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      Dietary intake of animal-based products and likelihood of follicular lymphoma and survival: A population-based family case-control study

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          Abstract

          Background

          The association between dietary intake of foods of animal origin and follicular lymphoma (FL) risk and survival is uncertain. In this study, we examined the relationship between dietary intake of dairy foods and fats, meat, fish and seafoods, and the likelihood of FL and survival.

          Methods

          We conducted a population-based family case-control study in Australia between 2011 and 2016 and included 710 cases, 303 siblings and 186 spouse/partner controls. We assessed dietary intake of animal products prior to diagnosis (the year before last) using a structured food frequency questionnaire and followed-up cases over a median of 6.9 years using record linkage to national death data. We examined associations with the likelihood of FL using logistic regression and used Cox regression to assess association with all-cause and FL-specific mortality among cases.

          Results

          We observed an increased likelihood of FL with increasing daily quantity of oily fish consumption in the year before last (highest category OR = 1.96, CI = 1.02–3.77; p-trend 0.06) among cases and sibling controls, but no associations with spouse/partner controls. We found no association between the likelihood of FL and the consumption of other types of fish or seafood, meats or dairy foods and fats. In FL cases, we found no association between meat or oily fish intake and all-cause or FL-specific mortality.

          Conclusion

          Our study showed suggestive evidence of a positive association between oily fish intake and the likelihood of FL, but findings varied by control type. Further investigation of the potential role of environmental contaminants in oily fish on FL etiology is warranted.

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          Most cited references50

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          Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                04 January 2023
                2022
                : 9
                : 1048301
                Affiliations
                [1] 1Centre for Big Data Research in Health, University of New South Wales , Sydney, NSW, Australia
                [2] 2Cancer Epidemiology Division, Cancer Council Victoria, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne , Parkville, VIC, Australia
                [3] 3Douglass Hanly Moir Pathology , Macquarie Park, NSW, Australia
                [4] 4Department of Clinical Medicine, Faculty of Medicine, Health and Human Science, Macquarie University , Sydney, NSW, Australia
                [5] 5Royal Melbourne Hospital, Peter MacCallum Cancer Centre, University of Melbourne , Melbourne, VIC, Australia
                [6] 6Epworth Healthcare and Sir Peter MacCallum Department of Oncology, University of Melbourne , Parkville, VIC, Australia
                [7] 7St. Vincent's Hospital, University of New South Wales , Sydney, NSW, Australia
                [8] 8Department of Haematology, Prince of Wales Hospital, University of New South Wales , Sydney, NSW, Australia
                [9] 9St. George Hospital , Kogarah, NSW, Australia
                [10] 10St. George Clinical School, University of New South Wales , Kogarah, NSW, Australia
                [11] 11Clinical Haematology, Monash Health and Monash University , Clayton, VIC, Australia
                [12] 12New South Wales Health Pathology, University of New South Wales , Sydney, NSW, Australia
                [13] 13Gosford Hospital, The University of Newcastle , Callaghan, NSW, Australia
                [14] 14Concord Repatriation General Hospital, University of Sydney , Concord, NSW, Australia
                [15] 15Liverpool Hospital, Western Sydney University , Liverpool, NSW, Australia
                [16] 16Border Medical Oncology Research Unit, Rural Medical School , Albury, NSW, Australia
                [17] 17School of Public Health and Preventive Medicine, Monash University , Melbourne, VIC, Australia
                [18] 18Precision Medicine, School of Clinical Sciences at Monash Health, Monash University , Melbourne, VIC, Australia
                [19] 19The Kirby Institute, University of New South Wales , Sydney, NSW, Australia
                Author notes

                Edited by: Fabiana Ourique, Universidade Federal de Juiz de Fora, Brazil

                Reviewed by: Shaokang Wang, Southeast University, China; Emmanouella Magriplis, Agricultural University of Athens, Greece

                *Correspondence: Claire M. Vajdic ✉ cvajdic@ 123456kirby.unsw.edu.au

                This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition

                Article
                10.3389/fnut.2022.1048301
                9846614
                07c28166-5a7a-44d3-a75e-8e35e84314ef
                Copyright © 2023 Odutola, van Leeuwen, Bassett, Bruinsma, Turner, Seymour, Prince, Milliken, Hertzberg, Roncolato, Opat, Lindeman, Tiley, Trotman, Verner, Harvey, Underhill, Benke, Giles and Vajdic.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 September 2022
                : 12 December 2022
                Page count
                Figures: 0, Tables: 6, Equations: 0, References: 51, Pages: 14, Words: 7545
                Funding
                Funded by: National Health and Medical Research Council, doi 10.13039/501100000925;
                Categories
                Nutrition
                Original Research

                follicular lymphoma,risk,survival,animal product,fish
                follicular lymphoma, risk, survival, animal product, fish

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