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Rahman ML, Breeze CE, Shu XO, Wong JYY, Blechter B, Cardenas A, Wang X, Ji BT, Hu W, Cai Q, Hosgood HD, Yang G, Shi J, Long J, Gao YT, Bell DA, Zheng W, Rothman N, Lan Q. Epigenome-wide association study of lung cancer among never smokers in two prospective cohorts in Shanghai, China. Thorax 2024; 79:735-744. [PMID: 38702190 PMCID: PMC11251856 DOI: 10.1136/thorax-2023-220352] [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: 04/19/2023] [Accepted: 02/17/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND The aetiology of lung cancer among individuals who never smoked remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Epigenetic alterations, particularly DNA methylation (DNAm) changes, have emerged as potential drivers. Yet, few prospective epigenome-wide association studies (EWAS), primarily focusing on peripheral blood DNAm with limited representation of never smokers, have been conducted. METHODS We conducted a nested case-control study of 80 never-smoking incident lung cancer cases and 83 never-smoking controls within the Shanghai Women's Health Study and Shanghai Men's Health Study. DNAm was measured in prediagnostic oral rinse samples using Illumina MethylationEPIC array. Initially, we conducted an EWAS to identify differentially methylated positions (DMPs) associated with lung cancer in the discovery sample of 101 subjects. The top 50 DMPs were further evaluated in a replication sample of 62 subjects, and results were pooled using fixed-effect meta-analysis. RESULTS Our study identified three DMPs significantly associated with lung cancer at the epigenome-wide significance level of p<8.22×10-8. These DMPs were identified as cg09198866 (MYH9; TXN2), cg01411366 (SLC9A10) and cg12787323. Furthermore, examination of the top 1000 DMPs indicated significant enrichment in epithelial regulatory regions and their involvement in small GTPase-mediated signal transduction pathways. Additionally, GrimAge acceleration was identified as a risk factor for lung cancer (OR=1.19 per year; 95% CI 1.06 to 1.34). CONCLUSIONS While replication in a larger sample size is necessary, our findings suggest that DNAm patterns in prediagnostic oral rinse samples could provide novel insights into the underlying mechanisms of lung cancer in never smokers.
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Affiliation(s)
- Mohammad L Rahman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Charles E Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Xiao-Ou Shu
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Batel Blechter
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Andres Cardenas
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
| | - Xuting Wang
- Immunity, Inflammation and Diseases Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Wei Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Qiuyin Cai
- Vanderbilt University, Nashville, Tennessee, USA
| | - H Dean Hosgood
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Gong Yang
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Jirong Long
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA
| | | | - Douglas A Bell
- Immunity, Inflammation and Diseases Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Wei Zheng
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
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Deng Y, Tsai CW, Chang WS, Xu Y, Huang M, Bau DT, Gu J. The Significant Associations between Epigenetic Clocks and Bladder Cancer Risks. Cancers (Basel) 2024; 16:2357. [PMID: 39001419 PMCID: PMC11240392 DOI: 10.3390/cancers16132357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 05/29/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024] Open
Abstract
Bladder cancer is an age-related disease, with over three-quarters of cases occurring in individuals aged 65 years and older. Accelerated biological aging has been linked to elevated cancer risks. Epigenetic clocks serve as excellent predictors of biological age, yet it remains unclear whether they are associated with bladder cancer risk. In this large case-control study, we assessed the associations between four well-established epigenetic clocks-HannumAge, HorvathAge, GrimAge, and PhenoAge-and bladder cancer risk. Utilizing single nucleotide polymorphisms (SNPs), which were identified in a genome-wide association study (GWAS), linked to these clocks as instruments, we constructed a weighted genetic risk score (GRS) for each clock. We discovered that higher HannumAge and HorvathAge GRS were significantly associated with increased bladder cancer risk (OR = 1.69 per SD increase, 95% CI, 1.44-1.98, p = 1.56 × 10-10 and OR = 1.09 per SD increase, 95% CI, 1.00-1.19, p = 0.04, respectively). Employing a summary statistics-based Mendelian randomization (MR) method, inverse-variance weighting (IVW), we found consistent risk estimates for bladder cancer with both HannumAge and HorvathAge. Sensitivity analyses using weighted median analysis and MR-Egger regression further supported the validity of the IVW method. However, GrimAge and PhenoAge were not associated with bladder cancer risk. In conclusion, our data provide the first evidence that accelerated biological aging is associated with elevated bladder cancer risk.
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Affiliation(s)
- Yang Deng
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200031, China
| | - Chia-Wen Tsai
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Wen-Shin Chang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Yifan Xu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maosheng Huang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Da-Tian Bau
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Jian Gu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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3
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Li DL, Hodge AM, Southey MC, Giles GG, Milne RL, Dugué PA. Self-rated health, epigenetic ageing, and long-term mortality in older Australians. GeroScience 2024:10.1007/s11357-024-01211-2. [PMID: 38795183 DOI: 10.1007/s11357-024-01211-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/16/2024] [Indexed: 05/27/2024] Open
Abstract
Self-rated health (SRH) is a subjective indicator of overall health based on a single questionnaire item. Previous evidence found that it is a strong predictor of mortality, although the underlying mechanism is poorly understood. Epigenetic age is an objective, emerging biomarker of health, estimated using DNA methylation data at hundreds of sites across the genome. This study aimed to assess the overlap and interaction between SRH and epigenetic ageing in predicting mortality risk. We used DNA methylation data from 1059 participants in the Melbourne Collaborative Cohort Study (mean age: 69 years) to calculate three age-adjusted measures of epigenetic ageing: GrimAge, PhenoAge, and DunedinPACE. SRH was assessed using a five-category questionnaire item ("excellent, very good, good, fair, poor"). Cox models were used to assess the associations of SRH, epigenetic ageing, and their interaction, with all-cause mortality over up to 17 years of follow-up (Ndeaths = 345). The association of SRH with mortality per category increase was HR = 1.29; 95%CI: 1.14-1.46. The association was slightly attenuated after adjusting for all three epigenetic ageing measures (HR = 1.25, 95%CI: 1.10-1.41). A strong gradient was observed in the association of GrimAge (Pinteraction = 0.006) and DunedinPACE (Pinteraction = 0.002) with mortality across worsening SRH strata. For example, the association between DunedinPACE and mortality in participants with "excellent" SRH was HR = 1.02, 95%CI: 0.73-1.43 and for "fair/poor" HR = 1.72, 95%CI: 1.35-2.20. SRH and epigenetic ageing were synergistic risk factors of mortality in our study. These findings suggest that consideration of subjective and objective factors may improve general health assessment, which has implications for the ongoing development of molecular markers of ageing.
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Affiliation(s)
- Danmeng Lily Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.
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Bian L, Ma Z, Fu X, Ji C, Wang T, Yan C, Dai J, Ma H, Hu Z, Shen H, Wang L, Zhu M, Jin G. Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study. eLife 2024; 13:RP91101. [PMID: 38687190 PMCID: PMC11060710 DOI: 10.7554/elife.91101] [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] [Indexed: 05/02/2024] Open
Abstract
Background Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer. Methods Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs). Results Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001). Conclusions Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle. Funding This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
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Affiliation(s)
- Lijun Bian
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Xiangjin Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Chen Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Tianpei Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical SciencesBeijingChina
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
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5
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Malyutina S, Chervova O, Maximov V, Nikitenko T, Ryabikov A, Voevoda M. Blood-Based Epigenetic Age Acceleration and Incident Colorectal Cancer Risk: Findings from a Population-Based Case-Control Study. Int J Mol Sci 2024; 25:4850. [PMID: 38732069 PMCID: PMC11084311 DOI: 10.3390/ijms25094850] [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: 04/03/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
This study investigates the association between epigenetic age acceleration (EAA) derived from DNA methylation and the risk of incident colorectal cancer (CRC). We utilized data from a random population sample of 9,360 individuals (men and women, aged 45-69) from the HAPIEE Study who had been followed up for 16 years. A nested case-control design yielded 35 incident CRC cases and 354 matched controls. Six baseline epigenetic age (EA) measures (Horvath, Hannum, PhenoAge, Skin and Blood (SB), BLUP, and Elastic Net (EN)) were calculated along with their respective EAAs. After adjustment, the odds ratios (ORs) for CRC risk per decile increase in EAA ranged from 1.20 (95% CI: 1.04-1.39) to 1.44 (95% CI: 1.21-1.76) for the Horvath, Hannum, PhenoAge, and BLUP measures. Conversely, the SB and EN EAA measures showed borderline inverse associations with ORs of 0.86-0.87 (95% CI: 0.76-0.99). Tertile analysis reinforced a positive association between CRC risk and four EAA measures (Horvath, Hannum, PhenoAge, and BLUP) and a modest inverse relationship with EN EAA. Our findings from a prospective population-based-case-control study indicate a direct association between incident CRC and four markers of accelerated baseline epigenetic age. In contrast, two markers showed a negative association or no association. These results warrant further exploration in larger cohorts and may have implications for CRC risk assessment and prevention.
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Affiliation(s)
- Sofia Malyutina
- Research Institute of Internal and Preventive Medicine-Branch of Institute of Cytology and Genetics SB RAS, Novosibirsk 630089, Russia; (V.M.); (T.N.); (A.R.); (M.V.)
| | | | - Vladimir Maximov
- Research Institute of Internal and Preventive Medicine-Branch of Institute of Cytology and Genetics SB RAS, Novosibirsk 630089, Russia; (V.M.); (T.N.); (A.R.); (M.V.)
| | - Tatiana Nikitenko
- Research Institute of Internal and Preventive Medicine-Branch of Institute of Cytology and Genetics SB RAS, Novosibirsk 630089, Russia; (V.M.); (T.N.); (A.R.); (M.V.)
| | - Andrew Ryabikov
- Research Institute of Internal and Preventive Medicine-Branch of Institute of Cytology and Genetics SB RAS, Novosibirsk 630089, Russia; (V.M.); (T.N.); (A.R.); (M.V.)
| | - Mikhail Voevoda
- Research Institute of Internal and Preventive Medicine-Branch of Institute of Cytology and Genetics SB RAS, Novosibirsk 630089, Russia; (V.M.); (T.N.); (A.R.); (M.V.)
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6
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Li DL, Hodge AM, Cribb L, Southey MC, Giles GG, Milne RL, Dugué PA. Body Size, Diet Quality, and Epigenetic Aging: Cross-Sectional and Longitudinal Analyses. J Gerontol A Biol Sci Med Sci 2024; 79:glae026. [PMID: 38267386 PMCID: PMC10953795 DOI: 10.1093/gerona/glae026] [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: 09/04/2023] [Indexed: 01/26/2024] Open
Abstract
Epigenetic age is an emerging marker of health that is highly predictive of disease and mortality risk. There is a lack of evidence on whether lifestyle changes are associated with changes in epigenetic aging. We used data from 1 041 participants in the Melbourne Collaborative Cohort Study with blood DNA methylation measures at baseline (1990-1994, mean age: 57.4 years) and follow-up (2003-2007, mean age: 68.8 years). The Alternative Healthy Eating Index-2010 (AHEI-2010), the Mediterranean Dietary Score, and the Dietary Inflammatory Index were used as measures of diet quality, and weight, waist circumference, and waist-to-hip ratio as measures of body size. Five age-adjusted epigenetic aging measures were considered: GrimAge, PhenoAge, PCGrimAge, PCPhenoAge, and DunedinPACE. Multivariable linear regression models including restricted cubic splines were used to assess the cross-sectional and longitudinal associations of body size and diet quality with epigenetic aging. Associations between weight and epigenetic aging cross-sectionally at both time points were positive and appeared greater for DunedinPACE (per SD: β ~0.24) than for GrimAge and PhenoAge (β ~0.10). The longitudinal associations with weight change were markedly nonlinear (U-shaped) with stable weight being associated with the lowest epigenetic aging at follow-up, except for DunedinPACE, for which only weight gain showed a positive association. We found negative, linear associations for AHEI-2010 both cross-sectionally and longitudinally. Other adiposity measures and dietary scores showed similar results. In middle-aged to older adults, declining diet quality and weight gain may increase epigenetic age, while the association for weight loss may require further investigation. Our study sheds light on the potential of weight management and dietary improvement in slowing aging processes.
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Affiliation(s)
- Danmeng Lily Li
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- 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
| | - Lachlan Cribb
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Melissa C Southey
- 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
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, 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
| | - 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
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7
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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. Reply to: Comments on "Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer". Int J Cancer 2023; 153:1545-1546. [PMID: 37387529 DOI: 10.1002/ijc.34644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023]
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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Chen JQ, Salas LA, Wiencke JK, Koestler DC, Molinaro AM, Andrew AS, Seigne JD, Karagas MR, Kelsey KT, Christensen BC. Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes. Cancer Epidemiol Biomarkers Prev 2023; 32:1328-1337. [PMID: 37527159 PMCID: PMC10543967 DOI: 10.1158/1055-9965.epi-23-0331] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/06/2023] [Accepted: 07/28/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Immune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival. METHODS Bladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival. RESULTS Higher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93-0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23-1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04-1.41), neutrophil (HR = 1.04, 95% CI = 1.03-1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03-1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival. CONCLUSIONS We identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes. IMPACT The findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.
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Affiliation(s)
- Ji-Qing Chen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - Lucas A. Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Devin C. Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas
| | - Annette M. Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Angeline S. Andrew
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - John D. Seigne
- Department of Surgery, Section of Urology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - Karl T. Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island
| | - Brock C. Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
- Departments of Molecular and Systems Biology, and Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
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9
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Allegra A, Caserta S, Mirabile G, Gangemi S. Aging and Age-Related Epigenetic Drift in the Pathogenesis of Leukemia and Lymphomas: New Therapeutic Targets. Cells 2023; 12:2392. [PMID: 37830606 PMCID: PMC10572300 DOI: 10.3390/cells12192392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
One of the traits of cancer cells is abnormal DNA methylation patterns. The idea that age-related epigenetic changes may partially explain the increased risk of cancer in the elderly is based on the observation that aging is also accompanied by comparable changes in epigenetic patterns. Lineage bias and decreased stem cell function are signs of hematopoietic stem cell compartment aging. Additionally, aging in the hematopoietic system and the stem cell niche have a role in hematopoietic stem cell phenotypes linked with age, such as leukemia and lymphoma. Understanding these changes will open up promising pathways for therapies against age-related disorders because epigenetic mechanisms are reversible. Additionally, the development of high-throughput epigenome mapping technologies will make it possible to identify the "epigenomic identity card" of every hematological disease as well as every patient, opening up the possibility of finding novel molecular biomarkers that can be used for diagnosis, prediction, and prognosis.
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Affiliation(s)
- Alessandro Allegra
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Santino Caserta
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Giuseppe Mirabile
- Division of Hematology, Department of Human Pathology in Adulthood and Childhood “Gaetano Barresi”, University of Messina, Via Consolare Valeria, 98125 Messina, Italy; (S.C.); (G.M.)
| | - Sebastiano Gangemi
- Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98125 Messina, Italy;
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10
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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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11
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Blechter B, Cardenas A, Shi J, Wong JYY, Hu W, Rahman ML, Breeze C, Downward GS, Portengen L, Zhang Y, Ning B, Ji BT, Cawthon R, Li J, Yang K, Bozack A, Dean Hosgood H, Silverman DT, Huang Y, Rothman N, Vermeulen R, Lan Q. Household air pollution and epigenetic aging in Xuanwei, China. ENVIRONMENT INTERNATIONAL 2023; 178:108041. [PMID: 37354880 DOI: 10.1016/j.envint.2023.108041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/19/2023] [Accepted: 06/13/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Household air pollution (HAP) from indoor combustion of solid fuel is a global health burden linked to lung cancer. In Xuanwei, China, lung cancer rate for nonsmoking women is among the highest in the world and largely attributed to high levels of polycyclic aromatic hydrocarbons (PAHs) that are produced from combustion of smoky (bituminous) coal used for cooking and heating. Epigenetic age acceleration (EAA), a DNA methylation-based biomarker of aging, has been shown to be highly correlated with biological processes underlying the susceptibility of age-related diseases. We aim to assess the association between HAP exposure and EAA. METHODS We analyzed data from 106 never-smoking women from Xuanwei, China. Information on fuel type was collected using a questionnaire, and validated exposure models were used to predict levels of 43 HAP constituents. Exposure clusters were identified using hierarchical clustering. EAA was derived for five epigenetic clocks defined as the residuals resulting from regressing each clock on chronological age. We used generalized estimating equations to test associations between exposure clusters derived from predicted levels of HAP exposure, ambient 5-methylchrysene (5-MC), a PAH previously found to be associated with risk of lung cancer, and EAA, while accounting for repeated-measurements and confounders. RESULTS We observed an increase in GrimAge EAA for clusters with 31 and 33 PAHs reflecting current (β = 0.77 y per standard deviation (SD) increase, 95 % CI:0.36,1.19) and childhood (β = 0.92 y per SD, 95 % CI:0.40,1.45) exposure, respectively. 5-MC (ng/m3-year) was found to be associated with GrimAge EAA for current (β = 0.15 y, 95 % CI:0.05,0.25) and childhood (β = 0.30 y, 95 % CI:0.13,0.47) exposure. CONCLUSIONS Our findings suggest that exposure to PAHs from indoor smoky coal combustion, particularly 5-MC, is associated with GrimAge EAA, a biomarker of mortality.
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Affiliation(s)
- Batel Blechter
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
| | - Andres Cardenas
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Junming Shi
- Department of Biostatistics, UC Berkeley School of Public Health, Berkeley, CA, USA
| | - Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Wei Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Mohammad L Rahman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Charles Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - George S Downward
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Lützen Portengen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Yongliang Zhang
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Bofu Ning
- Xuanwei Center of Diseases Control, Xuanwei, Yunnan, China
| | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Richard Cawthon
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jihua Li
- Quijing Center for Diseases Control and Prevention, Quijing, Yunnan, China
| | - Kaiyun Yang
- Department of Cardiothoracic Surgery, Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, Yunnan, China
| | - Anne Bozack
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - H Dean Hosgood
- Division of Epidemiology, Albert Einstein College of Medicine, New York, NY, USA
| | - Debra T Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Yunchao Huang
- Department of Cardiothoracic Surgery, Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, Yunnan, China
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Roel Vermeulen
- Department of Biostatistics, UC Berkeley School of Public Health, Berkeley, CA, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
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12
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Joyce BT, Chen X, Gao T, Zheng Y, Nannini DR, Liu L, Henkle BE, Kalhan R, Washko G, Kunisaki KM, Thyagarajan B, Vaughan DE, Gross M, Jacobs DR, Lloyd-Jones D, Hou L. Associations between GrimAge acceleration and pulmonary function in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Epigenomics 2023; 15:693-703. [PMID: 37694401 PMCID: PMC10503465 DOI: 10.2217/epi-2023-0164] [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: 05/09/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023] Open
Abstract
Background: The objective of this research was to determine whether pulmonary function is associated with epigenetic aging (GrimAge) and whether GrimAge predicts emphysema. Methods: This prospective study examined 1042 participants enrolled as part of a community-based longitudinal cohort. The cross-sectional associations between pulmonary function and GrimAge, measured at study year (Y) 20 (participant ages 40-45 years), and prospective associations with emphysema at Y25 were examined. Results: At Y20, forced expiratory volume in 1 s (FEV1) and FEV1/forced vital capacity (FVC) were negatively associated with GrimAge; for Y0-Y10 cumulative measures, only the FEV1/FVC ratio was associated with GrimAge at Y15 and Y20. Emphysema at Y25 was associated with GrimAge at Y15 and Y20. Conclusion: Pulmonary function was associated with GrimAge during early and mid-life; GrimAge partially mediated the association between pulmonary function and emphysema.
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Affiliation(s)
- Brian T Joyce
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Xuefen Chen
- Department of Epidemiology of Health Statistics, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201318, China
| | - Tao Gao
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Drew R Nannini
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lei Liu
- Division of Biostatistics, Washington University, St. Louis, MO 63110, USA
| | - Benjamin E Henkle
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
- University of Minnesota, Minneapolis, MN 55455, USA
| | - Ravi Kalhan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - George Washko
- Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Ken M Kunisaki
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
- University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Douglas E Vaughan
- Potocsnak Longevity Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Myron Gross
- University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Potocsnak Longevity Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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13
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Hao J, Liu T, Xiu Y, Yuan H, Xu D. High DNA methylation age deceleration defines an aggressive phenotype with immunoexclusion environments in endometrial carcinoma. Front Immunol 2023; 14:1208223. [PMID: 37388735 PMCID: PMC10303802 DOI: 10.3389/fimmu.2023.1208223] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Like telomere shortening, global DNA hypomethylation occurs progressively with cellular divisions or in vivo aging and functions as a mitotic clock to restrain malignant transformation/progression. Several DNA-methylation (DNAm) age clocks have been established to precisely predict chronological age using normal tissues, but show DNAm age drift in tumors, which suggests disruption of this mitotic clock during carcinogenesis. Little is known about DNAm age alterations and biological/clinical implications in endometrial cancer (EC). Here we address these issues by analyzing TCGA and GSE67116 cohorts of ECs. Horvath clock analysis of these tumors unexpectedly revealed that almost 90% of them exhibited DNAm age deceleration (DNAmad) compared to patient chronological age. Combined with an additional clock named Phenoage, we identified a subset of tumors (82/429) with high DNAmad (hDNAmad+) as assessed by both clocks. Clinically, hDNAmad+ tumors were associated with advanced diseases and shorter patient survival, compared to hDNAmad- ones. Genetically, hDNAmad+ tumors were characterized by higher copy number alterations (CNAs) whereas lower tumor mutation burden. Functionally, hDNAmad+ tumors were enriched with cell cycle and DNA mismatch repair pathways. Increased PIK3CA alterations and downregulation of SCGB2A1, the inhibitor of PI3K kinase, in hDNAmad+ tumors, might promote tumor growth/proliferation and stemness. In addition, the inactivation of aging drivers/tumor suppressors (TP53, RB1, and CDKN2A) while enhanced telomere maintenance occurred more frequently in hDNAmad+ tumors, which supports sustained tumor growth. Prominently, hDNAmad+ tumors were featured with immunoexclusion microenvironments, accompanied by significantly higher levels of VTCN1 expression while lower PD-L1 and CTLA4 expression, which indicates their poor response to immune checkpoint inhibitor (ICI)-based immunotherapy. We further showed significantly higher levels of DNMT3A and 3B expression in hDNAmad+ than in hDNAmad- tumors. Thus, the tumor suppressive function of aging-like DNA hypomethylation is severely impaired in hDNAmad+ tumors, likely due to enhanced expression of DNMT3A/3B and dysregulated aging regulators. Our findings not only enrich biological knowledge of EC pathogenesis but also help improve EC risk stratification and precision ICI immunotherapy.
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Affiliation(s)
- Jing Hao
- Department of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, China
- Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application, Jinan, China
| | - Tiantian Liu
- Department of Pathology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuchen Xiu
- Department of Pathology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huiyang Yuan
- Department of Urology, Qilu Hospital of Shandong University, Jinan, China
| | - Dawei Xu
- Department of Medicine, Bioclinicum and Center for Molecular Medicine (CMM), Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden
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14
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Gaylord A, Cohen A, Kupsco A. Biomarkers of aging through the life course: A Recent Literature Update. CURRENT OPINION IN EPIDEMIOLOGY AND PUBLIC HEALTH 2023; 2:7-17. [PMID: 38130910 PMCID: PMC10732539 DOI: 10.1097/pxh.0000000000000018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Purpose of review The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course. Recent findings The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches. Summary Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.
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Affiliation(s)
- Abigail Gaylord
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Alan Cohen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec, Canada
- Research Center on Aging and Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
- Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Allison Kupsco
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
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15
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Nannini DR, Cortese R, Egwom P, Palaniyandi S, Hildebrandt GC. Time to relapse in chronic lymphocytic leukemia and DNA-methylation-based biological age. Clin Epigenetics 2023; 15:81. [PMID: 37165442 PMCID: PMC10170738 DOI: 10.1186/s13148-023-01496-8] [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: 02/14/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023] Open
Abstract
Chronic lymphocytic leukemia (CLL) is a mature B cell neoplasm with a predilection for older individuals. While previous studies have identified epigenetic signatures associated with CLL, whether age-related DNA methylation changes modulate CLL relapse remains elusive. In this study, we examined the association between epigenetic age acceleration and time to CLL relapse in a publicly available dataset. DNA methylation profiling of 35 CLL patients prior to initiating chemoimmunotherapy was performed using the Infinium HumanMethylation450 BeadChip. Four epigenetic age acceleration metrics (intrinsic epigenetic age acceleration [IEAA], extrinsic epigenetic age acceleration [EEAA], PhenoAge acceleration [PhenoAA], and GrimAge acceleration [GrimAA]) were estimated from blood DNA methylation levels. Linear, quantile, and logistic regression and receiver operating characteristic curve analyses were conducted to assess the association between each epigenetic age metric and time to CLL relapse. EEAA (p = 0.011) and PhenoAA (p = 0.046) were negatively and GrimAA (p = 0.040) was positively associated with time to CLL relapse. Simultaneous assessment of EEAA and GrimAA in male patients distinguished patients who relapsed early from patients who relapsed later (p = 0.039). No associations were observed with IEAA. These findings suggest epigenetic age acceleration prior to chemoimmunotherapy initiation is associated with time to CLL relapse. Our results provide novel insight into the association between age-related DNA methylation changes and CLL relapse and may serve has biomarkers for treatment relapse, and potentially, treatment selection.
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Affiliation(s)
- Drew R Nannini
- Department of Internal Medicine, School of Medicine, University of Missouri at Columbia, MA408 Medical Science Building, Columbia, MO, 65212, USA.
| | - Rene Cortese
- Department of Child Health and Department of Obstetrics, Gynecology, and Women's Health, School of Medicine, University of Missouri at Columbia, Columbia, MO, 65212, USA
- Ellis Fischel Cancer Center, University of Missouri at Columbia, Columbia, MO, 65212, USA
| | - Peter Egwom
- Department of Internal Medicine, School of Medicine, University of Missouri at Columbia, MA408 Medical Science Building, Columbia, MO, 65212, USA
| | - Senthilnathan Palaniyandi
- Ellis Fischel Cancer Center, University of Missouri at Columbia, Columbia, MO, 65212, USA
- Division of Hematology and Medical Oncology, School of Medicine, University of Missouri at Columbia, Columbia, MO, 65212, USA
| | - Gerhard C Hildebrandt
- Ellis Fischel Cancer Center, University of Missouri at Columbia, Columbia, MO, 65212, USA
- Division of Hematology and Medical Oncology, School of Medicine, University of Missouri at Columbia, Columbia, MO, 65212, USA
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16
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Mak JKL, McMurran CE, Kuja-Halkola R, Hall P, Czene K, Jylhävä J, Hägg S. Clinical biomarker-based biological aging and risk of cancer in the UK Biobank. Br J Cancer 2023:10.1038/s41416-023-02288-w. [PMID: 37120669 DOI: 10.1038/s41416-023-02288-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND Despite a clear link between aging and cancer, there has been inconclusive evidence on how biological age (BA) may be associated with cancer incidence. METHODS We studied 308,156 UK Biobank participants with no history of cancer at enrolment. Using 18 age-associated clinical biomarkers, we computed three BA measures (Klemera-Doubal method [KDM], PhenoAge, homeostatic dysregulation [HD]) and assessed their associations with incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, and melanoma) using Cox proportional-hazards models. RESULTS A total of 35,426 incident cancers were documented during a median follow-up of 10.9 years. Adjusting for common cancer risk factors, 1-standard deviation (SD) increment in the age-adjusted KDM (hazard ratio = 1.04, 95% confidence interval = 1.03-1.05), age-adjusted PhenoAge (1.09, 1.07-1.10), and HD (1.02, 1.01-1.03) was significantly associated with a higher risk of any cancer. All BA measures were also associated with increased risks of lung and colorectal cancers, but only PhenoAge was associated with breast cancer risk. Furthermore, we observed an inverse association between BA measures and prostate cancer, although it was attenuated after removing glycated hemoglobin and serum glucose from the BA algorithms. CONCLUSIONS Advanced BA quantified by clinical biomarkers is associated with increased risks of any cancer, lung cancer, and colorectal cancer.
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Affiliation(s)
- Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Christopher E McMurran
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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17
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Mavromatis LA, Rosoff DB, Bell AS, Jung J, Wagner J, Lohoff FW. Multi-omic underpinnings of epigenetic aging and human longevity. Nat Commun 2023; 14:2236. [PMID: 37076473 PMCID: PMC10115892 DOI: 10.1038/s41467-023-37729-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/28/2023] [Indexed: 04/21/2023] Open
Abstract
Biological aging is accompanied by increasing morbidity, mortality, and healthcare costs; however, its molecular mechanisms are poorly understood. Here, we use multi-omic methods to integrate genomic, transcriptomic, and metabolomic data and identify biological associations with four measures of epigenetic age acceleration and a human longevity phenotype comprising healthspan, lifespan, and exceptional longevity (multivariate longevity). Using transcriptomic imputation, fine-mapping, and conditional analysis, we identify 22 high confidence associations with epigenetic age acceleration and seven with multivariate longevity. FLOT1, KPNA4, and TMX2 are novel, high confidence genes associated with epigenetic age acceleration. In parallel, cis-instrument Mendelian randomization of the druggable genome associates TPMT and NHLRC1 with epigenetic aging, supporting transcriptomic imputation findings. Metabolomics Mendelian randomization identifies a negative effect of non-high-density lipoprotein cholesterol and associated lipoproteins on multivariate longevity, but not epigenetic age acceleration. Finally, cell-type enrichment analysis implicates immune cells and precursors in epigenetic age acceleration and, more modestly, multivariate longevity. Follow-up Mendelian randomization of immune cell traits suggests lymphocyte subpopulations and lymphocytic surface molecules affect multivariate longevity and epigenetic age acceleration. Our results highlight druggable targets and biological pathways involved in aging and facilitate multi-omic comparisons of epigenetic clocks and human longevity.
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Affiliation(s)
- Lucas A Mavromatis
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Daniel B Rosoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program, University of Oxford, Oxford, UK
| | - Andrew S Bell
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Jeesun Jung
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Josephin Wagner
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Falk W Lohoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
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18
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Should We Expect an Increase in the Number of Cancer Cases in People with Long COVID? Microorganisms 2023; 11:microorganisms11030713. [PMID: 36985286 PMCID: PMC10051562 DOI: 10.3390/microorganisms11030713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
The relationship between viral infections and the risk of developing cancer is well known. Multiple mechanisms participate in and determine this process. The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in the deaths of millions of people worldwide. Although the effects of COVID-19 are limited for most people, a large number of people continue to show symptoms for a long period of time (long COVID). Several studies have suggested that cancer could also be a potential long-term complication of the virus; however, the causes of this risk are not yet well understood. In this review, we investigated arguments that could support or reject this possibility.
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19
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Michaud DS, Chung M, Zhao N, Koestler DC, Lu J, Platz EA, Kelsey KT. Epigenetic age and lung cancer risk in the CLUE II prospective cohort study. Aging (Albany NY) 2023; 15:617-629. [PMID: 36750177 PMCID: PMC9970317 DOI: 10.18632/aging.204501] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023]
Abstract
BACKGROUND Epigenetic age, a robust marker of biological aging, has been associated with obesity, low-grade inflammation and metabolic diseases. However, few studies have examined associations between different epigenetic age measures and risk of lung cancer, despite great interest in finding biomarkers to assist in risk stratification for lung cancer screening. METHODS A nested case-control study of lung cancer from the CLUE II cohort study was conducted using incidence density sampling with 1:1 matching of controls to lung cancer cases (n = 208 matched pairs). Prediagnostic blood samples were collected in 1989 (CLUE II study baseline) and stored at -70°C. DNA was extracted from buffy coat and DNA methylation levels were measured using Illumina MethylationEPIC BeadChip Arrays. Three epigenetic age acceleration (i.e., biological age is greater than chronological age) measurements (Horvath, Hannum and PhenoAge) were examined in relation to lung cancer risk using conditional logistic regression. RESULTS We did not observe associations between the three epigenetic age acceleration measurements and risk of lung cancer overall; however, inverse associations for the two Hannum age acceleration measures (intrinsic and extrinsic) were observed in men and among younger participants, but not in women or older participants. We did not observe effect modification by time from blood draw to diagnosis. CONCLUSION Findings from this study do not support a positive association between three different biological age acceleration measures and risk of lung cancer. Additional studies are needed to address whether epigenetic age is associated with lung cancer in never smokers.
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Affiliation(s)
- Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Mei Chung
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Tufts University, Boston, MA 02111, USA,Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition, Tufts University, Boston, MA 02111, USA
| | - Naisi Zhao
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Devin C. Koestler
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA,University of Kansas Cancer Center, Kansas City, KS 66160, USA
| | - Jiayun Lu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA,The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21231, USA
| | - Karl T. Kelsey
- Department of Epidemiology, Brown University, Providence, RI 02903, USA,Department of Pathology and Laboratory Medicine, Brown University, Providence, RI 02903, USA
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20
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Ma Z, Zhu C, Wang H, Ji M, Huang Y, Wei X, Zhang J, Wang Y, Yin R, Dai J, Xu L, Ma H, Hu Z, Jin G, Zhu M, Shen H. Association between biological aging and lung cancer risk: Cohort study and Mendelian randomization analysis. iScience 2023; 26:106018. [PMID: 36852276 PMCID: PMC9958377 DOI: 10.1016/j.isci.2023.106018] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/14/2022] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Chronological age only represents the passage of time, whereas biological age reflects the physiology states and the susceptibility to morbidity and mortality. The association between biological age and lung cancer risk remains controversial. Hence, we conducted a prospective analysis in the UK Biobank study and two-sample Mendelian randomization analysis to investigate this association. Biological aging was evaluated by PhenoAgeAccel, derived from routine clinical biomarkers. Independent of chronological age, PhenoAgeAccel was positively associated with the risk of overall and histological subtypes of lung cancer. There was a joint effect of PhenoAgeAccel and genetics in lung cancer incidence. In Mendelian randomization analysis, the genetically predicted PhenoAgeAccel was associated with the increased risk of overall lung cancer, small cell, and squamous cell carcinoma. Our findings suggest PhenoAgeAccel is an independent risk factor for lung cancer, which could be incorporated with polygenic risk score to identify high-risk individuals for lung cancer.
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Affiliation(s)
- Zhimin Ma
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Hui Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Mengmeng Ji
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yanqian Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xiaoxia Wei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Rong Yin
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100000, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Corresponding author
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Corresponding author
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China,Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100000, China,Corresponding author
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21
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Lu AT, Binder AM, Zhang J, Yan Q, Reiner AP, Cox SR, Corley J, Harris SE, Kuo PL, Moore AZ, Bandinelli S, Stewart JD, Wang C, Hamlat EJ, Epel ES, Schwartz JD, Whitsel EA, Correa A, Ferrucci L, Marioni RE, Horvath S. DNA methylation GrimAge version 2. Aging (Albany NY) 2022; 14:9484-9549. [PMID: 36516495 PMCID: PMC9792204 DOI: 10.18632/aging.204434] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022]
Abstract
We previously described a DNA methylation (DNAm) based biomarker of human mortality risk DNAm GrimAge. Here we describe version 2 of GrimAge (trained on individuals aged between 40 and 92) which leverages two new DNAm based estimators of (log transformed) plasma proteins: high sensitivity C-reactive protein (logCRP) and hemoglobin A1C (logA1C). We evaluate GrimAge2 in 13,399 blood samples across nine study cohorts. After adjustment for age and sex, GrimAge2 outperforms GrimAge in predicting mortality across multiple racial/ethnic groups (meta P=3.6x10-167 versus P=2.6x10-144) and in terms of associations with age related conditions such as coronary heart disease, lung function measurement FEV1 (correlation= -0.31, P=1.1x10-136), computed tomography based measurements of fatty liver disease. We present evidence that GrimAge version 2 also applies to younger individuals and to saliva samples where it tracks markers of metabolic syndrome. DNAm logCRP is positively correlated with morbidity count (P=1.3x10-54). DNAm logA1C is highly associated with type 2 diabetes (P=5.8x10-155). DNAm PAI-1 outperforms the other age-adjusted DNAm biomarkers including GrimAge2 in correlating with triglyceride (cor=0.34, P=9.6x10-267) and visceral fat (cor=0.41, P=4.7x10-41). Overall, we demonstrate that GrimAge version 2 is an attractive epigenetic biomarker of human mortality and morbidity risk.
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Affiliation(s)
- Ake T. Lu
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA 92121, USA
| | - Alexandra M. Binder
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA 90095, USA
| | - Joshua Zhang
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Qi Yan
- San Diego Institute of Science, Altos Labs, San Diego, CA 92121, USA
| | - Alex P. Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Simon R. Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, Scotland, UK
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, Scotland, UK
| | - Sarah E. Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, Scotland, UK
| | - Pei-Lun Kuo
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Ann Z. Moore
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Stefania Bandinelli
- Geriatric Unit, Local Health Unit Tuscany Centre, Firenze, Tuscany 40125, Italy
| | - James D. Stewart
- Dept. of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27516-8050, USA
| | - Cuicui Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Elissa J. Hamlat
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143-0848, USA
| | - Elissa S. Epel
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143-0848, USA
| | - Joel D. Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Eric A. Whitsel
- Dept. of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27516-8050, USA
- Dept. of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Adolfo Correa
- Departments of Medicine and Population Health Science, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, Scotland, UK
| | - Steve Horvath
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA 92121, USA
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
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22
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Yu C, Hodge AM, Wong EM, Joo JE, Makalic E, Schmidt DF, Buchanan DD, Severi G, Hopper JL, English DR, Giles GG, Milne RL, Southey MC, Dugué PA. Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation? Epigenetics 2022; 17:1838-1847. [PMID: 35726372 PMCID: PMC9621069 DOI: 10.1080/15592294.2022.2088038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 05/19/2022] [Accepted: 05/31/2022] [Indexed: 12/15/2022] Open
Abstract
Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene-environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10-14), ~0.6% (P = 2 × 10-7), and ~8.7% (P = 7 × 10-87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: -0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: -0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Jihoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine Universités Paris-Saclay, Uvsq, Villejuif, France
- Department of Statistics, Computer Science and Applications “G. Parenti”, University of Florence, Firenze, Italy
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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23
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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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24
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Li X, Schöttker B, Holleczek B, Brenner H. Associations of DNA methylation algorithms of aging and cancer risk: Results from a prospective cohort study. EBioMedicine 2022; 81:104083. [PMID: 35636319 PMCID: PMC9157462 DOI: 10.1016/j.ebiom.2022.104083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 11/18/2022] Open
Abstract
Background Previous studies have shown that three DNA methylation (DNAm) based algorithms of aging, DNAm PhenoAge acceleration (AgeAccelPheno), DNAm GrimAge acceleration (AgeAccelGrim), and mortality risk score (MRscore), to be strong predictors of mortality and aging related outcomes. We aimed to investigate if and to what extent these algorithms predict cancer. Methods In four subsets (n = 727, 1003, 910, and 412) of a population-based cohort from Germany, DNA methylation in whole blood was measured using the Infinium Methylation EPIC BeadChip kit or the Infinium HumanMethylation450K BeadChip Assay (Illumina.Inc, San Diego, CA, USA). AgeAccelPheno, AgeAccelGrim, and a revised MRscore based on 8 CpGs only (MRscore-8CpGs), were calculated. Hazard ratios (HRs) were calculated to assess associations of the three DNAm algorithms with total cancer risk and risk of invasive breast, lung, prostate, and colorectal cancer incidence. Findings During 17 years of follow-up, a total of 697 malignant tumors (thereof breast cancer = 75, lung cancer = 146, prostate cancer = 114, colorectal cancer = 155) were observed. All three algorithms showed strong positive associations with lung cancer risk in a dose response manner, with adjusted HRs per SD increase in AgeAccelPheno, AgeAccelGrim, and MRscore-8CpGs, of 1·37 (1·03-1·82), 1·74 (1·11-2·73), and 2·06 (1·39-3·06), respectively. By contrast, strong inverse associations were seen with breast cancer risk [adjusted HRs 0·65 (0·49-0·86), 0·45 (0·25-0·80), and 0·42 (0·25-0·70), respectively]. Weak positive associations of MRscore-8CpGs were seen with total cancer risk. Interpretation The DNAm algorithms, particularly the MRscore-8CpGs, have potential to contribute to site-specific cancer risk prediction. Funding The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany). The work of Xiangwei Li was supported by a grant from Fondazione Cariplo (Bando Ricerca Malattie invecchiamento, #2017-0653).
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Affiliation(s)
- Xiangwei Li
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Network Aging Research, Heidelberg University, Bergheimer Straße 20, 69115 Heidelberg, Germany
| | | | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Network Aging Research, Heidelberg University, Bergheimer Straße 20, 69115 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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25
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Musa J, Kim K, Zheng Y, Qu Y, Joyce BT, Wang J, Nannini DR, Gursel DB, Silas O, Abdulkareem FB, Imade G, Akanmu AS, Wei JJ, Kocherginsky M, Kim KYA, Wehbe F, Achenbach CJ, Anorlu R, Simon MA, Sagay A, Ogunsola FT, Murphy RL, Hou L. Accelerated Epigenetic Age Among Women with Invasive Cervical Cancer and HIV-Infection in Nigeria. Front Public Health 2022; 10:834800. [PMID: 35570901 PMCID: PMC9099239 DOI: 10.3389/fpubh.2022.834800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Invasive cervical cancer (ICC) is a serious public health burden in Nigeria, where human immunodeficiency virus (HIV) remains highly prevalent. Previous research suggested that epigenetic age acceleration (EAA) could play a role in detection of HIV-associated ICC. However, little research has been conducted on this topic in Africa where the population is most severely affected by HIV-associated ICC. Here, we investigated the association between ICC and EAA using cervical tissues of ICC-diagnosed Nigerian women living with HIV. Methods We included 116 cervical tissue samples from three groups of Nigerian women in this study: (1) HIV+/ICC+ (n = 39); (2) HIV+/ICC- (n = 53); and (3) HIV-/ICC + (n = 24). We utilized four DNA methylation-based EAA estimators; IEAA, EEAA, GrimAA, and PhenoAA. We compared EAA measurements across the 3 HIV/ICC groups using multiple linear regression models. We also compared EAA between 26 tumor tissues and their surrounding normal tissues using paired t-tests. We additionally performed a receiver operating characteristics (ROC) curve analysis to illustrate the area under the curve (AUC) of EAA in ICC. Results We found the most striking associations between HIV/ICC status and PhenoAge acceleration (PhenoAA). Among HIV-positive women, PhenoAA was on average 13.4 years higher in women with ICC compared to cancer-free women (P = 0.005). PhenoAA was 20.7 and 7.1 years higher in tumor tissues compared to surrounding normal tissues among HIV-positive women (P = 0.009) and HIV-negative women (P = 0.284), respectively. We did not find substantial differences in PhenoAA between HIV-positive and HIV-negative women with ICC. Conclusion PhenoAA is associated with ICC in HIV-infected women in our study. Our findings suggest that PhenoAA may serve as a potential biomarker for further risk stratification of HIV-associated ICC in Nigeria and similar resource-constrained settings.
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Affiliation(s)
- Jonah Musa
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Jos, Nigeria
| | - Kyeezu Kim
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Yinan Zheng
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Yishu Qu
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Brian T. Joyce
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Jun Wang
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Drew R. Nannini
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Demirkan B. Gursel
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | | | | | - Godwin Imade
- Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Jos, Nigeria
| | - Alani S. Akanmu
- Department of Hematology and Blood Transfusion, Lagos University Teaching Hospital and College of Medicine, University of Lagos, Lagos, Nigeria
| | - Jian-Jun Wei
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Masha Kocherginsky
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Kwang-Youn A. Kim
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Firas Wehbe
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Chad J. Achenbach
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Rose Anorlu
- Department of Obstetrics and Gynecology, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Melissa A. Simon
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Atiene Sagay
- Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Jos, Nigeria
| | - Folasade T. Ogunsola
- Department of Medical Microbiology, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Robert L. Murphy
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Lifang Hou
- Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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26
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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: 25] [Impact Index Per Article: 12.5] [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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27
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Morales Berstein F, McCartney DL, Lu AT, Tsilidis KK, Bouras E, Haycock P, Burrows K, Phipps AI, Buchanan DD, Cheng I, Martin RM, Davey Smith G, Relton CL, Horvath S, Marioni RE, Richardson TG, Richmond RC. Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study. eLife 2022; 11:e75374. [PMID: 35346416 PMCID: PMC9049976 DOI: 10.7554/elife.75374] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Epigenetic clocks have been associated with cancer risk in several observational studies. Nevertheless, it is unclear whether they play a causal role in cancer risk or if they act as a non-causal biomarker. Methods We conducted a two-sample Mendelian randomization (MR) study to examine the genetically predicted effects of epigenetic age acceleration as measured by HannumAge (nine single-nucleotide polymorphisms (SNPs)), Horvath Intrinsic Age (24 SNPs), PhenoAge (11 SNPs), and GrimAge (4 SNPs) on multiple cancers (i.e. breast, prostate, colorectal, ovarian and lung cancer). We obtained genome-wide association data for biological ageing from a meta-analysis (N = 34,710), and for cancer from the UK Biobank (N cases = 2671-13,879; N controls = 173,493-372,016), FinnGen (N cases = 719-8401; N controls = 74,685-174,006) and several international cancer genetic consortia (N cases = 11,348-122,977; N controls = 15,861-105,974). Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR. Individual study estimates were pooled using fixed effect meta-analysis. Sensitivity analyses included MR-Egger, weighted median, weighted mode and Causal Analysis using Summary Effect Estimates (CAUSE) methods, which are robust to some of the assumptions of the IVW approach. Results Meta-analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR = 1.12 per year increase in GrimAge acceleration, 95% CI 1.04-1.20, p = 0.002). The direction of the genetically predicted effects was consistent across main and sensitivity MR analyses. Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR = 1.15, 95% CI 1.09-1.21, p = 0.006), than rectal cancer (IVW OR = 1.05, 95% CI 0.97-1.13, p = 0.24). Results were less consistent for associations between other epigenetic clocks and cancers. Conclusions GrimAge acceleration may increase the risk of colorectal cancer. Findings for other clocks and cancers were inconsistent. Further work is required to investigate the potential mechanisms underlying the results. Funding FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z which is part of grant 218495/Z/19/Z). KKT was supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme) and by the Hellenic Republic's Operational Programme 'Competitiveness, Entrepreneurship & Innovation' (OΠΣ 5047228). PH was supported by Cancer Research UK (C18281/A29019). RMM was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). RMM is a National Institute for Health Research Senior Investigator (NIHR202411). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GDS and CLR were supported by the Medical Research Council (MC_UU_00011/1 and MC_UU_00011/5, respectively) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). REM was supported by an Alzheimer's Society project grant (AS-PG-19b-010) and NIH grant (U01 AG-18-018, PI: Steve Horvath). RCR is a de Pass Vice Chancellor's Research Fellow at the University of Bristol.
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Affiliation(s)
- Fernanda Morales Berstein
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonLondonUnited Kingdom
- Department of Hygiene and Epidemiology, School of Medicine, University of IoanninaIoanninaGreece
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, School of Medicine, University of IoanninaIoanninaGreece
| | - Philip Haycock
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Epidemiology, School of Public Health, University of WashingtonSeattleUnited States
| | - Daniel D Buchanan
- Department of Clinical Pathology, Melbourne Medical School, University of MelbourneParkvilleAustralia
| | - Iona Cheng
- Cancer Prevention Institute of CaliforniaFremontUnited States
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
- National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of BristolBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Biostatistics, Fielding School of Public Health, University of California, Los AngelesLos AngelesUnited States
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghEdinburghUnited Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
- Novo Nordisk Research CentreOxfordUnited Kingdom
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical SchoolBristolUnited Kingdom
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Shen W, He J, Hou T, Si J, Chen S. Common Pathogenetic Mechanisms Underlying Aging and Tumor and Means of Interventions. Aging Dis 2022; 13:1063-1091. [PMID: 35855334 PMCID: PMC9286910 DOI: 10.14336/ad.2021.1208] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/07/2021] [Indexed: 11/22/2022] Open
Abstract
Recently, there has been an increase in the incidence of malignant tumors among the older population. Moreover, there is an association between aging and cancer. During the process of senescence, the human body suffers from a series of imbalances, which have been shown to further accelerate aging, trigger tumorigenesis, and facilitate cancer progression. Therefore, exploring the junctions of aging and cancer and searching for novel methods to restore the junctions is of great importance to intervene against aging-related cancers. In this review, we have identified the underlying pathogenetic mechanisms of aging-related cancers by comparing alterations in the human body caused by aging and the factors that trigger cancers. We found that the common mechanisms of aging and cancer include cellular senescence, alterations in proteostasis, microbiota disorders (decreased probiotics and increased pernicious bacteria), persistent chronic inflammation, extensive immunosenescence, inordinate energy metabolism, altered material metabolism, endocrine disorders, altered genetic expression, and epigenetic modification. Furthermore, we have proposed that aging and cancer have common means of intervention, including novel uses of common medicine (metformin, resveratrol, and rapamycin), dietary restriction, and artificial microbiota intervention or selectively replenishing scarce metabolites. In addition, we have summarized the research progress of each intervention and revealed their bidirectional effects on cancer progression to compare their reliability and feasibility. Therefore, the study findings provide vital information for advanced research studies on age-related cancers. However, there is a need for further optimization of the described methods and more suitable methods for complicated clinical practices. In conclusion, targeting aging may have potential therapeutic effects on aging-related cancers.
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Affiliation(s)
- Weiyi Shen
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
- Institute of Gastroenterology, Zhejiang University, Hangzhou 310016, Zhejiang, China.
- Prevention and Treatment Research Center for Senescent Disease, Zhejiang University School of Medicine, Zhejiang, China
| | - Jiamin He
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
- Institute of Gastroenterology, Zhejiang University, Hangzhou 310016, Zhejiang, China.
- Prevention and Treatment Research Center for Senescent Disease, Zhejiang University School of Medicine, Zhejiang, China
| | - Tongyao Hou
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
- Institute of Gastroenterology, Zhejiang University, Hangzhou 310016, Zhejiang, China.
- Prevention and Treatment Research Center for Senescent Disease, Zhejiang University School of Medicine, Zhejiang, China
- Correspondence should be addressed to: Dr. Shujie Chen (), Dr. Jianmin Si () and Dr. Tongyao Hou (), Department of Gastroenterology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, Zhejiang, China
| | - Jianmin Si
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
- Institute of Gastroenterology, Zhejiang University, Hangzhou 310016, Zhejiang, China.
- Prevention and Treatment Research Center for Senescent Disease, Zhejiang University School of Medicine, Zhejiang, China
- Correspondence should be addressed to: Dr. Shujie Chen (), Dr. Jianmin Si () and Dr. Tongyao Hou (), Department of Gastroenterology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, Zhejiang, China
| | - Shujie Chen
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
- Institute of Gastroenterology, Zhejiang University, Hangzhou 310016, Zhejiang, China.
- Prevention and Treatment Research Center for Senescent Disease, Zhejiang University School of Medicine, Zhejiang, China
- Correspondence should be addressed to: Dr. Shujie Chen (), Dr. Jianmin Si () and Dr. Tongyao Hou (), Department of Gastroenterology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, Zhejiang, China
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29
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Yu C, Hodge AM, Wong EM, Joo JE, Makalic E, Schmidt D, Buchanan DD, Hopper JL, Giles GG, Southey MC, Dugué PA. Association of FOXO3 Blood DNA Methylation with Cancer Risk, Cancer Survival, and Mortality. Cells 2021; 10:cells10123384. [PMID: 34943892 PMCID: PMC8699522 DOI: 10.3390/cells10123384] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/22/2021] [Accepted: 11/26/2021] [Indexed: 12/29/2022] Open
Abstract
Genetic variants in FOXO3 are associated with longevity. Here, we assessed whether blood DNA methylation at FOXO3 was associated with cancer risk, survival, and mortality. We used data from eight prospective case–control studies of breast (n = 409 cases), colorectal (n = 835), gastric (n = 170), kidney (n = 143), lung (n = 332), prostate (n = 869), and urothelial (n = 428) cancer and B-cell lymphoma (n = 438). Case–control pairs were matched on age, sex, country of birth, and smoking (lung cancer study). Conditional logistic regression was used to assess associations between cancer risk and methylation at 45 CpGs of FOXO3 included on the HumanMethylation450 assay. Mixed-effects Cox models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations with cancer survival (total n = 2286 deaths). Additionally, using data from 1088 older participants, we assessed associations of FOXO3 methylation with overall and cause-specific mortality (n = 354 deaths). Methylation at a CpG in the first exon region of FOXO3 (6:108882981) was associated with gastric cancer survival (HR = 2.39, 95% CI: 1.60–3.56, p = 1.9 × 10−5). Methylation at three CpGs in TSS1500 and gene body was associated with lung cancer survival (p < 6.1 × 10−5). We found no evidence of associations of FOXO3 methylation with cancer risk and mortality. Our findings may contribute to understanding the implication of FOXO3 in longevity.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
| | - Allison M. Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Jihoon Eric Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- Victorian Comprehensive Cancer Centre, University of Melbourne Centre for Cancer Research, Parkville, VIC 3010, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Daniel Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC 3168, Australia;
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- Victorian Comprehensive Cancer Centre, University of Melbourne Centre for Cancer Research, Parkville, VIC 3010, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3000, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Graham G. Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (J.L.H.)
- Correspondence:
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Anghel SA, Ioniță-Mîndrican CB, Luca I, Pop AL. Promising Epigenetic Biomarkers for the Early Detection of Colorectal Cancer: A Systematic Review. Cancers (Basel) 2021; 13:4965. [PMID: 34638449 PMCID: PMC8508438 DOI: 10.3390/cancers13194965] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/22/2021] [Accepted: 09/29/2021] [Indexed: 12/12/2022] Open
Abstract
In CRC, screening compliance is decreased due to the experienced discomfort associated with colonoscopy, although this method is the gold standard in terms of sensitivity and specificity. Promoter DNA methylation (hypomethylation or hypermethylation) has been linked to all CRC stages. Study objectives: to systematically review the current knowledge on approved biomarkers, reveal new potential ones, and inspect tactics that can improve performance. This research was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines; the risk of bias was evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies criteria (QUADAS-2). The Web of Science® Core Collection, MEDLINE® and Scopus® databases were searched for original articles published in peer-reviewed journals with the specific keywords "colorectal cancer", "early detection", "early-stage colorectal cancer", "epigenetics", "biomarkers", "DNA methylation biomarkers", "stool or blood or tissue or biopsy", "NDRG4", "BMP3", "SEPT9", and "SDC2". Based on eligibility criteria, 74 articles were accepted for analysis. mSDC2 and mSEPT9 were frequently assessed in studies, alone or together as part of the ColoDefense panel test-the latter with the greatest performance. mBMP3 may not be an appropriate marker for detecting CRC. A panel of five methylated binding sites of the CTCF gene holds the promise for early-stage specific detection of CRC. CRC screening compliance and accuracy can be enhanced by employing a stool mt-DNA methylation test.
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Affiliation(s)
- Sorina Andreea Anghel
- Department of Clinical Laboratory, Food Safety, "Carol Davila" University of Medicine and Pharmacy, 6 Traian Vuia Street, 020945 Bucharest, Romania
- Department of Molecular Cell Biology, Institute of Biochemistry, Splaiul Independentei 296, 060031 Bucharest, Romania
| | - Corina-Bianca Ioniță-Mîndrican
- Department of Clinical Laboratory, Food Safety, "Carol Davila" University of Medicine and Pharmacy, 6 Traian Vuia Street, 020945 Bucharest, Romania
- Department of Toxicology, Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, 020945 Bucharest, Romania
| | - Ioana Luca
- Department of Clinical Laboratory, Food Safety, "Carol Davila" University of Medicine and Pharmacy, 6 Traian Vuia Street, 020945 Bucharest, Romania
| | - Anca Lucia Pop
- Department of Clinical Laboratory, Food Safety, "Carol Davila" University of Medicine and Pharmacy, 6 Traian Vuia Street, 020945 Bucharest, Romania
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31
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Liu T, Wang J, Xiu Y, Wu Y, Xu D. DNA Methylation Age Drift Is Associated with Poor Outcomes and De-Differentiation in Papillary and Follicular Thyroid Carcinomas. Cancers (Basel) 2021; 13:cancers13194827. [PMID: 34638311 PMCID: PMC8508076 DOI: 10.3390/cancers13194827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/19/2021] [Accepted: 09/23/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Normal human tissues contain an epigenetic clock resulting from the age-dependent DNA methylation signature, which is the so-called DNA methylation (DNAm) age and can be used to precisely predict chronological age of healthy individuals. Abnormal DNAm age drift has been implicated in cancer risk and pathogenesis. Here, we observed that highly drifted DNAm age (HDDA) occurred in approximately 1/3 tumors derived from patients with papillary and follicular thyroid carcinomas. HDDA is significantly associated with dedifferentiation of tumor cells and poor patient outcomes. Thus, HDDA may serve as a new prognostic factor for thyroid carcinoma. Abstract Alterations in global DNA methylation play a critical role in both aging and cancer, and DNA methylation (DNAm) age drift has been implicated in cancer risk and pathogenesis. In the present study, we analyzed the TCGA cohort of papillary and follicular thyroid carcinoma (PTC and FTC) for their DNAm age and association with clinic-pathological features. In 54 noncancerous thyroid (NT) samples, DNAm age was highly correlated with patient chronological age (R2 = 0.928, p = 2.6 × 10−31), but drifted to younger than chronological age in most specimens, especially those from patients >50 years old. DNAm age in 502 tumors was also correlated with patient chronological age, but to a much lesser extent (R2 = 0.403). Highly drifted DNAm age (HDDA) was identified in 161 tumors, among which were 101 with DNAm age acceleration while 60 with DNAm age deceleration. Tumors with HDDA were characterized by the robust aberrations in metabolic activities, extracellular microenvironment components and inflammation/immunology responses, and dedifferentiation. Importantly, HDDA in tumors independently predicted shorter disease-free survival of patients. Collectively, NT thyroids from TC patients have younger DNAm age, while HDDA frequently occurs in TCs, and contributes to the TC progression and poor patient outcomes. HDDA may serve as a new prognostic factor for TCs.
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Affiliation(s)
- Tiantian Liu
- Pathology Department, School of Basic Medical Science, Shandong University, Jinan 250012, China; (J.W.); (Y.X.)
- Correspondence:
| | - Jiansheng Wang
- Pathology Department, School of Basic Medical Science, Shandong University, Jinan 250012, China; (J.W.); (Y.X.)
| | - Yuchen Xiu
- Pathology Department, School of Basic Medical Science, Shandong University, Jinan 250012, China; (J.W.); (Y.X.)
| | - Yujiao Wu
- Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine (CMM), Karolinska University Hospital Solna, Karolinsk Institutet, SE-171 76 Stockholm, Sweden; (Y.W.); (D.X.)
| | - Dawei Xu
- Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine (CMM), Karolinska University Hospital Solna, Karolinsk Institutet, SE-171 76 Stockholm, Sweden; (Y.W.); (D.X.)
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32
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Yu C, Jordahl KM, Bassett JK, Joo JE, Wong EM, Brinkman MT, Schmidt DF, Bolton DM, Makalic E, Brasky TM, Shadyab AH, Tinker LF, Longano A, Hopper JL, English DR, Milne RL, Bhatti P, Southey MC, Giles GG, Dugué PA. Smoking Methylation Marks for Prediction of Urothelial Cancer Risk. Cancer Epidemiol Biomarkers Prev 2021; 30:2197-2206. [PMID: 34526299 DOI: 10.1158/1055-9965.epi-21-0313] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/22/2021] [Accepted: 09/10/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Self-reported information may not accurately capture smoking exposure. We aimed to evaluate whether smoking-associated DNA methylation markers improve urothelial cell carcinoma (UCC) risk prediction. METHODS Conditional logistic regression was used to assess associations between blood-based methylation and UCC risk using two matched case-control samples: 404 pairs from the Melbourne Collaborative Cohort Study (MCCS) and 440 pairs from the Women's Health Initiative (WHI) cohort. Results were pooled using fixed-effects meta-analysis. We developed methylation-based predictors of UCC and evaluated their prediction accuracy on two replication data sets using the area under the curve (AUC). RESULTS The meta-analysis identified associations (P < 4.7 × 10-5) for 29 of 1,061 smoking-associated methylation sites, but these were substantially attenuated after adjustment for self-reported smoking. Nominally significant associations (P < 0.05) were found for 387 (36%) and 86 (8%) of smoking-associated markers without/with adjustment for self-reported smoking, respectively, with same direction of association as with smoking for 387 (100%) and 79 (92%) markers. A Lasso-based predictor was associated with UCC risk in one replication data set in MCCS [N = 134; odds ratio per SD (OR) = 1.37; 95% CI, 1.00-1.90] after confounder adjustment; AUC = 0.66, compared with AUC = 0.64 without methylation information. Limited evidence of replication was found in the second testing data set in WHI (N = 440; OR = 1.09; 95% CI, 0.91-1.30). CONCLUSIONS Combination of smoking-associated methylation marks may provide some improvement to UCC risk prediction. Our findings need further evaluation using larger data sets. IMPACT DNA methylation may be associated with UCC risk beyond traditional smoking assessment and could contribute to some improvements in stratification of UCC risk in the general population.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Kristina M Jordahl
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.,Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jihoon Eric Joo
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Maree T Brinkman
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Department of Data Science & AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Damien M Bolton
- Department of Surgery, University of Melbourne and Olivia Newton-John Cancer Centre, Austin Hospital, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Theodore M Brasky
- Division of Medical Oncology, The Ohio State University College of Medicine, Columbus, Ohio
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anthony Longano
- Department of Anatomical Pathology, Eastern Health, Box Hill Hospital, Box Hill, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. .,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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33
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Yu C, Wong EM, Joo JE, Hodge AM, Makalic E, Schmidt D, Buchanan DD, Severi G, Hopper JL, English DR, Giles GG, Southey MC, Dugué PA. Epigenetic Drift Association with Cancer Risk and Survival, and Modification by Sex. Cancers (Basel) 2021; 13:cancers13081881. [PMID: 33919912 PMCID: PMC8070898 DOI: 10.3390/cancers13081881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 01/13/2023] Open
Abstract
Simple Summary Ageing is the strongest cancer risk factor, and men and women exhibit different risk profiles in terms of incidence and survival. DNA methylation is known to strongly vary by age and sex. Epigenetic drift refers to age-related DNA methylation changes and the tendency for increasing discordance between epigenomes over time, but it remains unknown to what extent the epigenetic drift contributes to cancer risk and survival. The aims of this study were to identify age-associated, sex-associated and sexually dimorphic age-associated (age-by-sex-associated) DNA methylation markers and investigate whether age- and age-by-sex-associated markers are associated with cancer risk and survival. Our study, which used a total of 2754 matched case–control pairs with DNA methylation in pre-diagnostic blood, is the first large study to examine the association between sex-specific epigenetic drift and cancer development and progression. The results may be useful for cancer early diagnosis and prediction of prognosis. Abstract To investigate age- and sex-specific DNA methylation alterations related to cancer risk and survival, we used matched case–control studies of colorectal (n = 835), gastric (n = 170), kidney (n = 143), lung (n = 332), prostate (n = 869) and urothelial (n = 428) cancers, and mature B-cell lymphoma (n = 438). Linear mixed-effects models were conducted to identify age-, sex- and age-by-sex-associated methylation markers using a discovery (controls)-replication (cases) strategy. Replication was further examined using summary statistics from Generation Scotland (GS). Associations between replicated markers and risk of and survival from cancer were assessed using conditional logistic regression and Cox models (hazard ratios (HR)), respectively. We found 32,659, 23,141 and 48 CpGs with replicated associations for age, sex and age-by-sex, respectively. The replication rates for these CpGs using GS summary data were 94%, 86% and 91%, respectively. Significant associations for cancer risk and survival were identified at some individual age-related CpGs. Opposite to previous findings using epigenetic clocks, there was a strong negative trend in the association between epigenetic drift and risk of colorectal cancer. Methylation at two CpGs overlapping TMEM49 and ARX genes was associated with survival of overall (HR = 0.91, p = 7.7 × 10−4) and colorectal (HR = 1.52, p = 1.8 × 10−4) cancer, respectively, with significant age-by-sex interaction. Our results may provide markers for cancer early detection and prognosis prediction.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Jihoon Eric Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC 3010, Australia
| | - Allison M. Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; (A.M.H.); (D.R.E.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Daniel Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; (J.E.J.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC 3010, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3000, Australia
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine Universités Paris-Saclay, UVSQ, Gustave Roussy, 94805 Villejuif, France;
- Department of Statistics, Computer Science and Applications “G. Parenti”, University of Florence, 50121 Firenze, Italy
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Dallas R. English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; (A.M.H.); (D.R.E.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Graham G. Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; (A.M.H.); (D.R.E.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; (A.M.H.); (D.R.E.)
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (C.Y.); (E.M.W.); (G.G.G.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; (A.M.H.); (D.R.E.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia; (E.M.); (D.S.); (J.L.H.)
- Correspondence:
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