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Chalitsios CV, Markozannes G, Papagiannopoulos C, Aglago EK, Berndt SI, Buchanan DD, Campbell PT, Cao Y, Chan AT, Dimou N, Drew DA, French AJ, Georgeson P, Giannakis M, Gruber SB, Gunter MJ, Harrison TA, Hoffmeister M, Hsu L, Huang WY, Hullar MAJ, Huyghe JR, Lynch BM, Moreno V, Newton CC, Nowak JA, Obón-Santacana M, Ogino S, Qu C, Schmit SL, Steinfelder RS, Sun W, Thomas CE, Toland AE, Trinh QM, Ugai T, Um CY, Van Guelpen B, Zaidi SH, Murphy N, Peters U, Phipps AI, Tsilidis KK. Waist Circumference, a Body Shape Index, and Molecular Subtypes of Colorectal Cancer: A Pooled Analysis of Four Cohort Studies. Cancer Epidemiol Biomarkers Prev 2025; 34:568-577. [PMID: 39898780 DOI: 10.1158/1055-9965.epi-24-1534] [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: 10/17/2024] [Revised: 11/27/2024] [Accepted: 01/29/2025] [Indexed: 02/04/2025] Open
Abstract
BACKGROUND Waist circumference (WC) and its allometric counterpart, "a body shape index" (ABSI), are risk factors for colorectal cancer; however, it is uncertain whether associations with these body measurements are limited to specific molecular subtypes of the disease. METHODS Data from 2,772 colorectal cancer cases and 3,521 controls were pooled from four cohort studies within the Genetics and Epidemiology of Colorectal Cancer Consortium. Four molecular markers (BRAF mutation, KRAS mutation, CpG island methylator phenotype, and microsatellite instability) were analyzed individually and in combination (Jass types). Multivariable logistic and multinomial logistic models were used to assess the associations of WC and ABSI with overall colorectal cancer risk and, in case-only analyses, to evaluate heterogeneity by molecular subtype, respectively. RESULTS Higher WC (ORper 5 cm = 1.06, 95% confidence interval, 1.04-1.09) and ABSI (ORper 1-SD = 1.07, 95% confidence interval, 1.00-1.14) were associated with elevated colorectal cancer risk. There was no evidence of heterogeneity between the molecular subtypes. No difference was observed regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancers, as well as in early- and late-onset colorectal cancers. Associations did not differ in the Jass-type analysis. CONCLUSIONS Higher WC and ABSI were associated with elevated colorectal cancer risk; however, they do not differentially influence all four major molecular mutations involved in colorectal carcinogenesis but underscore the importance of maintaining a healthy body weight in colorectal cancer prevention. IMPACT The proposed results have potential utility in colorectal cancer prevention.
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Affiliation(s)
| | - Georgios Markozannes
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | | | - Elom K Aglago
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia
- University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Australia
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Yin Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
- Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Amy J French
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia
- University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, Australia
| | - Marios Giannakis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Stephen B Gruber
- Department of Medical Oncology and Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, California
| | - Marc J Gunter
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - 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 Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Meredith A J Hullar
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Victor Moreno
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Christina C Newton
- Department of Population Science, American Cancer Society, Atlanta, Georgia
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mireia Obón-Santacana
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Claire E Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
- Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Quang M Trinh
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, Georgia
| | - Bethany Van Guelpen
- Department of Diagnostics and Intervention, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Syed H Zaidi
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
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Albers FEM, Swain CTV, Lou MWC, Dashti SG, Rinaldi S, Viallon V, Karahalios A, Brown KA, Gunter MJ, Milne RL, English DR, Lynch BM. Insulin and Insulin-like Growth Factor and Risk of Postmenopausal Estrogen Receptor-Positive Breast Cancer: A Case-Cohort Analysis. Cancer Epidemiol Biomarkers Prev 2025; 34:541-549. [PMID: 39808164 DOI: 10.1158/1055-9965.epi-24-1304] [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: 09/04/2024] [Revised: 11/07/2024] [Accepted: 01/09/2025] [Indexed: 01/16/2025] Open
Abstract
BACKGROUND Higher concentration of insulin-like growth factor-1 (IGF-1) increases postmenopausal breast cancer risk, but evidence for insulin and c-peptide is limited. Furthermore, not all studies have accounted for potential confounding by biomarkers from other biological pathways, and not all were restricted to estrogen receptor (ER)-positive breast cancer. METHODS This was a case-cohort study of 1,223 postmenopausal women (347 with ER-positive breast cancer) from the Melbourne Collaborative Cohort Study. We measured insulin, c-peptide, IGF-1, insulin-like growth factor binding protein-3, and biomarkers of inflammatory and sex-steroid hormone pathways. Poisson regression with a robust variance estimator was used to estimate risk ratios (RR) and 95% confidence intervals (95% CI) for ER-positive breast cancer per doubling plasma concentration and for quartiles, without and with adjustment for other, potentially confounding biomarkers. RESULTS ER-positive breast cancer risk was not associated with doubling of insulin (RR = 0.97, 95% CI, 0.82-1.14) or c-peptide (RR = 1.01, 95% CI, 0.80-1.26). Risk seemed to decrease with doubling IGF-1 (RR = 0.80, 95% CI, 0.62-1.03) and insulin-like growth factor binding protein-3 (RR = 0.62, 95% CI, 0.41-0.90). RRs were not meaningfully different when exposures were modeled as quartiles. RRs were less than unity but imprecise after adjustment for inflammatory and sex-steroid hormone biomarkers. CONCLUSIONS Circulating insulin, c-peptide, and IGF-1 were not positively associated with risk of ER-positive breast cancer in this case-cohort analysis of postmenopausal women. IMPACT Associations between insulin and c-peptide and risk of ER-positive breast cancer in postmenopausal women are likely to be weak.
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Affiliation(s)
- Frances E M Albers
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Christopher T V Swain
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, Australia
| | - Makayla W C Lou
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), 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, Kansas
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Cancer Epidemiology and Prevention Research Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Brigid M Lynch
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
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3
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Zheng HT, Li DL, Lou MWC, Hodge AM, Southey MC, Giles GG, Milne RL, Lynch BM, Dugué PA. Physical activity and DNA methylation-based markers of ageing in 6208 middle-aged and older Australians: cross-sectional and longitudinal analyses. GeroScience 2025; 47:2263-2274. [PMID: 39508977 PMCID: PMC11979085 DOI: 10.1007/s11357-024-01408-5] [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: 09/12/2024] [Accepted: 10/21/2024] [Indexed: 11/15/2024] Open
Abstract
Epigenetic age quantifies biological age using DNA methylation information and is a potential pathway by which physical activity benefits general health. We aimed to assess the cross-sectional and longitudinal associations between physical activity and epigenetic age in middle-aged and older Australians. Blood DNA methylation data for 6208 participants (40% female) in the Melbourne Collaborative Cohort Study (MCCS) were available at baseline (1990-1994, mean age, 59 years) and, of those, for 1009 at follow-up (2003-2007, mean age, 69 years). Physical activity measurements (weighted scores at baseline and follow-up and total MET hours per week at follow-up) were calculated from self-reported questionnaire data. Five blood methylation-based markers of ageing (PCGrimAge, PCPhenoAge, bAge, DNAmFitAge, and DunedinPACE) and four fitness-related markers (DNAmGaitspeed, DNAmGripmax, DNAmVO2max, and DNAmFEV1) were calculated and adjusted for age. Linear regression was used to examine the cross-sectional and longitudinal associations between physical activity and epigenetic age. Effect modification by age, sex, and BMI was assessed. At baseline, a standard deviation (SD) increment in physical activity was associated with 0.03-SD (DNAmFitAge, 95%CI = 0.01, 0.06, P = 0.02) to 0.07-SD (bAge, 95%CI = 0.04, 0.09, P = 2 × 10-8) lower epigenetic age. These associations were attenuated after adjustment for other lifestyle variables. Only weak evidence was found for the longitudinal association (N = 1009) of changes in physical activity and epigenetic age (e.g. DNAmFitAge: adjusted β = - 0.04, 95%CI = - 0.08, 0.01). The associations were not modified by age, sex, or BMI. In middle-aged and older Australians, higher levels of self-reported physical activity were associated with slightly lower epigenetic age.
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Affiliation(s)
- Haoxin Tina Zheng
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Danmeng Lily Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Makayla W C Lou
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, 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
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- 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
| | - 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
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- 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
| | - Brigid M Lynch
- 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
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
- 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.
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4
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Chu YL, Georgeson P, Clendenning M, Mahmood K, Walker R, Como J, Joseland S, Preston SG, Rice T, Lynch BM, Milne RL, Southey MC, Giles GG, Phipps AI, Hopper JL, Win AK, Rosty C, Macrae FA, Winship I, Jenkins MA, Buchanan DD, Joo JE. Intratumoural pks +Escherichia coli is associated with risk of metachronous colorectal cancer and adenoma development in people with Lynch syndrome. EBioMedicine 2025; 114:105661. [PMID: 40158390 PMCID: PMC11995779 DOI: 10.1016/j.ebiom.2025.105661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 03/07/2025] [Accepted: 03/07/2025] [Indexed: 04/02/2025] Open
Abstract
BACKGROUND The adverse gut microbiome may underlie the variability in risks of colorectal cancer (CRC) and metachronous CRC in people with Lynch syndrome (LS). The role of pks+/-Escherichia coli (pks+/-E. coli), Enterotoxigenic Bacteroides fragilis (ETBF), and Fusobacterium nucleatum (Fn) in CRCs and adenomas in people with LS is unknown. METHODS A total of 358 LS cases, including 386 CRCs, 90 adenomas, 195 normal colonic mucosa DNA from the Australasian Colon Cancer Family Registry were tested using multiplex TaqMan qPCR. Logistic regression was used to compare the intratumoural prevalence of each bacteria in Lynch CRCs with 1336 sporadic CRCs. Cox proportional-hazards regression estimated the associations of each bacteria with the risk of metachronous CRC and neoplasia. FINDINGS Pks+ E. coli (odds ratio [95% confidence interval] = 1.60 [1.08-2.35], P = 0.017), pks-E. coli (3.87 [2.58-5.80], P < 0.001) and Fn (19.47 [13.32-28.87], P < 0.001) were significantly enriched in LS CRCs when compared with sporadic CRCs. Pks+ E. coli in the initial CRC was associated with an increased risk of metachronous CRC (hazard ratio [95% confidence interval] = 2.32 [1.29-4.17], P = 0.005) and metachronous colorectal neoplasia (1.51 [1.02-2.23], P = 0.040) when compared with CRCs without pks+ E. coli. INTERPRETATION Pks+ E. coli, pks-E. coli, and Fn are enriched within LS CRCs, suggesting possible roles in CRC development in LS. Having intratumoural pks+ E. coli is associated with increased risk of metachronous CRC, suggesting that, if validated, people with LS might benefit from pks+ E. coli screening and eradication. FUNDING This work was funded by an NHMRC Investigator grant (GNT1194896) and a Cancer Australia/Cancer Council NSW co-funded grant (GNT2012914).
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Affiliation(s)
- Yen Lin Chu
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark Clendenning
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Bioinformatics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Romy Walker
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Julia Como
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Sharelle Joseland
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Susan G Preston
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - Toni Rice
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, 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, Parkville, 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
| | - 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
| | - 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
| | - Amanda I Phipps
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Aung K Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christophe Rosty
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; University of Queensland, Brisbane, Queensland, Australia; Envoi Specialist Pathologists, Brisbane, Queensland, Australia
| | - Finlay A Macrae
- Colorectal Medicine and Genetics, The Royal Melbourne Hospital, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Melbourne, Victoria, Australia; Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Melbourne, Victoria, Australia; Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- 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, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Melbourne, Victoria, Australia
| | - Jihoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia.
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5
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Nagle CM, Ibiebele TI, Na R, Bandera EV, Cramer D, Doherty JA, Giles GG, Goodman MT, Hanley GE, Harris HR, Jensen A, Kjaer SK, Lee A, McGuire V, Milne RL, Qin B, Richardson J, Sasamoto N, Schildkraut JM, Sieh W, Terry KL, Titus L, Trabert B, Wentzensen N, Wu AH, Berchuck A, Pike MC, Pearce CL, Webb PM. Diet and survival after a diagnosis of ovarian cancer: a pooled analysis from the Ovarian Cancer Association Consortium. Am J Clin Nutr 2025; 121:758-768. [PMID: 39921094 PMCID: PMC12002190 DOI: 10.1016/j.ajcnut.2025.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2024] [Revised: 01/23/2025] [Accepted: 02/03/2025] [Indexed: 02/10/2025] Open
Abstract
BACKGROUND Prognosis after a diagnosis of invasive epithelial ovarian cancer is poor. Some studies have suggested modifiable behaviors, like diet, are associated with survival but the evidence is inconsistent. OBJECTIVES This study aims to pool data from studies conducted around the world to evaluate the relationships among dietary indices, foods, and nutrients from food sources and survival after a diagnosis of ovarian cancer. METHODS This analysis from the Multidisciplinary Ovarian Cancer Outcomes Group within the Ovarian Cancer Association Consortium included 13 studies with 7700 individuals with ovarian cancer, who completed food-frequency questionnaires regarding their prediagnosis diet. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CI) for associations with overall survival were estimated using Cox proportional hazards models. RESULTS Overall, there was no association between any of the 7 dietary indices (representing prediagnosis diet) evaluated and survival; however, associations differed by tumor stage. Although there were no consistent associations among those with advanced disease, among those with earlier stage (local/regional) disease, higher scores on the alternate Healthy Eating Index (aHR quartile 4 compared with 1 = 0.66, 95% CI: 0.50, 0.87), Healthy Eating Index-2015 (aHR: 0.75; 95% CI: 0.59, 0.97), and alternate Mediterranean diet (aHR: 0.76; 95% CI: 0.60, 0.98) were associated with better survival. Better survival was also observed for individuals with early-stage disease who reported higher intakes of dietary components that contribute to the healthy diet indices (aHR for Q4 compared with Q1: vegetables 0.71; 95% CI: 0.56, 0.91), tomatoes (aHR: 0.72; 95% CI: 0.57, 0.91) and nuts and seeds (aHR 0.71; 95% CI: 0.55, 0.92). In contrast, there were suggestions of worse survival with higher scores on 2 of the 3 inflammatory indices and higher intake of trans-fatty acids. CONCLUSIONS Adherence to a more healthy, less-inflammatory diet may confer a survival benefit for individuals with early-stage ovarian cancer.
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Affiliation(s)
- Christina M Nagle
- Gynaecological Cancers Group, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Torukiri I Ibiebele
- Gynaecological Cancers Group, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Renhua Na
- Gynaecological Cancers Group, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute, New Brunswick, NJ, United States
| | - Daniel Cramer
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Jennifer A Doherty
- Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - 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
| | - Marc T Goodman
- Cancer Prevention and Control Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Community and Population Health Research Institute, Los Angeles, CA, United States
| | - Gillian E Hanley
- Department of Obstetrics & Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Holly R Harris
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, United States; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States
| | - Allan Jensen
- Department of Virus, Lifestyle and Genes, Danish Cancer Institute, Copenhagen, Denmark
| | - Susanne K Kjaer
- Department of Virus, Lifestyle and Genes, Danish Cancer Institute, Copenhagen, Denmark; Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Alice Lee
- Department of Public Health, California State University, Fullerton, Fullerton, CA, United States
| | - Valerie McGuire
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, United States
| | - 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
| | - Bo Qin
- Cancer Prevention and Control Program, Rutgers Cancer Institute, New Brunswick, NJ, United States
| | - Jean Richardson
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Patient advocate
| | - Naoko Sasamoto
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Joellen M Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Weiva Sieh
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Linda Titus
- Dartmouth Cancer Center, Lebanon, NH03756, United States
| | - Britton Trabert
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States; Department of Obstetrics and Gynecology, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, United States
| | - Nicolas Wentzensen
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Andrew Berchuck
- Division of Gynecologic Oncology, Duke University School of Medicine, Durham, NC, United States
| | - Malcolm C Pike
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Penelope M Webb
- Gynaecological Cancers Group, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; University of Queensland, School of Public Health, Brisbane, Queensland, Australia.
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6
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Cribb L, Hodge AM, Southey MC, Giles GG, Milne RL, Dugué PA. Dietary factors and DNA methylation-based markers of ageing in 5310 middle-aged and older Australian adults. GeroScience 2025; 47:1685-1698. [PMID: 39298107 PMCID: PMC11978581 DOI: 10.1007/s11357-024-01341-7] [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: 07/02/2024] [Accepted: 09/05/2024] [Indexed: 09/21/2024] Open
Abstract
The role of nutrition in healthy ageing is acknowledged but details of optimal dietary composition are still uncertain. We aimed to investigate the cross-sectional associations between dietary exposures, including macronutrient composition, food groups, specific foods, and overall diet quality, with methylation-based markers of ageing. Blood DNA methylation data from 5310 participants (mean age 59 years) in the Melbourne Collaborative Cohort Study were used to calculate five methylation-based measures of ageing: PCGrimAge, PCPhenoAge, DunedinPACE, ZhangAge, TelomereAge. For a range of dietary exposures, we estimated (i) the 'equal-mass substitution effect', which quantifies the effect of adding the component of interest to the diet while keeping overall food mass constant, and (ii) the 'total effect', which quantifies the effect of adding the component of interest to the current diet. For 'equal-mass substitution effects', the strongest association for macronutrients was for fibre intake (e.g. DunedinPACE, per 12 g/day - 0.10 [standard deviations]; 95%CI - 0.15, - 0.05, p < 0.001). Associations were positive for protein (e.g. PCGrimAge, per 33 g/day 0.04; 95%CI 0.01-0.08, p = 0.005). For food groups, the evidence tended to be weak, though sugar-sweetened drinks showed positive associations, as did artificially-sweetened drinks (e.g. DunedinPACE, per 91 g/day 0.06, 95%CI 0.03-0.08, p < 0.001). 'Total effect' estimates were generally very similar. Scores reflecting overall diet quality suggested that healthier diets were associated with lower levels of ageing markers. High intakes of fibre and low intakes of protein and sweetened drinks, as well as overall healthy diets, showed the most consistent associations with lower methylation-based ageing in our study.
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Affiliation(s)
- Lachlan Cribb
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Level 3, MIMR, 27-31, Wright St, Clayton, VIC, 3168, 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, Level 3, MIMR, 27-31, Wright St, Clayton, VIC, 3168, 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, Level 3, MIMR, 27-31, Wright St, Clayton, VIC, 3168, 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, Level 3, MIMR, 27-31, Wright St, Clayton, VIC, 3168, 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, Level 3, MIMR, 27-31, Wright St, Clayton, VIC, 3168, 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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7
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Ashtree DN, Orr R, Lane MM, Akbaraly TN, Bonaccio M, Costanzo S, Gialluisi A, Grosso G, Lassale C, Martini D, Monasta L, Santomauro D, Stanaway J, Jacka FN, O'Neil A. Estimating the Burden of Common Mental Disorders Attributable to Lifestyle Factors: Protocol for the Global Burden of Disease Lifestyle and Mental Disorder (GLAD) Project. JMIR Res Protoc 2025; 14:e65576. [PMID: 40085831 PMCID: PMC11953606 DOI: 10.2196/65576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 12/06/2024] [Accepted: 12/26/2024] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) collects and calculates risk-outcome data for modifiable lifestyle exposures (eg, dietary intake) and physical health outcomes (eg, cancers). These estimates form a critical digital resource tool, the GBD VizHub data visualization tool, for governments and policy makers to guide local, regional, and global health decisions. Despite evidence showing the contributions of lifestyle exposures to common mental disorders (CMDs), such as depression and anxiety, GBD does not currently generate these lifestyle exposure-mental disorder outcome pairings. This gap is due to a lack of uniformly collected and analyzed data about these exposures as they relate to CMDs. Such data are required to quantify whether, and to what degree, the global burden of CMDs could be reduced by targeting lifestyle factors at regional and global levels. We have established the Global burden of disease Lifestyle And mental Disorder (GLAD) Taskforce to address this gap. OBJECTIVE This study aims to generate the necessary estimates to afford the inclusion of lifestyle exposures as risk factors for CMDs in the GBD study and the GBD digital visualization tools, initially focusing on the relationship between dietary intake and CMDs. METHODS The GLAD project is a multicenter, collaborative effort to integrate lifestyle exposures as risk factors for CMDs in the GBD study. To achieve this aim, global epidemiological studies will be recruited to conduct harmonized data analyses estimating the risk, odds, or hazards of lifestyle exposures with CMD outcomes. Initially, these models will focus on the relationship between dietary intake, as defined by the GBD, and anxiety and depression. RESULTS As of August 2024, 18 longitudinal cohort studies from 9 countries (Australia: n=4; Brazil: n=1; France: n=1; Italy: n=3; The Netherlands: n=3; New Zealand: n=1; South Africa: n=1; Spain: n=1; and United Kingdom: n=3) have agreed to participate in the GLAD project. CONCLUSIONS Our comprehensive, collaborative approach allows for the concurrent execution of a harmonized statistical analysis protocol across multiple, internationally renowned epidemiological cohorts. These results will be used to inform the GBD study and incorporate lifestyle risk factors for CMD in the GBD digital platform. Consequently, given the worldwide influence of the GBD study, findings from the GLAD project can offer valuable insights to policy makers worldwide around lifestyle-based mental health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/65576.
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Affiliation(s)
- Deborah N Ashtree
- IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Australia
| | - Rebecca Orr
- IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Australia
| | - Melissa M Lane
- IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Australia
| | - Tasnime N Akbaraly
- Université Montpellier, Institut National de Santé et de Recherche Médicale (INSERM), Desbrest Institute of Epidemiology and Public Health (IDESP), F-34090 Montpellier, France
| | - Marialaura Bonaccio
- IRCCS Neuromed, Research Unit of Epidemiology and Prevention, Pozzilli, Italy
| | - Simona Costanzo
- IRCCS Neuromed, Research Unit of Epidemiology and Prevention, Pozzilli, Italy
| | - Alessandro Gialluisi
- IRCCS Neuromed, Research Unit of Epidemiology and Prevention, Pozzilli, Italy
- Department of Medicine and Surgery, Libera Università Mediterranea (LUM) University, Casamassima (Bari), Italy
| | - Giuseppe Grosso
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Camille Lassale
- ISGlobal, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Madrid, Spain
| | - Daniela Martini
- Division of Human Nutrition, Environmental and Nutritional Sciences, University of Milan, DeFENS-Department of Food, Milan, Italy
| | - Lorenzo Monasta
- Institute for Maternal and Child Health - IRCCS Burlo Garofolo, Trieste, Italy
| | - Damian Santomauro
- Queensland Centre for Mental Health Research, Wacol, Australia
- Faculty of Medicine, School of Public Health, University of Queensland, Herston, Australia
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States
| | - Jeffrey Stanaway
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States
| | - Felice N Jacka
- IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Australia
- Department of Immunology, Therapeutics, and Vaccines, James Cook University, Queensland, Australia
| | - Adrienne O'Neil
- IMPACT (the Institute for Mental and Physical Health and Clinical Translation), Food & Mood Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Australia
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8
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Viart NM, Renault AL, Eon-Marchais S, Jiao Y, Fuhrmann L, El Houdigui SM, Le Gal D, Cavaciuti E, Dondon MG, Beauvallet J, Raynal V, Stoppa-Lyonnet D, Vincent-Salomon A, Andrieu N, Southey MC, Lesueur F. Breast tumors from ATM pathogenic variant carriers display a specific genome-wide DNA methylation profile. Breast Cancer Res 2025; 27:36. [PMID: 40069712 PMCID: PMC11899765 DOI: 10.1186/s13058-025-01988-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: 11/18/2024] [Accepted: 02/27/2025] [Indexed: 03/14/2025] Open
Abstract
BACKGROUND The ataxia-telangiectasia mutated (ATM) kinase phosphorylates and activates several downstream targets that are essential for DNA damage repair, cell cycle inhibition and apoptosis. Germline biallelic inactivation of the ATM gene causes ataxia-telangiectasia (A-T), and heterozygous pathogenic variant (PV) carriers are at increased risk of cancer, notably breast cancer. This study aimed to investigate whether DNA methylation profiling can be useful as a biomarker to identify tumors arising in ATM PV carriers, which may help for the management and optimal tailoring of therapies of these patients. METHODS Breast tumor enriched DNA was prepared from 2 A-T patients, 27 patients carrying an ATM PV, 6 patients carrying a variant of uncertain clinical significance and 484 noncarriers enrolled in epidemiological studies conducted in France and Australia to investigate genetic and nongenetic factors involved in breast cancer susceptibility. Genome-wide DNA methylation analysis was performed using the Illumina Infinium HumanMethylation EPIC and 450K BeadChips. Correlation between promoter methylation and gene expression was assessed for 10 tumors for which transcriptomic data were available. RESULTS We found that the ATM promoter was hypermethylated in 62% of tumors of heterozygous PV carriers compared to the mean methylation level of ATM promoter in tumors of noncarriers. Gene set enrichment analyses identified 47 biological pathways enriched in hypermethylated genes involved in neoplastic, neurodegenerative and metabolic-related pathways in tumor of PV carriers. Among the 327 differentially methylated promoters, promoters of ARHGAP40, SCGB3A1 (HIN-1), and CYBRD1 (DCYTB) were hypermethylated and associated with a lower gene expression in these tumors. Moreover, using three different deep learning algorithms (logistic regression, random forest and XGBoost), we identified a set of 27 additional biomarkers predictive of ATM status, which could be used in the future to provide evidence for or against pathogenicity in ATM variant classification strategies. CONCLUSIONS We showed that breast tumors that arise in women who carry an ATM PV display a specific genome-wide DNA methylation profile. Specifically, the methylation pattern of 27 key gene promoters was predictive of ATM PV status of the women. These genes may also represent new medical prevention and therapeutic targets for these women.
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Affiliation(s)
- Nicolas M Viart
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | - Anne-Laure Renault
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
- Monash University, Clayton, VIC; University of Melbourne, Parkville, VIC, Australia
| | | | - Yue Jiao
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | | | | | - Dorothée Le Gal
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | - Eve Cavaciuti
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | | | - Juana Beauvallet
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | - Virginie Raynal
- ICGex Next-Generation Sequencing Platform, Institut Curie, PSL University, Paris, France
| | | | | | - Nadine Andrieu
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France
| | - Melissa C Southey
- Monash University, Clayton, VIC; University of Melbourne, Parkville, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Fabienne Lesueur
- Inserm, U1331, Institut Curie, PSL University, Mines ParisTech, Paris, France.
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9
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Zarean E, Li S, Wong EM, Makalic E, Milne RL, Giles GG, McLean C, Southey MC, Dugué PA. Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours. Epigenomics 2025; 17:105-114. [PMID: 39711216 PMCID: PMC11792870 DOI: 10.1080/17501911.2024.2441653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 12/10/2024] [Indexed: 12/24/2024] Open
Abstract
AIMS Clustering algorithms have been widely applied to tumor DNA methylation datasets to define methylation-based cancer subtypes. This study aimed to evaluate the agreement between subtypes obtained from common clustering strategies. MATERIALS & METHODS We used tumor DNA methylation data from 409 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS) and 781 breast tumors from The Cancer Genome Atlas (TCGA). Agreement was assessed using the adjusted Rand index for various combinations of number of CpGs, number of clusters and clustering algorithms (hierarchical, K-means, partitioning around medoids, and recursively partitioned mixture models). RESULTS Inconsistent agreement patterns were observed for between-algorithm and within-algorithm comparisons, with generally poor to moderate agreement (ARI <0.7). Results were qualitatively similar in the MCCS and TCGA, showing better agreement for moderate number of CpGs and fewer clusters (K = 2). Restricting the analysis to CpGs that were differentially-methylated between tumor and normal tissue did not result in higher agreement. CONCLUSION Our study highlights that common clustering strategies involving an arbitrary choice of algorithm, number of clusters and number of methylation sites are likely to identify different DNA methylation-based breast tumor subtypes.
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Affiliation(s)
- Elaheh Zarean
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Shuai Li
- 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
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Enes Makalic
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Roger L. Milne
- 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
| | - 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
| | - Catriona McLean
- Anatomical Pathology, Alfred Health, The Alfred Hospital, Melbourne, 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, Melbourne Medical School, The University of Melbourne, Parkville, 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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10
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Zarean E, Li S, Wong EM, Makalic E, Milne RL, Giles GG, McLean C, Southey MC, Dugué PA. Tumour DNA methylation markers associated with breast cancer survival: a replication study. Breast Cancer Res 2025; 27:9. [PMID: 39825380 PMCID: PMC11740461 DOI: 10.1186/s13058-024-01955-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: 09/23/2024] [Accepted: 12/20/2024] [Indexed: 01/20/2025] Open
Abstract
BACKGROUND Tumour DNA methylation has been investigated as a potential marker for breast cancer survival, but findings often lack replication across studies. METHODS This study sought to replicate previously reported associations for individual CpG sites and multi-CpG signatures using an Australian sample of 425 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS). Candidate methylation sites (N = 22) and signatures (N = 3) potentially associated with breast cancer survival were identified from five prior studies that used The Cancer Genome Atlas (TCGA) methylation dataset, which shares key characteristics with the MCCS: comparable sample size, tissue type (formalin-fixed paraffin-embedded; FFPE), technology (Illumina HumanMethylation450 array), and participant characteristics (age, ancestry, and disease subtype and severity). Cox proportional hazard regression analyses were conducted to assess associations between these markers and both breast cancer-specific survival and overall survival, adjusting for relevant participant characteristics. RESULTS Our findings revealed partial replication for both individual CpG sites (9 out of 22) and multi-CpG signatures (2 out of 3). These associations were maintained after adjustment for participant characteristics and were stronger for breast cancer-specific mortality than for overall mortality. In fully-adjusted models, strong associations were observed for a CpG in PRAC2 (per standard deviation [SD], HR = 1.67, 95%CI: 1.24-2.25) and a signature based on 28 CpGs developed using elastic net (per SD, HR = 1.48, 95%CI: 1.09-2.00). CONCLUSIONS While further studies are needed to confirm and expand on these findings, our study suggests that DNA methylation markers hold promise for improving breast cancer prognostication.
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Affiliation(s)
- Elaheh Zarean
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, Australia
| | - Shuai Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, 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, 246 Clayton Road, Clayton, VIC, 3168, Australia
| | - Enes Makalic
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, 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
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, 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
| | - Catriona McLean
- Anatomical Pathology, Alfred Health, The Alfred Hospital, Melbourne, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 246 Clayton Road, Clayton, VIC, 3168, 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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MacInnis RJ, Jenkins MA, Milne RL, John EM, Daly MB, Andrulis IL, Colonna SV, Phillips KA, Le Marchand L, Newcomb PA, Phipps AI, Schmit SL, Macrae FA, Buchanan DD, Gallinger S, Pai RK, Samadder NJ, Giles GG, Southey MC, Hopper JL, Terry MB. Menopausal hormone therapy: assessing associations with breast and colorectal cancers by familial risk. JNCI Cancer Spectr 2025; 9:pkae121. [PMID: 39673461 PMCID: PMC11700558 DOI: 10.1093/jncics/pkae121] [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: 08/19/2024] [Revised: 10/21/2024] [Accepted: 11/29/2024] [Indexed: 12/16/2024] Open
Abstract
Menopausal users of hormone replacement therapy (HRT) are at increased breast cancer risk and decreased colorectal cancer (CRC) risk compared with individuals who have never used HRT, but these opposing associations may differ by familial risk of breast cancer and CRC. We harmonized data from 3 cohorts and generated separate breast cancer and CRC familial risk scores based on cancer family history. We defined moderate or strong family history as a risk score of 0.4 or higher, where 0.4 was equivalent to a 50-year-old woman with 1 parent diagnosed with either breast cancer or CRC at 55 years of age. Of 24 486 women assessed, 1243 and 405 were diagnosed with incident breast cancer and CRC, respectively. For breast cancer, menopausal HRT ever use versus never use hazard ratios were 1.27 (95% CI = 1.11 to 1.45) for a breast cancer familial risk score below 0.4 and 1.01 (95% CI = 0.82 to 1.25) for a breast cancer familial risk score of 0.4 or higher (Pdifference = .08). For CRC, menopausal HRT hazard ratios were 0.63 (95% CI = 0.50 to 0.78) for a CRC familial risk score below 0.4 and 1.21 (95% CI = 0.73 to 2.00) for a CRC familial risk score of 0.4 or higher (Pdifference = .03). Associations with menopausal HRT use that apply to the general population may not hold for women at moderate or strong familial risk of these cancers.
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Affiliation(s)
- Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC 3002, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
- Cancer Research Centre, University of Melbourne, Parkville, VIC 3053, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC 3002, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia
| | - Esther M John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States
- Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA 94305, United States
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA 19111, United States
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X5, Canada
| | - Sarah V Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, United States
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, VIC 3010, Australia
| | - kConFab Investigators
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, VIC 3010, Australia
- The Kathleen Cuningham Foundation Consortium for Research into Familial Aspects of Breast Cancer (kConFab), Research Department, Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, United States
| | - Polly A Newcomb
- Epidemiology Department, University of Washington, Seattle, WA 98195, United States
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States
| | - Amanda I Phipps
- Epidemiology Department, University of Washington, Seattle, WA 98195, United States
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH 44196, United States
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH 44106, United States
| | - Finlay A Macrae
- Department of Colorectal Medicine and Genetics, Royal Melbourne Hospital, VIC 3052, Australia
| | - Daniel D Buchanan
- Cancer Research Centre, University of Melbourne, Parkville, VIC 3053, Australia
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3010, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC 3052, Australia
| | - Steven Gallinger
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X5, Canada
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON M5G 1M1, Canada
- Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ 85259, United States
| | - Niloy J Samadder
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Phoenix, AZ 85259, United States
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC 3002, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC 3002, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3800, Australia
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC 3010, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC 3053, Australia
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, United States
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, United States
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12
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Turbić A, Vandenput L, Gandham A, Lorentzon M. Effects of Synbiotic Supplementation on Bone and Metabolic Health in Caucasian Postmenopausal Women: Rationale and Design of the OsteoPreP Trial. Nutrients 2024; 16:4219. [PMID: 39683612 DOI: 10.3390/nu16234219] [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: 11/12/2024] [Revised: 11/28/2024] [Accepted: 12/04/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND/OBJECTIVES Correction of decreased diversity of the gut microbiome, which is characteristic of menopause, by supplementation with a synbiotic may attenuate or prevent dysbiosis processes and preserve bone mass. We describe the rationale and design of the OsteoPreP trial aimed at evaluating the effects of 12 months of supplementation with a synbiotic on bone and metabolic health in postmenopausal Caucasian women. METHODS This is a randomized, double-blinded, placebo-controlled trial among 160 Caucasian, postmenopausal women with no current diagnosis of osteoporosis or supplementation with pro- or prebiotics, and no medical treatment affecting bone turnover. Dual-energy X-ray absorptiometry scans will be conducted at screening to confirm absence of osteoporosis. The primary outcome is the relative change (%) in total bone mineral density of the distal tibia at 12 months post-treatment between the active and placebo groups, as determined via high-resolution peripheral quantitative computed tomography. Secondary outcomes are the effects on immune system modulation and cognition, gut microbiota composition, and musculoskeletal and metabolic functions, with particular emphasis on blood glucose regulation. CONCLUSIONS The trial will inform on the efficacy and safety of a synbiotic containing both aerobic and anerobic bacterial strains and a prebiotic fiber on reduction in bone loss and on indices of blood glucose regulation. This trial may pave the way for an exciting field of translational research and be the underpinnings of the prevention strategy of osteoporosis and the management of metabolic dysfunction in postmenopausal women. The trial is registered with clinicaltrials.gov (NCT05348694).
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Affiliation(s)
- Alisa Turbić
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3000, Australia
| | - Liesbeth Vandenput
- Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, 41345 Gothenburg, Sweden
| | - Anoohya Gandham
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3000, Australia
- Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Mattias Lorentzon
- Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, 41345 Gothenburg, Sweden
- Region Västra Götaland, Department of Geriatric Medicine, Sahlgrenska University Hospital, 43153 Mölndal, Sweden
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13
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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; 48:401-413. [PMID: 38472646 PMCID: PMC11588973 DOI: 10.1002/gepi.22556] [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: 07/31/2023] [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 HealthThe University of MelbourneCarltonVictoriaAustralia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVictoriaAustralia
- Murdoch Children's Research InstituteRoyal Children's HospitalParkvilleVictoriaAustralia
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
- Genetic Technologies Ltd.FitzroyVictoriaAustralia
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - Vivienne F. C. Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public HealthSeoul National UniversitySeoulKorea
- Genomic Medicine InstituteSeoul National UniversityEuigwahakgwan #402, Seoul National University College of Medicine, 103, Daehak‐ro, Jongno‐guSeoulSouth Korea
- Institute of Health and EnvironmentSeoul National University1st GwanakRoSeoulSouth Korea
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVictoriaAustralia
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVictoriaAustralia
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVictoriaAustralia
- Department of Clinical PathologyThe University of MelbourneParkvilleVictoriaAustralia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneCarltonVictoriaAustralia
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14
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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; 46:5505-5515. [PMID: 38795183 PMCID: PMC11493901 DOI: 10.1007/s11357-024-01211-2] [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: 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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15
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Kwan BPM, Lynch BM, Edbrooke L, Hodge A, Swain CTV. Are the Relationships of Physical Activity and Television Viewing Time With Mortality Robust to Confounding? A Study, Utilizing E-Values, From the Melbourne Collaborative Cohort Study. J Phys Act Health 2024; 21:1105-1113. [PMID: 39322218 DOI: 10.1123/jpah.2024-0218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/11/2024] [Accepted: 07/25/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND Physical activity and sedentary behavior are associated with health outcomes. However, evidence may be affected by confounding bias. This study aimed to examine the relationships of physical activity and television (TV) viewing time with all-cause, cardiovascular, and cancer mortality in a cohort of Australian adults, and determine the robustness of these relationships to residual and unmeasured confounding. METHODS Data from 27,317 Melbourne Collaborative Cohort Study participants (mean age = 66) were used. Physical activity was assessed using the International Physical Activity Questionnaire-Short Form and categorized as insufficient, sufficient, or more than sufficient. TV viewing time was categorized as low, moderate, or high. Multivariable Cox regression models were used to evaluate associations of interest. E-values were calculated to assess the strength of unmeasured confounders required to negate the observed results. RESULTS For highest versus lowest physical activity category, the hazard ratio was 0.67 (95% confidence interval, 0.56-0.81) for all-cause mortality; E-values ranged between 1.79 and 2.44. Results were similar for cardiovascular mortality; however, hazard ratios were lower (0.72; 95% confidence interval, 0.51-1.01) and E-values much smaller (1.00-2.12) for cancer mortality. For highest versus lowest TV viewing time category, the hazard ratio was 1.08 (1.01-1.15) for all-cause mortality; E-values ranged between 1.00 and 1.37. Results were similar for cardiovascular and cancer mortality. CONCLUSIONS Physical activity and TV viewing time were associated with mortality. The robustness to unmeasured/residual confounding was moderate for physical activity (all-cause and cardiovascular mortality), but weaker for physical activity (cancer mortality) and TV viewing time in this study of Australian adults.
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Affiliation(s)
- Baldwin Pok Man Kwan
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia
| | - Brigid M Lynch
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC, Australia
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Lara Edbrooke
- Department of Physiotherapy, Melbourne School of Health Sciences, The University of Melbourne, Carlton, VIC, Australia
- Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Allison Hodge
- Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC, Australia
| | - Christopher T V Swain
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, VIC, Australia
- Department of Physiotherapy, Melbourne School of Health Sciences, The University of Melbourne, Carlton, VIC, Australia
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16
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Wesselink E, Gauderman W, Berndt SI, Brenner H, Buchanan DD, Campbell PT, Chan AT, Chang-Claude J, Cotterchoi M, Gunter MJ, Hoffmeister M, Joshi AD, Newton CC, Pai RK, Pellatt AJ, Phipps AI, Song M, Um CY, van Guelpen B, White E, Peters U, van Duijnhoven FJB. Calcium intake and genetic variants in the calcium sensing receptor in relation to colorectal cancer mortality: an international consortium study of 18,952 patients. BJC REPORTS 2024; 2:63. [PMID: 39233917 PMCID: PMC11368808 DOI: 10.1038/s44276-024-00077-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/06/2024]
Abstract
Background Research on calcium intake as well as variants in the calcium sensor receptor (CaSR) gene and their interaction in relation to CRC survival is still limited. Methods Data from 18,952 CRC patients, were included. Associations between primarily pre-diagnostic dietary (n = 13.085), supplemental (n = 11,837), total calcium intake (n = 5970) as well as 325 single nucleotide polymorphisms (SNPs) of the CaSR gene (n = 15,734) in relation to CRC-specific and all-cause mortality were assessed using Cox proportional hazard models. Also interactions between calcium intake and variants in the CaSR gene were assessed. Results During a median follow-up of 4.8 years (IQR 2.4-8.4), 6801 deaths occurred, of which 4194 related to CRC. For all-cause mortality, no associations were observed for the highest compared to the lowest sex- and study-specific quartile of dietary (HR 1.00, 95%CI 0.92-1.09), supplemental (HR 0.97, 95%CI 0.89-1.06) and total calcium intake (HR 0.99, 95%CI 0.88-1.11). No associations with CRC-specific mortality were observed either. Interactions were observed between supplemental calcium intake and several SNPs of the CaSR gene. Conclusion Calcium intake was not associated with all-cause or CRC-specific mortality in CRC patients. The association between supplemental calcium intake and all-cause and CRC-specific mortality may be modified by genetic variants in the CaSR gene.
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Affiliation(s)
- Evertine Wesselink
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - William Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - 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 Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC Australia
- University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, VIC Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC Australia
| | - Peter T. Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 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
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Michelle Cotterchoi
- Prevention and Cancer Control, Cancer Care Ontario, Toronto, ON Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
- Department of Epidemiology and Biostatistics School of Public Health Imperial College London, London, UK
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Amit D. Joshi
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA USA
| | - Christina C. Newton
- Department of Population Science, American Cancer Society, Atlanta, Georgia USA
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ USA
| | - Andrew J. Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Amanda I. Phipps
- Department of Epidemiology, University of Washington, Seattle, WA USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
- Departments of Epidemiology and Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA USA
| | - Caroline Y. Um
- Department of Population Science, American Cancer Society, Atlanta, Georgia USA
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Emily White
- Department of Epidemiology, University of Washington, Seattle, WA USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA USA
| | - Ulrike Peters
- Department of Epidemiology, University of Washington, Seattle, WA USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA USA
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17
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Kabthymer RH, Karim MN, Itsiopoulos C, Hodge AM, De Courten B. Association of low carbohydrate diet score with the risk of type 2 diabetes in an Australian population: A longitudinal study. Diabetes Metab Syndr 2024; 18:103049. [PMID: 38838612 DOI: 10.1016/j.dsx.2024.103049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
AIMS We aimed to assess the association of a low carbohydrate diet score (LCD) with the incidence of type 2 diabetes (T2D) using Melbourne Collaborative Cohort Study (MCCS) data. METHODS Between 1990 and 1994, the MCCS recruited 41,513 people aged 40-69 years. The first and second follow-ups were conducted in 1995-1998 and 2003-2007, respectively. We analyzed data from 39,185 participants. LCD score was calculated at baseline as the percentage of energy from carbohydrate, fat, and protein. The higher the score the less percentage of carbohydrates contributed to energy intake. The association of LCD quintiles with the incidence of diabetes was assessed using modified Poisson regression, adjusted for lifestyle, obesity, socioeconomic and other confounders. Mediation of the association by adiposity (BMI) was assessed. RESULTS LCD was positively associated with diabetes risk. Higher LCD score (p for trend = 0.001) was associated with increased risk of T2D. Quintile 5 (38 % energy from carbohydrates) versus quintile 1 (55 % energy from carbohydrates) showed a 20 % increased diabetes risk (incidence risk ratio (IRR) = 1.20 (95 % CI: 1.05-1.37)). A further adjustment for BMI (Body Mass Index) and WHR (Waist-to-Hip-Ratio) eliminated the association. Mediation analysis demonstrated that BMI mediated 76 % of the LCD & diabetes association. CONCLUSIONS Consuming a low carbohydrate diet, reflected as a high LCD score, may increase the risk of T2D which is largely explained by obesity. Results highlight the need for further studies, including clinical trials investigating the effects of a low carbohydrate diet in T2D.
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Affiliation(s)
- Robel Hussen Kabthymer
- Department of Medicine, School of Clinical Sciences, Monash University, 3168, Melbourne, VIC, Australia.
| | - Md Nazmul Karim
- School of Public Health and Preventive Medicine, Monash University, 3004, Melbourne, VIC, Australia.
| | - Catherine Itsiopoulos
- School of Health and Biomedical Sciences, RMIT University, 3085, Melbourne, VIC, Australia.
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, 3004, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
| | - Barbora De Courten
- Department of Medicine, School of Clinical Sciences, Monash University, 3168, Melbourne, VIC, Australia; School of Health and Biomedical Sciences, RMIT University, 3085, Melbourne, VIC, Australia.
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18
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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 PMCID: PMC11130059 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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19
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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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20
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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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21
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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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22
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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: 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] [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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23
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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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24
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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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25
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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: 4] [Impact Index Per Article: 2.0] [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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26
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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: 3] [Impact Index Per Article: 1.5] [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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27
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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: 3.5] [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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28
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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 PMCID: PMC11314296 DOI: 10.1007/s10549-023-06903-5] [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: 12/09/2022] [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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29
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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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30
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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: 9] [Impact Index Per Article: 4.5] [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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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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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 2023; 129:1232-1241. [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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33
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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: 1.0] [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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34
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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: 10] [Impact Index Per Article: 5.0] [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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35
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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: 6] [Impact Index Per Article: 3.0] [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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36
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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: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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
- 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
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37
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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: 2.7] [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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38
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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: 0.7] [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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39
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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: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [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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40
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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: 14] [Impact Index Per Article: 4.7] [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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41
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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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Lane MM, Lotfaliany M, Forbes M, Loughman A, Rocks T, O’Neil A, Machado P, Jacka FN, Hodge A, Marx W. 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; 14:3309. [PMID: 36014818 PMCID: PMC9415636 DOI: 10.3390/nu14163309] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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Affiliation(s)
- Melissa M. Lane
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
| | - Mojtaba Lotfaliany
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
| | - Malcolm Forbes
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
- Mental Health, Drugs & Alcohol Service, University Hospital Geelong, Barwon Health, Geelong, VIC 3220, Australia
- Department of Psychiatry, University of Melbourne, Parkville, VIC 3050, Australia
| | - Amy Loughman
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
| | - Tetyana Rocks
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
| | - Adrienne O’Neil
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
| | - Priscila Machado
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, VIC 3125, Australia
- Center for Epidemiological Research in Nutrition and Health, University of Sao Paulo, Sao Paulo 01246-904, Brazil
| | - Felice N. Jacka
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- College of Public Health, Medical & Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
| | - Allison Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Wolfgang Marx
- Food & Mood Centre, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Geelong, VIC 3220, Australia
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43
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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] [MESH Headings] [Grants] [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 to 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. 2303 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 to 39 followed by either an early (from age 40 to 49) (early decreasing heavy; HR = 1.75, 95% CI: 1.25-2.44) or late decrease (from age 60 to 69) (late decreasing heavy; HR = 1.94, 95% CI: 1.28-2.93), and moderate intake (mean <60 g/day) at age 20 to 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.
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Affiliation(s)
- Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, 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, Melbourne, Victoria, Australia
| | - Yi Yang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 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, 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, Melbourne, Victoria, Australia
- Physical Activity Laboratory, Baker IDI Heart and Diabetes Institute, 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
| | - 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
| | - 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
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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44
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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: 0.3] [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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45
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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: 3.0] [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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46
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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: 17] [Impact Index Per Article: 5.7] [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: 27] [Impact Index Per Article: 9.0] [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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48
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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: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
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Affiliation(s)
- Christopher T V Swain
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.
- Department of Physiotherapy, The University of Melbourne, Melbourne, Australia.
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, 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, Melbourne, Australia
| | - David W Dunstan
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
- Behaviour, Environment and Cognition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - Neville Owen
- Centre for Urban Transitions, Swinburne University, Melbourne, VIC, Australia
- Behavioural Epidemiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Yi Yang
- 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
| | - Harindra Jayasekara
- 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
- Centre for Alcohol Policy Research, La Trobe University, Bundoora, Melbourne, Australia
| | - James R Hébert
- Cancer Prevention and Control Program &, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, 29201, USA
| | - Nitin Shivappa
- Cancer Prevention and Control Program &, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, 29201, USA
| | - 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, Melbourne, Australia
| | - Roger L Milne
- 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
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - 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, Melbourne, 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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49
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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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50
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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: 9] [Impact Index Per Article: 3.0] [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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