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      Dietary Pattern and Risk of Multiple Myeloma in Two Large Prospective US Cohort Studies

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          Abstract

          Background

          The limited data on specific dietary components and risk of multiple myeloma (MM) show no consistent association. Studies have not examined the association of dietary pattern with MM risk.

          Methods

          In prospective cohorts of 69 751 women (Nurses’ Health Study, 1984–2014) and 47 232 men (Health Professionals Follow-up Study, 1986–2014), we examined the association between dietary pattern and risk of MM using Cox proportional hazard models. Diet was assessed repeatedly every 4 years with food frequency questionnaires and was used to calculate dietary patterns including the Alternate Healthy Eating Index-2010, Alternate Mediterranean Diet, Dietary Approaches to Stop Hypertension, Prudent and Western patterns, the empirical dietary inflammatory pattern (EDIP), and empirical dietary indices for insulin resistance (EDIR) and hyperinsulinemia (EDIH).

          Results

          During 2 792 257 person-years of follow-up, we identified 478 incident MM cases (215 women, 263 men). In men, high EDIP was statistically significantly associated with a 16% increase in MM risk (hazard ratio [HR] = 1.16, 95% confidence interval [CI] = 1.02 to 1.32 per 1-SD increase). Moreover, EDIR and EDIH had a suggestive positive association (EDIR: HR = 1.09, 95% CI = 0.96 to 1.24; and EDIH: HR = 1.11, 95% CI = 0.97 to 1.28 per 1-SD increase). We observed no other associations with MM risk in men and no associations for any dietary pattern with MM risk in women.

          Conclusions

          We present the first evidence for a role of diets with higher inflammatory or insulinemic potential in MM development. Further studies are warranted to explore these associations in other populations, including the apparent restriction to men.

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          Most cited references34

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          Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements.

          Previous cohort studies of fat intake and risk of coronary heart disease (CHD) have been inconsistent, probably due in part to methodological differences and various limitations, including inadequate dietary assessment and incomplete adjustment for total energy intake. The authors analyzed repeated assessment of diet from the Nurses' Health Study to examine the associations between intakes of four major types of fat (saturated, monounsaturated, polyunsaturated, and trans fats) and risk of CHD during 14 years of follow-up (1980-1994) by using alternative methods for energy adjustment. In particular, the authors compared four risk models for energy adjustment: the standard multivariate model, the energy-partition model, the nutrient residual model, and the multivariate nutrient density model. Within each model, the authors compared four different approaches for analyzing repeated dietary measurements: baseline diet only, the most recent diet, and two different algorithms for calculating cumulative average diets. The substantive results were consistent across all models; that is, higher intakes of saturated and trans fats were associated with increased risk of CHD, while higher intakes of monounsaturated and polyunsaturated fats were associated with reduced risk. When nutrients were considered as continuous variables, the four energy-adjustment methods yielded similar associationS. However, the interpretation of the relative risks differed across models. In addition, within each model, the methods using the cumulative averages in general yielded stronger associations than did those using either only baseline diet or the most recent diet. When the nutrients were categorized according to quintiles, the residual and the nutrient density models, which gave similar results, yielded statistically more significant tests for linear trend than did the standard and the partition models.
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            Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption.

            The reproducibility and validity of responses for 55 specific foods and beverages on a self-administered food frequency questionnaire were evaluated. One hundred and seventy three women from the Nurses' Health Study completed the questionnaire twice approximately 12 months apart and also recorded their food consumption for seven consecutive days, four times during the one-year interval. For the 55 foods, the mean of correlation coefficients between frequencies of intake for first versus second questionnaire was 0.57 (range = 0.24 for fruit punch to 0.93 for beer). The mean of correlation coefficients between the dietary records and first questionnaire was 0.44 (range = 0.09 for yellow squash to 0.83 for beer and tea) and between the dietary records and the second questionnaire was 0.52 (range = 0.08 for spinach to 0.90 for tea). Ratios of within- to between-person variance for the 55 foods were computed using the mean four one-week dietary records for each person as replicate measurements. For most foods this ratio was greater than 1.0 (geometric mean of ratios = 1.88), ranging from 0.25 (skimmed milk) to 14.76 (spinach). Correlation coefficients comparing questionnaire and dietary record for the 55 foods were corrected for the within-person variation (mean corrected value = 0.55 for dietary record versus first questionnaire and 0.66 versus the second). Mean daily amounts of each food calculated by the questionnaire and by the dietary record were also compared; the observed differences suggested that responses to the questionnaire tended to over-represent socially desirable foods. This analysis documents the validity and reproducibility of the questionnaire for measuring specific foods and beverages, as well as the large within-person variation for food intake measured by dietary records. Differences in the degree of validity for specific foods revealed in this type of analysis can be useful in improving questionnaire design and in interpreting findings from epidemiological studies that use the instrument.
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              Obesity as a Major Risk Factor for Cancer

              The number of cancer cases caused by being obese is estimated to be 20% with the increased risk of malignancies being influenced by diet, weight change, and body fat distribution together with physical activity. Reports from the International Agency for Research into Cancer and the World Cancer Research Fund (WCRF) have shown that the strongest evidence exists for an association of obesity with the following cancer types: endometrial, esophageal adenocarcinoma, colorectal, postmenopausal breast, prostate, and renal, whereas the less common malignancies are leukemia, non-Hodgkin's lymphoma, multiple myeloma, malignant melanoma, and thyroid tumours. To be able to develop novel methods in prevention and treatment, we first must understand the underlying processes which link cancer to obesity. Four main systems have been identified as potential producers of cancer in obesity: insulin, insulin-like growth factor-I, sex steroids, and adipokines. Various novel candidate mechanisms have been proposed: chronic inflammation, oxidative stress, crosstalk between tumour cells and surrounding adipocytes, migrating adipose stromal cells, obesity-induced hypoxia, shared genetic susceptibility, and the functional defeat of immune function. Herein, we review the major pathogenic links between obesity and susceptibility to cancer.
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                Author and article information

                Journal
                JNCI Cancer Spectr
                JNCI Cancer Spectr
                jncics
                Jnci Cancer Spectrum
                Oxford University Press
                2515-5091
                June 2019
                27 April 2019
                27 April 2019
                : 3
                : 2
                : pkz025
                Affiliations
                [1 ]Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
                [2 ]Department of Nutrition, Simmons University, Boston, MA
                [3 ]Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH
                [4 ]Department of Public Health Sciences, Washington University School of Medicine, St. Louis, MO
                [5 ]Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
                [6 ]Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
                [7 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
                Author notes
                Correspondence to: Brenda M. Birmann, ScD, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 (e-mail: brenda.birmann@ 123456channing.harvard.edu ).
                Author information
                http://orcid.org/0000-0003-1329-4637
                http://orcid.org/0000-0002-0159-482X
                http://orcid.org/0000-0001-8193-7150
                http://orcid.org/0000-0002-7550-5498
                Article
                pkz025
                10.1093/jncics/pkz025
                6532330
                31149654
                7833c423-00e0-4f7c-8448-dee6ba579223
                © The Author(s) 2019. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 September 2018
                : 4 March 2019
                : 22 March 2019
                Page count
                Pages: 9
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: UM1 CA167552
                Award ID: R01 HL35464
                Award ID: UM1 CA186107
                Award ID: P01 CA87969
                Award ID: K99 CA207736
                Award ID: R00 CA207736
                Award ID: R21 CA198239
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