning
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Aaron L Meyers
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Julia Como
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Sharelle Joseland
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Susan G Preston
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Natalie Diepenhorst
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Julie Toner
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Danielle J Ingle
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Norelle L Sherry
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
- Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia
| | - Andrew Metz
- Endoscopy Unit, Department of Gastroenterology and Hepatology, The Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Finlay A Macrae
- Colorectal Medicine and Genetics, The Royal Melbourne Hospital, Parkville, VIC, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne, VIC, Australia
- Department of Medicine, The University of Melbourne, Parkville, VIC, Australia
| | - Ingrid M Winship
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne, VIC, Australia
- Department of Medicine, The University of Melbourne, Parkville, VIC, Australia
| | - Christophe Rosty
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
- Envoi Specialist Pathologists, Brisbane, QLD, Australia
- University of Queensland, Brisbane, QLD, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, VIC, Australia.
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia.
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne, VIC, Australia.
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Peng Y, Bassett JK, Hodge AM, Melaku YA, Afshar N, Hopper JL, MacInnis RJ, Lynch BM, Smith-Warner SA, Giles GG, Milne RL, Jayasekara H. Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study. Cancer Epidemiol Biomarkers Prev 2024; 33:43-54. [PMID: 37943161 DOI: 10.1158/1055-9965.epi-23-0945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND We examined associations between adherence to adaptations of the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations and total, exposure-related and site-specific cancer risk. METHODS A total of 20,001 participants ages 40 to 69 years at enrollment into the Melbourne Collaborative Cohort Study in 1990 to 1994, who had diet, body size, and lifestyle reassessed in 2003 to 2007 ("baseline"), were followed-up through June 2021. We constructed diet and standardized lifestyle scores based on core WCRF/AICR recommendations on diet, alcohol intake, body size and physical activity, and additional scores incorporating weight change, sedentary behavior, and smoking. Associations with cancer risk were estimated using Cox regression, adjusting for confounders. RESULTS During follow-up (mean = 16 years), 4,710 incident cancers were diagnosed. For highest quintile ("most adherent") of the standardized lifestyle score, compared with lowest ("least adherent"), a HR of 0.82 [95% confidence interval (CI): 0.74-0.92] was observed for total cancer. This association was stronger with smoking included in the score (HR = 0.74; 95% CI: 0.67-0.81). A higher score was associated with lower breast and prostate cancer risk for the standardized score, and with lung, stomach, rectal, and pancreatic cancer risk when the score included smoking. Our analyses identified alcohol use, waist circumference and smoking as key drivers of associations with total cancer risk. CONCLUSIONS Adherence to WCRF/AICR cancer prevention recommendations is associated with lower cancer risk. IMPACT With <0.2% of our sample fully adherent to the recommendations, the study emphasizes the vast potential for preventing cancer through modulation of lifestyle habits.
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Affiliation(s)
- Yang Peng
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- School of Public Health, The University of Queensland, Queensland, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Yohannes Adama Melaku
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- FHMRI Sleep, College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Nina Afshar
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Brigid M Lynch
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Stephanie A Smith-Warner
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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6
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Albers FE, Lou MW, Dashti SG, Swain CT, Rinaldi S, Viallon V, Karahalios A, Brown KA, Gunter MJ, Milne RL, English DR, Lynch BM. Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis. RESEARCH SQUARE 2023:rs.3.rs-3406466. [PMID: 37886482 PMCID: PMC10602098 DOI: 10.21203/rs.3.rs-3406466/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Purpose Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways. Methods This analysis included 1,208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders. Results Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI: 1.03 to 1.44), androstenedione (RR: 1.20, 95% CI: 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI: 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI: 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI: 0.98 to 1.28), estrone (RR: 1.21, 95% CI: 0.99 to 1.48), total estradiol (RR: 1.19, 95% CI: 1.02 to 1.39) and free estradiol (RR: 1.22, 95% CI: 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR: 0.83, 95% CI: 0.66 to 1.05). Conclusion Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex hormone-driven nature of postmenopausal breast cancer.
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Mizrahi D, Swain CTV, Bruinsma F, Hodge A, Taylor N, Lynch BM. The Relationship Between Psychological Distress and Physical Activity Is Non-linear and Differs by Domain: a Cross-Sectional Study. Int J Behav Med 2023; 30:673-681. [PMID: 36180761 PMCID: PMC9524734 DOI: 10.1007/s12529-022-10130-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND There is increasing evidence for the relationship between physical activity (PA), sedentary behaviour and mental health. Limited data exists on sex-specific associations. We aimed to identify associations between PA dose and domain and television time with psychological distress, including sex-stratified models. METHODS A total of 22,176 adults from the Melbourne Collaborative Cohort Study follow-up 2 cohort (2003-2007) participated in this cross-sectional study. Occupational, household, transport, leisure PA, hours watching television and psychological distress were assessed. Restricted cubic splines were used to examine the relationships between PA domains, television viewing time and psychological distress. RESULTS The relationships between PA and psychological distress were non-linear (p < 0.05) and differed by PA domain. There were dose-dependent, inverse associations between distress with transport (B[95% CI] = -0.39[-0.49, -0.30]) and leisure PA (B[95% CI] = -0.35[-0.46, -0.25]). The effect estimates for transport and leisure PA with distress were larger for women. For household domain, a U-shaped curve with an elongated tail was seen. Median PA was associated with lower distress compared with lower quantities (B[95% CI] = -0.12[-0.22, -0.03]); however, this association was not evident with increasing household PA. There were no clear associations between occupational PA and distress. Higher television viewing was associated with higher distress (B[95% CI] = 0.16[0.02, 0.30]). CONCLUSIONS Increasing PA and reducing television viewing may contribute to reduced psychological distress, particularly in women. Future interventions should incorporate leisure and transport PA and decrease television viewing to assess the impact on mental health.
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Affiliation(s)
- David Mizrahi
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia
- Prince of Wales Clinical School, UNSW Sydney, Sydney, Australia
| | | | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Allison Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Natalie Taylor
- School of Population Health, UNSW Sydney, Sydney, Australia
| | - Brigid M Lynch
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.
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8
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Vingrys K, Mathai ML, McAinch AJ, Bassett JK, de Courten M, Stojanovska L, Millar L, Giles GG, Hodge AM, Apostolopoulos V. Intake of polyphenols from cereal foods and colorectal cancer risk in the Melbourne Collaborative Cohort Study. Cancer Med 2023; 12:19188-19202. [PMID: 37702114 PMCID: PMC10557875 DOI: 10.1002/cam4.6514] [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: 05/10/2023] [Revised: 08/16/2023] [Accepted: 08/29/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Cereal-derived polyphenols have demonstrated protective mechanisms in colorectal cancer (CRC) models; however, confirmation in human studies is lacking. Therefore, this study examined the association between cereal polyphenol intakes and CRC risk in the Melbourne Collaborative Cohort Study (MCCS), a prospective cohort study in Melbourne, Australia that recruited participants between 1990 and 1994 to investigate diet-disease relationships. METHODS Using food frequency questionnaire diet data matched to polyphenol data, dietary intakes of alkylresorcinols, phenolic acids, lignans, and total polyphenols from cereals were estimated. Hazard ratios (HRs) and 95% confidence intervals for CRC risk were estimated for quintiles of intake with the lowest quintile as the comparison category, using multivariable adjusted Cox proportional hazards models with age as the time axis adjusted for sex, socio-economic status, alcohol consumption, fibre intake, country of birth, total energy intake, physical activity and smoking status. RESULTS From 35,245 eligible adults, mean (SD) age 54.7 (8.6) years, mostly female (61%) and Australian-born (69%), there were 1394 incident cases of CRC (946 colon cancers and 448 rectal cancers). Results for total cereal polyphenol intake showed reduced HRs in Q2 (HR: 0.80; 95% CI, 0.68-0.95) and Q4 (HR: 0.75; 95% CI, 0.62-0.90), and similar for phenolic acids. Alkylresorcinol intake showed reduced HR in Q3 (HR: 0.80; 95% CI, 0.67-0.95) and Q4 (HR: 0.79; 95% CI, 0.66-0.95). CONCLUSIONS Overall, the present study showed little evidence of association between intakes of cereal polyphenols and CRC risk. Future investigations may be useful to understand associations between cereal-derived polyphenols and additional cancers in different populations.
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Affiliation(s)
- Kristina Vingrys
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- VU First Year College®Victoria UniversityMelbourneVictoriaAustralia
| | - Michael L. Mathai
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
| | - Andrew J. McAinch
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- Australian Institute for Musculoskeletal Science (AIMSS)Victoria UniversityMelbourneVictoriaAustralia
| | - Julie K. Bassett
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
| | - Maximilian de Courten
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- Mitchell Institute for Education and Health PolicyVictoria UniversityMelbourneVictoriaAustralia
| | - Lily Stojanovska
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- Department of Nutrition and Health, College of Medicine and Health SciencesUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Lynne Millar
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- Telethon Kids InstituteNedlandsWAAustralia
| | - Graham G. Giles
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneParkvilleVictoriaAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVictoriaAustralia
| | - Allison M. Hodge
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Vasso Apostolopoulos
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
- Australian Institute for Musculoskeletal Science (AIMSS)Victoria UniversityMelbourneVictoriaAustralia
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9
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Dugué PA, Yu C, Hodge AM, Wong EM, Joo JE, Jung CH, Schmidt D, Makalic E, Buchanan DD, Severi G, English DR, Hopper JL, Milne RL, Giles GG, Southey MC. Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer. Int J Cancer 2023; 153:489-498. [PMID: 36919377 DOI: 10.1002/ijc.34513] [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: 01/28/2021] [Revised: 03/16/2021] [Accepted: 03/22/2021] [Indexed: 03/16/2023]
Abstract
Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.
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Affiliation(s)
- Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - JiHoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Chol-Hee Jung
- Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine Universités Paris-Saclay, UVSQ, Gustave Roussy, Villejuif, France
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
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10
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Dowty JG, Yu C, Hosseinpour M, Joo JE, Wong EM, Nguyen-Dumont T, Rosenbluh J, Giles GG, Milne RL, MacInnis RJ, Dugué PA, Southey MC. Heritable methylation marks associated with prostate cancer risk. Fam Cancer 2023; 22:313-317. [PMID: 36708485 PMCID: PMC10275808 DOI: 10.1007/s10689-022-00325-w] [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: 12/13/2021] [Accepted: 12/09/2022] [Indexed: 01/29/2023]
Abstract
DNA methylation marks that are inherited from parents to offspring are known to play a role in cancer risk and could explain part of the familial risk for cancer. We therefore conducted a genome-wide search for heritable methylation marks associated with prostate cancer risk. Peripheral blood DNA methylation was measured for 133 of the 469 members of 25 multiple-case prostate cancer families, using the EPIC array. We used these families to systematically search the genome for methylation marks with Mendelian patterns of inheritance, then we tested the 1,000 most heritable marks for association with prostate cancer risk. After correcting for multiple testing, 41 heritable methylation marks were associated with prostate cancer risk. Separate analyses, based on 869 incident cases and 869 controls from a prospective cohort study, showed that 9 of these marks near the metastable epiallele VTRNA2-1 were also nominally associated with aggressive prostate cancer risk in the population.
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Affiliation(s)
- James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Parkville, VIC, Australia
| | - Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
| | - Mahnaz Hosseinpour
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, 3010, Parkville, VIC, Australia
- Cancer Research Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, 3800, Clayton, VIC, Australia
| | - Jihoon Eric Joo
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, 3010, Parkville, VIC, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia
| | - Joseph Rosenbluh
- Cancer Research Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, 3800, Clayton, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Parkville, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Parkville, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Parkville, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3010, Parkville, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 3168, Clayton, VIC, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia.
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, 3010, Parkville, VIC, Australia.
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11
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Kabthymer RH, Karim MN, Hodge AM, de Courten B. High cereal fibre but not total fibre is associated with a lower risk of type 2 diabetes: Evidence from the Melbourne Collaborative Cohort Study. Diabetes Obes Metab 2023; 25:1911-1921. [PMID: 36932835 PMCID: PMC10946543 DOI: 10.1111/dom.15054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
AIM To assess the associations of total dietary fibre and fibre from different food sources (ie, cereal, fruit and vegetables) with the risk of diabetes. MATERIALS AND METHODS The Melbourne Collaborative Cohort Study enrolled 41 513 participants aged 40 to 69 years from 1990 to 1994. The first and second follow-ups were conducted in 1994 to 1998 and 2003 to 2007, respectively. Self-reported diabetes incidence was recorded at both follow-ups. We analysed data from 39 185 participants, with a mean follow-up of 13.8 years. The relationships between dietary fibre intake (total, fruit, vegetable and cereal fibre) and the incidence of diabetes were assessed using modified Poisson regression, adjusted for dietary, lifestyle, obesity, socioeconomic and other possible confounders. Fibre intake was categorized into quintiles. RESULTS At total of 1989 incident cases were identified over both follow-up surveys. Total fibre intake was not associated with diabetes risk. Higher intake of cereal fibre (P for trend = 0.003), but not fruit (P for trend = 0.3) and vegetable fibre (P for trend = 0.5), was protective against diabetes. For cereal fibre, quintile 5 versus quintile 1 showed a 25% reduction in diabetes risk (incidence risk ratio [IRR] 0.75, 95% confidence interval [CI] 0.63-0.88). For fruit fibre, only quintile 2 versus quintile 1 showed a 16% risk reduction (IRR 0.84, 95% CI 0.73-0.96). Adjustment for body mass index (BMI) and waist-to-hip ratio eliminated the association and mediation analysis showed that BMI mediated 36% of the relationship between fibre and diabetes. CONCLUSION Intake of cereal fibre and, to a lesser extent, fruit fibre, may reduce the risk of diabetes, while total fibre showed no association. Our data suggest that specific recommendations regarding dietary fibre intake may be needed to prevent diabetes.
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Affiliation(s)
- Robel Hussen Kabthymer
- Department of Medicine, School of Clinical SciencesMonash UniversityMelbourneVictoriaAustralia
| | - Md Nazmul Karim
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Barbora de Courten
- Department of Medicine, School of Clinical SciencesMonash UniversityMelbourneVictoriaAustralia
- School of Health and Biomedical SciencesRMIT UniversityMelbourneVictoriaAustralia
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12
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Albanes D, Alcala K, Alcala N, Amos CI, Arslan AA, Bassett JK, Brennan P, Cai Q, Chen C, Feng X, Freedman ND, Guida F, Hung RJ, Hveem K, Johansson M, Johansson M, Koh WP, Langhammer A, Milne RL, Muller D, Onwuka J, Sørgjerd EP, Robbins HA, Sesso HD, Severi G, Shu XO, Sieri S, Smith-Byrne K, Stevens V, Tinker L, Tjønneland A, Visvanathan K, Wang Y, Wang R, Weinstein S, Yuan JM, Zahed H, Zhang X, Zheng W. The blood proteome of imminent lung cancer diagnosis. Nat Commun 2023; 14:3042. [PMID: 37264016 PMCID: PMC10235023 DOI: 10.1038/s41467-023-37979-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Identification of risk biomarkers may enhance early detection of smoking-related lung cancer. We measured between 392 and 1,162 proteins in blood samples drawn at most three years before diagnosis in 731 smoking-matched case-control sets nested within six prospective cohorts from the US, Europe, Singapore, and Australia. We identify 36 proteins with independently reproducible associations with risk of imminent lung cancer diagnosis (all p < 4 × 10-5). These include a few markers (e.g. CA-125/MUC-16 and CEACAM5/CEA) that have previously been reported in studies using pre-diagnostic blood samples for lung cancer. The 36 proteins include several growth factors (e.g. HGF, IGFBP-1, IGFP-2), tumor necrosis factor-receptors (e.g. TNFRSF6B, TNFRSF13B), and chemokines and cytokines (e.g. CXL17, GDF-15, SCF). The odds ratio per standard deviation range from 1.31 for IGFBP-1 (95% CI: 1.17-1.47) to 2.43 for CEACAM5 (95% CI: 2.04-2.89). We map the 36 proteins to the hallmarks of cancer and find that activation of invasion and metastasis, proliferative signaling, tumor-promoting inflammation, and angiogenesis are most frequently implicated.
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13
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Vingrys K, Mathai ML, Apostolopoulos V, Bassett JK, de Courten M, Stojanovska L, Millar L, Giles GG, Milne RL, Hodge AM, McAinch AJ. Estimated dietary intake of polyphenols from cereal foods and associated lifestyle and demographic factors in the Melbourne Collaborative Cohort Study. Sci Rep 2023; 13:8556. [PMID: 37237174 PMCID: PMC10220042 DOI: 10.1038/s41598-023-35501-0] [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: 08/10/2022] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Cereal foods are consumed globally and are important sources of polyphenols with potential health benefits, yet dietary intakes are unclear. We aimed to calculate the dietary intakes of polyphenols from cereal foods in the Melbourne Collaborative Cohort Study (MCCS), and describe intakes by demographic and lifestyle factors. We estimated intakes of alkylresorcinols, lignans and phenolic acids in n = 39,892 eligible MCCS participants, using baseline dietary data (1990-1994) from a 121-item FFQ containing 17 cereal foods, matched to a polyphenol database developed from published literature and Phenol-Explorer Database. Intakes were estimated within groups according to lifestyle and demographic factors. The median (25th-75th percentile) intake of total polyphenols from cereal foods was 86.9 mg/day (51.4-155.8). The most consumed compounds were phenolic acids, with a median intake of 67.1 mg (39.5-118.8), followed by alkylresorcinols of 19.7 mg (10.8-34.6). Lignans made the smallest contribution of 0.50 mg (0.13-0.87). Higher polyphenol intakes were associated with higher relative socio-economic advantage and prudent lifestyles, including lower body mass index (BMI), non-smoking and higher physical activity scores. The findings based on polyphenol data specifically matched to the FFQ provide new information on intakes of cereal polyphenols, and how they might vary according to lifestyle and demographic factors.
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Affiliation(s)
- Kristina Vingrys
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia.
- VU First Year College ®, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia.
| | - Michael L Mathai
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
| | - Vasso Apostolopoulos
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
- Australian Institute for Musculoskeletal Science (AIMSS), Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
| | - Maximilian de Courten
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
- Mitchell Institute for Education and Health Policy, Victoria University, 300 Queen St, Melbourne, VIC, Australia
| | - Lily Stojanovska
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
- Department of Nutrition and Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Lynne Millar
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
- Telethon Kids Institute, 15 Hospital Avenue, Nedlands, WA, 6009, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Andrew J McAinch
- Institute for Health and Sport, Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
- Australian Institute for Musculoskeletal Science (AIMSS), Victoria University, PO Box 14428, Melbourne, VIC, 8001, Australia
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14
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Lane MM, Lotfalian M, Hodge A, O'Neil A, Travica N, Jacka FN, Rocks T, Machado P, Forbes M, Ashtree DN, Marx W. High ultra-processed food consumption is associated with elevated psychological distress as an indicator of depression in adults from the Melbourne Collaborative Cohort Study. J Affect Disord 2023; 335:57-66. [PMID: 37149054 DOI: 10.1016/j.jad.2023.04.124] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 04/14/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Few studies have tested longitudinal associations between ultra-processed food consumption and depressive outcomes. As such, further investigation and replication are necessary. The aim of this study is to examine associations of ultra-processed food intake with elevated psychological distress as a marker for depression after 15 years. METHOD Data from the Melbourne Collaborative Cohort Study (MCCS) were analysed (n = 23,299). We applied the NOVA food classification system to a food frequency questionnaire (FFQ) to determine ultra-processed food intake at baseline. We categorised energy-adjusted ultra-processed food consumption into quartiles by using the distribution of the dataset. Psychological distress was measured by the ten-item Kessler Psychological Distress Scale (K10). We fitted unadjusted and adjusted logistic regression models to assess the association of ultra-processed food consumption (exposure) with significant psychological distress (outcome and defined as K10 ≥ 20). We fitted additional logistic regression models to determine whether these associations were modified by sex, age and body mass index. RESULTS After adjusting for sociodemographic characteristics and lifestyle and health-related behaviours, participants with the highest relative intake of ultra-processed food were at increased odds of significant psychological distress compared to participants with the lowest intake (aOR: 1.23; 95%CI: 1.10, 1.38, p for trend = 0.001). We found no evidence for an interaction of sex, age and body mass index with ultra-processed food intake. CONCLUSION Higher ultra-processed food intake at baseline was associated with subsequent elevated psychological distress as an indicator of depression at follow-up. Further prospective and intervention studies are necessary to identify possible underlying pathways, specify the precise attributes of ultra-processed food that confer harm, and optimise nutrition-related and public health strategies for common mental disorders.
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Affiliation(s)
- Melissa M Lane
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia.
| | - Mojtaba Lotfalian
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
| | - Allison Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Adrienne O'Neil
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
| | - Nikolaj Travica
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
| | - Felice N Jacka
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia; Centre for Adolescent Health, Murdoch Children's Research Institute, VIC, Australia; Black Dog Institute, NSW, Australia; James Cook University, QLD, Australia
| | - Tetyana Rocks
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
| | - Priscila Machado
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3220, Australia; Center for Epidemiological Research in Nutrition and Health, University of Sao Paulo, Av. Dr. Arnaldo, 715, Sao Paulo 01246-904, Brazil
| | - Malcolm Forbes
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia; Mental Health, Drugs & Alcohol Service, University Hospital Geelong, Barwon Health, VIC 3220, Australia; Department of Psychiatry, University of Melbourne, Parkville, VIC 3050, Australia
| | - Deborah N Ashtree
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
| | - Wolfgang Marx
- Deakin University, IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Geelong, Australia
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15
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Wang ME, Hodge AM, Li SX, Southey MC, Giles GG, Dugué PA. Adiposity and plasma concentrations of kynurenine pathway metabolites and traditional markers of inflammation. Obes Res Clin Pract 2023:S1871-403X(23)00028-5. [PMID: 37121824 DOI: 10.1016/j.orcp.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 05/02/2023]
Abstract
AIM The kynurenine pathway is increasingly recognised to play a role in inflammation and disease. We assessed the cross-sectional and longitudinal associations of adiposity measures (body mass index, waist-hip ratio, waist circumference and fat mass ratio) with plasma concentrations of kynurenine pathway metabolites and traditional markers of inflammation. METHODS We used data from 970 Melbourne Collaborative Cohort Study participants who had plasma markers measured at baseline (median age 59 years) and follow-up (median age 70 years). Linear regression was used to assess cross-sectional and longitudinal associations between four adiposity measures and concentrations of i) nine kynurenine pathway metabolites; ii) two derived markers; iii) eight traditional inflammatory markers. RESULTS Cross-sectionally, most kynurenine metabolites were strongly associated with adiposity measures at both time points; associations were generally stronger than for most inflammation markers except CRP (e.g. body mass index at baseline, quinolinic acid (per S.D. β = 0.30, 95%CI: 0.24-0.36, P = 10-21), kynurenine (β = 0.25, 95%CI: 0.19-0.31, P = 10-16) and CRP (β = 0.31, 95%CI: 0.25-0.37, P = 10-24), and remained largely unchanged after adjustment for confounders. Longitudinally, changes in adiposity measures over approximately a decade were positively associated with changes in kynurenine metabolite concentrations (in particular for 3-hydroxyanthranilic acid, kynurenine and quinolinic acid), and more strongly so than for other markers of inflammation, including CRP. CONCLUSIONS In middle-aged and older adults, plasma concentrations of kynurenine metabolites are strongly associated with adiposity, both cross-sectionally and longitudinally. Our study demonstrates that kynurenine metabolites may be valuable markers to monitor the adverse consequences of obesity.
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Affiliation(s)
- Mengmei E Wang
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Allison M Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Sherly X Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
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16
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Nichols HB, House MG, Yarosh R, Mitra S, Goldberg M, Bertrand KA, Eliassen AH, Giles GG, Jones ME, Milne RL, O'Brien KM, Palmer JR, Sandin S, Willett WC, Yin W, Sandler DP, Swerdlow AJ, Schoemaker MJ. Hypertensive conditions of pregnancy, preterm birth, and premenopausal breast cancer risk: a premenopausal breast cancer collaborative group analysis. Breast Cancer Res Treat 2023; 199:323-334. [PMID: 37020102 DOI: 10.1007/s10549-023-06903-5] [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: 12/09/2022] [Accepted: 02/17/2023] [Indexed: 04/07/2023]
Abstract
PURPOSE Women with preeclampsia are more likely to deliver preterm. Reports of inverse associations between preeclampsia and breast cancer risk, and positive associations between preterm birth and breast cancer risk are difficult to reconcile. We investigated the co-occurrence of preeclampsia/gestational hypertension with preterm birth and breast cancer risk using data from the Premenopausal Breast Cancer Collaborative Group. METHODS Across 6 cohorts, 3096 premenopausal breast cancers were diagnosed among 184,866 parous women. We estimated multivariable hazard ratios (HR) and 95% confidence intervals (CI) for premenopausal breast cancer risk using Cox proportional hazards regression. RESULTS Overall, preterm birth was not associated (HR 1.02, 95% CI 0.92, 1.14), and preeclampsia was inversely associated (HR 0.86, 95% CI 0.76, 0.99), with premenopausal breast cancer risk. In stratified analyses using data from 3 cohorts, preterm birth associations with breast cancer risk were modified by hypertensive conditions in first pregnancies (P-interaction = 0.09). Preterm birth was positively associated with premenopausal breast cancer in strata of women with preeclampsia or gestational hypertension (HR 1.52, 95% CI: 1.06, 2.18), but not among women with normotensive pregnancy (HR = 1.09, 95% CI: 0.93, 1.28). When stratified by preterm birth, the inverse association with preeclampsia was more apparent, but not statistically different (P-interaction = 0.2), among women who did not deliver preterm (HR = 0.82, 95% CI 0.68, 1.00) than those who did (HR = 1.07, 95% CI 0.73, 1.56). CONCLUSION Findings support an overall inverse association of preeclampsia history with premenopausal breast cancer risk. Estimates for preterm birth and breast cancer may vary according to other conditions of pregnancy.
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Affiliation(s)
- Hazel B Nichols
- Department of Epidemiology, Hazel B. Nichols, University of North Carolina at Chapel Hill Gillings School of Global Public Health, 2104F McGavran-Greenberg Hall, 135 Dauer Drive, Chapel Hill, Chapel Hill, NC, 27599-7435, USA.
| | | | - Rina Yarosh
- Department of Epidemiology, Hazel B. Nichols, University of North Carolina at Chapel Hill Gillings School of Global Public Health, 2104F McGavran-Greenberg Hall, 135 Dauer Drive, Chapel Hill, Chapel Hill, NC, 27599-7435, USA
| | - Sara Mitra
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Mandy Goldberg
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, USA
| | | | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Graham G Giles
- Cancer Epidemiology Division Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Roger L Milne
- Cancer Epidemiology Division Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, USA
- Boston University Chobanian & Avedisian School of Medicine, Boston University, Boston, USA
| | - Sven Sandin
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet, Solna, Sweden
- Department of Psychiatry Icahn School of Medicine at Mount Sinai, New York, USA
- Seaver Autism Center for Research and Treatment at Mount Sinai Icahn School of Medicine at Mount Sinai, New York, USA
| | - Walter C Willett
- Channing Division of Network Medicine, Department of Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Weiyao Yin
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet, Solna, Sweden
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, USA
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Real World Solutions IQVIA, Amsterdam, The Netherlands
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17
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Hussain SM, Ackerman IN, Wang Y, English DR, Wluka AE, Giles GG, Cicuttini FM. Trajectories of body mass index from early adulthood to late midlife and incidence of total knee arthroplasty for osteoarthritis: findings from a prospective cohort study. Osteoarthritis Cartilage 2023; 31:397-405. [PMID: 36521732 DOI: 10.1016/j.joca.2022.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To examine the association between body mass index (BMI) trajectories from early adulthood to late midlife and risk of total knee arthroplasty (TKA) for osteoarthritis. METHODS 24,368 participants from the Melbourne Collaborative Cohort Study with weight collected during 1990-1994, 1995-1998, and 2003-2007, recalled weight at age 18-21 years, and height measured during 1990-1994 were included. Incident TKA from 2003 to 2007 to December 2018 was determined by linking cohort records to the National Joint Replacement Registry. RESULTS Using group-based trajectory modelling, six distinct trajectories (TR) of BMI from early adulthood (age 18-21 years) to late midlife (approximately 62 years) were identified: lower normal to normal BMI (TR1; 19.7% population), normal BMI to borderline overweight (TR2; 36.7%), normal BMI to overweight (TR3; 26.8%), overweight to borderline obese (TR4; 3.5%), normal BMI to class 1 obesity (TR5; 10.1%), overweight to class 2 obesity (TR6; 3.2%). Over 12.4 years, 1,328 (5.4%) had TKA. The hazard ratios for TKA increased in all TR compared to TR1 [from TR2: 2.03 (95% CI 1.64-2.52) to TR6: 8.59 (6.44-11.46)]. 28.4% of TKA could be prevented if individuals followed the trajectory one lower, an average weight reduction of 8-12 kg from early adulthood to late midlife, saving $AUS 373 million/year. Most reduction would occur in TR2 (population attributable fraction 37.9%, 95% CI 26.7-47.3%) and TR3 (26.8%, 20.0-31.2%). CONCLUSIONS Prevention of weight gain from young adulthood to late midlife in order to reduce overweight/obesity has the potential to significantly reduce the cost and burden of TKA.
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Affiliation(s)
- S M Hussain
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
| | - I N Ackerman
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
| | - Y Wang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
| | - D R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC 3010, Australia.
| | - A E Wluka
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
| | - G G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia.
| | - F M Cicuttini
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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18
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Do WL, Sun D, Meeks K, Dugué PA, Demerath E, Guan W, Li S, Chen W, Milne R, Adeyemo A, Agyemang C, Nassir R, Manson JE, Shadyab AH, Hou L, Horvath S, Assimes TL, Bhatti P, Jordahl KM, Baccarelli AA, Smith AK, Staimez LR, Stein AD, Whitsel EA, Narayan KV, Conneely KN. Epigenome-wide meta-analysis of BMI in nine cohorts: Examining the utility of epigenetically predicted BMI. Am J Hum Genet 2023; 110:273-283. [PMID: 36649705 PMCID: PMC9943731 DOI: 10.1016/j.ajhg.2022.12.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
This study sought to examine the association between DNA methylation and body mass index (BMI) and the potential of BMI-associated cytosine-phosphate-guanine (CpG) sites to provide information about metabolic health. We pooled summary statistics from six trans-ethnic epigenome-wide association studies (EWASs) of BMI representing nine cohorts (n = 17,034), replicated these findings in the Women's Health Initiative (WHI, n = 4,822), and developed an epigenetic prediction score of BMI. In the pooled EWASs, 1,265 CpG sites were associated with BMI (p < 1E-7) and 1,238 replicated in the WHI (FDR < 0.05). We performed several stratified analyses to examine whether these associations differed between individuals of European and African descent, as defined by self-reported race/ethnicity. We found that five CpG sites had a significant interaction with BMI by race/ethnicity. To examine the utility of the significant CpG sites in predicting BMI, we used elastic net regression to predict log-normalized BMI in the WHI (80% training/20% testing). This model found that 397 sites could explain 32% of the variance in BMI in the WHI test set. Individuals whose methylome-predicted BMI overestimated their BMI (high epigenetic BMI) had significantly higher glucose and triglycerides and lower HDL cholesterol and LDL cholesterol compared to accurately predicted BMI. Individuals whose methylome-predicted BMI underestimated their BMI (low epigenetic BMI) had significantly higher HDL cholesterol and lower glucose and triglycerides. This study confirmed 553 and identified 685 CpG sites associated with BMI. Participants with high epigenetic BMI had poorer metabolic health, suggesting that the overestimation may be driven in part by cardiometabolic derangements characteristic of metabolic syndrome.
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Affiliation(s)
- Whitney L. Do
- Laney Graduate School, Emory University, Atlanta, GA, USA
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China,Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Karlijn Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA,Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Ellen Demerath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Shengxu Li
- Children’s Minnesota Research Institute, Childrens Minnesota, Minneapolis, MN, USA
| | - Wei Chen
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Roger Milne
- Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Abedowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles Agyemang
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Steve Horvath
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Parveen Bhatti
- Cancer Control Research, BC Cancer, Vancouver, BC, Canada
| | | | - Andrea A. Baccarelli
- Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | - Alicia K. Smith
- Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Lisa R. Staimez
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Aryeh D. Stein
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Eric A. Whitsel
- Departments of Epidemiology and Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - K.M. Venkat Narayan
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Karen N. Conneely
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA,Corresponding author
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19
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Afshar N, Hodge AM, Shivappa N, Hébert JR, Giles GG, English DR, Milne RL. Dietary Inflammatory Index, Alternative Healthy Eating Index-2010, Mediterranean Diet Score and the risk of pancreatic cancer. Cancer Epidemiol 2023; 82:102295. [PMID: 36395705 DOI: 10.1016/j.canep.2022.102295] [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: 08/22/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Previous studies of dietary patterns and pancreatic cancer risk have been inconclusive; we aimed to investigate the association of Mediterranean Diet Score (MDS), Alternative Healthy Eating Index-2010 (AHEI-2010), and Dietary Inflammatory Index (DII®) with risk of pancreatic cancer. METHODS We used data from the Melbourne Collaborative Cohort Study including 33,690 men and women aged 40-69 years at recruitment in 1990-1994. A total of 258 incident cases of pancreatic cancer was identified over an average of 23.7 years of follow-up. Hazard ratios (HR) were estimated using Cox regression, with age as the underlying time metric, adjusting for potential confounders including sex, height, country of birth, education, socio-economic position, physical activity, energy intake, smoking status, pack-years smoking, years since quitting smoking, and alcohol intake. RESULTS A healthier diet as assessed by the AHEI-2010 was associated with a lower risk of pancreatic cancer [HRQuartile4 vs Quartile1 = 0.58; 95%CI 0.40 - 0.85; p for trend 0.003]. Weaker but consistent evidence was observed for the other indexes [DII® HRQuartile4 vs Quartile1 = 1.30; 95%CI 0.82 - 2.06; p for trend 0.1], [MDS HRCategory3 vs Category1 = 0.79; 95%CI 0.49 - 1.26; p for trend 0.06]. CONCLUSION Adherence to a healthier diet, as assessed by the AHEI-2010, may reduce the risk of pancreatic cancer.
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Affiliation(s)
- Nina Afshar
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Carlton, VIC 3010, Australia.
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Carlton, VIC 3010, Australia
| | - Nitin Shivappa
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene St., Columbia, SC 29208, USA; Department of Nutrition, Connecting Health Innovations LLC, 1417 Gregg St., Columbia, SC 29201, USA
| | - James R Hébert
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene St., Columbia, SC 29208, USA; Department of Nutrition, Connecting Health Innovations LLC, 1417 Gregg St., Columbia, SC 29201, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Carlton, VIC 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Rd, Clayton, VIC 3168, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Carlton, VIC 3010, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St, Carlton, VIC 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Rd, Clayton, VIC 3168, Australia
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20
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Jung AY, Ahearn TU, Behrens S, Middha P, Bolla MK, Wang Q, Arndt V, Aronson KJ, Augustinsson A, Beane Freeman LE, Becher H, Brenner H, Canzian F, Carey LA, Czene K, Eliassen AH, Eriksson M, Evans DG, Figueroa JD, Fritschi L, Gabrielson M, Giles GG, Guénel P, Hadjisavvas A, Haiman CA, Håkansson N, Hall P, Hamann U, Hoppe R, Hopper JL, Howell A, Hunter DJ, Hüsing A, Kaaks R, Kosma VM, Koutros S, Kraft P, Lacey JV, Le Marchand L, Lissowska J, Loizidou MA, Mannermaa A, Maurer T, Murphy RA, Olshan AF, Olsson H, Patel AV, Perou CM, Rennert G, Shibli R, Shu XO, Southey MC, Stone J, Tamimi RM, Teras LR, Troester MA, Truong T, Vachon CM, Wang SS, Wolk A, Wu AH, Yang XR, Zheng W, Dunning AM, Pharoah PDP, Easton DF, Milne RL, Chatterjee N, Schmidt MK, García-Closas M, Chang-Claude J. Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies. J Natl Cancer Inst 2022; 114:1706-1719. [PMID: 35723569 PMCID: PMC9949579 DOI: 10.1093/jnci/djac117] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 03/22/2022] [Accepted: 05/03/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear. METHODS Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided. RESULTS Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like. CONCLUSIONS This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.
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Affiliation(s)
- Audrey Y Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg (UCCH), Hamburg, Germany
| | - Thomas U Ahearn
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pooja Middha
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, ON, Canada
| | | | - Laura E Beane Freeman
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Heiko Becher
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lisa A Carey
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - CTS Consortium
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Manchester Academic Health Science Centre, North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Jonine D Figueroa
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Pascal Guénel
- Institut national de la santé et de la recherche médicale (INSERM), University Paris-Saclay, Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, Villejuif, France
| | - Andreas Hadjisavvas
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus Institute of Neurology and Genetics, Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Reiner Hoppe
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - David J Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Veli-Matti Kosma
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Stella Koutros
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - James V Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Oncology Institute, Warsaw, Poland
| | - Maria A Loizidou
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus Institute of Neurology and Genetics, Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Tabea Maurer
- Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg (UCCH), Hamburg, Germany
| | - Rachel A Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- BC Cancer Agency, Cancer Control Research, Vancouver, BC, Canada
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Håkan Olsson
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Alpa V Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Charles M Perou
- Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gad Rennert
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Rana Shibli
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Lauren R Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Institut national de la santé et de la recherche médicale (INSERM), University Paris-Saclay, Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, Villejuif, France
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Sophia S Wang
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaohong R Yang
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Nilanjan Chatterjee
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Montserrat García-Closas
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg (UCCH), Hamburg, Germany
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21
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O'Connor H, Li S, Hodge A, Callaway L, David Mclntyre H, Barrett H, Wilkinson SA, Nitert MD. Gut microbiome composition is similar between pregnant women with excess body fat with healthy and less healthy dietary intake patterns. J Hum Nutr Diet 2022. [PMID: 36471554 DOI: 10.1111/jhn.13123] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Dietary composition influences the composition of the gut microbiota in healthy adults. Little is known about the effect of dietary patterns on gut microbiota composition in pregnancy. This cross-sectional study aimed to investigate the associations between two diet quality scores adapted from the Australian Recommended Food Score (ARFS) and the Mediterranean Dietary Score (MDS) with the composition of the gut microbiota in pregnant women with excess body fat at 28 weeks' gestation. METHODS Women from the Study of Probiotics IN Gestational diabetes (SPRING) who had completed a food frequency questionnaire (FFQ; n = 395) were classified according to tertiles of ARFS and the MDS. Higher dietary pattern scores in both the ARFS and the MDS represent better diet quality. Gut microbiota composition was assessed using 16S rRNA gene amplicon sequencing and analysed using MicrobiomeAnalyst in a subset of 196 women with faecal samples. RESULTS No significant difference was found in alpha or beta diversity. A higher ARFS was associated with a higher abundance of Ruminococcus and lower abundance of Akkermansia, whereas a higher MDS was associated with a higher abundance of Ruminococcus and Butyricicoccus, though these changes disappeared after correction for multiple testing. CONCLUSION These results suggest that dietary patterns defined by the ARFS and MDS were not associated with gut microbiota composition in pregnant women classified as overweight and obese at 28 weeks' gestation within this study.
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Affiliation(s)
- Hannah O'Connor
- School of Human Movement and Nutrition Sciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Sherly Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.,MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Cambridge, UK.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
| | - Allison Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
| | - Leonie Callaway
- Women's and Newborns, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia.,Faculty of Medicine, The University of Queensland, St Lucia, Queensland, Australia.,Mater Research Institute, The University of Queensland, South Brisbane, Queensland, Australia
| | - Harold David Mclntyre
- Mater Research Institute, The University of Queensland, South Brisbane, Queensland, Australia
| | - Helen Barrett
- Mater Research Institute, The University of Queensland, South Brisbane, Queensland, Australia
| | - Shelley A Wilkinson
- School of Human Movement and Nutrition Sciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Marloes Dekker Nitert
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
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22
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Yu C, Hodge AM, Wong EM, Joo JE, Makalic E, Schmidt DF, Buchanan DD, Severi G, Hopper JL, English DR, Giles GG, Milne RL, Southey MC, Dugué PA. Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation? Epigenetics 2022; 17:1838-1847. [PMID: 35726372 PMCID: PMC9621069 DOI: 10.1080/15592294.2022.2088038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 05/19/2022] [Accepted: 05/31/2022] [Indexed: 12/15/2022] Open
Abstract
Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene-environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10-14), ~0.6% (P = 2 × 10-7), and ~8.7% (P = 7 × 10-87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: -0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: -0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Jihoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine Universités Paris-Saclay, Uvsq, Villejuif, France
- Department of Statistics, Computer Science and Applications “G. Parenti”, University of Florence, Firenze, Italy
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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23
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Kynurenine Pathway Metabolites in the Blood and Cerebrospinal Fluid Are Associated with Human Aging. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:5019752. [PMID: 36312896 PMCID: PMC9616658 DOI: 10.1155/2022/5019752] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/24/2022] [Indexed: 12/03/2022]
Abstract
The kynurenine pathway is implicated in aging, longevity, and immune regulation, but longitudinal studies and assessment of the cerebrospinal fluid (CSF) are lacking. We investigated tryptophan (Trp) and downstream kynurenine metabolites and their associations with age and change over time in four cohorts using comprehensive, targeted metabolomics. The study included 1574 participants in two cohorts with repeated metabolite measurements (mean age at baseline 58 years ± 8 SD and 62 ± 10 SD), 3161 community-dwelling older adults (age range 71-74 years), and 109 CSF donors (mean age 73 years ± 7 SD). In the first two cohorts, age was associated with kynurenine (Kyn), quinolinic acid (QA), and the kynurenine to tryptophan ratio (KTR), and inversely with Trp. Consistent with these findings, Kyn, QA, and KTR increased over time, whereas Trp decreased. Similarly, QA and KTR were higher in community-dwelling older adults of age 74 compared to 71, whereas Trp was lower. Kyn and QA were more strongly correlated with age in the CSF compared to serum and increased in a subset of participants with repeated CSF sampling (n = 33) over four years. We assessed associations with frailty and mortality in two cohorts. QA and KTR were most strongly associated with mortality and frailty. Our study provides robust evidence of changes in tryptophan and kynurenine metabolism with human aging and supports links with adverse health outcomes. Our results suggest that aging activates the inflammation and stress-driven kynurenine pathway systemically and in the brain, but we cannot determine whether this activation is harmful or adaptive. We identified a relatively stronger age-related increase of the potentially neurotoxic end-product QA in brain.
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24
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Dugué PA, Bodelon C, Chung FF, Brewer HR, Ambatipudi S, Sampson JN, Cuenin C, Chajès V, Romieu I, Fiorito G, Sacerdote C, Krogh V, Panico S, Tumino R, Vineis P, Polidoro S, Baglietto L, English D, Severi G, Giles GG, Milne RL, Herceg Z, Garcia-Closas M, Flanagan JM, Southey MC. Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies. Breast Cancer Res 2022; 24:59. [PMID: 36068634 PMCID: PMC9446544 DOI: 10.1186/s13058-022-01554-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/12/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer. METHODS Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage. RESULTS None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics. CONCLUSION We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.
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Affiliation(s)
- Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.
| | - Clara Bodelon
- Divison of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA
| | - Felicia F Chung
- International Agency for Research On Cancer (IARC), Lyon, France
- Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, Malaysia
| | - Hannah R Brewer
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Srikant Ambatipudi
- International Agency for Research On Cancer (IARC), Lyon, France
- AMCHSS, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Joshua N Sampson
- Divison of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA
| | - Cyrille Cuenin
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Veronique Chajès
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Isabelle Romieu
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Giovanni Fiorito
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e Della Scienza University-Hospital, Turin, Italy
| | - Vittorio Krogh
- Department of Research, Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, MI, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia Federico II University, Naples, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research AIRE-ONLUS, Ragusa, Italy
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, 56126, Pisa, Italy
| | - Dallas English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Gianluca Severi
- CESP UMR1018, Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, Villejuif, France
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Zdenko Herceg
- International Agency for Research On Cancer (IARC), Lyon, France
| | | | - James M Flanagan
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia
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25
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Higher Ultra-Processed Food Consumption Is Associated with Greater High-Sensitivity C-Reactive Protein Concentration in Adults: Cross-Sectional Results from the Melbourne Collaborative Cohort Study. Nutrients 2022; 14:nu14163309. [PMID: 36014818 PMCID: PMC9415636 DOI: 10.3390/nu14163309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/10/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Few studies have examined associations between ultra-processed food intake and biomarkers of inflammation, and inconsistent results have been reported in the small number of studies that do exist. As such, further investigation is required. Methods: Cross-sectional baseline data from the Melbourne Collaborative Cohort Study (MCCS) were analysed (n = 2018). We applied the NOVA food classification system to data from a food frequency questionnaire (FFQ) to determine ultra-processed food intake (g/day). The outcome was high-sensitivity C-reactive protein concentration (hsCRP; mg/L). We fitted unadjusted and adjusted linear regression analyses, with sociodemographic characteristics and lifestyle- and health-related behaviours as covariates. Supplementary analyses further adjusted for body mass index (kg/m2). Sex was assessed as a possible effect modifier. Ultra-processed food intake was modelled as 100 g increments and the magnitude of associations expressed as estimated relative change in hsCRP concentration with accompanying 95% confidence intervals (95%CIs). Results: After adjustment, every 100 g increase in ultra-processed food intake was associated with a 4.0% increase in hsCRP concentration (95%CIs: 2.1−5.9%, p < 0.001). Supplementary analyses showed that part of this association was independent of body mass index (estimated relative change in hsCRP: 2.5%; 95%CIs: 0.8−4.3%, p = 0.004). No interaction was observed between sex and ultra-processed food intake. Conclusion: Higher ultra-processed food intake was cross-sectionally associated with elevated hsCRP, which appeared to occur independent of body mass index. Future prospective and intervention studies are necessary to confirm directionality and whether the observed association is causal.
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Cribb L, Hodge AM, Yu C, Li SX, English DR, Makalic E, Southey MC, Milne RL, Giles GG, Dugué PA. Inflammation and Epigenetic Aging Are Largely Independent Markers of Biological Aging and Mortality. J Gerontol A Biol Sci Med Sci 2022; 77:2378-2386. [PMID: 35926479 PMCID: PMC9799220 DOI: 10.1093/gerona/glac147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Indexed: 01/20/2023] Open
Abstract
Limited evidence exists on the link between inflammation and epigenetic aging. We aimed to (a) assess the cross-sectional and prospective associations of 22 inflammation-related plasma markers and a signature of inflammaging with epigenetic aging and (b) determine whether epigenetic aging and inflammaging are independently associated with mortality. Blood samples from 940 participants in the Melbourne Collaborative Cohort Study collected at baseline (1990-1994) and follow-up (2003-2007) were assayed for DNA methylation and 22 inflammation-related markers, including well-established markers (eg, interleukins and C-reactive protein) and metabolites of the tryptophan-kynurenine pathway. Four measures of epigenetic aging (PhenoAge, GrimAge, DunedinPoAm, and Zhang) and a signature of inflammaging were considered, adjusted for age, and transformed to Z scores. Associations were assessed using linear regression, and mortality hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated using Cox regression. Cross-sectionally, most inflammation-related markers were associated with epigenetic aging measures, although with generally modest effect sizes (regression coefficients per SD ≤ 0.26) and explaining altogether between 1% and 11% of their variation. Prospectively, baseline inflammation-related markers were not, or only weakly, associated with epigenetic aging after 11 years of follow-up. Epigenetic aging and inflammaging were strongly and independently associated with mortality, for example, inflammaging: HR = 1.41, 95% CI = 1.27-1.56, p = 2 × 10-10, which was only slightly attenuated after adjustment for 4 epigenetic aging measures: HR = 1.35, 95% CI = 1.22-1.51, p = 7 × 10-9). Although cross-sectionally associated with epigenetic aging, inflammation-related markers accounted for a modest proportion of its variation. Inflammaging and epigenetic aging are essentially nonoverlapping markers of biological aging and may be used jointly to predict mortality.
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Affiliation(s)
- Lachlan Cribb
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Allison M Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Sherly X Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia,Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia,Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Pierre-Antoine Dugué
- Address correspondence to: Pierre-Antoine Dugué, PhD, Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, TRF Building 7.22., Monash Medical Centre, 3168 Clayton, VIC, Australia. E-mail:
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27
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Wang SE, Hodge A, Dashti SG, Dixon-Suen SC, Castaño-Rodríguez N, Thomas R, Giles G, Boussioutas A, Kendall B, English DR. Diet and risk of Barrett's oesophagus: Melbourne collaborative cohort study. Br J Nutr 2022; 129:1-10. [PMID: 35837679 PMCID: PMC10011587 DOI: 10.1017/s0007114522002112] [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: 03/05/2022] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022]
Abstract
Barrett's oesophagus (BE) is the precursor of oesophageal adenocarcinoma, which has become the most common type of oesophageal cancer in many Western populations. Existing evidence on diet and risk of BE predominantly comes from case-control studies, which are subject to recall bias in measurement of diet. We aimed to investigate the potential effect of diet, including macronutrients, carotenoids, food groups, specific food items, beverages and dietary scores, on risk of BE in over 20 000 participants of the Melbourne Collaborative Cohort Study. Diet at baseline (1990-1994) was measured using a food frequency questionnaire. The outcome was BE diagnosed between baseline and follow-up (2007-2010). Logistic regression models were used to estimate OR and 95 % CI for diet in relation to risk of BE. Intakes of leafy vegetables and fruit were inversely associated with risk of BE (highest v. lowest quartile: OR = 0·59; CI: 0·38, 0·94; P-trend = 0·02 and OR = 0·58; CI: 0·37, 0·93; P-trend = 0·02 respectively), as were dietary fibre and carotenoids. Stronger associations were observed for food than the nutrients found in them. Positive associations were observed for discretionary food (OR = 1·54; CI: 0·97, 2·44; P-trend = 0·04) and total fat intake (OR per 10 g/d = 1·11; CI: 1·00, 1·23), the association for fat was less robust in sensitivity analyses. No association was observed for meat, protein, dairy products or diet scores. Diet is a potential modifiable risk factor for BE. Public health and clinical guidelines that incorporate dietary recommendations could contribute to reduction in risk of BE and, thereby, oesophageal adenocarcinoma.
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Affiliation(s)
- Sabrina E. Wang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Allison Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Suzanne C. Dixon-Suen
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia
| | - Natalia Castaño-Rodríguez
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Robert Thomas
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Alex Boussioutas
- Department of Gastroenterology, The Alfred, Melbourne, VIC, Australia
- Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Bradley Kendall
- Department of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Dallas R. English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
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28
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Wang SE, Dashti SG, Hodge AM, Dixon-Suen SC, Castaño-Rodríguez N, Thomas RJ, Giles GG, Milne RL, Boussioutas A, Kendall BJ, English DR. Mechanisms for the sex-specific effect of H. pylori on risk of gastroesophageal reflux disease and Barrett's oesophagus. Cancer Epidemiol Biomarkers Prev 2022; 31:1630-1637. [PMID: 35654416 DOI: 10.1158/1055-9965.epi-22-0234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/22/2022] [Accepted: 05/24/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mechanisms for how Helicobacter pylori infection affects risk of gastroesophageal reflux disease (GERD) and Barrett's oesophagus (BE) are incompletely understood and might differ by sex. METHODS In a case-control study nested in the Melbourne Collaborative Cohort Study with 425 GERD cases and 169 BE cases (identified at 2007-10 follow-up), we estimated sex-specific odds ratios for participants who were H. pylori seronegative versus seropositive at baseline (1990-94). To explore possible mechanisms, we 1) compared patterns of H. pylori-induced gastritis by sex using serum pepsinogen-I and gastrin-17 data and 2) quantified the effect of H. pylori seronegativity on BE mediated by GERD using causal mediation analysis. RESULTS For men, H. pylori seronegativity was associated with 1.69-fold (CI:1.03-2.75) and 2.28-fold (CI:1.27-4.12) higher odds of GERD and BE, respectively. No association was observed for women. H. pylori-induced atrophic antral gastritis was more common in men (68%) than in women (56%; p=0.015). For men, 5 of the 15 per 1,000 excess BE risk from being seronegative was mediated by GERD. CONCLUSIONS Men, but not women, who were H. pylori seronegative had increased risks of GERD and BE. A possible explanation might be sex-differences in patterns of H. pylori-induced atrophic antral gastritis, which could lead to less erosive reflux for men. Evidence of GERD mediating the effect of H. pylori on BE risk among men supports this proposed mechanism. IMPACT The findings highlight the importance of investigating sex differences in the effect of H. pylori on risk of GERD and BE in future studies.
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Affiliation(s)
| | | | | | | | | | | | | | - Roger L Milne
- Cancer Council Victoria, Melbourne, Victoria, Australia
| | | | | | - Dallas R English
- Melbourne School of Population and Global Health, Melbourne, Australia
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29
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Steinberg J, Iles MM, Lee JY, Wang X, Law MH, Smit AK, Nguyen‐Dumont T, Giles GG, Southey MC, Milne RL, Mann GJ, Bishop DT, MacInnis RJ, Cust AE. Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts. Br J Dermatol 2022; 186:823-834. [PMID: 34921685 PMCID: PMC9545863 DOI: 10.1111/bjd.20956] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 11/11/2021] [Accepted: 12/11/2021] [Indexed: 12/05/2022]
Abstract
BACKGROUND Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS-based risk prediction, particularly assessment of calibration (comparing predicted to observed risks). OBJECTIVES To evaluate PRS-based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry. METHODS We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case-cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single-nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta-analysis (PRS68), 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. RESULTS Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62-0·68] and MCCS (E/O = 0·63, 95% CI 0·56-0·72). For UKB, calibration was improved by PRS adjustment, with PRS50-adjusted risks E/O = 0·91, 95% CI 0·87-0·95. The discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07-0·10, P < 0·0001), and higher than for PRS45-adjusted risks (Δ area under the curve 0·02-0·04, P < 0·001). CONCLUSIONS A PRS derived from a larger, more diverse meta-analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations.
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Affiliation(s)
- Julia Steinberg
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
| | - Mark M. Iles
- Leeds Institute for Data AnalyticsUniversity of LeedsLeedsUK
| | - Jin Yee Lee
- School of Public HealthThe University of SydneySydneyNSWAustralia
| | - Xiaochuan Wang
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
| | - Matthew H. Law
- Statistical Genetics LaboratoryQIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical InnovationQueensland University of TechnologyKelvin GroveQLDAustralia
| | - Amelia K. Smit
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
| | - Tu Nguyen‐Dumont
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Department of Clinical PathologyThe University of MelbourneMelbourneVICAustralia
| | - Graham G. Giles
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
| | - Roger L. Milne
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Graham J. Mann
- John Curtin School of Medical ResearchAustralian National UniversityCanberraACTAustralia
| | | | - Robert J. MacInnis
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Anne E. Cust
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
- Melanoma Institute AustraliaThe University of SydneySydneyNSWAustralia
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30
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Ribeiro AI, Fraga S, Severo M, Kelly-Irving M, Delpierre C, Stringhini S, Kivimaki M, Joost S, Guessous I, Severi G, Giles G, Sacerdote C, Vineis P, Barros H. Association of neighbourhood disadvantage and individual socioeconomic position with all-cause mortality: a longitudinal multicohort analysis. Lancet Public Health 2022; 7:e447-e457. [PMID: 35487230 DOI: 10.1016/s2468-2667(22)00036-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Few studies have examined the interactions between individual socioeconomic position and neighbourhood deprivation and the findings so far are heterogeneous. Using a large sample of diverse cohorts, we investigated the interaction effect of neighbourhood socioeconomic deprivation and individual socioeconomic position, assessed using education, on mortality. METHODS We did a longitudinal multicohort analysis that included six cohort studies participating in the European LIFEPATH consortium: the CoLaus (Lausanne, Switzerland), E3N (France), EPIC-Turin (Turin, Italy), EPIPorto (Porto, Portugal), Melbourne Collaborative Cohort Study (Melbourne, VIC, Australia), and Whitehall II (London, UK) cohorts. All participants with data on mortality, educational attainment, and neighbourhood deprivation were included in the present study. The data sources were the databases of each cohort study. Poisson regression was used to estimate the mortality rates and associations (relative risk, 95% CIs) with neighbourhood deprivation (Q1 being least deprived to Q5 being the most deprived). Baseline educational attainment was used as an indicator of individual socioeconomic position. Estimates were combined using pooled analysis and the relative excess risk due to the interaction was computed to identify additive interactions. FINDINGS The cohorts comprised a total population of 168 801 individuals. The recruitment dates were 2003-06 for CoLaus, 1989-91 for E3N, 1992-98 for EPIC-Turin, 1999-2003 for EPIPorto, 1990-94 for MCCS, and 1991-94 for Whitehall II. We use baseline data only and mortality data obtained using record linkage. Age-adjusted mortality rates were higher among participants residing in more deprived neighbourhoods than those in the least deprived neighbourhoods (Q1 least deprived neighbourhoods, 369·7 per 100 000 person-years [95% CI 356·4-383·2] vs Q5-most deprived neighbourhoods 445·7 per 100 000 person-years [430·2-461·7]), but the magnitude of the association varied according to educational attainment (relative excess risk due to interaction=0·18, 95% CI 0·08-0·28). The relative risk for Q5 versus Q1 was 1·31 (1·23-1·40) among individuals with primary education or less, but less pronounced among those with secondary education (1·12; 1·04-1·21) and tertiary education (1·16; 1·07-1·27). Associations remained after adjustment for individual-level factors, such as smoking, physical activity, and alcohol intake, among others. INTERPRETATION Our study suggests that the detrimental health effect of living in disadvantaged neighbourhoods is more pronounced among individuals with low education attainment, amplifying social inequalities in health. This finding is relevant to policies aimed at reducing health inequalities, suggesting that these issues should be addressed at both the individual level and the community level. FUNDING The European Commission, European Regional Development Fund, the Portugese Foundation for Science and Technology.
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Affiliation(s)
- Ana Isabel Ribeiro
- Epidemiology Research Unit, Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal.
| | - Silvia Fraga
- Epidemiology Research Unit, Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal
| | - Milton Severo
- Epidemiology Research Unit, Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal
| | - Michelle Kelly-Irving
- INSERM, UMR1027, Toulouse, France; Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Cyrille Delpierre
- INSERM, UMR1027, Toulouse, France; Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Silvia Stringhini
- Centre for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Mika Kivimaki
- University College London, Department of Epidemiology and Public Health, London, UK; Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Stéphane Joost
- Laboratory of Geographic Information Systems, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Division of Primary Care Medicine, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals, Switzerland; Geographic Information for Research and Analysis in Public Health Lab, Lausanne, Switzerland; La Source, School of Nursing, University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Idris Guessous
- Division of Primary Care Medicine, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals, Switzerland; Geographic Information for Research and Analysis in Public Health Lab, Lausanne, Switzerland
| | - Gianluca Severi
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia; Centre for Research in Epidemiology and Population Health, INSERM U1018, Université Paris-Saclay, Villejuif, France; Human Genetics Foundation, Turin, Italy
| | - Graham Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Centre for Cancer Prevention, Turin, Italy
| | - Paolo Vineis
- Medical Research Council and Public Health England Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Henrique Barros
- Epidemiology Research Unit, Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal
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Dugué PA, Hodge AM, Ulvik A, Ueland PM, Midttun Ø, Rinaldi S, MacInnis RJ, Li SX, Meyer K, Navionis AS, Flicker L, Severi G, English DR, Vineis P, Tell GS, Southey MC, Milne RL, Giles GG. Association of Markers of Inflammation, the Kynurenine Pathway and B Vitamins with Age and Mortality, and a Signature of Inflammaging. J Gerontol A Biol Sci Med Sci 2022; 77:826-836. [PMID: 34117761 DOI: 10.1093/gerona/glab163] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Inflammation is a key feature of aging. We aimed to (i) investigate the association of 34 blood markers potentially involved in inflammatory processes with age and mortality and (ii) develop a signature of "inflammaging." METHODS Thirty-four blood markers relating to inflammation, B vitamin status, and the kynurenine pathway were measured in 976 participants in the Melbourne Collaborative Cohort Study at baseline (median age = 59 years) and follow-up (median age = 70 years). Associations with age and mortality were assessed using linear and Cox regression, respectively. A parsimonious signature of inflammaging was developed and its association with mortality was compared with 2 marker scores calculated across all markers associated with age and mortality, respectively. RESULTS The majority of markers (30/34) were associated with age, with stronger associations observed for neopterin, cystatin C, interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), several markers of the kynurenine pathway and derived indices KTR (kynurenine/tryptophan ratio), PAr index (ratio of 4-pyridoxic acid and the sum of pyridoxal 5'-phosphate and pyridoxal), and HK:XA (3-hydroxykynurenine/xanthurenic acid ratio). Many markers (17/34) showed an association with mortality, in particular IL-6, neopterin, C-reactive protein, quinolinic acid, PAr index, and KTR. The inflammaging signature included 10 markers and was strongly associated with mortality (hazard ratio [HR] per SD = 1.40, 95% CI: 1.24-1.57, p = 2 × 10-8), similar to scores based on all age-associated (HR = 1.38, 95% CI: 1.23-1.55, p = 4 × 10-8) and mortality-associated markers (HR = 1.43, 95% CI: 1.28-1.60, p = 1 × 10-10), respectively. Strong evidence of replication of the inflammaging signature association with mortality was found in the Hordaland Health Study. CONCLUSION Our study highlights the key role of the kynurenine pathway and vitamin B6 catabolism in aging, along with other well-established inflammation-related markers. A signature of inflammaging based on 10 markers was strongly associated with mortality.
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Affiliation(s)
- Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Per M Ueland
- Department of Clinical Science, University of Bergen, Norway
| | | | - Sabina Rinaldi
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sherly X Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Medical Research Council Epidemiology Unit, University of Cambridge, UK
| | | | - Anne-Sophie Navionis
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Leon Flicker
- Medical School, University of Western Australia, Perth, Australia
- WA Centre for Health and Ageing of the University of Western Australia, Perth, Australia
| | - Gianluca Severi
- Centre for Research into Epidemiology and Population Health (CESP), Faculté de Medicine, Université Paris-Saclay, Inserm, Villejuif, France
- Institut Gustave Roussy, Villejuif, France
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Grethe S Tell
- Department of Global Public Health and Primary Care, University of Bergen, Norway
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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Swain CTV, Bassett JK, Hodge AM, Dunstan DW, Owen N, Yang Y, Jayasekara H, Hébert JR, Shivappa N, MacInnis RJ, Milne RL, English DR, Lynch BM. Television viewing time and all-cause mortality: interactions with BMI, physical activity, smoking, and dietary factors. Int J Behav Nutr Phys Act 2022; 19:30. [PMID: 35305675 PMCID: PMC8934515 DOI: 10.1186/s12966-022-01273-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022] Open
Abstract
Background Higher levels of time spent sitting (sedentary behavior) contribute to adverse health outcomes, including earlier death. This effect may be modified by other lifestyle factors. We examined the association of television viewing (TV), a common leisure-time sedentary behavior, with all-cause mortality, and whether this is modified by body mass index (BMI), physical activity, smoking, alcohol intake, soft drink consumption, or diet-associated inflammation. Methods Using data from participants in the Melbourne Collaborative Cohort Study, flexible parametric survival models assessed the time-dependent association of self-reported TV time (three categories: < 2 h/day, 2–3 h/day, > 3 h/day) with all-cause mortality. Interaction terms were fitted to test whether there was effect modification of TV time by the other risk factors. Results From 19,570 participants, 4,417 deaths were reported over a median follow up of 14.5 years. More TV time was associated with earlier mortality; however, this relationship diminished with increasing age. The hazard ratio (HR) and 95% confidence interval (95% CI) for > 3 h/day compared with < 2 h/day of TV time was 1.34 (1.16, 1.55) at 70 years, 1.14 (1.04, 1.23) at 80 years, and 0.95 (0.84, 1.06) at 90 years. The TV time/mortality relationship was more evident in participants who were physically inactive (compared with active; p for interaction < 0.01) or had a higher dietary inflammatory index score (compared with a lower score; p for interaction = 0.03). No interactions were detected between TV time and BMI, smoking, alcohol intake, nor soft-drink consumption (all p for interaction > 0.16). Conclusions The relationship between TV time and all-cause mortality may change with age. It may also be more pronounced in those who are otherwise inactive or who have a pro-inflammatory diet. Supplementary Information The online version contains supplementary material available at 10.1186/s12966-022-01273-5.
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The BC Generations Project as a Tumor Tissue Resource for Cancer Research. Curr Oncol 2022; 29:1262-1268. [PMID: 35200606 PMCID: PMC8870926 DOI: 10.3390/curroncol29020107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/16/2022] Open
Abstract
Population-based cohort studies can be a resource for tumor specimens, annotated with demographic, lifestyle, and health history data, that support innovative studies of cancer. Our aim was to establish and test a process for accessing tumor samples, held at pathology laboratories around British Columbia (BC), for participants of the BC Generations Project (BCGP). Through the BC Cancer Registry, we identified pathology reports for 1100 (93%) of the 1180 incident solid cancer cases diagnosed in BCGP as of 2019. Using manually abstracted data from the reports, we successfully retrieved 183 (92%) of the 200 formalin-fixed, paraffin-embedded (FFPE) blocks (breast, lung, bladder, and pancreas cancer cases) that we requested from pathology laboratories. No important differences in retrieval rates by cancer site, sample location (Greater Vancouver vs. Outside Greater Vancouver), sample type (biopsy vs. excision) or year of diagnosis were identified. A text mining solution recently implemented by the Registry will allow us to automate the process for data abstraction and should capture pathology reports for 100% of all newly diagnosed BCGP cancer cases moving forward. This will further enhance the utility of BCGP as a high-quality tumor tissue research resource.
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Bassett JK, MacInnis RJ, Yang Y, Hodge AM, Lynch BM, English DR, Giles GG, Milne RL, Jayasekara H. Alcohol intake trajectories during the life course and risk of alcohol-related cancer: a prospective cohort study. Int J Cancer 2022; 151:56-66. [PMID: 35182083 DOI: 10.1002/ijc.33973] [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: 09/28/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 11/10/2022]
Abstract
We examined associations between sex-specific alcohol intake trajectories and alcohol-related cancer risk using data from 22,756 women and 15,701 men aged 40-69 years at baseline in the Melbourne Collaborative Cohort Study. Alcohol intake for 10-year periods from age 20 until the decade encompassing recruitment, calculated using recalled beverage-specific frequency and quantity, was used to estimate group-based sex-specific intake trajectories. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for primary invasive alcohol-related cancer (upper aerodigestive tract, breast, liver and colorectum). Three distinct alcohol intake trajectories for women (lifetime abstention, stable light, increasing moderate) and six for men (lifetime abstention, stable light, stable moderate, increasing heavy, early decreasing heavy, late decreasing heavy) were identified. 2,303 incident alcohol-related cancers were diagnosed during 485,525 person-years in women and 789 during 303,218 person-years in men. For men, compared with lifetime abstention, heavy intake (mean≥60 g/day) at age 20-39 followed by either an early (from age 40-49) (early decreasing heavy; HR=1.75, 95% CI: 1.25-2.44) or late decrease (from age 60-69) (late decreasing heavy; HR=1.94, 95% CI: 1.28-2.93), and moderate intake (mean<60 g/day) at age 20-39 increasing to heavy intake in middle-age (increasing heavy; HR=1.45, 95% CI: 1.06-1.97) were associated with increased risk of alcohol-related cancer. For women, compared with lifetime abstention, increasing intake from age 20 (increasing moderate) was associated with increased alcohol-related cancer risk (HR=1.25, 95% CI: 1.06-1.48). Similar associations were observed for colorectal (men) and breast cancer. Heavy drinking during early adulthood might increase cancer risk later in life. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Yi Yang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Brigid M Lynch
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Physical Activity Laboratory, Baker IDI Heart and Diabetes Institute, 75 Commercial Road, Melbourne, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.,School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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Labadie JD, Savas S, Harrison TA, Banbury B, Huang Y, Buchanan DD, Campbell PT, Gallinger SJ, Giles GG, Gunter MJ, Hoffmeister M, Hsu L, Jenkins MA, Lin Y, Ogino S, Phipps AI, Slattery ML, Steinfelder RS, Sun W, Van Guelpen B, Hua X, Figuieredo JC, Pai RK, Nassir R, Qi L, Chan AT, Peters U, Newcomb PA. Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival. Sci Rep 2022; 12:127. [PMID: 34996992 PMCID: PMC8741984 DOI: 10.1038/s41598-021-03945-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 12/06/2021] [Indexed: 12/16/2022] Open
Abstract
Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10-8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30-1.69, p = 8.47 × 10-9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65-2.77, p = 9.19 × 10-9 and rs144717887, HR = 2.01, 95% CI 1.57-2.58, p = 3.14 × 10-8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.
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Affiliation(s)
- Julia D Labadie
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Sevtap Savas
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John's, NL, Canada
- Discipline of Oncology, Faculty of Medicine, Memorial University, St. John's, NL, Canada
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Barb Banbury
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yuhan Huang
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Daniel D Buchanan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
- Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Peter T Campbell
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Steven J Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Medicine, School of Clinical Sciences at Monash Health, Monash University, VIC, Australia
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research On Cancer, World Health Organization, Lyon, France
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Shuji Ogino
- Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Immunology Program, Dana-Farber Harvard Cancer Center, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Xinwei Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Jane C Figuieredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Lihong Qi
- Department of Public Health Sciences, University of California Davis, Davis, CA, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
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36
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Wang SE, Kendall BJ, Hodge AM, Dixon-Suen SC, Dashti SG, Makalic E, Williamson EM, Thomas RJS, Giles GG, English DR. Demographic and lifestyle risk factors for gastroesophageal reflux disease and Barrett's esophagus in Australia. Dis Esophagus 2022; 35:6354029. [PMID: 34409990 DOI: 10.1093/dote/doab058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/23/2021] [Accepted: 07/31/2021] [Indexed: 12/11/2022]
Abstract
We examined demographic and lifestyle risk factors for incidence of gastroesophageal reflux disease (GERD) and Barrett's esophagus (BE) in an Australian cohort of 20,975 participants aged 40-63 at recruitment (1990-1994). Information on GERD and BE was collected between 2007 and 2010. GERD symptoms were defined as self-reported heartburn or acid regurgitation. BE was defined as endoscopically confirmed columnar-lined esophagus. Risk factors for developing GERD symptoms, BE diagnosis, age at symptom onset, and age at BE diagnosis were quantified using regression. During a mean follow-up of 15.8 years, risk of GERD symptoms was 7.5% (n = 1,318) for daily, 7.5% (n = 1,333) for 2-6 days/week, and 4.3% (n = 751) for 1 day/week. There were 210 (1.0%) endoscopically diagnosed BE cases, of whom 141 had histologically confirmed esophageal intestinal metaplasia. Female sex, younger age, lower socioeconomic position (SEP) and educational attainment, and former smoking were associated with higher GERD risk. Male sex and smoking were associated with earlier GERD symptom onset. Men, older participants, those with higher SEP, and former smokers were at higher BE risk. There was some evidence higher SEP was associated with earlier BE diagnosis. GERD and BE had different demographic risk factors but shared similar lifestyle factors. Earlier GERD symptom onset for men and smokers might have contributed to higher BE risk. The SEP patterns observed for GERD and BE suggest potential inequity in access to care. These findings would be important in the development of clinical risk prediction models for early detection of BE.
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Affiliation(s)
- Sabrina E Wang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Bradley J Kendall
- Department of Medicine, The University of Queensland, Brisbane, Australia.,Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, Australia
| | - Allison M Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | | | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Elizabeth M Williamson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.,Health Data Research UK, London, UK
| | - Robert J S Thomas
- Department of Medicine, The University of Melbourne, Melbourne, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
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37
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Yu C, Hodge AM, Wong EM, Joo JE, Makalic E, Schmidt D, Buchanan DD, Hopper JL, Giles GG, Southey MC, Dugué PA. Association of FOXO3 Blood DNA Methylation with Cancer Risk, Cancer Survival, and Mortality. Cells 2021; 10:cells10123384. [PMID: 34943892 PMCID: PMC8699522 DOI: 10.3390/cells10123384] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/22/2021] [Accepted: 11/26/2021] [Indexed: 12/29/2022] Open
Abstract
Genetic variants in FOXO3 are associated with longevity. Here, we assessed whether blood DNA methylation at FOXO3 was associated with cancer risk, survival, and mortality. We used data from eight prospective case–control studies of breast (n = 409 cases), colorectal (n = 835), gastric (n = 170), kidney (n = 143), lung (n = 332), prostate (n = 869), and urothelial (n = 428) cancer and B-cell lymphoma (n = 438). Case–control pairs were matched on age, sex, country of birth, and smoking (lung cancer study). Conditional logistic regression was used to assess associations between cancer risk and methylation at 45 CpGs of FOXO3 included on the HumanMethylation450 assay. Mixed-effects Cox models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations with cancer survival (total n = 2286 deaths). Additionally, using data from 1088 older participants, we assessed associations of FOXO3 methylation with overall and cause-specific mortality (n = 354 deaths). Methylation at a CpG in the first exon region of FOXO3 (6:108882981) was associated with gastric cancer survival (HR = 2.39, 95% CI: 1.60–3.56, p = 1.9 × 10−5). Methylation at three CpGs in TSS1500 and gene body was associated with lung cancer survival (p < 6.1 × 10−5). We found no evidence of associations of FOXO3 methylation with cancer risk and mortality. Our findings may contribute to understanding the implication of FOXO3 in longevity.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
| | - Allison M. Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Jihoon Eric Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- Victorian Comprehensive Cancer Centre, University of Melbourne Centre for Cancer Research, Parkville, VIC 3010, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Daniel Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC 3168, Australia;
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- Victorian Comprehensive Cancer Centre, University of Melbourne Centre for Cancer Research, Parkville, VIC 3010, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3000, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Graham G. Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
- Correspondence:
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Association between Diet Quality Indices and Incidence of Type 2 Diabetes in the Melbourne Collaborative Cohort Study. Nutrients 2021; 13:nu13114162. [PMID: 34836416 PMCID: PMC8622769 DOI: 10.3390/nu13114162] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 12/15/2022] Open
Abstract
Type 2 diabetes mellitus is a common condition whose incidence is increasing worldwide, and for which obesity and diet are important risk factors. The aim of this study was to assess the association of three diet quality scores with diabetes risk and how much of the association was mediated through body size. The Melbourne Collaborative Cohort Study recruited 41,513 men and women aged 40-69 years during 1990-1994. At baseline, data were collected on lifestyle and diet, anthropometric measures were performed. Incident diabetes was assessed by self-report at follow-up surveys in 1994-1998 and 2003-2007. The associations between the dietary inflammatory index (DII®), Mediterranean Diet Score (MDS) and the Alternative Healthy Eating Index-2010 and incident diabetes were assessed using Poisson regression, adjusting for age, sex, physical activity, smoking, alcohol consumption, socio-economic status (area based) and family history of diabetes. Data from 39,185 participants were included in the analysis and 1989 cases of diabetes were identified. Both DII and AHEI-2010 were associated with diabetes incidence, but MDS was not. In the top quintile of DII (most pro-inflammatory) vs. the least inflammatory quintile IRR was 1.49 95% CI (1.30, 1.72), p trend across quintiles <0.001. For AHEI-2010 the IRR was 0.67 (0.58, 0.78), p trend <0.001 for the healthiest vs. the least healthy quintile. Mediation analysis indicated that body size (body mass index/waist to hip ratio) mediated 35-48% of the association with incident diabetes for the AHEI and DII. Healthier diets may reduce risk of diabetes both by reducing weight gain and other mechanisms such as reducing inflammation.
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Hussain SM, Wang Y, Heath AK, Giles GG, English DR, Eyles DW, Williamson EJ, Graves SE, Wluka AE, Cicuttini FM. Association between circulating 25-hydroxyvitamin D concentrations and hip replacement for osteoarthritis: a prospective cohort study. BMC Musculoskelet Disord 2021; 22:887. [PMID: 34666727 PMCID: PMC8524987 DOI: 10.1186/s12891-021-04779-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/01/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To examine the association between circulating 25(OH)D concentrations and incidence of total hip replacement for osteoarthritis in a prospective cohort study. METHODS This study examined a random sample of 2651 participants in the Melbourne Collaborative Cohort Study who had 25(OH)D concentrations measured from dried blood spots collected in 1990-1994. Participants who underwent total hip replacement for osteoarthritis between January 2001 and December 2018 were identified by linking the cohort records to the Australian Orthopaedic Association National Joint Replacement Registry. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) of total hip replacement for osteoarthritis in relation to 25(OH)D concentrations, adjusted for confounders. RESULTS Eighty-six men and eighty-seven women had a total hip replacement for osteoarthritis. Compared with men in the lowest (1st) quartile of 25(OH)D concentration, the HR for total hip replacement was 2.32 (95% CI 1.05, 5.13) for those in the 2nd quartile, 2.77 (95% CI 1.28, 6.00) for those in the 3rd quartile, and 1.73 (95% CI 0.75, 4.02) for those in the highest quartile of 25(OH)D concentrations (p for trend 0.02). There was little evidence of an association in women. CONCLUSIONS Higher circulating 25(OH)D concentrations were associated with an increased risk of total hip replacement for osteoarthritis in men but not in women. Although the underlying mechanism warrants further investigation, our findings highlight the need to determine the optimal levels of circulating 25(OH)D to reduce the risk of hip osteoarthritis.
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Affiliation(s)
- Sultana Monira Hussain
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
| | - Yuanyuan Wang
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Graham G Giles
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Darryl W Eyles
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, 4076, Australia
| | - Elizabeth J Williamson
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Stephen E Graves
- Department of Surgery, Flinders University, Bedford Park, SA, 5042, Australia
- Australian Orthopaedic Association National Joint Replacement Registry, Discipline of Public Health, School of Population Health & Clinical Practice, University of Adelaide, Adelaide, SA, 5005, Australia
| | - Anita E Wluka
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Flavia M Cicuttini
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
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Li SX, Milne RL, Nguyen-Dumont T, English DR, Giles GG, Southey MC, Antoniou AC, Lee A, Winship I, Hopper JL, Terry MB, MacInnis RJ. Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models. Cancers (Basel) 2021; 13:5194. [PMID: 34680343 PMCID: PMC8534072 DOI: 10.3390/cancers13205194] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/30/2021] [Accepted: 10/13/2021] [Indexed: 11/20/2022] Open
Abstract
Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.
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Affiliation(s)
- Sherly X. Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Roger L. Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne 3800, Australia;
| | - Tú Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne 3800, Australia;
- Department of Clinical Pathology, University of Melbourne, Melbourne 3010, Australia
| | - Dallas R. English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne 3800, Australia;
| | - Melissa C. Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne 3800, Australia;
- Department of Clinical Pathology, University of Melbourne, Melbourne 3010, Australia
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (A.C.A.); (A.L.)
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (A.C.A.); (A.L.)
| | - Ingrid Winship
- Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne 3050, Australia;
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne 3050, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA;
| | - Robert J. MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (S.X.L.); (R.L.M.); (D.R.E.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne 3010, Australia;
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Wang SE, Hodge AM, Dashti SG, Dixon-Suen SC, Mitchell H, Thomas RJS, Williamson EM, Makalic E, Boussioutas A, Haydon AM, Giles GG, Milne RL, Kendall BJ, English DR. Diet and risk of gastro-oesophageal reflux disease in the Melbourne Collaborative Cohort Study. Public Health Nutr 2021; 24:5034-5046. [PMID: 33472714 PMCID: PMC11082811 DOI: 10.1017/s1368980021000197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/23/2020] [Accepted: 01/13/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To examine associations between diet and risk of developing gastro-oesophageal reflux disease (GERD). DESIGN Prospective cohort with a median follow-up of 15·8 years. Baseline diet was measured using a FFQ. GERD was defined as self-reported current or history of daily heartburn or acid regurgitation beginning at least 2 years after baseline. Sex-specific logistic regressions were performed to estimate OR for GERD associated with diet quality scores and intakes of nutrients, food groups and individual foods and beverages. The effect of substituting saturated fat for monounsaturated or polyunsaturated fat on GERD risk was examined. SETTING Melbourne, Australia. PARTICIPANTS A cohort of 20 926 participants (62 % women) aged 40-59 years at recruitment between 1990 and 1994. RESULTS For men, total fat intake was associated with increased risk of GERD (OR 1·05 per 5 g/d; 95 % CI 1·01, 1·09; P = 0·016), whereas total carbohydrate (OR 0·89 per 30 g/d; 95 % CI 0·82, 0·98; P = 0·010) and starch intakes (OR 0·84 per 30 g/d; 95 % CI 0·75, 0·94; P = 0·005) were associated with reduced risk. Nutrients were not associated with risk for women. For both sexes, substituting saturated fat for polyunsaturated or monounsaturated fat did not change risk. For both sexes, fish, chicken, cruciferous vegetables and carbonated beverages were associated with increased risk, whereas total fruit and citrus were associated with reduced risk. No association was observed with diet quality scores. CONCLUSIONS Diet is a possible risk factor for GERD, but food considered as triggers of GERD symptoms might not necessarily contribute to disease development. Potential differential associations for men and women warrant further investigation.
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Affiliation(s)
- Sabrina E Wang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | - Suzanne C Dixon-Suen
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Hazel Mitchell
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Robert JS Thomas
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Elizabeth M Williamson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
- Health Data Research UK, London, UK
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Alex Boussioutas
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Andrew M Haydon
- Department of Medical Oncology, Alfred Hospital, Melbourne, VIC, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Bradley J Kendall
- Department of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
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42
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Li SX, Hodge AM, MacInnis RJ, Bassett JK, Ueland PM, Midttun Ø, Ulvik A, Rinaldi S, Meyer K, Navionis AS, Shivappa N, Hébert JR, Flicker L, Severi G, Jayasekara H, English DR, Vineis P, Southey MC, Milne RL, Giles GG, Dugué PA. Inflammation-Related Marker Profiling of Dietary Patterns and All-cause Mortality in the Melbourne Collaborative Cohort Study. J Nutr 2021; 151:2908-2916. [PMID: 34320210 DOI: 10.1093/jn/nxab231] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/16/2021] [Accepted: 06/22/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Nutritional epidemiology research using self-reported dietary intake is prone to measurement error. Objective methods are being explored to overcome this limitation. OBJECTIVES We aimed to examine 1) the association between plasma markers related to inflammation and derive marker scores for dietary patterns [Mediterranean dietary score (MDS), energy-adjusted Dietary Inflammatory Index (E-DIITM), Alternative Healthy Eating Index 2010 (AHEI)] and 2) the associations of these marker scores with mortality. METHODS Weighted marker scores were derived from the cross-sectional association between 30 plasma markers and each dietary score (assessed using food-frequency questionnaires) using linear regression for 770 participants in the Melbourne Collaborative Cohort Study (aged 50-82 y). Prospective associations between marker scores and mortality (n = 249 deaths) were assessed using Cox regression (median follow-up: 14.4 y). RESULTS The MDS, E-DII, and AHEI were associated (P < 0.05) with 9, 14, and 11 plasma markers, respectively. Healthier diets (higher MDS and AHEI, and lower anti-inflammatory, E-DII) were associated with lower concentrations of kynurenines, neopterin, IFN-γ, cytokines, and C-reactive protein. Five of 6 markers common to the 3 dietary scores were components of the kynurenine pathway. The 3 dietary-based marker scores were highly correlated (Spearman ρ: -0.74, -0.82, and 0.93). Inverse associations (for 1-SD increment) were observed with all-cause mortality for the MDS marker score (HR: 0.84; 95% CI: 0.72-0.98) and the AHEI marker score (HR: 0.76; 95% CI: 0.66-0.89), whereas a positive association was observed with the E-DII marker score (HR: 1.18; 95% CI: 1.01-1.39). The same magnitude of effect was not observed for the respective dietary patterns. CONCLUSIONS Markers involved in inflammation-related processes are associated with dietary quality, including a substantial overlap between markers associated with the MDS, the E-DII, and the AHEI, especially kynurenines. Unfavorable marker scores, reflecting poorer-quality diets, were associated with increased mortality.
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Affiliation(s)
- Sherly X Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Per M Ueland
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | - Arve Ulvik
- Bevital A/S, Laboratoriebygget, Bergen, Norway
| | - Sabina Rinaldi
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Klaus Meyer
- Bevital A/S, Laboratoriebygget, Bergen, Norway
| | - Anne-Sophie Navionis
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Nitin Shivappa
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - James R Hébert
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - Leon Flicker
- WA Centre for Health and Ageing of the University of Western Australia, Crawley, Western Australia, Australia
| | - Gianluca Severi
- Centre de Recherche en Épidémiologie et Santé des Populations (CESP, Inserm U1018), Université Paris-Saclay, UPS, USQ, Gustave Roussy, Villejuif, France
- Human Genetics Foundation (HuGeF), Turin, Italy
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Pierre-Antoine Dugué
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
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Yu C, Jordahl KM, Bassett JK, Joo JE, Wong EM, Brinkman MT, Schmidt DF, Bolton DM, Makalic E, Brasky TM, Shadyab AH, Tinker LF, Longano A, Hopper JL, English DR, Milne RL, Bhatti P, Southey MC, Giles GG, Dugué PA. Smoking Methylation Marks for Prediction of Urothelial Cancer Risk. Cancer Epidemiol Biomarkers Prev 2021; 30:2197-2206. [PMID: 34526299 DOI: 10.1158/1055-9965.epi-21-0313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/22/2021] [Accepted: 09/10/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Self-reported information may not accurately capture smoking exposure. We aimed to evaluate whether smoking-associated DNA methylation markers improve urothelial cell carcinoma (UCC) risk prediction. METHODS Conditional logistic regression was used to assess associations between blood-based methylation and UCC risk using two matched case-control samples: 404 pairs from the Melbourne Collaborative Cohort Study (MCCS) and 440 pairs from the Women's Health Initiative (WHI) cohort. Results were pooled using fixed-effects meta-analysis. We developed methylation-based predictors of UCC and evaluated their prediction accuracy on two replication data sets using the area under the curve (AUC). RESULTS The meta-analysis identified associations (P < 4.7 × 10-5) for 29 of 1,061 smoking-associated methylation sites, but these were substantially attenuated after adjustment for self-reported smoking. Nominally significant associations (P < 0.05) were found for 387 (36%) and 86 (8%) of smoking-associated markers without/with adjustment for self-reported smoking, respectively, with same direction of association as with smoking for 387 (100%) and 79 (92%) markers. A Lasso-based predictor was associated with UCC risk in one replication data set in MCCS [N = 134; odds ratio per SD (OR) = 1.37; 95% CI, 1.00-1.90] after confounder adjustment; AUC = 0.66, compared with AUC = 0.64 without methylation information. Limited evidence of replication was found in the second testing data set in WHI (N = 440; OR = 1.09; 95% CI, 0.91-1.30). CONCLUSIONS Combination of smoking-associated methylation marks may provide some improvement to UCC risk prediction. Our findings need further evaluation using larger data sets. IMPACT DNA methylation may be associated with UCC risk beyond traditional smoking assessment and could contribute to some improvements in stratification of UCC risk in the general population.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Kristina M Jordahl
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.,Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jihoon Eric Joo
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Maree T Brinkman
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Department of Data Science & AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Damien M Bolton
- Department of Surgery, University of Melbourne and Olivia Newton-John Cancer Centre, Austin Hospital, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Theodore M Brasky
- Division of Medical Oncology, The Ohio State University College of Medicine, Columbus, Ohio
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anthony Longano
- Department of Anatomical Pathology, Eastern Health, Box Hill Hospital, Box Hill, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. .,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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Dugué PA, Hodge AM, Wong EM, Joo JE, Jung CH, Hopper JL, English DR, Giles GG, Milne RL, Southey MC. Methylation marks of prenatal exposure to maternal smoking and risk of cancer in adulthood. Int J Epidemiol 2021; 50:105-115. [PMID: 33169152 DOI: 10.1093/ije/dyaa210] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prenatal exposure to maternal smoking is detrimental to child health but its association with risk of cancer has seldom been investigated. Maternal smoking induces widespread and long-lasting DNA methylation changes, which we study here for association with risk of cancer in adulthood. METHODS Eight prospective case-control studies nested within the Melbourne Collaborative Cohort Study were used to assess associations between maternal-smoking-associated methylation marks in blood and risk of several cancers: breast (n = 406 cases), colorectal (n = 814), gastric (n = 166), kidney (n = 139), lung (n = 327), prostate (n = 847) and urothelial (n = 404) cancer and B-cell lymphoma (n = 426). We used conditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between cancer and five methylation scores calculated as weighted averages for 568, 19, 15, 28 and 17 CpG sites. Models were adjusted for confounders, including personal smoking history (smoking status, pack-years, age at starting and quitting) and methylation scores for personal smoking. RESULTS All methylation scores for maternal smoking were strongly positively associated with risk of urothelial cancer. Risk estimates were only slightly attenuated after adjustment for smoking history, other potential confounders and methylation scores for personal smoking. Potential negative associations were observed with risk of lung cancer and B-cell lymphoma. No associations were observed for other cancers. CONCLUSIONS We found that methylation marks of prenatal exposure to maternal smoking are associated with increased risk of urothelial cancer. Our study demonstrates the potential for using DNA methylation to investigate the impact of early-life, unmeasured exposures on later-life cancer risk.
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Affiliation(s)
- Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - JiHoon E Joo
- Department of Clinical Pathology, Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, The University of Melbourne, Parkville, VIC, Australia
| | - Chol-Hee Jung
- Melbourne Bioinformatics, The University of Melbourne, Parkville VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
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Jayasekara H, MacInnis RJ, Lujan‐Barroso L, Mayen‐Chacon A, Cross AJ, Wallner B, Palli D, Ricceri F, Pala V, Panico S, Tumino R, Kühn T, Kaaks R, Tsilidis K, Sánchez M, Amiano P, Ardanaz E, Chirlaque López MD, Merino S, Rothwell JA, Boutron‐Ruault M, Severi G, Sternby H, Sonestedt E, Bueno‐de‐Mesquita B, Boeing H, Travis R, Sandanger TM, Trichopoulou A, Karakatsani A, Peppa E, Tjønneland A, Yang Y, Hodge AM, Mitchell H, Haydon A, Room R, Hopper JL, Weiderpass E, Gunter MJ, Riboli E, Giles GG, Milne RL, Agudo A, English DR, Ferrari P. Lifetime alcohol intake, drinking patterns over time and risk of stomach cancer: A pooled analysis of data from two prospective cohort studies. Int J Cancer 2021; 148:2759-2773. [PMID: 33554339 PMCID: PMC9290950 DOI: 10.1002/ijc.33504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/22/2020] [Accepted: 01/04/2021] [Indexed: 02/03/2023]
Abstract
Alcohol consumption is causally linked to several cancers but the evidence for stomach cancer is inconclusive. In our study, the association between long-term alcohol intake and risk of stomach cancer and its subtypes was evaluated. We performed a pooled analysis of data collected at baseline from 491 714 participants in the European Prospective Investigation into Cancer and Nutrition and the Melbourne Collaborative Cohort Study. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for incident stomach cancer in relation to lifetime alcohol intake and group-based life course intake trajectories, adjusted for potential confounders including Helicobacter pylori infection. In all, 1225 incident stomach cancers (78% noncardia) were diagnosed over 7 094 637 person-years; 984 in 382 957 study participants with lifetime alcohol intake data (5 455 507 person-years). Although lifetime alcohol intake was not associated with overall stomach cancer risk, we observed a weak positive association with noncardia cancer (HR = 1.03, 95% CI: 1.00-1.06 per 10 g/d increment), with a HR of 1.50 (95% CI: 1.08-2.09) for ≥60 g/d compared to 0.1 to 4.9 g/d. A weak inverse association with cardia cancer (HR = 0.93, 95% CI: 0.87-1.00) was also observed. HRs of 1.48 (95% CI: 1.10-1.99) for noncardia and 0.51 (95% CI: 0.26-1.03) for cardia cancer were observed for a life course trajectory characterized by heavy decreasing intake compared to light stable intake (Phomogeneity = .02). These associations did not differ appreciably by smoking or H pylori infection status. Limiting alcohol use during lifetime, particularly avoiding heavy use during early adulthood, might help prevent noncardia stomach cancer. Heterogeneous associations observed for cardia and noncardia cancers may indicate etiologic differences.
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Affiliation(s)
- Harindra Jayasekara
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
- Centre for Alcohol Policy ResearchLa Trobe UniversityBundooraVictoriaAustralia
| | - Robert J. MacInnis
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
| | - Leila Lujan‐Barroso
- Unit of Nutrition and Cancer, Catalan Institute of Oncology ‐ ICO, Nutrition and Cancer Group, Bellvitge Biomedical Research Institute ‐ IDIBELL, L'Hospitalet de LlobregatBarcelonaSpain
- Department of Nursing of Public HealthMental Health and Maternity and Child Health School of Nursing Universitat de BarcelonaBarcelonaSpain
| | - Ana‐Lucia Mayen‐Chacon
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer, World Health OrganizationLyonFrance
| | - Amanda J. Cross
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Bengt Wallner
- Department of Surgical and Perioperatve Sciences, SurgeryUmeå University HospitalUmeåSweden
| | - Domenico Palli
- Cancer Risk Factors and Life‐Style Epidemiology UnitInstitute for Cancer Research, Prevention and Clinical Network – ISPROFlorenceItaly
| | - Fulvio Ricceri
- Department of Clinical and Biological SciencesUniversity of TurinTurinItaly
- Unit of Epidemiology, Regional Health Service ASL TO3GrugliascoItaly
| | - Valeria Pala
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di MilanoMilanItaly
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e ChirurgiaFederico II UniversityNaplesItaly
| | - Rosario Tumino
- Cancer Registry and Histopathology DepartmentProvincial Health Authority (ASP)RagusaItaly
| | - Tilman Kühn
- Division of Cancer EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Rudolf Kaaks
- Division of Cancer EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Kostas Tsilidis
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Maria‐Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP)GranadaSpain
- Instituto de Investigación Biosanitaria ibs.GRANADAGranadaSpain
- CIBER in Epidemiology and Public Health (CIBERESP)MadridSpain
- Universidad de GranadaGranadaSpain
| | - Pilar Amiano
- CIBER in Epidemiology and Public Health (CIBERESP)MadridSpain
- Public Health Division of Gipuzkoa, BioDonostia Research InstituteDonostia‐San SebastianSpain
| | - Eva Ardanaz
- CIBER in Epidemiology and Public Health (CIBERESP)MadridSpain
- Navarra Public Health InstitutePamplonaSpain
- IdiSNA, Navarra Institute for Health ResearchPamplonaSpain
| | - María Dolores Chirlaque López
- CIBER in Epidemiology and Public Health (CIBERESP)MadridSpain
- Department of EpidemiologyRegional Health Council, IMIB‐Arrixaca, Murcia UniversityMurciaSpain
| | - Susana Merino
- Public Health Directorate, Regional Government of AsturiasOviedoSpain
| | - Joseph A. Rothwell
- CESP (U1018), Faculté de médecineUniversité Paris‐Saclay, UVSQ, INSERMVillejuifFrance
- Gustave RoussyVillejuifFrance
| | | | - Gianluca Severi
- CESP (U1018), Faculté de médecineUniversité Paris‐Saclay, UVSQ, INSERMVillejuifFrance
- Gustave RoussyVillejuifFrance
- Department of StatisticsComputer Science and Applications “G. Parenti” (DISIA), University of FlorenceFlorenceItaly
| | - Hanna Sternby
- Department of SurgeryInstitution of Clinical Sciences Malmö, Lund UniversityLundSweden
| | - Emily Sonestedt
- Nutritional Epidemiology, Department of Clinical Sciences MalmöLund UniversityMalmöSweden
| | - Bas Bueno‐de‐Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM)BilthovenThe Netherlands
| | - Heiner Boeing
- Institute of Nutrition Science, University of PotsdamNuthetalGermany
| | - Ruth Travis
- Cancer Epidemiology Unit, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Torkjel M. Sandanger
- Department of Community Medicine, Faculty of Health SciencesUiT‐the Arctic University of NorwayTromsøNorway
| | | | - Anna Karakatsani
- Hellenic Health FoundationAthensGreece
- 2nd Pulmonary Medicine DepartmentSchool of Medicine, National Kapodistrian University of Athens, “ATTIKON” University HospitalHaidariGreece
| | | | - Anne Tjønneland
- Danish Cancer Society Research CenterCopenhagenDenmark
- Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
| | - Yi Yang
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
| | - Allison M. Hodge
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
| | - Hazel Mitchell
- School of Biotechnology and Biomolecular Sciences, University of New South WalesKensingtonNew South WalesAustralia
| | - Andrew Haydon
- Department of Medical OncologyAlfred HospitalMelbourneVictoriaAustralia
| | - Robin Room
- Centre for Alcohol Policy ResearchLa Trobe UniversityBundooraVictoriaAustralia
- Centre for Social Research on Alcohol and Drugs, Department of Public Health SciencesStockholm UniversityStockholmSweden
| | - John L. Hopper
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
| | - Elisabete Weiderpass
- Office of the Director, International Agency for Research on Cancer, World Health OrganizationLyonFrance
| | - Marc J. Gunter
- Nutritional Epidemiology Group, International Agency for Research on Cancer, World Health OrganizationLyonFrance
| | - Elio Riboli
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Graham G. Giles
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash UniversityClaytonVictoriaAustralia
| | - Roger L. Milne
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash UniversityClaytonVictoriaAustralia
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Catalan Institute of Oncology ‐ ICO, Nutrition and Cancer Group, Bellvitge Biomedical Research Institute ‐ IDIBELL, L'Hospitalet de LlobregatBarcelonaSpain
| | - Dallas R. English
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Centre for Epidemiology and BiostatisticsMelbourne School of Population and Global Health, The University of MelbourneMelbourneVictoriaAustralia
| | - Pietro Ferrari
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer, World Health OrganizationLyonFrance
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Li SX, Milne RL, Nguyen-Dumont T, Wang X, English DR, Giles GG, Southey MC, Antoniou AC, Lee A, Li S, Winship I, Hopper JL, Terry MB, MacInnis RJ. Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models. JNCI Cancer Spectr 2021; 5:pkab021. [PMID: 33977228 PMCID: PMC8099999 DOI: 10.1093/jncics/pkab021] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/13/2020] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. Methods We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). Results When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. Conclusions Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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Affiliation(s)
- Sherly X Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Xiaochuan Wang
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Ingrid Winship
- Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
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47
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Joo JE, Clendenning M, Wong EM, Rosty C, Mahmood K, Georgeson P, Winship IM, Preston SG, Win AK, Dugué PA, Jayasekara H, English D, Macrae FA, Hopper JL, Jenkins MA, Milne RL, Giles GG, Southey MC, Buchanan DD. DNA Methylation Signatures and the Contribution of Age-Associated Methylomic Drift to Carcinogenesis in Early-Onset Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13112589. [PMID: 34070516 PMCID: PMC8199056 DOI: 10.3390/cancers13112589] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The role of DNA methylation in the carcinogenesis of colorectal cancer (CRC) diagnosed <50 years of age (early-onset CRC or EOCRC) is currently unknown. In this study, we investigated the genome-wide DNA methylation of 97 tumour and 54 normal colonic mucosa samples from people with EOCRC with the aim of identifying unique DNA methylation signatures and determining the role of ageing-related DNA methylation drift and age-acceleration in EOCRC aetiology. We found extensive DNA methylation alterations associated with EOCRC carcinogenesis, including a unique signature comprising 234 loci compared with CRCs from people >50 years of age. CpGs that undergo ageing-related methylation drift were significantly altered in EOCRC, and accelerated ageing was also evident in normal mucosa from people with EOCRC. Our study is the first study to identify unique DNA methylation changes in EOCRC, contributing novel information that may aid future efforts towards EOCRC prevention. Abstract We investigated aberrant DNA methylation (DNAm) changes and the contribution of ageing-associated methylomic drift and age acceleration to early-onset colorectal cancer (EOCRC) carcinogenesis. Genome-wide DNAm profiling using the Infinium HM450K on 97 EOCRC tumour and 54 normal colonic mucosa samples was compared with: (1) intermediate-onset CRC (IOCRC; diagnosed between 50–70 years; 343 tumour and 35 normal); and (2) late-onset CRC (LOCRC; >70 years; 318 tumour and 40 normal). CpGs associated with age-related methylation drift were identified using a public dataset of 231 normal mucosa samples from people without CRC. DNAm-age was estimated using epiTOC2. Common to all three age-of-onset groups, 88,385 (20% of all CpGs) CpGs were differentially methylated between tumour and normal mucosa. We identified 234 differentially methylated genes that were unique to the EOCRC group; 13 of these DMRs/genes were replicated in EOCRC compared with LOCRCs from TCGA. In normal mucosa from people without CRC, we identified 28,154 CpGs that undergo ageing-related DNAm drift, and of those, 65% were aberrantly methylated in EOCRC tumours. Based on the mitotic-based DNAm clock epiTOC2, we identified age acceleration in normal mucosa of people with EOCRC compared with normal mucosa from the IOCRC, LOCRC groups (p = 3.7 × 10−16) and young people without CRC (p = 5.8 × 10−6). EOCRC acquires unique DNAm alterations at 234 loci. CpGs associated with ageing-associated drift were widely affected in EOCRC without needing the decades-long accrual of DNAm drift as commonly seen in intermediate- and late-onset CRCs. Accelerated ageing in normal mucosa from people with EOCRC potentially underlies the earlier age of diagnosis in CRC carcinogenesis.
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Affiliation(s)
- Jihoon E. Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
| | - Mark Clendenning
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
| | - Ee Ming Wong
- Precision Medicine, Monash Health, Monash University, Clayton, Melbourne 3800, Australia; (E.M.W.); (P.-A.D.); (R.L.M.); (G.G.G.); (M.C.S.)
| | - Christophe Rosty
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
- School of Medicine, University of Queensland, Herston, Brisbane 4006, Australia
- Envoi Pathology, Brisbane 4059, Australia
| | - Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
- Melbourne Bioinformatics, The University of Melbourne, Parkville, Melbourne 3010, Australia
| | - Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
| | - Ingrid M. Winship
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Melbourne 3050, Australia; (I.M.W.); (F.A.M.)
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne 3050, Australia
| | - Susan G. Preston
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
| | - Pierre-Antoine Dugué
- Precision Medicine, Monash Health, Monash University, Clayton, Melbourne 3800, Australia; (E.M.W.); (P.-A.D.); (R.L.M.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
| | - Harindra Jayasekara
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
| | - Dallas English
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
| | - Finlay A. Macrae
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Melbourne 3050, Australia; (I.M.W.); (F.A.M.)
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne 3050, Australia
- Colorectal Medicine and Genetics, The Royal Melbourne Hospital, Parkville, Melbourne 3050, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
| | - Roger L. Milne
- Precision Medicine, Monash Health, Monash University, Clayton, Melbourne 3800, Australia; (E.M.W.); (P.-A.D.); (R.L.M.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
| | - Graham G. Giles
- Precision Medicine, Monash Health, Monash University, Clayton, Melbourne 3800, Australia; (E.M.W.); (P.-A.D.); (R.L.M.); (G.G.G.); (M.C.S.)
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne 3010, Australia; (A.K.W.); (D.E.); (J.L.H.); (M.A.J.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
| | - Melissa C. Southey
- Precision Medicine, Monash Health, Monash University, Clayton, Melbourne 3800, Australia; (E.M.W.); (P.-A.D.); (R.L.M.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Melbourne 3010, Australia; (J.E.J.); (M.C.); (C.R.); (K.M.); (P.G.); (S.G.P.); (H.J.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Melbourne 3000, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Melbourne 3050, Australia
- Correspondence: ; Tel.: +61-3-8559-7004
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48
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Jayasekara H, MacInnis RJ, Juneja S, Bassett JK, Bruinsma F, Lynch BM, Hodge AM, Hopper JL, English DR, Giles GG, Milne RL. Smoking, alcohol consumption, body fatness, and risk of myelodysplastic syndromes: A prospective study. Leuk Res 2021; 109:106593. [PMID: 34237503 DOI: 10.1016/j.leukres.2021.106593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 04/07/2021] [Accepted: 04/07/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Harindra Jayasekara
- 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, Victoria, 3010, Australia.
| | - Robert J MacInnis
- 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, Victoria, 3010, Australia
| | - Surender Juneja
- Melbourne Health Pathology, Royal Melbourne Hospital, Parkville, Victoria, 3050, Australia; Peter MacCallum Cancer Centre, 305 Grattan Street, Melbourne, Victoria, 3000, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, 3004, Australia
| | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria, 3004, Australia
| | - Brigid M Lynch
- 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, Victoria, 3010, Australia
| | - Allison M Hodge
- 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, Victoria, 3010, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, 3010, Australia
| | - Dallas R English
- 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, Victoria, 3010, 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, Victoria, 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia
| | - Roger L Milne
- 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, Victoria, 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia
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49
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Diet scores and prediction of general and abdominal obesity in the Melbourne collaborative cohort study. Public Health Nutr 2021; 24:6157-6168. [PMID: 33875030 DOI: 10.1017/s1368980021001713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
OBJECTIVE To ascertain which of the Alternative Healthy Eating Index (AHEI) 2010, Dietary Inflammatory Index (DII®) and Mediterranean Diet Score (MDS) best predicted BMI and waist-to-hip circumference ratio (WHR). DESIGN Body size was measured at baseline (1990-1994) and in 2003-2007. Diet was assessed at baseline using a FFQ, along with age, sex, socio-economic status, smoking, alcohol drinking, physical activity and country of birth. Regression coefficients and 95 % CI for the association of baseline dietary scores with follow-up BMI and WHR were generated using multivariable linear regression, adjusting for baseline body size, confounders and energy intake. SETTING Population-based cohort in Melbourne, Australia. PARTICIPANTS Included were data from 11 030 men and 16 774 women aged 40-69 years at baseline. RESULTS Median (IQR) follow-up was 11·6 (10·7-12·8) years. BMI and WHR at follow-up were associated with baseline DII® (Q5 v. Q1 (BMI 0·41, 95 % CI 0·21, 0·61) and WHR 0·009, 95 % CI 0·006, 0·013)) and AHEI (Q5 v. Q1 (BMI -0·51, 95 % CI -0·68, -0·35) and WHR -0·011, 95 % CI -0·013, -0·008)). WHR, but not BMI, at follow-up was associated with baseline MDS (Group 3 most Mediterranean v. G1 (BMI -0·05, 95 % CI -0·23, 0·13) and WHR -0·004, 95 % CI -0·007, -0·001)). Based on Akaike's Information Criterion and Bayesian Information Criterion statistics, AHEI was a stronger predictor of body size than the other diet scores. CONCLUSIONS Poor quality or pro-inflammatory diets predicted overall and central obesity. The AHEI may provide the best way to assess the obesogenic potential of diet.
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Jayasekara H, Hodge AM, Haydon A, Room R, Hopper JL, English DR, Smith-Warner SA, Giles GG, Milne RL, MacInnis RJ. Prediagnosis alcohol intake and metachronous cancer risk in cancer survivors: A prospective cohort study. Int J Cancer 2021; 149:827-838. [PMID: 33872391 DOI: 10.1002/ijc.33603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/21/2021] [Accepted: 04/07/2021] [Indexed: 12/24/2022]
Abstract
Alcohol consumption is a known cause of cancer, but its role in the etiology of second primary (metachronous) cancer is uncertain. Associations between alcohol intake up until study enrollment (prediagnosis) and risk of metachronous cancer were estimated using 9435 participants in the Melbourne Collaborative Cohort Study who were diagnosed with their first invasive cancer after enrollment (1990-1994). Follow-up was from date of first invasive cancer until diagnosis of metachronous cancer, death or censor date (February 2018), whichever came first. Alcohol intake for 10-year periods from age 20 until decade encompassing baseline using recalled beverage-specific frequency and quantity was used to calculate baseline and lifetime intakes, and group-based intake trajectories. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs), adjusted for potential confounders. After a mean follow-up of 7 years, 1512 metachronous cancers were identified. A 10 g/d increment in prediagnosis lifetime alcohol intake (HR = 1.03, 95% CI = 1.00-1.06; Pvalue = .02) and an intake of ≥60 g/d (HR = 1.32, 95% CI = 1.01-1.73) were associated with increased metachronous cancer risk. We observed positive associations (per 10 g/d increment) for metachronous colorectal (HR = 1.07, 95% CI = 1.00-1.14), upper aero-digestive tract (UADT) (HR = 1.16, 95% CI = 1.00-1.34) and kidney cancer (HR = 1.24, 95% CI = 1.10-1.39). Although these findings were partly explained by effects of smoking, the association for kidney cancer remained unchanged when current smokers or obese individuals were excluded. Alcohol intake trajectories over the life course confirmed associations with metachronous cancer risk. Prediagnosis long-term alcohol intake, and particularly heavy drinking, may increase the risk of metachronous cancer, particularly of the colorectum, UADT and kidney.
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Affiliation(s)
- Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Centre for Alcohol Policy Research, La Trobe University, Bundoora, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Haydon
- Department of Medical Oncology, Alfred Hospital, Melbourne, Victoria, Australia
| | - Robin Room
- Centre for Alcohol Policy Research, La Trobe University, Bundoora, Victoria, Australia
- Centre for Social Research on Alcohol and Drugs, Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Stephanie A Smith-Warner
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
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