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Odutola MK, van Leeuwen MT, Turner J, Bruinsma F, Seymour JF, Prince HM, Milliken ST, Hertzberg M, Trotman J, Opat SS, Lindeman R, Roncolato F, Verner E, Harvey M, Tiley C, Underhill CR, Benke G, Giles GG, Vajdic CM. Associations between early-life growth pattern and body size and follicular lymphoma risk and survival: a family-based case-control study. Cancer Epidemiol 2022; 80:102241. [PMID: 36058036 DOI: 10.1016/j.canep.2022.102241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 08/04/2022] [Accepted: 08/21/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND The influence of early-life growth pattern and body size on follicular lymphoma (FL) risk and survival is unclear. In this study, we aimed to investigate the association between gestational age, growth during childhood, body size, changes in body shape over time, and FL risk and survival. METHODS We conducted a population-based family case-control study and included 706 cases and 490 controls. We ascertained gestational age, growth during childhood, body size and body shape using questionnaires and followed-up cases (median=83 months) using record linkage with national death records. We used a group-based trajectory modeling approach to identify body shape trajectories from ages 5-70. We examined associations with FL risk using unconditional logistic regression and used Cox regression to assess the association between body mass index (BMI) and all-cause and FL-specific mortality among cases. RESULTS We found no association between gestational age, childhood height and FL risk. We observed a modest increase in FL risk with being obese 5 years prior to enrolment (OR=1.43, 95 %CI=0.99-2.06; BMI ≥30 kg/m2) and per 5-kg/m2 increase in BMI 5 years prior to enrolment (OR=1.14, 95 %CI=0.99-1.31). The excess risk for obesity 5 years prior to enrolment was higher for ever-smokers (OR=2.00, 95 %CI=1.08-3.69) than never-smokers (OR=1.14, 95 %CI=0.71-1.84). We found no association between FL risk and BMI at enrolment, BMI for heaviest lifetime weight, the highest categories of adult weight or height, trouser size, body shape at different ages or body shape trajectory. We also observed no association between all-cause or FL-specific mortality and excess adiposity at or prior to enrolment. CONCLUSION We observed a weak association between elevated BMI and FL risk, and no association with all-cause or FL-specific mortality, consistent with previous studies. Future studies incorporating biomarkers are needed to elucidate possible mechanisms underlying the role of body composition in FL etiology.
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Affiliation(s)
- Michael K Odutola
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Marina T van Leeuwen
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Jennifer Turner
- Douglass Hanly Moir Pathology, Macquarie Park and Department of Clinical Medicine, Faculty of Medicine, Health and Human Science, Macquarie University, Sydney, Australia.
| | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
| | - John F Seymour
- Royal Melbourne Hospital, Peter MacCallum Cancer Centre and University of Melbourne, Melbourne, Victoria, Australia.
| | - H Miles Prince
- Epworth Healthcare and Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia.
| | - Samuel T Milliken
- St. Vincent's Hospital, Sydney and University of New South Wales, Sydney, New South Wales, Australia.
| | - Mark Hertzberg
- Department of Haematology, Prince of Wales Hospital and University of New South Wales, Sydney, New South Wales, Australia.
| | - Judith Trotman
- Concord Repatriation General Hospital and University of Sydney, Concord, New South Wales, Australia.
| | - Stephen S Opat
- Clinical Haematology, Monash Health and Monash University, Clayton, Australia.
| | - Robert Lindeman
- New South Wales Health Pathology and University of New South Wales, Sydney, New South Wales, Australia.
| | - Fernando Roncolato
- St. George Hospital, Kogarah and University of New South Wales, Sydney, New South Wales, Australia.
| | - Emma Verner
- Concord Repatriation General Hospital and University of Sydney, Concord, New South Wales, Australia.
| | - Michael Harvey
- Liverpool Hospital, Liverpool and Western Sydney University, New South Wales, Australia.
| | - Campbell Tiley
- Gosford Hospital and The University of Newcastle, New South Wales, Australia.
| | - Craig R Underhill
- Rural Medical School and Border Medical Oncology Research Unit, Albury, New South Wales, Australia.
| | - Geza Benke
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
| | - Claire M Vajdic
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia; The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.
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Odutola MK, van Leeuwen MT, Turner J, Bruinsma F, Seymour JF, Prince HM, Milliken ST, Trotman J, Verner E, Tiley C, Roncolato F, Underhill CR, Opat SS, Harvey M, Hertzberg M, Benke G, Giles GG, Vajdic CM. Associations between Smoking and Alcohol and Follicular Lymphoma Incidence and Survival: A Family-Based Case-Control Study in Australia. Cancers (Basel) 2022; 14:cancers14112710. [PMID: 35681690 PMCID: PMC9179256 DOI: 10.3390/cancers14112710] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/16/2022] [Accepted: 05/27/2022] [Indexed: 12/10/2022] Open
Abstract
The association between smoking and alcohol consumption and follicular lymphoma (FL) incidence and clinical outcome is uncertain. We conducted a population-based family case-control study (709 cases: 490 controls) in Australia. We assessed lifetime history of smoking and recent alcohol consumption and followed-up cases (median = 83 months). We examined associations with FL risk using unconditional logistic regression and with all-cause and FL-specific mortality of cases using Cox regression. FL risk was associated with ever smoking (OR = 1.38, 95%CI = 1.08−1.74), former smoking (OR = 1.36, 95%CI = 1.05−1.77), smoking initiation before age 17 (OR = 1.47, 95%CI = 1.06−2.05), the highest categories of cigarettes smoked per day (OR = 1.44, 95%CI = 1.04−2.01), smoking duration (OR = 1.53, 95%CI = 1.07−2.18) and pack-years (OR = 1.56, 95%CI = 1.10−2.22). For never smokers, FL risk increased for those exposed indoors to >2 smokers during childhood (OR = 1.84, 95%CI = 1.11−3.04). For cases, current smoking and the highest categories of smoking duration and lifetime cigarette exposure were associated with elevated all-cause mortality. The hazard ratio for current smoking and FL-specific mortality was 2.97 (95%CI = 0.91−9.72). We found no association between recent alcohol consumption and FL risk, all-cause or FL-specific mortality. Our study showed consistent evidence of an association between smoking and increased FL risk and possibly also FL-specific mortality. Strengthening anti-smoking policies and interventions may reduce the population burden of FL.
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Affiliation(s)
- Michael K. Odutola
- Centre for Big Data Research in Health, University of New South Wales, Sydney 2052, Australia; (M.K.O.); (M.T.v.L.)
| | - Marina T. van Leeuwen
- Centre for Big Data Research in Health, University of New South Wales, Sydney 2052, Australia; (M.K.O.); (M.T.v.L.)
| | - Jennifer Turner
- Department of Anatomical Pathology, Douglass Hanly Moir Pathology, Macquarie Park 2113, Australia;
- Department of Clinical Medicine, Faculty of Medicine, Health and Human Science, Macquarie University, North Ryde 2109, Australia
| | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (F.B.); (G.G.G.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville 3010, Australia
| | - John F. Seymour
- Royal Melbourne Hospital, Melbourne 3052, Australia;
- Peter MacCallum Cancer Centre, University of Melbourne, Parkville 3010, Australia;
| | - Henry M. Prince
- Peter MacCallum Cancer Centre, University of Melbourne, Parkville 3010, Australia;
- Epworth Healthcare, Richmond 3121, Australia
| | - Samuel T. Milliken
- St. Vincent’s Hospital, Sydney 2010, Australia;
- University of New South Wales, Sydney 2052, Australia; (F.R.); (M.H.)
| | - Judith Trotman
- Concord Repatriation General Hospital, Concord 2139, Australia; (J.T.); (E.V.)
- Faculty of Medicine and Health, University of Sydney, Concord 2139, Australia
| | - Emma Verner
- Concord Repatriation General Hospital, Concord 2139, Australia; (J.T.); (E.V.)
- Faculty of Medicine and Health, University of Sydney, Concord 2139, Australia
| | - Campbell Tiley
- Gosford Hospital, Gosford 2250, Australia;
- School of Medicine and Public Health, The University of Newcastle, Newcastle 2308, Australia
| | - Fernando Roncolato
- University of New South Wales, Sydney 2052, Australia; (F.R.); (M.H.)
- St. George Hospital, Kogarah 2217, Australia
| | - Craig R. Underhill
- Rural Medical School, Albury 2640, Australia;
- Border Medical Oncology Research Unit, Albury 2640, Australia
| | - Stephen S. Opat
- Clinical Haematology, Monash Health and Monash University, Clayton 3168, Australia;
| | - Michael Harvey
- Liverpool Hospital, Liverpool 2170, Australia;
- Western Sydney University, Sydney 2000, Australia
| | - Mark Hertzberg
- University of New South Wales, Sydney 2052, Australia; (F.R.); (M.H.)
- Department of Haematology, Prince of Wales Hospital, Sydney 2031, Australia
| | - Geza Benke
- School of Public Health and Preventive Medicine, Monash University, Melbourne 3004, Australia;
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne 3004, Australia; (F.B.); (G.G.G.)
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville 3010, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton 3168, Australia
| | - Claire M. Vajdic
- Centre for Big Data Research in Health, University of New South Wales, Sydney 2052, Australia; (M.K.O.); (M.T.v.L.)
- Kirby Institute, University of New South Wales, Sydney 2052, Australia
- Correspondence:
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Odutola MK, Benke G, Fritschi L, Giles GG, van Leeuwen MT, Vajdic CM. A systematic review and meta-analysis of occupational exposures and risk of follicular lymphoma. ENVIRONMENTAL RESEARCH 2021; 197:110887. [PMID: 33607095 DOI: 10.1016/j.envres.2021.110887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/03/2021] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND The etiology of follicular lymphoma (FL), a common non-Hodgkin lymphoma subtype, is largely unknown. OBJECTIVE We performed a systematic review and meta-analysis of observational studies examining the relationship between occupational exposures and FL risk. METHODS We searched Ovid MEDLINE, Ovid EMBASE, and Web of Science for eligible observational studies examining job titles or occupational exposures prior to January 1, 2020. We performed a narrative synthesis and used random-effects models to generate meta-estimates of relative risk (RR) with 95% confidence intervals (95%CI) for exposures reported by three or more studies. RESULTS Fifty-eight studies were eligible. Ten cohort and 37 case-control studies quantified FL risk in relation to any exposure to one or more occupational groups or agents. Eight cohort and 19 case-control studies examined dose-response relationships. We found evidence of a positive association with increasing plasma concentration of dichlorodiphenyldichloroethylene (DDE; meta-RR = 1.51, 95%CI = 0.99, 2.31; I2 = 0.0%) and polychlorinated biphenyls (PCBs; meta-RR = 1.47, 95%CI = 0.97, 2.24; I2 = 8.6%). We observed a positive association with exposure to any solvent (meta-RR = 1.16, 95%CI = 1.00, 1.34; I2 = 0.0%) and chlorinated solvents (meta-RR = 1.35, 95%CI = 1.09, 1.68; I2 = 0.0%). Single studies reported a significant positive dose-response association for exposure to any pesticide, hexachlorobenzene, any organophosphate, diazinon, metolachlor, carbaryl, lindane, trichloroethylene, oils/greases, and extremely low-frequency magnetic fields. Job title-only analyses suggested increased risk for medical doctors and spray painters, and decreased risk for bakers and teachers. Overall, studies demonstrated low risk of bias, but most studies examined small numbers of exposed cases. CONCLUSIONS Current evidence indicates a positive association between FL and occupational exposure to DDE, PCBs, any solvent and chlorinated solvents. Our findings may help guide policies and practices on the safe use of solvents and inform models of lymphomagenesis. Future studies with larger sample sizes and comprehensive quantitative exposure measures may elucidate other avoidable carcinogenic exposures.
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Affiliation(s)
- Michael K Odutola
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Geza Benke
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Lin Fritschi
- School of Public Health, Curtin University, Perth, Western Australia, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia; Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Australia; Precision Medicine, School of Clinical Sciences at Monash Health Monash University, Melbourne, Australia
| | - Marina T van Leeuwen
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Claire M Vajdic
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.
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