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Haas CB, Chen H, Harrison T, Fan S, Gago-Dominguez M, Castelao JE, Bolla MK, Wang Q, Dennis J, Michailidou K, Dunning AM, Easton DF, Antoniou AC, Hall P, Czene K, Andrulis IL, Mulligan AM, Milne RL, Fasching PA, Haeberle L, Garcia-Closas M, Ahearn T, Gierach GL, Haiman C, Maskarinec G, Couch FJ, Olson JE, John EM, Chenevix-Trench G, de Gonzalez AB, Jones M, Stone J, Murphy R, Aronson KJ, Wernli KJ, Hsu L, Vachon C, Tamimi RM, Lindström S. Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization. Breast Cancer Res Treat 2024:10.1007/s10549-024-07306-w. [PMID: 38653906 DOI: 10.1007/s10549-024-07306-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.
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Affiliation(s)
- Cameron B Haas
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Hongjie Chen
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Tabitha Harrison
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shaoqi Fan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Manuela Gago-Dominguez
- Health Research Institute of Santiago de Compostela Foundation (FIDIS), SERGAS, Cancer Genetics and Epidemiology Group, Santiago, Spain
| | - Jose E Castelao
- Unidad de Oncología Genética, Instituto de Investigación Sanitaria, Galicia Sur, Vigo, Spain
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - 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
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Prevision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Gretchen L Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gertraud Maskarinec
- Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Esther M John
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Geogia Chenevix-Trench
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Michael Jones
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, WA, Australia
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, The University of Melbourne, Melbourne, VIC, Australia
| | - Rachel Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - Kristan J Aronson
- Division of Cancer Care and Epidemiology, Department of Community Health and Epidemiology, Queen's University, Kingston, ON, K7L3N6, Canada
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celine Vachon
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
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Klapp R, Nimptsch K, Pischon T, Wilkens LR, Lim U, Guillermo C, Setiawan VW, Shepherd JA, Le Marchand L, Maskarinec G. The association of a healthy lifestyle index and imaging-based body fat distribution with glycemic status and Type 2 diabetes in the Multi Ethnic Cohort: a cross-sectional analysis. Eur J Clin Nutr 2024; 78:236-242. [PMID: 38097807 DOI: 10.1038/s41430-023-01381-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 03/13/2024]
Abstract
INTRODUCTION As several behaviors captured by the Lifestyle Risk Factor Index (LSRI) are protective against Type 2 diabetes (T2D) and may affect body fat distribution, we examined its relation with both outcomes. METHODS In a subset of the Multiethnic Cohort, participants from five ethnic groups (60-77 years) were assigned LSRI scores (one point each for consuming <1 (women)/<2 (men) alcoholic drinks/day, ≥1.5 physical activity hours/week, not smoking, and adhering to ≥3/7 dietary recommendations). All participants completed an extensive Quantitative Food Frequency Questionnaire to allow estimation of adherence to intake recommendations for fruits, vegetables, refined and whole grains, fish, processed and non-processed meat. Glycemic/T2D status was classified according to self-reports and fasting glucose. We estimated prevalence odds ratios (POR) of LSRI with glycemic/T2D status and DXA- and MRI-based body fat distribution using logistic regression. RESULTS Of 1713 participants, 43% had normoglycemia, 30% Pre-T2D, 9% Undiagnosed T2D, and 18% T2D. Overall, 39% scored 0-2, 49% 3, and 12% 4 LSRI points. T2D prevalence was 55% (POR 0.45; 95% confidence intervals 0.27, 0.76) lower for 4 vs. 0-2 LSRI points with weaker associations for abnormal glycemic status. Despite the low adherence to dietary recommendations (22%), this was the only component related to lower T2D prevalence. The inverse LSRI-T2D association was only observed among Latinos and Japanese Americans in ethnic-specific models. Visceral fat measures were higher in T2D patients and attenuated the LSRI-T2D association. CONCLUSION These findings support the role of a healthy lifestyle, especially diet, in T2D prevention with differences across ethnicity.
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Affiliation(s)
- Rebecca Klapp
- University of Hawai'i Cancer Center, Honolulu, HI, USA
| | | | - Tobias Pischon
- Max Delbrück Centrum für Molekulare Medizin, Berlin, Germany
| | | | - Unhee Lim
- University of Hawai'i Cancer Center, Honolulu, HI, USA
| | | | | | | | | | - Gertraud Maskarinec
- University of Hawai'i Cancer Center, Honolulu, HI, USA.
- Max Delbrück Centrum für Molekulare Medizin, Berlin, Germany.
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Maskarinec G, Shvetsov Y, Wong MC, Cataldi D, Bennett J, Garber AK, Buchthal SD, Heymsfield SB, Shepherd JA. Predictors of visceral and subcutaneous adipose tissue and muscle density: The ShapeUp! Kids study. Nutr Metab Cardiovasc Dis 2024; 34:799-806. [PMID: 38218711 PMCID: PMC10922397 DOI: 10.1016/j.numecd.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 12/02/2023] [Accepted: 12/14/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND AIMS Body fat distribution, i.e., visceral (VAT), subcutaneous adipose tissue (SAT) and intramuscular fat, is important for disease prevention, but sex and ethnic differences are not well understood. Our aim was to identify anthropometric, demographic, and lifestyle predictors for these outcomes. METHODS AND RESULTS The cross-sectional ShapeUp!Kids study was conducted among five ethnic groups aged 5-18 years. All participants completed questionnaires, anthropometric measurements, and abdominal MRI scans. VAT and SAT areas at four lumbar levels and muscle density were assessed manually. General linear models were applied to estimate coefficients of determination (R2) and to compare the fit of VAT and SAT prediction models. After exclusions, the study population had 133 male and 170 female participants. Girls had higher BMI-z scores, waist circumference (WC), and SAT than boys but lower VAT/SAT and muscle density. SAT, VAT, and VAT/SAT but not muscle density differed significantly by ethnicity. R2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R2 increased from 0.85 to 0.88 (girls) and 0.62 to 0.71 (boys) when WC was added while VAT models improved from 0.62 to 0.65 (girls) and 0.57 to 0.62 (boys). VAT values were significantly lower among Blacks than Whites with little difference for the other groups. CONCLUSION This analysis in a multiethnic population identified BMI-z scores and WC as the major predictors of MRI-derived SAT and VAT and highlights the important ethnic differences that need to be considered in diverse populations.
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Affiliation(s)
| | | | | | - Devon Cataldi
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | - Andrea K Garber
- University of California at San Francisco, San Francisco, CA, USA
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Tsuzaki J, Maskarinec G, Mapa V, Shvetsov YB, Park SY, Monroe KR, Lim U, Le Marchand L, Boushey CJ. Diet Quality and Body Mass Index Over 20 Years in the Multiethnic Cohort. J Acad Nutr Diet 2024; 124:194-204. [PMID: 36758897 PMCID: PMC10404631 DOI: 10.1016/j.jand.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 01/15/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023]
Abstract
BACKGROUND With increasing rates of overweight and obesity and disparities by ethnicity, it is important to understand the role of diet in ameliorating this health problem. OBJECTIVE This study examined the relation of diet quality as measured by the Healthy Eating Index 2015 with body mass index (BMI; calculated as kg/m2) and obesity among participants of the Multiethnic Cohort (MEC) in cross-sectional analyses at 3 time points (T-1, T-2, and T-3) over 20 years. DESIGN In a subset of 1,860 MEC participants, 3 cross-sectional analyses at cohort entry (1993 to 1996, T-1) and follow-ups in 2003 to 2008 (T-2) and 2013 to 2016 (T-3) were performed. PARTICIPANTS/SETTING The cohort consists of African American, Native Hawaiian, Japanese American, Latino, and White adults in Hawaii and California; mean age was 48 years at T-1. MAIN OUTCOME MEASURE BMI and weight status in relation to diet quality were measured. STATISTICAL ANALYSIS Linear and multinomial logistic regressions were applied to analyze the relation of diet quality with BMI and obesity, while adjusting for known confounders. RESULTS Healthy Eating Index 2015 increased by 6.1 and 5.1 units for men and women, respectively, from T-1 to T-3; the respective values for BMI were 1.5 and 2.4. Diet quality was inversely associated with BMI across time: BMI was lower by -0.47, -0.72, and -0.92 units for every 10-point increase in Healthy Eating Index 2015 scores at T-1, T-2, and T-3, respectively (P < .0001 for all). During the 20 years, the association was consistently high among Japanese American participants (-0.79, -0.87, and -1.02) and weakest in African American cohort members (-0.34, -0.37, and -0.40). Higher diet quality was related to lower odds of having obesity at all 3 time points; prevalence odds ratios were 0.72, 0.57, and 0.60. CONCLUSIONS These findings suggest that consuming a high-quality diet is related to lower BMI and rates of overweight and obesity but with the strongest association at an older age. To understand the ethnic differences, investigations of dietary habits and behaviors and/or fat distribution patterns will be needed in the future.
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Affiliation(s)
- Jenna Tsuzaki
- University of Hawaii Cancer Center, Honolulu, Hawaii
| | | | | | | | - Song-Yi Park
- University of Hawaii Cancer Center, Honolulu, Hawaii
| | | | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, Hawaii
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Lo YC, Chan TF, Jeon S, Maskarinec G, Taparra K, Nakatsuka N, Yu M, Chen CY, Lin YF, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. The accuracy of polygenic score models for anthropometric traits and Type II Diabetes in the Native Hawaiian Population. medRxiv 2023:2023.12.25.23300499. [PMID: 38234828 PMCID: PMC10793530 DOI: 10.1101/2023.12.25.23300499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations, and their accuracies have not been evaluated for Native Hawaiians. Using body mass index, height, and type-2 diabetes as examples of highly polygenic traits, we evaluated the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5,300 individuals. We evaluated both publicly available PGS models or genome-wide PGS models trained in this study using the largest available GWAS. We found evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also found that using the Native Hawaiian samples as an optimization cohort during training did not consistently improve PGS performance. Moreover, even the best performing PGS models among Native Hawaiians would have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.
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Affiliation(s)
- Ying-Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gertraud Maskarinec
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Kekoa Taparra
- Standard Health Care, Department of Radiation Oncology, Palo Alto, CA, USA
| | | | - Mingrui Yu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Chia-Yen Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Biogen, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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Maskarinec G, Kristal BS, Wilkens LR, Quintal G, Bogumil D, Setiawan VW, Le Marchand L. Risk Factors for Type 2 Diabetes in the Multiethnic Cohort. Can J Diabetes 2023; 47:627-635.e2. [PMID: 37406880 PMCID: PMC10761589 DOI: 10.1016/j.jcjd.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVES In this report, we investigated the association between established risk factors and type 2 diabetes (T2D) across 5 distinct ethnic groups and explored differences according to T2D definition within the Multiethnic Cohort (MEC) Study. METHODS Using the full MEC, with participants in Hawaii and Los Angeles (N=172,230), we applied Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). All participants completed questionnaires asking about demographics, anthropometrics, lifestyle factors, and regular diet. T2D status was determined from self-reported diagnosis/medication and Medicare claims. We assessed the associations between well-established risk factors and T2D in the full cohort, after stratification by ethnic group, according to the T2D definition, and in a biorepository subset. Effect modification by ethnicity was evaluated using Wald's tests. RESULTS Overall, 46,500 (27%) participants had an incident T2D diagnosis after a mean follow-up of 17.1±6.9 years. All predictors were significantly associated with T2D: overweight (HR=1.74), obesity (HR=2.90), red meat intake (HR=1.15), short (HR=1.04) and long (HR=1.08) sleep duration, and smoking (HR=1.26) predicted a significantly higher T2D incidence, whereas coffee (HR=0.90) and alcohol (HR=0.78) consumption, physical activity (HR=0.89), and diet quality (HR=0.96) were associated with lower T2D incidence. The strength of these associations was similar across ethnic groups with noteworthy disparities for overweight/obesity, physical activity, alcohol intake, coffee consumption, and diet quality. CONCLUSIONS These findings confirm the importance of known risk factors for T2D across ethnic groups, but small differences were detected that may contribute to disparate incidence rates in some ethnic groups, especially for obesity and physical activity.
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Affiliation(s)
- Gertraud Maskarinec
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, United States.
| | - Bruce S Kristal
- Division of Sleep Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Lynne R Wilkens
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, United States
| | - Gino Quintal
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, United States
| | - David Bogumil
- Preventive Medicine, University of Southern California, Los Angeles, California, United States
| | - Veronica W Setiawan
- Preventive Medicine, University of Southern California, Los Angeles, California, United States
| | - Loïc Le Marchand
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, United States
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Maskarinec G, Brown SM, Lee J, Bogumil D, Walsh C, Haiman CA, Setiawan VW, Shvetsov YB, Marchand LL. Association of Obesity and Type 2 Diabetes with Non-Hodgkin Lymphoma: The Multiethnic Cohort. Cancer Epidemiol Biomarkers Prev 2023; 32:1348-1355. [PMID: 37555836 PMCID: PMC10592150 DOI: 10.1158/1055-9965.epi-23-0565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Given the role of the immune system in non-Hodgkin lymphoma (NHL) etiology, obesity and type 2 diabetes (T2D) may impact NHL development. We examined the association of body mass index (BMI) and T2D with NHL in the multiethnic cohort (MEC). METHODS The MEC recruited >215,000 participants in Hawaii and Los Angeles from five racial/ethnic groups; NHL cases were identified through cancer registry linkages. T2D status, and BMI at age 21 and cohort entry were derived from repeated self-reports; for T2D, Medicare claims were also applied. HRs and 95% confidence intervals (CI) for BMI and T2D as predictors of NHL were determined using Cox regression adjusted for relevant covariates. RESULTS Among 192,424 participants, 3,472 (1.8%) with NHL and 68,850 (36%) with T2D after 19.2 ± 6.6 years follow-up, no significant association between T2D and NHL (HR, 1.04; 95% CI, 0.96-1.13) was observed. Stratification by BMI at cohort entry showed a significant association of T2D with NHL among individuals with normal weight only (HR, 1.18; 95% CI, 1.03-1.37). In a model with both BMI values plus T2D, only overweight (HR, 1.13; 95% CI, 1.01-1.26) and obesity (HR, 1.25; 95% CI, 0.99-1.59) at age 21 were associated with NHL incidence. Stratification by sex, race/ethnicity, and NHL subtype indicated no differences. CONCLUSIONS Our findings suggest an association between T2D and NHL incidence in several subgroups but not in the total population and an elevated risk related to early-life BMI. IMPACT Excess body weight in early life, rather than T2D, may be a predictor of NHL incidence.
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Affiliation(s)
| | | | - Jordyn Lee
- University of Hawaii Cancer Center, Honolulu, HI
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Garber AK, Bennett JP, Wong MC, Tian IY, Maskarinec G, Kennedy SF, McCarthy C, Kelly NN, Liu YE, Machen VI, Heymsfield SB, Shepherd JA. Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans. Am J Clin Nutr 2023; 118:812-821. [PMID: 37598747 PMCID: PMC10797509 DOI: 10.1016/j.ajcnut.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/08/2023] [Accepted: 08/14/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND New recommendations for the assessment of malnutrition and sarcopenia include body composition, specifically reduced muscle mass. Three-dimensional optical imaging (3DO) is a validated, accessible, and affordable alternative to dual X-ray absorptiometry (DXA). OBJECTIVE Identify strengths and weaknesses of 3DO for identification of malnutrition in participants with low body mass index (BMI) and eating disorders. DESIGN Participants were enrolled in the cross-sectional Shape Up! Adults and Kids studies of body shape, metabolic risk, and functional assessment and had BMI of <20 kg/m2 in adults or <85% of median BMI (mBMI) in children and adolescents. A subset was referred for eating disorders evaluation. Anthropometrics, scans, strength testing, and questionnaires were completed in clinical research centers. Lin's Concordance Correlation Coefficient (CCC) assessed agreement between 3DO and DXA; multivariate linear regression analysis examined associations between weight history and body composition. RESULTS Among 95 participants, mean ± SD BMI was 18.3 ± 1.4 kg/m2 in adult women (N = 56), 19.0 ± 0.6 in men (N = 14), and 84.2% ± 4.1% mBMI in children (N = 25). Concordance was excellent for fat-free mass (FFM, CCC = 0.97) and strong for appendicular lean mass (ALM, CCC = 0.86) and fat mass (FM, CCC = 0.87). By DXA, 80% of adults met the low FFM index criterion for malnutrition, and 44% met low ALM for sarcopenia; 52% of children and adolescents were <-2 z-score for FM. 3DO identified 95% of these cases. In the subset, greater weight loss predicted lower FFM, FM, and ALM by both methods; a greater percentage of weight regained predicted a higher percentage of body fat. CONCLUSIONS 3DO can accurately estimate body composition in participants with low BMI and identify criteria for malnutrition and sarcopenia. In a subset, 3DO detected changes in body composition expected with weight loss and regain secondary to eating disorders. These findings support the utility of 3DO for body composition assessment in patients with low BMI, including those with eating disorders. This trial was registered at clinicaltrials.gov as NCT03637855.
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Affiliation(s)
- Andrea K Garber
- Department of Pediatrics, University of California, San Francisco, CA, United States.
| | - Jonathan P Bennett
- Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, HI, United States; University of Hawai'i Cancer Center, Honolulu, HI, United States
| | - Michael C Wong
- University of Hawai'i Cancer Center, Honolulu, HI, United States
| | - Isaac Y Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | | | - Samantha F Kennedy
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, United States
| | - Cassidy McCarthy
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, United States
| | - Nisa N Kelly
- University of Hawai'i Cancer Center, Honolulu, HI, United States
| | - Yong E Liu
- University of Hawai'i Cancer Center, Honolulu, HI, United States
| | - Vanessa I Machen
- Department of Pediatrics, University of California, San Francisco, CA, United States
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, United States
| | - John A Shepherd
- University of Hawai'i Cancer Center, Honolulu, HI, United States
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Wong MC, Bennett JP, Quon B, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow D, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd JA. Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity. Am J Clin Nutr 2023; 118:657-671. [PMID: 37474106 PMCID: PMC10517211 DOI: 10.1016/j.ajcnut.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown. OBJECTIVES This study aimed to evaluate 3DO's accuracy and precision by subgroups of age, body mass index, and ethnicity. METHODS A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Student's t tests were performed between 3DO and DXA by subgroup to determine significant differences. RESULTS Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038). CONCLUSIONS A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).
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Affiliation(s)
- Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Jonathan P Bennett
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Brandon Quon
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Lambert T Leong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Isaac Y Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Yong E Liu
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Cassidy McCarthy
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Dominic Chow
- John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Sergi Pujades
- Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Andrea K Garber
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Gertraud Maskarinec
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | | | - John A Shepherd
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
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Chai W, Maskarinec G, Lim U, Boushey CJ, Wilkens LR, Setiawan VW, Le Marchand L, Randolph TW, Jenkins IC, Lampe JW, Hullar MA. Association of Habitual Intake of Probiotic Supplements and Yogurt with Characteristics of the Gut Microbiome in the Multiethnic Cohort Adiposity Phenotype Study. Gut Microbiome (Camb) 2023; 4:e14. [PMID: 38468639 PMCID: PMC10927272 DOI: 10.1017/gmb.2023.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Consumption of probiotics and/or yogurt could be a solution for restoring the balance of the gut microbiota. This study examined associations of regular intake of probiotic supplements or yogurt with the gut microbiota among a diverse population of older adults (N=1,861; 60-72 years). Fecal microbial composition was obtained from 16S rRNA gene sequencing (V1-V3 region). General Linear Models were used to estimate the associations of probiotic supplement or yogurt intake with microbiome measures adjusting for covariates. Compared to non-yogurt consumers (N=1,023), regular yogurt consumers (≥once/week, N=818) had greater Streptococcus (β=0.29, P=0.0003) and lower Odoribacter (β=-0.33, P<0.0001) abundance. The directions of the above associations were consistent across the five ethnic groups but stronger among Japanese Americans (Streptococcus: β=0.56, P=0.0009; Odoribacter: β=-0.62, P=0.0005). Regular intake of probiotic supplements (N=175) was not associated with microbial characteristics (i.e., alpha diversity and the abundance of 152 bacteria genera). Streptococcus is one of the predominant bacteria genera in yogurt products, which may explain the positive association between yogurt consumption and Streptococcus abundance. Our analyses suggest that changes in Odoribacter were independent of changes in Streptococcus abundance. Future studies may investigate whether these microbial genera and their sub-level species mediate potential pathways between yogurt consumption and health.
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Affiliation(s)
- Weiwen Chai
- Department of Nutrition and Health Sciences, University of Nebraska-Lincoln, Lincoln, NE
| | | | - Unhee Lim
- University of Hawai’i Cancer Center, Honolulu, HI
| | | | | | - V. Wendy Setiawan
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
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Bogumil D, Cortessis VK, Wilkens LR, Le Marchand L, Haiman CA, Maskarinec G, Setiawan VW. Interethnic Differences in Bladder Cancer Incidence and the Association between Type 2 Diabetes and Bladder Cancer in the Multiethnic Cohort Study. Cancer Res Commun 2023; 3:755-762. [PMID: 37377897 PMCID: PMC10153456 DOI: 10.1158/2767-9764.crc-22-0288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/11/2022] [Accepted: 04/04/2023] [Indexed: 06/29/2023]
Abstract
Background Research on the association between type 2 diabetes (T2D) and bladder cancer (BCA) risk among non-European ancestry populations is sparse to nonexistent, and most prior studies rely on a single baseline assessment of T2D status. Methods We estimated the T2D-BCA association using the Multiethnic Cohort Study of 185,059 men and women in California and Hawaii. Participants were African American, European American, Japanese American, Latin American, and Native Hawaiian, ages 45-75 years at enrollment (1993-1996). T2D was assessed by self-report at baseline, follow-up surveys, and Medicare claims. Cases were identified using Surveillance, Epidemiology and End Results Program cancer registries through 2016. Associations were estimated by race/ethnicity using Cox proportional hazards regression. Adjusted attributable fractions (AAF) and cumulative absolute risk of bladder cancer were estimated across groups. Results Over an average 19.7 years of follow-up 1,890 incident bladder cancer cases were diagnosed. Time-varying T2D was associated with bladder cancer in the multiethnic sample (HR = 1.17; 95% confidence interval, 1.05-1.30); however, the HR did not differ by race/ethnicity (P = 0.85). The AAF was 4.2% in the multiethnic sample and largest among Native Hawaiians (9.8%). Absolute risk of bladder cancer among European Americans without T2D was higher than all other groups with T2D. Conclusion T2D is significantly associated with bladder cancer risk in a multiethnic sample. Significance Those with T2D have higher incidence of bladder cancer, regardless of racial/ethnic group. Reducing T2D prevalence could substantially lower bladder cancer incidence among Native Hawaiians due to T2D being more common in this group. High absolute risk of bladder cancer among European Americans, regardless of T2D status, indicates that elevated bladder cancer risk in this group may be due to factors other than T2D. Future studies must explore reasons for this difference in incidence.
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Affiliation(s)
- David Bogumil
- Department of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Victoria K. Cortessis
- Department of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Christopher A. Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, California
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California
- Center for Genetic Epidemiology, Keck School of Medicine of University of Southern California, Los Angeles, California
| | | | - Veronica Wendy Setiawan
- Department of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, California
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California
- Center for Genetic Epidemiology, Keck School of Medicine of University of Southern California, Los Angeles, California
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12
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Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Wong JMW, Ebbeling CB, Ludwig DS, Irving BA, Scott MC, Stampley J, Davis B, Johannsen N, Matthews R, Vincellette C, Garber AK, Maskarinec G, Weiss E, Rood J, Varanoske AN, Pasiakos SM, Heymsfield SB, Shepherd JA. Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry. Am J Clin Nutr 2023; 117:802-813. [PMID: 36796647 PMCID: PMC10315406 DOI: 10.1016/j.ajcnut.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/24/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown. OBJECTIVES This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies. METHODS A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis. RESULTS The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. CONCLUSIONS Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).
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Affiliation(s)
- Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Jonathan P Bennett
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Lambert T Leong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Isaac Y Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Yong E Liu
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Cassidy McCarthy
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Julia M W Wong
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Cara B Ebbeling
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - David S Ludwig
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Brian A Irving
- Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Matthew C Scott
- Pennington Biomedical Research Center, Baton Rouge, LA, United States; Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - James Stampley
- Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Brett Davis
- Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Neil Johannsen
- Pennington Biomedical Research Center, Baton Rouge, LA, United States; Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Rachel Matthews
- Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Cullen Vincellette
- Louisiana State University, School of Kinesiology, Baton Rouge, LA, United States
| | - Andrea K Garber
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Gertraud Maskarinec
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Ethan Weiss
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Jennifer Rood
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Alyssa N Varanoske
- Military Nutrition Division, U.S. Army Research Institute of Environmental Medicine, Natick, MA, United States; Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
| | - Stefan M Pasiakos
- Military Nutrition Division, U.S. Army Research Institute of Environmental Medicine, Natick, MA, United States
| | | | - John A Shepherd
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
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13
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Valdez D, Bunnell A, Wolfgruber T, Altamirano A, Quon B, Maskarinec G, Sadowski P, Shepherd J. Abstract P3-03-02: Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms. Cancer Res 2023. [DOI: 10.1158/1538-7445.sabcs22-p3-03-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Abstract
Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms Dustin Valdez12, Arianna Bunnell2, Thomas Wolfgruber1, Aleen V. Altamirano3, Brandon Quon1, Gertraud Maskarinec1, Peter Sadowski2, John A. Shepherd1 1 University of Hawaii Cancer Center, Honolulu, HI 2 University of Hawaii at Manoa, Honolulu, HI 3 Instituto Radiodiagnóstico, Managua, Nicaragua Background: Breast cancer is the second leading cause of cancer-related death among women in Hawaii and the Pacific. However, while there are programs like the Breast and Cervical Cancer Early Detection Program (BCCEDP) implemented throughout the Pacific, the lack of access to mammography screening and low screening participation rates contributes to very high advanced breast cancer rates in most cases over 50%. Portable breast ultrasound is a promising screening technology for low resource areas. However, without mammography, mammographic density is not available for risk modeling to determine who should participate in screening programs or at what frequency. In this study, we ask if breast ultrasound (US) images can be used to derive an equivalent mammographic density for risk modeling. We utilized artificial intelligence to derive breast density from diagnostic ultrasound images and compared to BI-RADS mammographic density in an established breast cancer risk model1. Methods: We selected women with negative screening visit who either later developed cancer (positives) or did not (negatives) over a 10-year period. Temporally-matched negative mammographic and ultrasound images, cancer outcome status and cancer risk information were sourced from the Hawaii and Pacific Islands Mammography Registry. US images had to have occurred within a year of the mammogram. BI-RADS mammographic density was derived using an existing deep neural network model2. Mammographic density was estimated from US images by training a deep-learning convolutional neural network model. A hold out set of images (Test set of 20% of the total) was used to compare 10-year breast cancer risk using the Tyrer-Cuzick (TC) risk model1 when calculated using breast density from either mammograms or US. The AUC values, confidence intervals, ROC plots and Pearson correlation were calculated and compared. Results: Over the 10-year study period, 1337 had matched mammograms and US images and 65 went on to develop breast cancer. Using the test set, the Pearson’s correlation between breast density from mammography and US was 0.31 (moderate correlation). There were no covariates found to improve this association. The AUC for TC 10-year personal risk was higher when breast density from mammograms was used 0.71 (95% CI=0.57-0.86) versus US images 0.65 (95% CI=0.53-0.76). Conclusion: Overall breast cancer risk was similar when breast density was derived from either mammograms or US. The performance of our US breast density model is expected to improve further when more US training data becomes available. Breast cancer screening programs exclusively using US imaging may be able to provide equivalent risk modeling to clinics using mammography. 1.Tyrer J, Duffy SW, Cuzick J (2004). A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004 Apr 15;23(7):1111-30. doi: 10.1002/sim.1668. Erratum in: Stat Med. 2005 Jan 15;24(1):156. PMID: 15057881. 2. Wu, N., K. J. Geras, Y. Shen, J. Su, S. G. Kim, E. Kim, S. Wolfson, L. Moy and K. Cho (2018). Breast Density Classification with Deep Convolutional Neural Networks. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
Citation Format: Dustin Valdez, Arianna Bunnell, Thomas Wolfgruber, Aleen Altamirano, Brandon Quon, Gertraud Maskarinec, Peter Sadowski, John Shepherd. Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-02.
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Visvanathan K, Mondul AM, Zeleniuch-Jacquotte A, Wang M, Gail MH, Yaun SS, Weinstein SJ, McCullough ML, Eliassen AH, Cook NR, Agnoli C, Almquist M, Black A, Buring JE, Chen C, Chen Y, Clendenen T, Dossus L, Fedirko V, Gierach GL, Giovannucci EL, Goodman GE, Goodman MT, Guénel P, Hallmans G, Hankinson SE, Horst RL, Hou T, Huang WY, Jones ME, Joshu CE, Kaaks R, Krogh V, Kühn T, Kvaskoff M, Lee IM, Mahamat-Saleh Y, Malm J, Manjer J, Maskarinec G, Millen AE, Mukhtar TK, Neuhouser ML, Robsahm TE, Schoemaker MJ, Sieri S, Sund M, Swerdlow AJ, Thomson CA, Ursin G, Wactawski-Wende J, Wang Y, Wilkens LR, Wu Y, Zoltick E, Willett WC, Smith-Warner SA, Ziegler RG. Circulating vitamin D and breast cancer risk: an international pooling project of 17 cohorts. Eur J Epidemiol 2023; 38:11-29. [PMID: 36593337 PMCID: PMC10039648 DOI: 10.1007/s10654-022-00921-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/21/2022] [Indexed: 01/04/2023]
Abstract
Laboratory and animal research support a protective role for vitamin D in breast carcinogenesis, but epidemiologic studies have been inconclusive. To examine comprehensively the relationship of circulating 25-hydroxyvitamin D [25(OH)D] to subsequent breast cancer incidence, we harmonized and pooled participant-level data from 10 U.S. and 7 European prospective cohorts. Included were 10,484 invasive breast cancer cases and 12,953 matched controls. Median age (interdecile range) was 57 (42-68) years at blood collection and 63 (49-75) years at breast cancer diagnosis. Prediagnostic circulating 25(OH)D was either newly measured using a widely accepted immunoassay and laboratory or, if previously measured by the cohort, calibrated to this assay to permit using a common metric. Study-specific relative risks (RRs) for season-standardized 25(OH)D concentrations were estimated by conditional logistic regression and combined by random-effects models. Circulating 25(OH)D increased from a median of 22.6 nmol/L in consortium-wide decile 1 to 93.2 nmol/L in decile 10. Breast cancer risk in each decile was not statistically significantly different from risk in decile 5 in models adjusted for breast cancer risk factors, and no trend was apparent (P-trend = 0.64). Compared to women with sufficient 25(OH)D based on Institute of Medicine guidelines (50- < 62.5 nmol/L), RRs were not statistically significantly different at either low concentrations (< 20 nmol/L, 3% of controls) or high concentrations (100- < 125 nmol/L, 3% of controls; ≥ 125 nmol/L, 0.7% of controls). RR per 25 nmol/L increase in 25(OH)D was 0.99 [95% confidence intervaI (CI) 0.95-1.03]. Associations remained null across subgroups, including those defined by body mass index, physical activity, latitude, and season of blood collection. Although none of the associations by tumor characteristics reached statistical significance, suggestive inverse associations were seen for distant and triple negative tumors. Circulating 25(OH)D, comparably measured in 17 international cohorts and season-standardized, was not related to subsequent incidence of invasive breast cancer over a broad range in vitamin D status.
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Affiliation(s)
- Kala Visvanathan
- Departments of Epidemiology and Oncology, Johns Hopkins Bloomberg School of Public Health and Kimmel Cancer Center, Baltimore, MD, USA
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Anne Zeleniuch-Jacquotte
- Departments of Population Health and Environmental Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Molin Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mitchell H Gail
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shiaw-Shyuan Yaun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nancy R Cook
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, IRCCS National Cancer Institute Foundation, Milan, Italy
| | - Martin Almquist
- Department of Surgery, Skane University Hospital, Lund, Sweden
| | - Amanda Black
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Julie E Buring
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chu Chen
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yu Chen
- Departments of Population Health and Environmental Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Tess Clendenen
- Departments of Population Health and Environmental Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Veronika Fedirko
- Department of Epidemiology, Rollins School of Public Health and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Gretchen L Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gary E Goodman
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marc T Goodman
- Cancer Prevention and Control Research Program, Cedars Sinai Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, USA
| | - Pascal Guénel
- Center for Research in Epidemiology and Population Health (CESP), French National Institute of Health and Medical Research (INSERM), University Paris-Saclay, Villejuif, France
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | | | - Tao Hou
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Corrine E Joshu
- Departments of Epidemiology and Oncology, Johns Hopkins Bloomberg School of Public Health and Kimmel Cancer Center, Baltimore, MD, USA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, IRCCS National Cancer Institute Foundation, Milan, Italy
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Global Food Security, Queen's University, Belfast, Northern Ireland
| | - Marina Kvaskoff
- Center for Research in Epidemiology and Population Health (CESP), French National Institute of Health and Medical Research (INSERM), University Paris-Saclay, Villejuif, France
| | - I-Min Lee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yahya Mahamat-Saleh
- Center for Research in Epidemiology and Population Health (CESP), French National Institute of Health and Medical Research (INSERM), University Paris-Saclay, Villejuif, France
| | - Johan Malm
- Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Jonas Manjer
- Department of Surgery, Skane University Hospital, Lund University, Malmö, Sweden
| | - Gertraud Maskarinec
- Cancer Epidemiology Program, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Amy E Millen
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY, USA
| | - Toqir K Mukhtar
- Department of Primary Care and Public Health, Imperial College, London, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marian L Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trude E Robsahm
- Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Sabina Sieri
- Epidemiology and Prevention Unit, IRCCS National Cancer Institute Foundation, Milan, Italy
| | - Malin Sund
- Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Cynthia A Thomson
- Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona and University of Arizona Cancer Center, Tucson, AZ, USA
| | - Giske Ursin
- Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY, USA
| | - Ying Wang
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Lynne R Wilkens
- Population Sciences in the Pacific, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Yujie Wu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Emilie Zoltick
- Department of Population Medicine, Harvard Medical School, Boston, MA, USA
| | - Walter C Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephanie A Smith-Warner
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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15
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Jannasch F, Dietrich S, Bishop TRP, Pearce M, Fanidi A, O'Donoghue G, O'Gorman D, Marques-Vidal P, Vollenweider P, Bes-Rastrollo M, Byberg L, Wolk A, Hashemian M, Malekzadeh R, Poustchi H, Luft VC, de Matos SMA, Kim J, Kim MK, Kim Y, Stern D, Lajous M, Magliano DJ, Shaw JE, Akbaraly T, Kivimaki M, Maskarinec G, Le Marchand L, Martínez-González MÁ, Soedamah-Muthu SS, Wareham NJ, Forouhi NG, Schulze MB. Associations between exploratory dietary patterns and incident type 2 diabetes: a federated meta-analysis of individual participant data from 25 cohort studies. Eur J Nutr 2022; 61:3649-3667. [PMID: 35641800 PMCID: PMC9464116 DOI: 10.1007/s00394-022-02909-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 05/09/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE In several studies, exploratory dietary patterns (DP), derived by principal component analysis, were inversely or positively associated with incident type 2 diabetes (T2D). However, findings remained study-specific, inconsistent and rarely replicated. This study aimed to investigate the associations between DPs and T2D in multiple cohorts across the world. METHODS This federated meta-analysis of individual participant data was based on 25 prospective cohort studies from 5 continents including a total of 390,664 participants with a follow-up for T2D (3.8-25.0 years). After data harmonization across cohorts we evaluated 15 previously identified T2D-related DPs for association with incident T2D estimating pooled incidence rate ratios (IRR) and confidence intervals (CI) by Piecewise Poisson regression and random-effects meta-analysis. RESULTS 29,386 participants developed T2D during follow-up. Five DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, were associated with higher incidence of T2D. The strongest association was observed for a DP comprising these food groups besides others (IRRpooled per 1 SD = 1.104, 95% CI 1.059-1.151). Although heterogeneity was present (I2 = 85%), IRR exceeded 1 in 18 of the 20 meta-analyzed studies. Original DPs associated with lower T2D risk were not confirmed. Instead, a healthy DP (HDP1) was associated with higher T2D risk (IRRpooled per 1 SD = 1.057, 95% CI 1.027-1.088). CONCLUSION Our findings from various cohorts revealed positive associations for several DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, adding to the evidence-base that links DPs to higher T2D risk. However, no inverse DP-T2D associations were confirmed.
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Affiliation(s)
- Franziska Jannasch
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. .,NutriAct Competence Cluster Nutrition Research Potsdam-Berlin, Nuthetal, Germany. .,German Center for Diabetes Research, Munich-Neuherberg, Germany.
| | - Stefan Dietrich
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,Department of Food Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Tom R P Bishop
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Matthew Pearce
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Anouar Fanidi
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Gráinne O'Donoghue
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Donal O'Gorman
- School of Health and Human Performance, Dublin City University, Dublin, Ireland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Maira Bes-Rastrollo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Liisa Byberg
- Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Alicja Wolk
- Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Maryam Hashemian
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Biology Department, School of Arts and Sciences, Utica College, Utica, NY, USA
| | - Reza Malekzadeh
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Poustchi
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Vivian C Luft
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | | | - Jihye Kim
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, South Korea
| | - Mi Kyung Kim
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, South Korea
| | - Yeonjung Kim
- Division of Health and Nutrition Survey and Analysis, Korea Disease Control Prevention Agency, Seoul, South Korea
| | - Dalia Stern
- CONACyT-Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Martin Lajous
- CONACyT-Center for Research on Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Tasnime Akbaraly
- Inserm U 1018, Université Paris-Saclay, UVSQ, Villejuif, Maison des Sciences de l'Homme - SUD, Montpellier, France.,Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | | | - Miguel Ángel Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.,Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA
| | - Sabita S Soedamah-Muthu
- Center of Research On Psychological and Somatic Disorders (CORPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.,Institute for Food, Nutrition and Health, University of Reading, Reading, RG6 6AR, UK
| | | | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,NutriAct Competence Cluster Nutrition Research Potsdam-Berlin, Nuthetal, Germany.,German Center for Diabetes Research, Munich-Neuherberg, Germany
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16
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Ward SV, Burton A, Tamimi RM, Pereira A, Garmendia ML, Pollan M, Boyd N, Dos-Santos-Silva I, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, López-Ridaura R, Bertrand K, Kwong A, Ursin G, Lee E, Ma H, Vinnicombe S, Moss S, Allen S, Ndumia R, Vinayak S, Teo SH, Mariapun S, Peplonska B, Bukowska-Damska A, Nagata C, Hopper J, Giles G, Ozmen V, Aribal ME, Schüz J, Van Gils CH, Wanders JOP, Sirous R, Sirous M, Hipwell J, Kim J, Lee JW, Dickens C, Hartman M, Chia KS, Scott C, Chiarelli AM, Linton L, Flugelman AA, Salem D, Kamal R, McCormack V, Stone J. The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density. Breast Cancer Res 2022; 24:49. [PMID: 35836268 PMCID: PMC9284807 DOI: 10.1186/s13058-022-01545-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/29/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Early age at menarche and tall stature are associated with increased breast cancer risk. We examined whether these associations were also positively associated with mammographic density, a strong marker of breast cancer risk. METHODS Participants were 10,681 breast-cancer-free women from 22 countries in the International Consortium of Mammographic Density, each with centrally assessed mammographic density and a common set of epidemiologic data. Study periods for the 27 studies ranged from 1987 to 2014. Multi-level linear regression models estimated changes in square-root per cent density (√PD) and dense area (√DA) associated with age at menarche and adult height in pooled analyses and population-specific meta-analyses. Models were adjusted for age at mammogram, body mass index, menopausal status, hormone therapy use, mammography view and type, mammographic density assessor, parity and height/age at menarche. RESULTS In pooled analyses, later age at menarche was associated with higher per cent density (β√PD = 0.023 SE = 0.008, P = 0.003) and larger dense area (β√DA = 0.032 SE = 0.010, P = 0.002). Taller women had larger dense area (β√DA = 0.069 SE = 0.028, P = 0.012) and higher per cent density (β√PD = 0.044, SE = 0.023, P = 0.054), although the observed effect on per cent density depended upon the adjustment used for body size. Similar overall effect estimates were observed in meta-analyses across population groups. CONCLUSIONS In one of the largest international studies to date, later age at menarche was positively associated with mammographic density. This is in contrast to its association with breast cancer risk, providing little evidence of mediation. Increased height was also positively associated with mammographic density, particularly dense area. These results suggest a complex relationship between growth and development, mammographic density and breast cancer risk. Future studies should evaluate the potential mediation of the breast cancer effects of taller stature through absolute breast density.
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Affiliation(s)
- Sarah V Ward
- School of Population and Global Health, The University of Western Australia, Perth, Australia
| | - Anya Burton
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France
- Translation Health Sciences, University of Bristol, Bristol, UK
| | - Rulla M Tamimi
- Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, USA
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Marina Pollan
- Cancer and Environmental Epidemiology Unit, Instituto de Salud Carlos III, Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Norman Boyd
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Beatriz Perez-Gomez
- Cancer and Environmental Epidemiology Unit, Instituto de Salud Carlos III, Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
| | - Martín Lajous
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | | | - Ava Kwong
- Division of Breast Surgery, Faculty of Medicine, University of Hong Kong, Pok Fu Lam, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Pok Fu Lam, Hong Kong, China
- Hong Kong Hereditary Breast Cancer Family Registry, Pok Fu Lam, Hong Kong, China
| | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eunjung Lee
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Huiyan Ma
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, USA
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, UK
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | | | - Soo-Hwang Teo
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Beata Peplonska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Łódź, Poland
| | - Agnieszka Bukowska-Damska
- Department of Physiology, Pathophysiology and Clinical Immunology,, Medical University of Lodz., Łódź, Poland
| | - Chisato Nagata
- Department of Epidemiology and Preventive Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Vahit Ozmen
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Mustafa Erkin Aribal
- Department of Radiology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Joachim Schüz
- School of Population and Global Health, The University of Western Australia, Perth, Australia
| | - Carla H Van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johanna O P Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Reza Sirous
- Radiology Department, George Washington University Hospital, Washington, DC, USA
| | - Mehri Sirous
- Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | - John Hipwell
- Centre for Medical Image Computing, University College London, London, UK
| | - Jisun Kim
- Asan Medical Center, Seoul, Republic of Korea
| | | | - Caroline Dickens
- Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
| | - Kee-Seng Chia
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Anna M Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, ON, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Anath Arzee Flugelman
- National Cancer Control Center, Lady Davis Carmel Medical Center, Faculty of Medicine, Technion-Israel Institute Technology, Haifa, Israel
| | - Dorria Salem
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Valerie McCormack
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France.
| | - Jennifer Stone
- School of Population and Global Health, The University of Western Australia, Perth, Australia
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17
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Maskarinec G, Raquinio P, Kristal BS, Franke AA, Buchthal SD, Ernst TM, Monroe KR, Shepherd JA, Shvetsov YB, Le Marchand L, Lim U. Body Fat Distribution, Glucose Metabolism, and Diabetes Status Among Older Adults: The Multiethnic Cohort Adiposity Phenotype Study. J Epidemiol 2022; 32:314-322. [PMID: 33642515 PMCID: PMC9189316 DOI: 10.2188/jea.je20200538] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND As the proportion of visceral (VAT) to subcutaneous adipose tissue (SAT) may contribute to type 2 diabetes (T2D) development, we examined this relation in a cross-sectional design within the Multiethnic Cohort that includes Japanese Americans known to have high VAT. The aim was to understand how ectopic fat accumulation differs by glycemic status across ethnic groups with disparate rates of obesity, T2D, and propensity to accumulate VAT. METHODS In 2013-2016, 1,746 participants aged 69.2 (standard deviation, 2.7) years from five ethnic groups completed questionnaires, blood collections, and whole-body dual X-ray absorptiometry and abdominal magnetic resonance imaging scans. Participants with self-reported T2D and/or medication were classified as T2D, those with fasting glucose >125 and 100-125 mg/dL as undiagnosed cases (UT2D) and prediabetes (PT2D), respectively. Using linear regression, we estimated adjusted means of adiposity measures by T2D status. RESULTS Overall, 315 (18%) participants were classified as T2D, 158 (9%) as UT2D, 518 (30%) as PT2D, and 755 (43%) as normoglycemic (NG), with significant ethnic differences (P < 0.0001). In fully adjusted models, VAT, VAT/SAT, and percent liver fat increased significantly from NG, PT2D, UT2D, to T2D (P < 0.001). Across ethnic groups, the VAT/SAT ratio was lowest for NG participants and highest for T2D cases. Positive trends were observed in all groups except African Americans, with highest VAT/SAT in Japanese Americans. CONCLUSION These findings indicate that VAT plays an important role in T2D etiology, in particular among Japanese Americans with high levels of ectopic adipose tissue, which drives the development of T2D to a greater degree than in other ethnic groups.
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Affiliation(s)
| | | | - Bruce S. Kristal
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | | | | | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, HI, USA
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18
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Bogumil D, Cortessis V, Wilkens L, Setiawan VW, Haiman C, Marchand LL, Maskarinec G. Abstract 734: History of diabetes is differentially associated with urothelial cancer risk in understudied racial/ethnic groups in the Multiethnic Cohort Study (MEC). Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Type 2 diabetes (T2D) is an established risk factor for urothelial cancer (UC), however there is very little data characterizing this association in populations of non-European ancestry. Understanding the relationship between T2DM and UC across multiple populations is important since both risk and severity of each condition differ according to race and ethnicity.
Methods: We estimated the association between T2D and UC among 185,587 MEC participants who were between 45 and 75 years old at enrollment (1993-1996) and from five major racial/ethnic groups (African Americans, Japanese Americans, Native Hawaiians, Latinos, and non-Hispanic whites). T2D status was assessed by self-report at cohort entry. UC cases were identified through linkages to statewide SEER cancer registries. We estimated the association between T2D and UC using Cox regression, adjusting for known confounders (smoking duration, smoking intensity, alcohol consumption, body mass index), stratifying on sex, age, and ethnicity. Using the full model, we assessed heterogeneity of associations by race/ethnicity.
Results: Over a mean of 19 years of follow-up, 2,398 incident UC cases were identified. The prevalence of T2D was highest among African Americans (15.7%), followed by Latinos (15.6%), Native Hawaiians (14.8%), Japanese Americans (10.5%), and non-Hispanic whites (5.9%). The age-standardized UC incidence rates were highest among whites (60/100,000) followed by Japanese Americans (42/100,000), Native Hawaiians (40/100,000), African Americans (37/100,000), and Latinos (29/100,000). Incidence of UC was 1.11 (95% CI: 0.97, 1.26)-fold greater in those who reported a history of T2D, and this association differed between racial-ethnic groups (pheterogeneity=0.04). Estimates of the association between T2D and UC achieved statistical significance among African Americans (HR=1.49; 95% CI: 1.12, 1.98) and Native Hawaiians (HR=1.68; 95% CI: 1.08, 2.60), but not among non-Hispanic whites (HR=1.06; 95% CI: 0.78, 1.43), Latinos (HR=0.96; 95% CI: 0.71, 1.29), or Japanese Americans (HR=0.95; 95% CI: 0.75, 1.21).
Conclusions: In this multiethnic analysis, estimates of the association between T2DM and UC were strongest among African American and Native Hawaiian participants. The apparently stronger associations in these groups may be due to greater severity and/or poorer control of T2DM. African Americans and Native Hawaiians may benefit from targeted interventions to better manage T2D in these populations to reduce risk of UC.
Citation Format: David Bogumil, Victoria Cortessis, Lynne Wilkens, Veronica Wendy Setiawan, Christopher Haiman, Loic Le Marchand, Gertraud Maskarinec. History of diabetes is differentially associated with urothelial cancer risk in understudied racial/ethnic groups in the Multiethnic Cohort Study (MEC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 734.
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19
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Lozano CP, Wilkens LR, Shvetsov YB, Maskarinec G, Park SY, Shepherd JA, Boushey CJ, Hebert JR, Wirth MD, Ernst T, Randolph T, Lim U, Lampe JW, Le Marchand L, Hullar MAJ. Associations of the Dietary Inflammatory Index with total adiposity and ectopic fat through the gut microbiota, LPS, and C-reactive protein in the Multiethnic Cohort-Adiposity Phenotype Study. Am J Clin Nutr 2022; 115:1344-1356. [PMID: 34871345 PMCID: PMC9071464 DOI: 10.1093/ajcn/nqab398] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/29/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Mechanisms linking a proinflammatory diet to obesity remain under investigation. The ability of diet to influence the gut microbiome (GM) in creating chronic low-grade systemic inflammation provides a plausible connection to adiposity. OBJECTIVES Assess whether any associations seen between the Energy-Adjusted Dietary Inflammatory Index (E-DII score), total fat mass, visceral adipose tissue (VAT), or liver fat (percentage volume) operated through the GM or microbial related inflammatory factors, in a multiethnic cross-sectional study. METHODS In the Multiethnic Cohort-Adiposity Phenotype Study (812 men, 843 women, aged 60-77 y) we tested whether associations between the E-DII and total adiposity, VAT, and liver fat function through the GM, LPS, and high-sensitivity C-reactive protein (hs-CRP). DXA-derived total fat mass, MRI-measured VAT, and MRI-based liver fat were measured. Participants provided stool and fasting blood samples and completed an FFQ. Stool bacterial DNA was amplified and the 16S rRNA gene was sequenced at the V1-V3 region. E-DII score was computed from FFQ data, with a higher E-DII representing a more proinflammatory diet. The associations between E-DII score, GM (10 phyla, 28 genera, α diversity), and adiposity phenotypes were examined using linear regression and mediation analyses, adjusting for confounders. RESULTS There were positive total effects (c) between E-DII and total fat mass (c = 0.68; 95% CI: 0.47, 0.90), VAT (c = 4.61; 95% CI: 2.95, 6.27), and liver fat (c = 0.40; 95% CI: 0.27, 0.53). The association between E-DII score and total fat mass was mediated by LPS, Flavonifractor, [Ruminococcus] gnavus group, and Tyzzerella. The association between E-DII score and ectopic fat occurred indirectly through Fusobacteria, Christensenellaceae R-7 group, Coprococcus 2, Escherichia-Shigella, [Eubacterium] xylanophilum group, Flavonifractor, Lachnoclostridium, [Ruminococcus] gnavus group, Tyzzerella, [Ruminococcus] gnavus group (VAT only), and α diversity (liver fat only). There was no significant association between E-DII score and adiposity phenotype through hs-CRP. CONCLUSIONS Associations found between E-DII and adiposity phenotypes occurred through the GM and LPS.
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Affiliation(s)
| | | | | | | | - Song-Yi Park
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | | | - James R Hebert
- University of South Carolina,Cancer Prevention and Control Program, Department of Epidemiology and Biostatistics, Arnold School of Public Health, Columbia, SC, USA
| | - Michael D Wirth
- University of South Carolina,Cancer Prevention and Control Program, Department of Epidemiology and Biostatistics, Arnold School of Public Health, Columbia, SC, USA
| | - Thomas Ernst
- University of Maryland, Department of Diagnostic Radiology and Nuclear Medicine, Baltimore, MD, USA
| | - Timothy Randolph
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, USA
| | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Johanna W Lampe
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, USA
| | | | - Meredith A J Hullar
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, WA, USA
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20
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Wang X, Chen H, Kapoor PM, Su YR, Bolla MK, Dennis J, Dunning AM, Lush M, Wang Q, Michailidou K, Pharoah PD, Hopper JL, Southey MC, Koutros S, Freeman LEB, Stone J, Rennert G, Shibli R, Murphy RA, Aronson K, Guénel P, Truong T, Teras LR, Hodge JM, Canzian F, Kaaks R, Brenner H, Arndt V, Hoppe R, Lo WY, Behrens S, Mannermaa A, Kosma VM, Jung A, Becher H, Giles GG, Haiman CA, Maskarinec G, Scott C, Winham S, Simard J, Goldberg MS, Zheng W, Long J, Troester MA, Love MI, Peng C, Tamimi R, Eliassen H, García-Closas M, Figueroa J, Ahearn T, Yang R, Evans DG, Howell A, Hall P, Czene K, Wolk A, Sandler DP, Taylor JA, Swerdlow AJ, Orr N, Lacey JV, Wang S, Olsson H, Easton DF, Milne RL, Hsu L, Kraft P, Chang-Claude J, Lindström S. A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women. Cancer Res Commun 2022; 2:211-219. [PMID: 36303815 PMCID: PMC9604427 DOI: 10.1158/2767-9764.crc-21-0119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 06/14/2023]
Abstract
Background Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. Methods We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. Results After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). Conclusion In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Impact Our study suggests a limited role of gene-environment interactions in breast cancer risk.
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Affiliation(s)
- Xiaoliang Wang
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Hongjie Chen
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Pooja Middha Kapoor
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Manjeet K. Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Joe Dennis
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Alison M. Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Michael Lush
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Qin Wang
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Paul D.P. Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, 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, The University of Melbourne, Victoria, Australia
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetic, NCI, NIH, Bethesda, Maryland
| | | | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Crawley, Australia
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
| | - Rana Shibli
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
| | - Rachel A. Murphy
- Cancer Control Research, BC Cancer and School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Kristan Aronson
- Public Health Sciences, Queen's University, Kingston, Canada
| | - Pascal Guénel
- Université Paris-Saclay, Inserm, CESP, Team Exposome and Heredity, Villejuif, France
| | - Thérèse Truong
- Université Paris-Saclay, Inserm, CESP, Team Exposome and Heredity, Villejuif, France
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, Georgia
| | - James M. Hodge
- Department of Population Science, American Cancer Society, Atlanta, Georgia
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Reiner Hoppe
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, German
| | - Wing-Yee Lo
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, German
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Veli-Matti Kosma
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heiko Becher
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | | | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Stacey Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, Quebec, Canada
| | - Mark S. Goldberg
- Department of Medicine, McGill University, Montréal, Quebec, Canada; Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, Quebec, Canada
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jirong Long
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Melissa A. Troester
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Michael I. Love
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Cheng Peng
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women's Hospital, Boston, Massachusetts
| | - Rulla Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | - Jonine Figueroa
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetic, NCI, NIH, Bethesda, Maryland
| | - Rose Yang
- Division of Cancer Epidemiology and Genetic, NCI, NIH, Bethesda, Maryland
| | - D. Gareth Evans
- Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Genomic Medicine, St Mary's Hospital, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Dale P. Sandler
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, North Carolina
| | - Jack A. Taylor
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, North Carolina
| | - Anthony J. Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
- Division of Breast Cancer Research, The Institute of Cancer Research, London, United K.ingdom
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom
| | - James V. Lacey
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California
| | - Sophia Wang
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California
| | - Håkan Olsson
- Departments of Oncology and Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
- Deceased
| | - Douglas F. Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Sara Lindström
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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Maskarinec G, Shvetsov YB, Wong MC, Garber A, Monroe K, Ernst TM, Buchthal SD, Lim U, Marchand LL, Heymsfield SB, Shepherd JA. Subcutaneous and visceral fat assessment by DXA and MRI in older adults and children. Obesity (Silver Spring) 2022; 30:920-930. [PMID: 35253409 PMCID: PMC10181882 DOI: 10.1002/oby.23381] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/16/2021] [Accepted: 12/30/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Given the importance of body fat distribution in chronic disease development, feasible methods to assess body fat are essential. This study compared dual-energy x-ray absorptiometry (DXA) in measuring visceral and subcutaneous adipose tissue (VAT and SAT) with magnetic resonance imaging (MRI). METHODS VAT and SAT were assessed using similar DXA and MRI protocols among 1,795 elderly participants of the Adiposity Phenotype Study (APS) and 309 children/adolescents in Shape Up! Kids (SKids). Spearman correlations, Bland-Altman plots, and coefficients of determination (R2 ) assessed agreement between DXA and MRI measures. RESULTS DXA overestimated SAT values in APS (315 vs. 229 cm2 ) and SKids (212 vs. 161 cm2 ), whereas DXA underestimated VAT measures (141 vs. 167 cm2 ) in adults only. The correlations between DXA and MRI values were stronger for SAT than VAT (APS: r = 0.92 vs. 0.88; SKids: 0.90 vs. 0.74). Bland-Altman plots confirmed better agreement for SAT than VAT despite differences by sex, ethnicity, and weight status with respective R2 values for SAT and VAT of 0.88 and 0.84 (APS) and 0.81 and 0.69 (SKids). CONCLUSION These findings indicate that SAT by DXA reflects MRI measures in children and older adults, whereas agreement for VAT is weaker for individuals with low VAT levels.
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Affiliation(s)
- Gertraud Maskarinec
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Yurii B. Shvetsov
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Michael C. Wong
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Andrea Garber
- School of Medicine, University of California at San Francisco, San Francisco, California, USA
| | - Kristine Monroe
- Preventive Medicine, University of Southern California, Los Angeles, California, USA
| | - Thomas M. Ernst
- Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Steven D. Buchthal
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Unhee Lim
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Loïc Le Marchand
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | | | - John A. Shepherd
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
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Valdez D, Cruz T, Rania S, Badowski G, Cassel K, Wolfgruber T, Grosskreutz S, Dulana LJ, Adonay R, Maskarinec G, Shepherd JA. Technical note: Low clinical efficacy, but good acceptability of a point-of-care electronic palpation device for breast cancer screening for a lower middle-income environment. Med Phys 2022; 49:2663-2671. [PMID: 35106767 PMCID: PMC9007865 DOI: 10.1002/mp.15499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/30/2021] [Accepted: 01/19/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Late-stage breast cancer rates in the Pacific where mammography services are limited are exceedingly high: Marshall Islands (61%), Palau (94%), and Samoa (79%). Due to the limited medical resources in these areas an alternative accessible technology is needed. The iBreast Exam (iBE) is a point-of-care electronic palpitation device that has a reported sensitivity of 86%. However, little is known about the performance and acceptability of this device for women in the Pacific. METHODS A total of 39 women (ages 42-73 years) were recruited in Guam with 19 women having a mammogram requiring biopsy (Breast Imaging-Reporting and Data System [BI-RADS] category 4 or above) and 20 women with a negative screening mammogram before the study visit. Participants received an iBE exam and completed a 26-item breast health questionnaire to evaluate the iBE. Furthermore, the performance characteristics of the iBE were tested using gelatin breast phantoms in terms of tumor size, tumor depth, and overall breast stiffness. RESULTS The iBE had a sensitivity of 20% (two true positives to eight false negatives) and specificity of 92% (24 false positives to 278 true negatives) when analyzed based on the location of the tumor by quadrant. The iBE also had generally poor agreement according to a Cohen's kappa value of 0.068. The phantom experiments showed that the iBE can detect tumors as deep as 2.5 cm, but only if the lesion is greater than 8 mm in diameter. However, the iBE did demonstrate acceptability; 67% of the women reported that they had high trust in iBE as an early detection device. CONCLUSIONS The iBE had generally poor sensitivity and specificity when tested in a clinical setting which does not allow its use as a screening tool. IMPACT This study demonstrates the need for an alternative screening method other than electronic palpation for lower-middle-income areas.
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Affiliation(s)
- Dustin Valdez
- Graduate Program in Human Nutrition, University of Hawai’i Manoa, Honolulu, Hawaii, 96822, USA
- University of Hawai’i Cancer Center, Honolulu, Honolulu, Hawaii, 96813, USA
| | | | - Stephanie Rania
- University of Hawai’i Cancer Center, Honolulu, Honolulu, Hawaii, 96813, USA
| | | | - Kevin Cassel
- University of Hawai’i Cancer Center, Honolulu, Honolulu, Hawaii, 96813, USA
| | - Thomas Wolfgruber
- University of Hawai’i Cancer Center, Honolulu, Honolulu, Hawaii, 96813, USA
| | | | | | - Roy Adonay
- Guam Radiology Consultants, Tamuning, 96913, Guam
| | | | - John A. Shepherd
- University of Hawai’i Cancer Center, Honolulu, Honolulu, Hawaii, 96813, USA
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Valdez D, Fukui J, Wolfgruber T, Leong L, Maskarinec G, Shepherd J. Abstract P3-01-13: Comparing portable and clinical ultrasound systems using 3D printed breast phantom inserts. Cancer Res 2022. [DOI: 10.1158/1538-7445.sabcs21-p3-01-13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Late-stage breast cancer rates in the Pacific where mammography services are limited are exceedingly high. Therefore, alternative accessible breast cancer screening technologies such as portable ultrasound is needed. However, little is known about the performance of portable ultrasound when compared to clinical ultrasound for use in breast cancer screening. By utilizing 3D printing technology, it is possible to design breast phantom inserts to replicate various types of lesions. In this study, we utilized 3D printed breast phantom inserts to compare portable and clinical ultrasound lesion detection performance. Methods: Four different breast inserts were designed using FreeCAD (version 0.19) to replicate different lesion detection properties. The first insert compares lesion shape, the second insert investigates depth and size, the third insert looks at fiber diameter, and the fourth insert looks at clusters. The four inserts were printed using a photopolymer resign (Formlabs Inc Rigid resign, Somerville, MA, USA) and then placed in a gelatin-based breast phantom designed for ultrasound use. Using the portable ultrasound (GE Vscan Extend) and clinical ultrasound (Philips EPIQ 5G), various images were captured of identical angle and orientation for both devices. The number of lesions visualized were counted and presented as a percentage of lesions detected. Results: The portable ultrasound had a 100% lesion detection rate for breast insert 1, 90.3% for breast insert 2, 70% for breast insert 3 and 55.8% for breast insert 4. Clinical ultrasound had 100% lesion detection rate for breast insert 1, 93.1% for breast insert 2, 76.6% for breast insert 3, and 99% for breast insert 4. Conclusion: Portable ultrasound shows comparable lesion detection capabilities to clinical ultrasound in 3 of the 4 breast phantom insert tests. Portable ultrasound may have potential as a capable accessible breast cancer screening device in areas without mammography.
Citation Format: Dustin Valdez, Jami Fukui, Thomas Wolfgruber, Lambert Leong, Gertraud Maskarinec, John Shepherd. Comparing portable and clinical ultrasound systems using 3D printed breast phantom inserts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-01-13.
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Affiliation(s)
| | - Jami Fukui
- University of Hawaii Cancer Center, Honolulu, HI
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Haraldsdottir A, Steingrimsdottir L, Maskarinec G, Adami HO, Aspelund T, Valdimarsdottir UA, Bjarnason R, Thorsdottir I, Halldorsson TI, Gunnarsdottir I, Tryggvadottir L, Gudnason V, Birgisdottir BE, Torfadottir JE. Growth Rate in Childhood and Adolescence and the Risk of Breast and Prostate Cancer: A Population-Based Study. Am J Epidemiol 2022; 191:320-330. [PMID: 34643238 DOI: 10.1093/aje/kwab250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 09/16/2021] [Accepted: 10/06/2021] [Indexed: 11/12/2022] Open
Abstract
Growth rate is regulated by hormonal pathways that might affect early cancer development. We explored the association between rate of growth in height from ages 8 to 13 years (childhood) and from age 13 to attainment of adult height (adolescence), as measured at study entry, and the risk of breast or prostate cancer. Participants were 2,037 Icelanders born during 1915-1935, who took part in the Reykjavik Study, established in 1967. Height measurements were obtained from school records and at study entry. We used multivariable Cox regression models to calculate hazard ratios with 95% confidence intervals of breast and prostate cancer by rates of growth in tertiles. During a mean follow-up of 66 years (women) and 64 years (men), 117 women were diagnosed with breast cancer and 118 men with prostate cancer (45 with advanced disease). Women in the highest growth-rate tertile in adolescence had a higher risk of breast cancer (hazard ratio = 2.4, 95% confidence interval: 1.3, 4.3) compared with women in the lowest tertile. A suggestive inverse association was observed for highest adolescent growth rate in men and advanced prostate cancer: hazard ratio = 0.4, 95% confidence interval: 0.2, 1.0. Rapid growth, particularly in adolescence may affect cancer risk later in life.
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Chen H, Fan S, Stone J, Thompson DJ, Douglas J, Li S, Scott C, Bolla MK, Wang Q, Dennis J, Michailidou K, Li C, Peters U, Hopper JL, Southey MC, Nguyen-Dumont T, Nguyen TL, Fasching PA, Behrens A, Cadby G, Murphy RA, Aronson K, Howell A, Astley S, Couch F, Olson J, Milne RL, Giles GG, Haiman CA, Maskarinec G, Winham S, John EM, Kurian A, Eliassen H, Andrulis I, Evans DG, Newman WG, Hall P, Czene K, Swerdlow A, Jones M, Pollan M, Fernandez-Navarro P, McConnell DS, Kristensen VN, Rothstein JH, Wang P, Habel LA, Sieh W, Dunning AM, Pharoah PDP, Easton DF, Gierach GL, Tamimi RM, Vachon CM, Lindström S. Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci. Breast Cancer Res 2022; 24:27. [PMID: 35414113 PMCID: PMC9006574 DOI: 10.1186/s13058-022-01524-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/02/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. METHODS We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. RESULTS We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes. CONCLUSIONS Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.
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Affiliation(s)
- Hongjie Chen
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA
| | - Shaoqi Fan
- grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Jennifer Stone
- grid.1012.20000 0004 1936 7910School of Population and Global Health, University of Western Australia, Crawley, Australia
| | - Deborah J. Thompson
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Julie Douglas
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA ,grid.60094.3b0000 0001 2270 6467Department of Mathematics and Statistics, Skidmore College, Saratoga Springs, NY USA
| | - Shuai Li
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK ,grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Christopher Scott
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Manjeet K. Bolla
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- grid.417705.00000 0004 0609 0940Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus ,grid.417705.00000 0004 0609 0940Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Christopher Li
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Ulrike Peters
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - John L. Hopper
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia
| | - Melissa C. Southey
- grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Tu Nguyen-Dumont
- grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Tuong L. Nguyen
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia
| | - Peter A. Fasching
- grid.411668.c0000 0000 9935 6525Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Annika Behrens
- grid.411668.c0000 0000 9935 6525Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Gemma Cadby
- grid.1012.20000 0004 1936 7910School of Population and Global Health, University of Western Australia, Crawley, Australia
| | - Rachel A. Murphy
- grid.17091.3e0000 0001 2288 9830Cancer Control Research, BC Cancer and School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Kristan Aronson
- grid.410356.50000 0004 1936 8331Public Health Sciences, Queen’s University, Kingston, Canada
| | - Anthony Howell
- grid.5379.80000000121662407Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Susan Astley
- grid.5379.80000000121662407Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Fergus Couch
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Janet Olson
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Roger L. Milne
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia ,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Graham G. Giles
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.5379.80000000121662407Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK ,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Christopher A. Haiman
- grid.42505.360000 0001 2156 6853Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Gertraud Maskarinec
- grid.410445.00000 0001 2188 0957Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
| | - Stacey Winham
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Esther M. John
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Allison Kurian
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Heather Eliassen
- grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA ,grid.62560.370000 0004 0378 8294Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Irene Andrulis
- grid.250674.20000 0004 0626 6184Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - D. Gareth Evans
- grid.5379.80000000121662407Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK ,grid.462482.e0000 0004 0417 0074Genomic Medicine, St Mary’s Hospital, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK ,grid.498924.a0000 0004 0430 9101NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - William G. Newman
- grid.5379.80000000121662407Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Per Hall
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anthony Swerdlow
- grid.18886.3fDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Michael Jones
- grid.18886.3fDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Marina Pollan
- grid.413448.e0000 0000 9314 1427Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Pablo Fernandez-Navarro
- grid.413448.e0000 0000 9314 1427Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Daniel S. McConnell
- grid.214458.e0000000086837370Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Vessela N. Kristensen
- grid.55325.340000 0004 0389 8485Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | | | - Joseph H. Rothstein
- grid.59734.3c0000 0001 0670 2351Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Pei Wang
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Laurel A. Habel
- grid.280062.e0000 0000 9957 7758Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Weiva Sieh
- grid.59734.3c0000 0001 0670 2351Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alison M. Dunning
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D. P. Pharoah
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Douglas F. Easton
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Gretchen L. Gierach
- grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Rulla M. Tamimi
- grid.5386.8000000041936877XDivision of Epidemiology, Population Health Science, Weill Cornell Medicine, New York, NY USA
| | - Celine M. Vachon
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Sara Lindström
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
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McVey T, Herzog T, Maskarinec G, Plaza K, Ching J, Legaspi J, Mak V, Orasud A, Quintal G. Cancer Health Disparities Research Training: A Qualitative Report. Pac Asia Inq 2022; 13:46-63. [PMID: 37501935 PMCID: PMC10373443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The Research Education Core of the Pacific Islands Partnership for Cancer Health Equity (PIPCHE) conducted a systematic review of participant learning. All students from both the University of Guam and the University of Hawai'i who have completed the program were asked two open-ended questions, which were then thematically analyzed. (1) What impact did the training have on your career? (2) What did you learn about cancer health disparities? Findings include themes such as expanding social networks, building professional skills, providing opportunities and funding, inspiring a future career in research, and giving back to the community. The results also indicate that students learned that cancer disparities research was complex and diverse, required cultural sensitivity, different areas of cancer research and education, the importance of mentor and peer relationships. Trainees spoke very favorably about the weekly seminar format. These findings are consistent with studies in other similar programs. The authors recommend future educational outcome research.
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Frankenfeld CL, Hullar MA, Maskarinec G, Monroe KR, Shepherd JA, Franke AA, Randolph TW, Wilkens LR, Boushey CJ, Le Marchand L, Lim U, Lampe JW. The Gut Microbiome Is Associated with Circulating Dietary Biomarkers of Fruit and Vegetable Intake in a Multiethnic Cohort. J Acad Nutr Diet 2022; 122:78-98. [PMID: 34226163 PMCID: PMC9019929 DOI: 10.1016/j.jand.2021.05.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 04/01/2021] [Accepted: 05/20/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Results from observational studies suggest high diet quality favorably influences the human gut microbiome. Fruit and vegetable consumption is often a key contributor to high diet quality. OBJECTIVE To evaluate measures of gut bacterial diversity and abundance in relation to serum biomarkers of fruit and vegetable intake. DESIGN Secondary analysis of cross-sectional data. PARTICIPANTS AND SETTING Men and women from Los Angeles, CA, and Hawai'i who participated in the Multiethnic Cohort-Adiposity Phenotype Study from 2013 to 2016 (N = 1,709). MAIN OUTCOME MEASURES Gut microbiome diversity and composition in relation to dietary biomarkers. STATISTICAL ANALYSIS Carotenoid (beta carotene, alpha carotene, cryptoxanthins, lutein, lycopene, and zeaxanthin), tocopherol (α, β + γ, and δ), and retinol concentrations were assessed in serum. The α and β diversity and composition of the gut microbiome were classified based on 16S rRNA gene sequencing of bacterial DNA from self-collected fecal samples. Global differences in microbial community profiles in relation dietary biomarkers were evaluated using multivariable permutational analysis of variance. Associations of α diversity (Shannon index), β diversity (weighted and unweighted UniFrac) with center log-ratio-transformed phyla and genera abundances were evaluated using linear regression, adjusted for covariates. RESULTS Increasing total carotenoid, beta carotene, alpha carotene, cryptoxanthin, and lycopene concentrations were associated with higher gut bacterial diversity (Shannon Index) (P < 0.001). Total tocopherol, α-tocopherol, and δ-tocopherol concentrations contributed significantly to more than 1% of the microbiome variation in gut bacterial community: total tocopherol: 1.74%; α-tocopherol: 1.70%; and δ-tocopherol: 1.16% (P < 0.001). Higher total carotenoid was associated with greater abundance of some genera relevant for microbial macronutrient metabolism (P < 0.001). CONCLUSIONS Objective biomarkers of fruit and vegetable intake, particularly carotenoids, were favorably associated with gut bacterial composition and diversity in this multiethnic population. These observations provide supportive evidence that fruit and vegetable intake is related to gut bacterial composition; more work is needed to elucidate how this influences host health.
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Affiliation(s)
- Cara L. Frankenfeld
- George Mason University, 4400 University Drive MS 5B7, Fairfax, VA, 22030,Associate Professor and Program Director, Master of Public Health Program; University of Puget Sound, 1500 N. Warner St, Tacoma, WA 98416
| | | | | | | | - John A. Shepherd
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Adrian A. Franke
- Cancer Biology Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Timothy W. Randolph
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109
| | - Lynne R. Wilkens
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Carol J. Boushey
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Loïc Le Marchand
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Unhee Lim
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813
| | - Johanna W. Lampe
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109
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Raquinio PASH, Maskarinec G, Dela Cruz R, Setiawan VW, Kristal BS, Wilkens LR, Le Marchand L. Type 2 Diabetes Among Filipino American Adults in the Multiethnic Cohort. Prev Chronic Dis 2021; 18:E98. [PMID: 34818147 PMCID: PMC8673944 DOI: 10.5888/pcd18.210240] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION Several Asian racial and ethnic groups, including individuals of Filipino ancestry, are at higher risk of developing type 2 diabetes than White individuals, despite their lower body mass index (BMI). This study examined determinants of type 2 diabetes among Filipino American adults in the Multiethnic Cohort Study. METHODS Participants in Hawaii and Los Angeles completed questionnaires on demographics, diet, and anthropometrics. Generational status was determined according to birthplace of participants and their parents. Based on self-reported data and data on medications, type 2 diabetes status was classified as no, prevalent, or incident. We used polytomous logistic regression, while adjusting for confounders, to obtain odds ratios. RESULTS Among 10,681 Multiethnic Cohort Study participants reporting any Filipino ancestry, 57% were 1st-, 17% were 2nd-, and 25% were 3rd-generation Filipino Americans. Overall, 13% and 17% of participants had a prevalent or incident type 2 diabetes diagnosis. Overweight and obesity and the presence of other risk factors increased from the 1st to subsequent generations. First-generation immigrants were less likely to report type 2 diabetes at cohort entry than immigrants of subsequent generations who were born in the US or whose parents were born in the US; only the prevalence of type 2 diabetes was significantly elevated in the 2nd generation compared with the 1st generation. CONCLUSION The results support the hypothesis that Filipino migrants adopt lifestyle factors of the host country and subsequent generations experience higher type 2 diabetes rates due to changes in risk factor patterns.
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Affiliation(s)
| | - Gertraud Maskarinec
- University of Hawaii Cancer Center, Honolulu, Hawaii
- University of Hawaii Cancer Center, 701 Ilalo St, Honolulu, HI 96813.
| | | | | | - Bruce S Kristal
- Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
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Wong MC, Ng BK, Tian I, Sobhiyeh S, Pagano I, Dechenaud M, Kennedy SF, Liu YE, Kelly NN, Chow D, Garber AK, Maskarinec G, Pujades S, Black MJ, Curless B, Heymsfield SB, Shepherd JA. A pose-independent method for accurate and precise body composition from 3D optical scans. Obesity (Silver Spring) 2021; 29:1835-1847. [PMID: 34549543 PMCID: PMC8570991 DOI: 10.1002/oby.23256] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE The aim of this study was to investigate whether digitally re-posing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose. METHODS Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs). RESULTS The analysis included 472 participants after exclusions. After re-posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female. CONCLUSIONS Re-posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.
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Affiliation(s)
- Michael C Wong
- Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Bennett K Ng
- Department of Emerging Growth and Incubation, Intel Corp., Santa Clara, California, USA
| | - Isaac Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Sima Sobhiyeh
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Ian Pagano
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Marcelline Dechenaud
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Samantha F Kennedy
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Yong E Liu
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Dominic Chow
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii, USA
| | - Andrea K Garber
- School of Medicine, University of California, San Francisco, California, USA
| | - Gertraud Maskarinec
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Sergi Pujades
- Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Michael J Black
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Brian Curless
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - John A Shepherd
- Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
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30
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Okada Y, Park SY, Wilkens LR, Maskarinec G, Shvetsov YB, Haiman C, Le Marchand L. White Rice Consumption and Risk for Colorectal Cancer among Japanese Americans: The Multiethnic Cohort Study. J Epidemiol 2021; 33:170-176. [PMID: 34380917 PMCID: PMC9939926 DOI: 10.2188/jea.je20200611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND White rice is a staple food for Japanese, a population at high risk for colorectal cancer (CRC). We investigated the association between white rice intake and CRC among Japanese Americans in the Multiethnic Cohort (MEC) study. METHODS The Multiethnic Cohort Study is a prospective study established in Hawaii and California in 1993-1996. Usual dietary intake was assessed by a validated quantitative food frequency questionnaire at baseline. Cox proportional hazards models were used to compute hazard ratios (HR) and 95% confidence intervals (CI) for quartiles of intake and to perform trend tests across sex-specific quartiles with adjustment for relevant confounders. RESULTS We identified 1,553 invasive CRC cases among 49,136 Japanese Americans (23,595 men and 25,541 women) during a mean follow-up of 19 years. White rice consumption was not associated with overall CRC incidence in men (p-trend = 0.11) or women (p-trend = 0.56). After excluding participants with a history of diabetes, the inverse associations were significant for CRC (p-trend = 0.03, HR for quartile 4 (Q4) vs. 1 = 0.81; 95% CI: 0.64-1.03) and tumors of the distal colon (p-trend = 0.006, HR for Q4 vs. Q1: 0.66; 0.44-0.99) among men but not women. CONCLUSIONS White rice consumption was not associated with an increased risk of overall CRC among Japanese Americans. An inverse association was observed with risk of CRC and distal colon cancer in men without a history of diabetes.
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Affiliation(s)
- Yuito Okada
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center.,Office of Public Health Studies, University of Hawaii
| | - Song-Yi Park
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center
| | - Lynne R Wilkens
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center
| | - Gertraud Maskarinec
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center
| | - Yurii B Shvetsov
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California
| | - Loïc Le Marchand
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center
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Ma E, Maskarinec G, Lim U, Boushey CJ, Wilkens LR, Setiawan VW, Le Marchand L, Randolph TW, Jenkins IC, Curtis KR, Lampe JW, Hullar MA. Long-term association between diet quality and characteristics of the gut microbiome in the multiethnic cohort study. Br J Nutr 2021; 128:1-10. [PMID: 34369335 PMCID: PMC8825880 DOI: 10.1017/s0007114521002968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
As past usual diet quality may affect gut microbiome (GM) composition, we examined the association of the Healthy Eating Index (HEI)-2015 assessed 21 and 9 years before stool collection with measures of fecal microbial composition in a subset of the Multiethnic Cohort. A total of 5936 participants completed a validated quantitative FFQ (QFFQ) at cohort entry (Q1, 1993-1996), 5280 at follow-up (Q3, 2003-2008) and 1685 also at a second follow-up (Adiposity Phenotype Study (APS), 2013-2016). All participants provided a stool sample in 2013-2016. Fecal microbial composition was obtained from 16S rRNA gene sequencing (V1-V3 regions). HEI-2015 scores were computed based on each QFFQ. Using linear regression adjusted for relevant covariates, we calculated associations of HEI-2015 scores with gut microbial diversity and 152 individual genera. The mean HEI-2015 scores increased from Q1 (67 (sd 10)) to Q3 (71 (sd 11)) and APS (72 (sd 10)). Alpha diversity assessed by the Shannon Index was significantly higher with increasing tertiles of HEI-2015. Of the 152 bacterial genera tested, seven (Anaerostipes, Coprococcus_2, Eubacterium eligens, Lachnospira, Lachnospiraceae_ND3007, Ruminococcaceae_UCG-013 and Ruminococcus_1) were positively and five (Collinsella, Parabacteroides, Ruminiclostridium_5, Ruminococcus gnavus and Tyzzerella) were inversely associated with HEI-2015 assessed in Q1, Q3 and APS. The estimates of change per unit of the HEI-2015 score associated with the abundance of these twelve genera were consistent across the three questionnaires. The quality of past diet, assessed as far as ∼20 years before stool collection, is equally predictive of GM composition as concurrently assessed diet, indicative of the long-term consistency of this relation.
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Affiliation(s)
- Erica Ma
- University of Hawai’i Cancer Center, Honolulu, HI
| | | | - Unhee Lim
- University of Hawai’i Cancer Center, Honolulu, HI
| | | | | | - V. Wendy Setiawan
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
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Maskarinec G, Raquinio PA, Setiawan VW, Ernst T, Franke AA, Buchthal SD, Shepherd JA, Wilkens LR, Lim U, Le Marchand L. Biomarker-based visceral adiposity score and incident type 2 diabetes in the multiethnic cohort. Ann Epidemiol 2021; 63:29-34. [PMID: 34298074 DOI: 10.1016/j.annepidem.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/16/2021] [Accepted: 07/06/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE Visceral adipose tissue (VAT) may be more important than subcutaneous fat in type 2 diabetes (T2D) etiology. We examined a VAT score developed in reference to MRI measurement of VAT in the Multiethnic Cohort (MEC) as a risk factor for incident T2D. METHODS Two nested case-control studies of cancer allowed calculation of the VAT score based on anthropometric measures and 8 biomarkers among 2,556 participants without T2D. Incident cases were identified from Medicare linkages and self-reports after blood draws in 2001-2006. Cox regression with age as time metric was applied to estimate the association of the VAT score with T2D. RESULTS During 10.1 ± 2.4 years, 355 incident T2D cases were identified. VAT scores were higher in T2D cases than among those without disease (5.06±0.43 vs. 4.95±0.41; P<0.0001) and significantly associated with T2D (HR = 2.70; 95%CI 1.60, 4.58 per unit) with similar values in men (HR = 2.99; 95%CI 1.03, 8.73) and women (HR = 2.61; 95%CI 1.39, 4.91). A significant association was observed in all five ethnic groups but only statistically significant among Japanese Americans (HR = 6.24; 95%CI 2.34, 16.68). CONCLUSION These findings support that VAT as estimated by a biomarker-based score predicts T2D incidence beyond BMI in particular among older adults of Japanese ancestry.
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Affiliation(s)
| | | | - Veronica W Setiawan
- Keck School of Medicine, University of Southern California, Los Angeles, CA.
| | - Thomas Ernst
- University of Maryland School of Medicine, Baltimore, MD.
| | | | | | | | | | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, HI.
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Maskarinec G, Raquinio P, Kristal BS, Setiawan VW, Wilkens LR, Franke AA, Lim U, Le Marchand L, Randolph TW, Lampe JW, Hullar MAJ. The gut microbiome and type 2 diabetes status in the Multiethnic Cohort. PLoS One 2021; 16:e0250855. [PMID: 34161346 PMCID: PMC8221508 DOI: 10.1371/journal.pone.0250855] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 04/15/2021] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND The gut microbiome may play a role in inflammation associated with type 2 diabetes (T2D) development. This cross-sectional study examined its relation with glycemic status within a subset of the Multiethnic Cohort (MEC) and estimated the association of circulating bacterial endotoxin (measured as plasma lipopolysaccharide-binding protein (LBP)) with T2D, which may be mediated by C-reactive protein (CRP). METHODS In 2013-16, cohort members from five ethnic groups completed clinic visits, questionnaires, and stool and blood collections. Participants with self-reported T2D and/or taking medication were considered T2D cases. Those with fasting glucose >125 and 100-125 mg/dL were classified as undiagnosed (UT2D) and pre-diabetes (PT2D) cases, respectively. We characterized the gut microbiome through 16S rRNA gene sequencing and measured plasma LBP and CRP by standard assays. Linear regression was applied to estimate associations of the gut microbiome community structure and LBP with T2D status adjusting for relevant confounders. RESULTS Among 1,702 participants (59.9-77.4 years), 735 (43%) were normoglycemic (NG), 506 (30%) PT2D, 154 (9%) UT2D, and 307 (18%) T2D. The Shannon diversity index decreased (ptrend = 0.05), while endotoxin, measured as LBP, increased (ptrend = 0.0003) from NG to T2D. Of 10 phyla, Actinobacteria (ptrend = 0.007), Firmicutes (ptrend = 0.003), and Synergistetes (ptrend = 0.02) were inversely associated and Lentisphaerae (ptrend = 0.01) was positively associated with T2D status. Clostridium sensu stricto 1, Lachnospira, and Peptostreptococcaceae were less, while Escherichia-Shigella and Lachnospiraceae were more abundant among T2D patients, but the associations with Actinobacteria, Clostridium sensu stricto 1, and Escherichia-Shigella may be due metformin use. PT2D/UT2D values were closer to NG than T2D. No indication was detected that CRP mediated the association of LBP with T2D. CONCLUSIONS T2D but not PT2D/UT2D status was associated with lower abundance of SCFA-producing genera and a higher abundance of gram-negative endotoxin-producing bacteria suggesting that the gut microbiome may contribute to chronic systemic inflammation and T2D through bacterial translocation.
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Affiliation(s)
- Gertraud Maskarinec
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
- * E-mail:
| | - Phyllis Raquinio
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
| | - Bruce S. Kristal
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Veronica W. Setiawan
- Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Lynne R. Wilkens
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
| | - Adrian A. Franke
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
| | - Unhee Lim
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
| | - Loïc Le Marchand
- Population Sciences in the Pacific, University of Hawai’i Cancer Center, Honolulu, Hawaii, United States of America
| | - Timothy W. Randolph
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Johanna W. Lampe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Meredith A. J. Hullar
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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Borrello K, Park SY, Lim U, Maskarinec G, Boushey C, Wilkens L, Randolph T, Marchand LL, Hullar M, Lampe J. Food Intakes Mediate Ethnic Differences in the Gut Microbiome. Curr Dev Nutr 2021. [DOI: 10.1093/cdn/nzab054_005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objectives
Gut microbial composition has been associated with diet quality and health outcomes and has also been found to vary across ethnic groups. We explored how ethnic differences in food consumption may mediate some of the ethnic variation in the gut microbiome (GM).
Methods
In a subset of 5,280 Multiethnic Cohort participants, based on a food frequency questionnaire administered in 2003–2008, we assessed overall diet quality using the Healthy Eating Index (HEI-2015; scores 0–100) and specific food consumption using the intake amounts of 10 component food groups used to define common diet quality indexes. GM composition was obtained from 16S rRNA gene sequencing of a stool sample (2013–2016; age 59–98 years) and estimated in genus proportions (as the centered log ratio (CLR) transformed counts). Using mediation analysis, we determined the % of the total ethnicity effect on the genus proportion for each of the ethnically divergent bacterial genera mediated by overall diet quality and component food groups, while adjusting for age, sex, total energy intake, body mass index, and antibiotics use.
Results
Overall diet quality was highest in Whites (mean HEI-2015 = 73.1) and African Americans (72.7), followed by Japanese Americans (71.3), Native Hawaiians (70.1) and Latinos (68.9). Of the 152 genera, 7 with the largest ethnic variation (CLR difference for the genus between most vs. least abundant ethnic group > 1) were examined for mediation. Five genera showed significant mediation of their ethnic differences through diet (P < 0.00,045 for Bonferroni correction), ranging from the −12% for Flavonifractor in African Americans vs. Whites mediated by vegetable intake to the 19% for Christensenellaceae R-7 group in Latinos vs. Whites mediated by overall diet quality. Large mediation effects were also seen by saturated fat for Ruminococcaceae NK4A214 in African Americans vs. Whites (18%) and by alcohol for Erysipelatoclostridium in Latinos vs. Whites (18%).
Conclusions
Overall diet quality and component foods may contribute substantially to ethnic differences in gut bacterial composition. These novel findings may help the development of targeted dietary interventions to improve the gut health of specific ethnic groups.
Funding Sources
This work was supported by the US National Cancer Institute (NCI) grants P01 CA168530, U01 CA164973, and P30 CA71789.
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Panizza C, Wilkens L, Shvetsov Y, Maskarinec G, Park SY, Shepherd J, Boushey C, Hebert J, Wirth M, Ernst T, Randolph T, Lim U, Lampe J, Le ML, Hullar M. Associations of the Dietary Inflammatory Index With Total Adiposity and Ectopic Fat and the Mediating Effect of the Gut Microbiota. Curr Dev Nutr 2021. [DOI: 10.1093/cdn/nzab054_028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Objectives
To assess, in a large multiethnic cross-sectional study, associations between the Dietary Inflammatory Index (DII®) and total adiposity and ectopic fat, and whether these associations are mediated by gut microbiota (GM).
Methods
Analyses used data from 1,655 participants (812 men, 843 women, 60–77 y) in the Adiposity Phenotype Study. At clinic visit (2013–2015), DXA-based total fat mass, MRI-based visceral adipose tissue (VAT) area at L1-L5 (cm2), and liver fat (% volume) were measured. Participants provided a stool sample and completed a validated food frequency questionnaire (FFQ). Stool bacterial DNA was amplified and the V1-V3 region of the 16S rRNA gene was sequenced. As ratios, GM data were centered log-ratio transformed. DII score was computed from FFQ data, with a higher DII representing a more inflammatory diet. The relationships between DII, GM and adiposity phenotypes were examined using linear regression and mediation analyses. Bootstrap 95% CI were calculated for the indirect effect (IE).
Results
DII was positively associated with total fat mass (β = 0.71 kg), VAT (β = 4.73 cm2), and liver fat (β = 0.40%) (P-values < 0.001). DII was negatively associated with Eubacterium xylanophilum (β = −2.86), and alpha diversity (β = −0.04), and positively associated with Tyzzerella (β = 5.78) (P-values < 0.001). An inverse relationship was found between E. xylanophilum, VAT (β = −0.11 cm2), and liver fat (β = −0.01%), and between alpha diversity and liver fat (β = −0.93%) (P-values < 0.001). Tyzzerella was positively associated with VAT (β = 0.04 cm2) (P < 0.001). The total effect of DII on VAT was partially mediated by E. xylanophilum (IE = 0.30) and Tyzzerella (IE = 0.26). The association between DII and liver fat was partially mediated by E. xylanophilum (IE = 0.02) and alpha diversity (IE = 0.04). GM did not mediate the total effect between DII and total fat mass.
Conclusions
The total effect of DII on ectopic fat was partially mediated by lower bacterial diversity and E. xylanophilum, a butyrate-producing genera often inversely associated with inflammation. The association between DII and ectopic fat was also mediated by an abundance of Tyzzerella, a genus previously found to be associated with low-quality diets. Following an anti-inflammatory diet may minimize intra-abdominal fat, in part through the indirect effect of the gut microbiota.
Funding Sources
NIH, NCI.
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Villegas-Valle RC, Lim U, Maskarinec G, Franke AA, Ernst T, Fan B, Álvarez-Hernández G, Candia-Plata MDC, Díaz-Zavala RG, Wilkens LR, Monroe KR, Valencia ME, Le Marchand L, Shepherd JA. Metabolic syndrome screening using visceral adipose tissue (VAT) from opportunistic MRI locations in a multi-ethnic population. Obes Res Clin Pract 2021; 15:227-234. [PMID: 34024755 DOI: 10.1016/j.orcp.2021.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To determine if visceral adipose tissue (VAT) area measured through MRI can be used opportunistically to assess the presence of cardiometabolic risk factors and compare its performance to simpler adiposity measures. METHODS A cross-sectional analysis was carried out on a subset of 1683 participants (856 women) from the Adiposity Phenotype Study (mean age=69.2y; range 59.9-77.4). The association of total VAT area (sum of four cross sections, L1-L2, L2-L3, L3-L4, L4-L5) and each location, as well as BMI and body fat % (per SD) with the metabolic syndrome (MetSx) or its components was evaluated through logistic regression analysis. RESULTS Total VAT can be accurately predicted using all sites evaluated (R2 range=0.82-0.96). In men, VAT did not show a superior association to MetSx compared to BMI in men. However, in women, VAT was consistently superior to BMI and body fat % in its association to MetSx, independent of ethnicity [odds ratio for BMI, body fat %and total VAT area=2.25 (95% CI: 1.93-2.62); 1.66 (95% CI: 1.36-2.03); 6.20 (95% CI: 4.69-8.21) respectively in all women]. Ethnic-specific odds ratios to MetSx in women ranged from 5.38 to 8.63 for total VAT area and 2.12-4.08 for BMI. CONCLUSION Total VAT area can be accurately predicted from individual VAT regions in men and women and offers superior association to BMI for MetSx in women but not in men for five ethnicities. Therefore, opportunistic screening for elevated VAT area in women may be warranted across multiple ethnic groups.
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Affiliation(s)
- Rosa C Villegas-Valle
- Graduate Program on Chemical and Biological Sciences, University of Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo, Sonora, 83000, Mexico.
| | - Unhee Lim
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
| | - Gertraud Maskarinec
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
| | - Adrian A Franke
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
| | - Thomas Ernst
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, 1356 Lusitana Street, University Tower, 7th Floor, Honolulu, HI, 96813, USA.
| | - Bo Fan
- Department of Epidemiology & Biostatistics, University of California-San Francisco, 550 16th Street, 2nd Floor, San Francisco, CA, 94158-2549, USA.
| | - Gerardo Álvarez-Hernández
- Department of Medicine and Health Sciences, University of Sonora, Avenida Luis Donaldo Colosio y Calle de la Reforma, Hermosillo, Sonora, 83000, Mexico.
| | - Maria Del Carmen Candia-Plata
- Department of Medicine and Health Sciences, University of Sonora, Avenida Luis Donaldo Colosio y Calle de la Reforma, Hermosillo, Sonora, 83000, Mexico.
| | - Rolando Giovanni Díaz-Zavala
- Department of Chemical and Biological Sciences, University of Sonora, Blvd. Luis Encinas y Rosales S/N, Hermosillo, Sonora, 83000, Mexico.
| | - Lynne R Wilkens
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles, CA, 90033, USA.
| | - Mauro E Valencia
- Department of Chemical and Biological Sciences, University of Sonora, Blvd. Luis Encinas y Rosales S/N, Hermosillo, Sonora, 83000, Mexico.
| | - Loïc Le Marchand
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
| | - John A Shepherd
- University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
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Fornili M, Perduca V, Fournier A, Jérolon A, Boutron-Ruault MC, Maskarinec G, Severi G, Baglietto L. Association between menopausal hormone therapy, mammographic density and breast cancer risk: results from the E3N cohort study. Breast Cancer Res 2021; 23:47. [PMID: 33865453 PMCID: PMC8053286 DOI: 10.1186/s13058-021-01425-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 04/01/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Menopausal hormone therapy (MHT) is a risk factor for breast cancer (BC). Evidence suggests that its effect on BC risk could be partly mediated by mammographic density. The aim of this study was to investigate the relationship between MHT, mammographic density and BC risk using data from a prospective study. METHODS We used data from a case-control study nested within the French cohort E3N including 453 cases and 453 matched controls. Measures of mammographic density, history of MHT use during follow-up and information on potential confounders were available for all women. The association between MHT and mammographic density was evaluated by linear regression models. We applied mediation modelling techniques to estimate, under the hypothesis of a causal model, the proportion of the effect of MHT on BC risk mediated by percent mammographic density (PMD) for BC overall and by hormone receptor status. RESULTS Among MHT users, 4.2% used exclusively oestrogen alone compared with 68.3% who used exclusively oestrogens plus progestogens. Mammographic density was higher in current users (for a 60-year-old woman, mean PMD 33%; 95% CI 31 to 35%) than in past (29%; 27 to 31%) and never users (24%; 22 to 26%). No statistically significant association was observed between duration of MHT and mammographic density. In past MHT users, mammographic density was negatively associated with time since last use; values similar to those of never users were observed in women who had stopped MHT at least 8 years earlier. The odds ratio of BC for current versus never MHT users, adjusted for age, year of birth, menopausal status at baseline and BMI, was 1.67 (95% CI, 1.04 to 2.68). The proportion of effect mediated by PMD was 34% for any BC and became 48% when the correlation between BMI and PMD was accounted for. These effects were limited to hormone receptor-positive BC. CONCLUSIONS Our results suggest that, under a causal model, nearly half of the effect of MHT on hormone receptor-positive BC risk is mediated by mammographic density, which appears to be modified by MHT for up to 8 years after MHT termination.
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Affiliation(s)
- M Fornili
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - V Perduca
- Laboratoire MAP 5 (UMR CNRS 8145), Université de Paris, Paris, France
| | - A Fournier
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP UMR1018, 94805, Villejuif, France
| | - A Jérolon
- Laboratoire MAP 5 (UMR CNRS 8145), Université de Paris, Paris, France
| | - M C Boutron-Ruault
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP UMR1018, 94805, Villejuif, France
| | - G Maskarinec
- University of Hawaii Cancer Center, Honolulu, USA
| | - G Severi
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, "Exposome and Heredity" team, CESP UMR1018, 94805, Villejuif, France.
- Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Florence, Italy.
| | - L Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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Nguyen TL, Schmidt DF, Makalic E, Maskarinec G, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, MacInnis RJ, Dugué PA, Dowty JG, Jenkins MA, Milne RL, Southey MC, Giles GG, Hopper JL. Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure. Int J Cancer 2020; 148:2193-2202. [PMID: 33197272 DOI: 10.1002/ijc.33396] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 10/28/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022]
Abstract
Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel F Schmidt
- Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | | | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- 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, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- 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, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- 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, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Graham G Giles
- 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, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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Farias AJ, Wu AH, Porcel J, Marchand LL, Wilkens LR, Monroe KR, Maskarinec G, Pandol SJ, Setiawan VW. Diabetes-Related Complications and Pancreatic Cancer Incidence in the Multiethnic Cohort. JNCI Cancer Spectr 2020; 4:pkaa035. [PMID: 33134820 PMCID: PMC7583154 DOI: 10.1093/jncics/pkaa035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/23/2020] [Accepted: 04/24/2020] [Indexed: 12/19/2022] Open
Abstract
Background People with diabetes are at an increased risk of developing pancreatic cancer. However, it is unclear whether diabetes-related complications are associated with risk of pancreatic cancer. Methods A nested matched case-control analysis was conducted among the fee-for-service Medicare participants of the prospective Multiethnic Cohort (n = ∼123 000). Between 2001 and 2014, 433 incident cases of pancreatic ductal adenocarcinoma were matched to 1728 controls by birth year, sex, race and ethnicity, and age at cohort entry. Participants were linked to data from the California and Hawaii cancer registries and Medicare claims. We used the diabetes complications severity index (DCSI) for the presence of 7 complications within 2 years prior to the diagnosis date of the index case. Multivariable conditional logistic regression was used to examine the association of DCSI with pancreatic cancer incidence. Results Diabetes was present among 45.4% of cases and 34.1% of controls. Cases had higher DCSI score compared with controls (score ≥4: 32.8% in cases; 21.2% in controls). The most prevalent diabetes-related complications for cases were cardiovascular disease (61.2%), nephropathy (31.2%), and cerebrovascular disease (21.7%). Individuals with diabetes (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.14 to 1.91), nephropathy (OR = 1.75, 95% CI = 1.32 to 2.33), cardiovascular disease (OR = 1.88, 95% CI = 1.45 to 2.44), and metabolic complications (OR = 6.61, 95% CI = 2.49 to 17.50) were at increased risk of pancreatic cancer. For every 1-unit increase in DCSI score, participants had 18% greater risk of pancreatic cancer (OR = 1.18, 95% CI = 1.11 to 1.25). Conclusions Participants with diabetes-related complications have an elevated risk of pancreatic cancer. Identifying diabetes-related complications may help identify high-risk groups who can be studied for development of early markers for this fatal cancer.
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Affiliation(s)
- Albert J Farias
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Jacqueline Porcel
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Stephen J Pandol
- Division of Gastroenterology, Departments of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Veronica Wendy Setiawan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Norris Comprehensive Cancer Center, Los Angeles, CA, USA
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40
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Maskarinec G, Garber AK, Wong MC, Kelly N, Kazemi L, Buchthal SD, Fearnbach N, Heymsfield SB, Shepherd JA. Predictors of liver fat among children and adolescents from five different ethnic groups. Obes Sci Pract 2020; 7:53-62. [PMID: 33680492 PMCID: PMC7909587 DOI: 10.1002/osp4.459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/27/2020] [Accepted: 09/28/2020] [Indexed: 12/21/2022] Open
Abstract
Objectives As rates of obesity around the world have increased, so has the detection of high level of liver fat in children and adolescents. This may put them at risk for cardiovascular disease later in life. This analysis of a cross‐sectional population‐based study of children and adolescents evaluated demographic and lifestyle determinants of percent liver fat. Methods Healthy participants (123 girls and 99 boys aged 5–17 years) recruited by convenience sampling in three locations completed questionnaires, anthropometric measurements, and dual X‐ray absorptiometry and magnetic resonance imaging (MRI) assessment. General linear models were applied to estimate the association of demographic, anthropometric, and dietary factors as well as physical activity with MRI‐based percent liver fat. Results The strongest predictor of liver fat was body mass index (BMI; p < 0.0001); overweight and obesity were associated with 0.5% and 1% higher liver fat levels. The respective adjusted mean percent values were 2.9 (95% CI 2.7, 3.1) and 3.4 (95% CI 3.2, 3.6) as compared to normal weight (2.4; 95% CI 2.3, 2.6). Mean percent liver fat was highest in Whites and African Americans, intermediate in Hispanic, and lowest among Asians and Native Hawaiians/Pacific Islanders (p < 0.0001). Age (p = 0.67), sex (p = 0.28), physical activity (p = 0.74), and diet quality (p = 0.70) were not significantly related with liver fat. Conclusions This study in multiethnic children and adolescents confirms the strong relationship of BMI with percent liver fat even in a population with low liver fat levels without detecting an association with age, sex, and dietary or physical activity patterns.
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Affiliation(s)
| | - Andrea K Garber
- University of California at San Francisco San Francisco California USA
| | | | - Nisa Kelly
- University of Hawaii Cancer Center Honolulu Hawaii USA
| | - Leila Kazemi
- University of Hawaii Cancer Center Honolulu Hawaii USA
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41
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Panizza CE, Wong MC, Kelly N, Liu YE, Shvetsov YB, Lowe DA, Weiss EJ, Heymsfield SB, Kennedy S, Boushey CJ, Maskarinec G, Shepherd JA. Diet Quality and Visceral Adiposity among a Multiethnic Population of Young, Middle, and Older Aged Adults. Curr Dev Nutr 2020; 4:nzaa090. [PMID: 33959689 PMCID: PMC8082229 DOI: 10.1093/cdn/nzaa090] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 03/30/2020] [Accepted: 05/20/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Visceral adiposity, more so than overall adiposity, is associated with chronic disease and mortality. There has been, to our knowledge, little research exploring the association between diet quality and visceral adipose tissue (VAT) among a mulitethnic population aged 18-80 y. OBJECTIVE The primary objective of this cross-sectional analysis was to examine the association between diet quality [Healthy Eating Index-2010 (HEI-2010) scores] and VAT among a multiethnic population of young, middle, and older aged adults in the United States. Secondary objectives were to repeat these analyses with overall adiposity and blood-based biomarkers for type 2 diabetes and cardiovascular disease risk as outcome measures. METHODS A total of 540 adults (dropped out: n = 4; age: 18-40 y, n = 220; 40-60 y, n = 183; 60-80 y, n = 133) were recruited across 3 sites (Honolulu County, San Francisco, and Baton Rouge) for the Shape Up! Adults study. Whole-body DXA, anthropometry, fasting blood draw, and questionnaires (food frequency, physical activity, and demographic characteristics) were completed. Linear regression was used to assess the associations between HEI-2010 tertiles and VAT and secondary outcome measures among all participants and age-specific strata, while adjusting for known confounders. RESULTS VAT, BMI (kg/m2), body fat percentage, total body fat, trunk fat, insulin, and insulin resistance were inversely related to diet quality (all P values < 0.004). When stratified by age, diet quality was inversely associated with VAT among participants aged 60-80 y (P < 0.006) and VAT/subcutaneous adipose tissue (SAT) among participants aged 40-60 y (P < 0.008). CONCLUSIONS Higher-quality diet was associated with lower VAT, overall adiposity, and insulin resistance among this multiethnic population of young, middle, and older aged adults with ages ranging from 18 to 80 y. More specifically, adherence to a high-quality diet may minimize VAT accumulation in adults aged 60-80 y and preferentially promote storage of SAT compared with VAT in adults aged 40-60 y.This study was registered at clinicaltrials.gov as NCT03637855.
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Affiliation(s)
| | | | - Nisa Kelly
- University of Hawaii Cancer Center, Honolulu, HI
| | - Yong En Liu
- University of Hawaii Cancer Center, Honolulu, HI
| | | | - Dylan A Lowe
- University of California-San Francisco, School of Medicine, San Francisco, CA
| | - Ethan J Weiss
- University of California-San Francisco, School of Medicine, San Francisco, CA
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Cruz RD, Park SY, Shvetsov Y, Boushey C, Monroe K, Marchand LL, Maskarinec G. Association of Diet Quality and Breast Cancer Incidence in the Multiethnic Cohort (MEC). Curr Dev Nutr 2020. [DOI: 10.1093/cdn/nzaa044_017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objectives
Healthy eating patterns assessed by diet quality indexes (DQIs) have been related to lower risk of cancer incidence and mortality; however, the association between DQIs and breast cancer risk is still unclear. This study investigated the relation of DQIs with breast cancer incidence among diverse women from the Multiethnic Cohort (MEC).
Methods
At baseline (1993–1996), 101,291 female participants of five major racial/ethnic groups (African Americans, Native Hawaiians, Japanese Americans, Latinos and whites) aged 45–75 years completed a survey including a validated food frequency questionnaire. Scores for Healthy Eating Index 2015 (HEI-2015), Alternate Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean diet score (aMED), and Dietary Approaches to Stop Hypertension (DASH) score were calculated and divided into quintiles (Q1-Q5). Cox regression was applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between DQIs and breast cancer risk, with adjustment for known risk factors including body mass index (BMI) among others.
Results
During a mean follow-up of 17.4 years, 7769 breast cancer cases were identified through linkage to tumor registries. The respective HRs for Q5 vs. Q1 were: 1.06 (95% CI, 0.98–1.14) for HEI-2015, 0.96 (95% CI, 0.90–1.04) for AHEI-2010, 1.01 (95% CI, 0.94–1.09) for aMED, and 0.95 (95% CI, 0.88–1.02) for DASH. No significant dose-response relations of DQIs with breast cancer risk were observed (all Ptrend ≥ 0.07). HRs analyzed by ethnic group also resulted in null findings with no significant dose-response relations and no significant Q5 vs. Q1 associations of DQIs with breast cancer risk (all Ptrend ≥ 0.14). For example, the respective HRs for the HEI-2015 by race/ethnicity were: 0.96 (95% CI, 0.81–1.14) for African Americans, 1.15 (95% CI, 0.90–1.46) for Native Hawaiians, 1.02 (95% CI, 0.89–1.17) for Japanese, 1.08 (95% CI, 0.88–1.33) for Latinas, and 1.08 (95% CI, 0.92–1.27) for whites.
Conclusions
Although adherence to DQIs was not associated with breast cancer risk overall or within racial/ethnic groups, nutrition remains important in breast cancer prevention as obesity, a strong modifiable risk factor, may be influenced by diet quality.
Funding Sources
This work was supported by grants from the National Cancer Institute.
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Affiliation(s)
| | | | | | | | - Kristine Monroe
- University of Southern California, Department of Preventive Medicine
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Maskarinec G, Hullar MAJ. Understanding the Interaction of Diet Quality with the Gut Microbiome and Their Effect on Disease. J Nutr 2020; 150:654-655. [PMID: 32006026 PMCID: PMC7138650 DOI: 10.1093/jn/nxaa015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/03/2020] [Accepted: 01/14/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Gertraud Maskarinec
- University of Hawaii Cancer Center, Honolulu, HI, USA,Address correspondence to GM (e-mail: )
| | - Meredith A J Hullar
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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Gram IT, Park SY, Maskarinec G, Wilkens LR, Haiman CA, Le Marchand L. Smoking and breast cancer risk by race/ethnicity and oestrogen and progesterone receptor status: the Multiethnic Cohort (MEC) study. Int J Epidemiol 2020; 48:501-511. [PMID: 30668861 DOI: 10.1093/ije/dyy290] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2018] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The purpose of this study was to examine if the smoking-related higher breast cancer risk was similar for the five race/ethnicity groups in the Multiethnic Cohort (MEC) study and by oestrogen (ER) and progesterone (PR) receptor status. METHODS From 1993 to 2013, we followed 67 313 women who were enrolled in the MEC study at 45-75 years of age. We identified breast cancer cases and tumour receptor status via linkage to the Hawaii and California Surveillance, Epidemiology and End Results Program cancer registries through December 2013. We used Cox proportional hazards regression to estimate multivariable-adjusted hazard ratios with 95% confidence intervals (CI). RESULTS During a mean follow-up of 16.7 years, we identified 4230 incident, invasive breast cancer cases. Compared with parous never smokers, parous ever smokers who had smoked more than 5 years before their first live childbirth had a higher risk of breast cancer overall of 31% (95% CI: 1.14-1.51). This higher risk was 51% (95% CI: 1.05-2.16) for African Americans, 66% (95% CI: 1.10-2.50) for Native Hawaiians, 42% (95% CI: 1.13-1.78) for Whites, 37% (95% CI: 1.17-1.61) for ER-positive (ER+) tumours and 33% (95% CI: 1.11-1.59) for PR+ tumours. No difference was suggested by racial/ethnic groups (Pheterogeneity = 0.15) or tumour receptor status (Pheterogeneity = 0.60 by ER status and 0.95 by PR status). CONCLUSIONS We find that the higher breast cancer risk related to smoking is similar across racial/ethnic groups and by ER and PR status, indicating that breast cancer should be considered as a smoking-related cancer.
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Affiliation(s)
- Inger T Gram
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Song-Yi Park
- Population Science in the Pacific, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Gertraud Maskarinec
- Population Science in the Pacific, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Lynne R Wilkens
- Population Science in the Pacific, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loïc Le Marchand
- Population Science in the Pacific, Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
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45
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Le Marchand L, Wilkens LR, Castelfranco AM, Monroe KR, Kristal BS, Cheng I, Maskarinec G, Hullar MA, Lampe JW, Shepherd JA, Franke A, Ernst T, Lim U. Circulating Biomarker Score for Visceral Fat and Risks of Incident Colorectal and Postmenopausal Breast Cancer: The Multiethnic Cohort Adiposity Phenotype Study. Cancer Epidemiol Biomarkers Prev 2020; 29:966-973. [PMID: 32132150 DOI: 10.1158/1055-9965.epi-19-1469] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 12/24/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Visceral adipose tissue (VAT) may play a greater role than subcutaneous fat in increasing cancer risk but is poorly estimated in epidemiologic studies. METHODS We developed a VAT prediction score by regression equations averaged across 100 least absolute shrinkage and selection operator models in a cross-sectional study of 1,801 older adults in the Multiethnic Cohort (MEC). The score was then used as proxy for VAT in case-control studies of postmenopausal breast (950 case-control pairs) and colorectal (831 case-control pairs) cancer in an independent sample in MEC. Abdominal MRI-derived VAT; circulating biomarkers of metabolic, hormonal, and inflammation dysfunctions; and ORs for incident cancer adjusted for BMI and other risk factors were assessed. RESULTS The final score, composed of nine biomarkers, BMI, and height, explained 11% and 15% more of the variance in VAT than BMI alone in men and women, respectively. The area under the receiver operator curve for VAT >150 cm2 was 0.90 in men and 0.86 in women. The VAT score was associated with risk of breast cancer [OR (95% confidence interval [CI]) by increasing tertiles: 1.00, 1.09 (0.86-1.39), 1.48 (1.16-1.89); P trend = 0.002] but not with colorectal cancer (P = 0.84), although an association [1.00, 0.98 (0.68-1.39), 1.24 (0.88-1.76); P trend = 0.08] was suggested for this cancer after excluding cases that occurred within 7 years of blood draw (P heterogeneity = 0.06). CONCLUSIONS The VAT score predicted risks of postmenopausal breast cancer and can be used for risk assessment in diverse populations. IMPACT These findings provide specific evidence for a role of VAT in breast cancer.
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Affiliation(s)
| | | | - Ann M Castelfranco
- Bekesy Laboratory of Neurobiology, Pacific Biosciences Research Center, University of Hawaii, Honolulu, Hawaii
| | - Kristine R Monroe
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Bruce S Kristal
- Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Iona Cheng
- School of Medicine, University of California-San Francisco, San Francisco, California
| | | | | | | | | | - Adrian Franke
- University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Thomas Ernst
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, Hawaii.
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46
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Maskarinec G, Ju D, Shvetsov YB, Horio D, Chan O, Loo LWM, Hernandez BY. Breast tumor tissue inflammation but not lobular involution is associated with survival among breast cancer patients in the Multiethnic Cohort. Cancer Epidemiol 2020; 65:101685. [PMID: 32058311 DOI: 10.1016/j.canep.2020.101685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/29/2020] [Accepted: 02/02/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND This study investigated the association of breast lobular involution status and three inflammatory markers as predictors of survival among breast cancer patients in the Multiethnic Cohort. METHODS Lobular involution was evaluated in tissue sections of normal breast tissue and COX-2, TNF-α, and TGF-β proteins were assessed by immunohistochemistry in tumor microarrays. A summary score added the expression levels of the three markers. Cox regression was applied to estimate hazard ratios (HRs) and 95 % confidence intervals (CI) with age as the time metric and adjustment for factors known to affect mortality. RESULTS Among 254 women (mean age = 61.7 ± 8.7 years) with pathologic blocks and follow-up information, 54 all-cause and 10 breast cancer-specific deaths were identified after a mean follow-up time of 16.0 ± 3.1 years. For 214 participants, an inflammatory score was available and 157 women had information on lobular involution. Lobular involution was not significantly associated with survival. Expression of both COX-2 and TNF-α were significant predictors of lower survival (p = 0.02 and 0.04), while the association for TGF-β was weaker (p = 0.09). When combined into one overall inflammation score, both intermediate (HR = 2.72; 95 % CI 0.90-8.28) and high (HR = 4.21; 95 % CI 1.51-11.8) scores were associated with higher mortality but only the latter was statistically significant. No significant association with breast cancer-specific mortality was detected. CONCLUSIONS These results suggest that strong expression of inflammatory markers in breast tissue predicts a poorer prognosis possibly due to a system-wide state of chronic inflammation.
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Affiliation(s)
| | - Dan Ju
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David Horio
- University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Owen Chan
- University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Lenora W M Loo
- University of Hawaii Cancer Center, Honolulu, HI, United States
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47
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Maskarinec G, Ciba M, Ju D, Shepherd JA, Ernst T, Wu AH, Monroe KR, Lim U, Wilkens LR, Le Marchand L. Association of Imaging-Based Body Fat Distribution and Mammographic Density in the Multiethnic Cohort Adiposity Phenotype Study. Cancer Epidemiol Biomarkers Prev 2019; 29:352-358. [PMID: 31727725 DOI: 10.1158/1055-9965.epi-19-1060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/13/2019] [Accepted: 11/05/2019] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND As the stronger association of obesity with postmenopausal breast cancer in Asian than white women may be due to body fat distribution, we examined the relation of adiposity measures with percent mammographic density (PMD), a strong predictor of breast cancer incidence. METHODS A total of 938 women from five ethnic groups (69.1 ± 2.7 years) in the Adiposity Phenotype Study (APS) underwent DXA and MRI imaging. PMD was assessed in routine mammograms using a computer-assisted method. Spearman correlation coefficients were computed and general linear models were applied to estimate regression coefficients (β) for PMD per 0.5 SD units of adiposity measures while adjusting for known confounders, including DXA total body fat. RESULTS For 701 (75%) of the participants (69.1 ± 2.7 years), valid mammograms were obtained. Whereas total body fat, the trunk-to-periphery fat ratio (TPFR), visceral fat (VAT), and subcutaneous fat (SAT) were inversely correlated with PMD (P < 0.0001), the VAT/SAT ratio correlated positively (r spearman = 0.10; P = 0.01). In fully adjusted models, PMD remained inversely related to TPFR and SAT and disappeared for VAT, while it was strengthened for VAT/SAT (β = 0.51; P = 0.009). This relation was stronger in Japanese Americans than other ethnic groups. CONCLUSIONS This is the first study to show an association of a high VAT/SAT ratio with greater PMD, a marker of breast cancer risk after taking into account total body fat. IMPACT The results indicate a link between the propensity to accumulate VAT and the amount of fat in the breast (1-PMD), which may influence the relation of obesity with breast cancer incidence.
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Affiliation(s)
| | - Michelle Ciba
- University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Dan Ju
- University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Anna H Wu
- University of Southern California, Los Angeles, California
| | | | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, Hawaii
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48
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Maskarinec G, Hullar MAJ, Monroe KR, Shepherd JA, Hunt J, Randolph TW, Wilkens LR, Boushey CJ, Le Marchand L, Lim U, Lampe JW. Fecal Microbial Diversity and Structure Are Associated with Diet Quality in the Multiethnic Cohort Adiposity Phenotype Study. J Nutr 2019; 149:1575-1584. [PMID: 31187868 PMCID: PMC6862930 DOI: 10.1093/jn/nxz065] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/07/2019] [Accepted: 03/18/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Variation in gut microbial community structure is partly attributed to variations in diet. A priori dietary indexes capture diet quality and have been associated with chronic disease risk. OBJECTIVES The aim of this study was to examine the association of diet quality, as assessed by the Healthy Eating Index, Alternative Healthy Eating Index-2010, alternate Mediterranean Diet, and the Dietary Approaches to Stop Hypertension Trial, with measures of fecal microbial community structure assessed in the Adiposity Phenotype Study (APS), an ethnically diverse study population with varied food intakes. METHODS Multiethnic Cohort Study members completed a validated quantitative food frequency questionnaire (QFFQ) at cohort entry (1993-1996) and, for the APS subset, at clinic visit (2013-2015), when they also provided a stool sample. DNA was extracted from stool, and the V1-V3 region of the 16S rRNA gene was amplified and sequenced. Dietary index scores were computed based on the QFFQ and an extensive nutritional database. Using linear regression adjusted for relevant covariates, we estimated associations of dietary quality with microbiome measures and computed adjusted mean values of microbial measures by tertiles of dietary index scores. RESULTS The 858 men and 877 women of white, Japanese American, Latino, Native Hawaiian, and African American ancestry had a mean age of 69.2 years at stool collection. Alpha diversity according to the Shannon index increased by 1-2% across tertiles of all 4 diet indexes measured at clinic visit. The mean relative abundance of the phylum Actinobacteria was 13-19% lower with higher diet quality across all 4 indexes (difference between tertile 3 and tertile 1 divided by tertile 1). Of the 104 bacterial genera tested, 21 (primarily from the phylum Firmicutes) were positively associated with at least 1 index after Bonferroni adjustment. CONCLUSION Diet quality was strongly associated with fecal microbial alpha diversity and beta diversity and several genera previously associated with human health.
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Affiliation(s)
| | - Meredith A J Hullar
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Kristine R Monroe
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA
| | | | - Jeani Hunt
- School of Public Health, University of Washington, Seattle, WA
| | - Timothy W Randolph
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | | | | | | | - Unhee Lim
- University of Hawaii Cancer Center, Honolulu, HI
| | - Johanna W Lampe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
- School of Public Health, University of Washington, Seattle, WA
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Guillermo C, Boushey CJ, Franke AA, Monroe KR, Lim U, Wilkens LR, Le Marchand L, Maskarinec G. Diet Quality and Biomarker Profiles Related to Chronic Disease Prevention: The Multiethnic Cohort Study. J Am Coll Nutr 2019; 39:216-223. [PMID: 31291155 DOI: 10.1080/07315724.2019.1635921] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Objective: To understand how diet quality affects chronic disease etiology, the associations of 4 a priori diet quality indices with blood levels of lipid-soluble micronutrients and biomarkers of inflammation, lipid, and glucose metabolism were examined in 5 ethnic groups.Methods: In a cross-sectional design, the Adiposity Phenotype Study, a subset of the Multiethnic Cohort in Hawaii and Los Angeles, recruited participants of white, African American, Native Hawaiian, Japanese American, and Latino ancestry. A total of 896 men and 910 women completed a validated quantitative food frequency questionnaire and anthropometric measurements and donated a fasting blood sample. Using general linear models, covariate-adjusted mean levels of lipid-soluble micronutrients (total carotenes, lycopene, total tocopherols, total lutein, cryptoxanthins), biomarkers of inflammation (C-reactive protein [CRP], tumor necrosis factor-[Formula: see text]), adipokines (adiponectin, leptin), lipids (total cholesterol, high-density lipoprotein cholesterol [HDL-C], triglycerides), and glucose metabolism (glucose, insulin, homeostatic model assessment of insulin resistance [HOMA-IR]) were computed across tertiles of 4 a priori dietary indices Healthy Eating Index (HEI)-2010, Alternative HEI (AHEI)-2010, alternate Mediterranean Diet (aMED), Dietary Approaches to Stop Hypertension (DASH); trends were evaluated in models with diet quality scores as continuous variables.Results: With better diet quality, levels of carotenes, lutein, cryptoxanthin, adiponectin, and HDL-C were significantly higher (ptrend < 0.01), whereas levels of CRP, leptin, total cholesterol, triglycerides, glucose, insulin, and HOMA-IR were inversely associated (ptrend < 0.05) with diet quality. With the exception of cryptoxanthins and triglycerides, the associations were consistent across ethnic groups.Conclusions: These findings confirm the association between diet quality and nutrition-related biomarkers and support the idea that a high-quality diet positively influences biologic pathways involved in chronic disease etiology across different ethnic groups.
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Affiliation(s)
- Cherie Guillermo
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Carol J Boushey
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Adrian A Franke
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - Unhee Lim
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Lynne R Wilkens
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Loïc Le Marchand
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Gertraud Maskarinec
- Population Sciences in the Pacific, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
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Chai W, Maskarinec G, Franke AA, Monroe KR, Park SY, Kolonel LN, Wilkens LR, Le Marchand L, Cooney RV. Association of serum γ-tocopherol levels with mortality: the Multiethnic Cohort Study. Eur J Clin Nutr 2019; 74:87-96. [PMID: 31243335 PMCID: PMC6930982 DOI: 10.1038/s41430-019-0460-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 06/07/2019] [Accepted: 06/11/2019] [Indexed: 01/24/2023]
Abstract
BACKGROUND/OBJECTIVES γ-Tocopherol has unique properties that protect against nitrogen oxide-mediated cellular damage. To elucidate the potential role of γ-tocopherol in the aging process, we examined the associations of serum γ-tocopherol levels with all-cause and cause-specific mortality. SUBJECTS/METHODS Among participants in the biorepository subcohort of the Multiethnic Cohort Study, pre-cancer diagnostic serum γ-tocopherol levels were measured in a subset of 3904 men and 4461 women. Of these, 22.7% of men and 13.5% of women died during a mean follow-up time of 9.6 ± 2.6 years. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for mortality associated with γ-tocopherol were estimated by Cox proportional hazards regression. RESULTS Positive associations of serum γ-tocopherol with all-cause, cancer, and cardiovascular disease mortality (CVD) (Ptrend < 0.05) were detected after adjusting for age, race/ethnicity, and serum cholesterol levels. The respective HRs (95% CIs) for the highest versus the lowest sex-specific γ-tocopherol quartile were 1.43 (1.17-1.74), 1.79 (1.22-2.64), and 1.52 (1.10-2.11) for men and 1.58 (1.25-2.00), 1.59 (1.05-2.41), and 1.59 (1.07-2.37) for women. Associations remained significant for all-cause mortality among women after further adjusting for smoking variables and history of cancer, CVD, diabetes, and hypertension at cohort entry (highest vs. lowest γ-tocopherol quartile: HR = 1.38; 95% CI = 1.08-1.75; Ptrend = 0.005). Overall, associations with all-cause mortality were consistent across race/ethnicity and were significant in three of ten sex-specific racial/ethnic groups in the fully adjusted models, with no interactions between ethnicity and γ-tocopherol. CONCLUSIONS The positive association between γ-tocopherol and mortality suggests a potential physiological role for γ-tocopherol in response to pathological conditions.
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Affiliation(s)
- Weiwen Chai
- Department of Nutrition and Health Sciences, University of Nebraska, Lincoln, NE, USA.
| | | | - Adrian A Franke
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Song-Yi Park
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Laurence N Kolonel
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Robert V Cooney
- Office of Public Health Studies, University of Hawaii at Manoa, Honolulu, HI, USA
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