1
|
Michels KB, Binder AM. Impact of folic acid supplementation on the epigenetic profile in healthy unfortified individuals - a randomized intervention trial. Epigenetics 2024; 19:2293410. [PMID: 38096372 PMCID: PMC10730197 DOI: 10.1080/15592294.2023.2293410] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Folate is an essential mediator in one-carbon metabolism, which provides methyl groups for DNA synthesis and methylation. The availability of active methyl groups can be influenced by the uptake of folic acid. We conducted a randomized intervention trial to test the influence of folic acid supplementation on DNA methylation in an unfortified population in Germany. A total of 16 healthy male volunteers (age range 23-61 y) were randomized to receive either 400 μg (n = 9) or 800 μg (n = 7) folic acid supplements daily for 8 weeks. Infinium Human Methylation 450K BeadChip Microarrays were used to assay site-specific DNA methylation across the genome. Microarray analyses were conducted on PBL DNA. We estimated several epigenetic clocks and mean DNA methylation across all autosomal probes on the array. AgeAccel was estimated as the residual variation in each metric. In virtually all participants, both serum and red blood cell (RBC) folate increased successively throughout the trial period. Participants with a larger increase in RBC folate had a larger increase in DNAmAge AgeAccel (Spearman Rho: 0.56, p-value = 0.03). No notable changes in the methylome resulting from the folic acid supplementation emerged. In this population with adequate folate levels derived from diet, an increase in RBC folate had a modest impact on the epigenetic clock predicting chronologic age.
Collapse
Affiliation(s)
- Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Alexandra M. Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| |
Collapse
|
2
|
Hajek A, Becher H, Brenner H, Holleczek B, Katzke V, Kaaks R, Minnerup H, Karch A, Baurecht H, Leitzmann M, Peters A, Gastell S, Ahrens W, Haug U, Nimptsch K, Pischon T, Michels KB, Dorrn A, Klett-Tammen CJ, Castell S, Willich SN, Keil T, Schipf S, Meinke-Franze C, Harth V, Obi N, König HH. Personality and the use of cancer screenings - Results of the German National Cohort. Prev Med Rep 2024; 41:102677. [PMID: 38533391 PMCID: PMC10963220 DOI: 10.1016/j.pmedr.2024.102677] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/28/2024] Open
Abstract
Objective To determine the association between personality characteristics and use of different cancer screenings. Methods We used data from the German National Cohort (NAKO; mean age was 53.0 years (SD: 9.2 years)) - a population-based cohort study. A total of 132,298 individuals were included in the analyses. As outcome measures, we used (self-reported): stool examination for blood (haemoccult test, early detection of bowel cancer), colonoscopy (screening for colorectal cancer), skin examination for moles (early detection of skin cancer), breast palpation by a doctor (early detection of breast cancer), x-ray examination of the breast ("mammography", early detection of breast cancer), cervical smear test, finger examination of the rectum (early detection of prostate cancer), and blood test for prostate cancer (determination of Prostate-Specific Antigen level). The established Big Five Inventory-SOEP was used to quantify personality factors. It was adjusted for several covariates based on the Andersen model. Unadjusted and adjusted multiple logistic regressions were computed. Results A higher probability of having a skin examination for moles, for example, was associated with a higher conscientiousness (OR: 1.07, p < 0.001), higher extraversion (OR: 1.03, p < 0.001), higher agreeableness (OR: 1.02, p < 0.001), lower openness to experience (OR: 0.98, p < 0.001) and higher neuroticism (OR: 1.07, p < 0.001) among the total sample. Depending on the outcome used, the associations slightly varied. Conclusions Particularly higher levels of extraversion, neuroticism and conscientiousness are associated with the use of different cancer screenings. Such knowledge may help to better understand non-participation in cancer screening examinations from a psychological perspective.
Collapse
Affiliation(s)
- André Hajek
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany
| | - Heiko Becher
- Heidelberg University Hospital, Heidelberg Institute of Global Health, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bernd Holleczek
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Saarland Cancer Registry, Saarbrücken, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heike Minnerup
- Institute for Epidemiology and Social Medicine, Faculty of Medicine, University of Münster, Münster, Germany
| | - André Karch
- Institute for Epidemiology and Social Medicine, Faculty of Medicine, University of Münster, Münster, Germany
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Sylvia Gastell
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
| | - Ulrike Haug
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
| | - Katharina Nimptsch
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany
- Charité - Universitaetsmedizin Berlin, Corporate Member of Freie Universitaet Berlin, Humboldt-Universitaet zu Berlin, Berlin, Germany
| | - Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Anja Dorrn
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | | | - Stefanie Castell
- Department for Epidemiology, Helmholtz Centre for Infection Research, Brunswick, Germany
| | - Stefan N. Willich
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany
| | - Sabine Schipf
- Institute for Community Medicine, Department SHIP/Clinical-Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Claudia Meinke-Franze
- Institute for Community Medicine, Department SHIP/Clinical-Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Volker Harth
- Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nadia Obi
- Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hans-Helmut König
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany
| |
Collapse
|
3
|
Stein MJ, Fischer B, Bohmann P, Ahrens W, Berger K, Brenner H, Günther K, Harth V, Heise JK, Karch A, Klett-Tammen CJ, Koch-Gallenkamp L, Krist L, Lieb W, Meinke-Franze C, Michels KB, Mikolajczyk R, Nimptsch K, Obi N, Peters A, Pischon T, Schipf S, Schmidt B, Stang A, Thierry S, Willich SN, Wirkner K, Leitzmann MF, Sedlmeier AM. Differences in Anthropometric Measures Based on Sex, Age, and Health Status: Findings From the German National Cohort (NAKO). Dtsch Arztebl Int 2024:arztebl.m2024.0016. [PMID: 38377337 DOI: 10.3238/arztebl.m2024.0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
BACKGROUND Obesity is a worldwide health problem. We conducted detailed analyses of anthropometric measures in a comprehensive, population-based, current cohort in Germany. METHODS In the German National Cohort (NAKO), we analyzed cross-sectional data on body-mass index (BMI), waist and hip circumference, subcutaneous (SAT) and visceral adipose tissue (VAT) as measured by ultrasound, and body fat percentage. The data were stratified by sex, age, and self-reported physicians' diagnoses of cardiovascular diseases (CVD), metabolic diseases (MetD), cardiometabolic diseases (CMD), and cancer. RESULTS Data were available from 204 751 participants (age, 49.9 ± 12.8 years; 50.5% women). Body size measures generally increased with age. Men had a higher BMI, larger waist circumference, and more VAT than women, while women had a larger hip circumference, more SAT, and a higher body fat percentage than men. For example, the mean BMI of participants over age 60 was 28.3 kg/m2 in men and 27.6 kg/m2 in women. CVD, MetD, and CMD were associated with higher anthropometric values, while cancer was not. For example, the mean BMI was 25.3 kg/m2 in healthy women, 29.4 kg/m2 in women with CMD, and 25.4 kg/m2 in women with cancer. CONCLUSION Obesity is widespread in Germany, with notable differences between the sexes in anthropometric values. Obesity was more common in older participants and those with chronic diseases other than cancer. Elevated values were especially common in multimorbid individuals.
Collapse
|
4
|
Pereira A, Garmendia ML, Leiva V, Corvalán C, Michels KB, Shepherd J. Breast composition during and after puberty: the Chilean Growth and Obesity Cohort Study. Breast Cancer Res 2024; 26:45. [PMID: 38475816 DOI: 10.1186/s13058-024-01793-x] [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: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Breast density (BD) is a strong risk factor for breast cancer. Little is known about how BD develops during puberty. Understanding BD trajectories during puberty and its determinants could be crucial for promoting preventive actions against breast cancer (BC) at early ages. The objective of this research is to characterize % fibroglandular volume (%FGV), absolute fibroglandular volume (AFGV), and breast volume (BV) at different breast Tanner stages until 4-year post menarche in a Latino cohort and to assess determinants of high %FGV and AFGV during puberty and in a fully mature breast. METHODS This is a longitudinal follow-up of 509 girls from low-middle socioeconomic status of the Southeast area of Santiago, recruited at a mean age of 3.5 years. The inclusion criteria were singleton birth born, birthweight between 2500 and 4500 g with no medical or mental disorder. A trained dietitian measured weight and height since 3.5 years old and sexual maturation from 8 years old (breast Tanner stages and age at menarche onset). Using standardized methods, BD was measured using dual-energy X-ray absorptiometry (DXA) in various developmental periods (breast Tanner stage B1 until 4 years after menarche onset). RESULTS In the 509 girls, we collected 1,442 breast DXA scans; the mean age at Tanner B4 was 11.3 years. %FGV increased across breast Tanner stages and peaked 250 days after menarche. AFGV and BV peaked 2 years after menarche onset. Girls in the highest quartiles of %FGV, AFGV, and BV at Tanner B4 and B5 before menarche onset had the highest values thereafter until 4 years after menarche onset. The most important determinants of %FGV and AFGV variability were BMI z-score (R2 = 44%) and time since menarche (R2 = 42%), respectively. CONCLUSION We characterize the breast development during puberty, a critical window of susceptibility. Although the onset of menarche is a key milestone for breast development, we observed that girls in the highest quartiles of %FGV and AFGV tracked in that group afterwards. Following these participants in adulthood would be of interest to understand the changes in breast composition during this period and its potential link with BC risk.
Collapse
Affiliation(s)
- Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Valeria Leiva
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, USA
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - John Shepherd
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, Honolulu, HI, USA
| |
Collapse
|
5
|
Herbolsheimer F, Peters A, Wagner S, Willich SN, Krist L, Pischon T, Nimptsch K, Gastell S, Brandes M, Brandes B, Schikowski T, Schmidt B, Michels KB, Mikolajczyk R, Harth V, Obi N, Castell S, Heise JK, Lieb W, Franzpötter K, Karch A, Teismann H, Völzke H, Meinke-Franze C, Leitzmann M, Stein MJ, Brenner H, Holleczek B, Weber A, Bohn B, Kluttig A, Steindorf K. Changes in physical activity and sedentary behavior during the first COVID-19 pandemic- restrictions in Germany: a nationwide survey : Running head: physical activity during the COVID-19 restrictions. BMC Public Health 2024; 24:433. [PMID: 38347566 PMCID: PMC10860251 DOI: 10.1186/s12889-024-17675-y] [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: 09/22/2023] [Accepted: 01/04/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic restrictions posed challenges to maintaining healthy lifestyles and physical well-being. During the first mobility restrictions from March to mid-July 2020, the German population was advised to stay home, except for work, exercise, and essential shopping. Our objective was to comprehensively assess the impact of these restrictions on changes in physical activity and sedentary behavior to identify the most affected groups. METHODS Between April 30, 2020, and May 12, 2020, we distributed a COVID-19-specific questionnaire to participants of the German National Cohort (NAKO). This questionnaire gathered information about participants' physical activity and sedentary behavior currently compared to the time before the restrictions. We integrated this new data with existing information on anxiety, depressive symptoms, and physical activity. The analyses focused on sociodemographic factors, social relationships, physical health, and working conditions. RESULTS Out of 152,421 respondents, a significant proportion reported altered physical activity and sedentary behavioral patterns due to COVID-19 restrictions. Over a third of the participants initially meeting the WHO's physical activity recommendation could no longer meet the guidelines during the restrictions. Participants reported substantial declines in sports activities (mean change (M) = -0.38; 95% CI: -.390; -.378; range from -2 to + 2) and reduced active transportation (M = -0.12; 95% CI: -.126; -.117). However, they also increased recreational physical activities (M = 0.12; 95% CI: .117; .126) while engaging in more sedentary behavior (M = 0.24; 95% CI: .240; .247) compared to pre-restriction levels. Multivariable linear and log-binomial regression models indicated that younger adults were more affected by the restrictions than older adults. The shift to remote work, self-rated health, and depressive symptoms were the factors most strongly associated with changes in all physical activity domains, including sedentary behavior, and the likelihood to continue following the physical activity guidelines. CONCLUSIONS Mobility patterns shifted towards inactivity or low-intensity activities during the nationwide restrictions in the spring of 2020, potentially leading to considerable and lasting health risks.
Collapse
Affiliation(s)
- Florian Herbolsheimer
- Division of Physical Activity, Prevention and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Sarah Wagner
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Stefan N Willich
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lilian Krist
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Katharina Nimptsch
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Sylvia Gastell
- German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, Germany
| | - Mirko Brandes
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Berit Brandes
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Tamara Schikowski
- IUF - Leibniz Research Institute for Environmental Medicine, Duesseldorf, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics, and Informatics, Interdisciplinary Center for Health Sciences , Medical Faculty of the Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Volker Harth
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Nadia Obi
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | - Jana K Heise
- Helmholtz Centre for Infection Research, Brunswick, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, University of Kiel, Kiel, Germany
| | | | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Henning Teismann
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Claudia Meinke-Franze
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | | | | | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | - Alexander Kluttig
- Institute for Medical Epidemiology, Biometrics, and Informatics, Interdisciplinary Center for Health Sciences , Medical Faculty of the Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Karen Steindorf
- Division of Physical Activity, Prevention and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
6
|
Klinger-König J, Erhardt A, Streit F, Völker MP, Schulze MB, Keil T, Fricke J, Castell S, Klett-Tammen CJ, Pischon T, Karch A, Teismann H, Michels KB, Greiser KH, Becher H, Karrasch S, Ahrens W, Meinke-Franze C, Schipf S, Mikolajczyk R, Führer A, Brandes B, Schmidt B, Emmel C, Leitzmann M, Konzok J, Peters A, Obi N, Brenner H, Holleczek B, Moreno Velásquez I, Deckert J, Baune BT, Rietschel M, Berger K, Grabe HJ. Childhood Trauma and Somatic and Mental Illness in Adulthood. Dtsch Arztebl Int 2024; 121:1-8. [PMID: 37876295 PMCID: PMC10916765 DOI: 10.3238/arztebl.m2023.0225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/26/2023]
Abstract
BACKGROUND Childhood trauma is associated with somatic and mental illness in adulthood. The strength of the association varies as a function of age, sex, and type of trauma. Pertinent studies to date have mainly focused on individual diseases. In this study, we investigate the association between childhood trauma and a multiplicity of somatic and mental illnesses in adulthood. METHODS Data from 156 807 NAKO Health Study participants were analyzed by means of logistic regressions, with adjustment for age, sex, years of education, and study site. The Childhood Trauma Screener differentiated between no/minor (n = 115 891) and moderate/severe childhood trauma (n = 40 916). The outcome variables were medical diagnoses of five somatic and two mental health conditions as stated in the clinical history. RESULTS Persons with childhood trauma were more likely to bear a diagnosis of all of the studied conditions: cancer (odds ratio [OR] = 1.10; 95% confidence interval: [1.05; 1.15]), myocardial infarction (OR = 1.13 [1.03; 1.24]), diabetes (OR = 1.16, [1.10; 1.23]), stroke (OR = 1.35 [1.23; 1.48]), chronic obstructive pulmonary disease (OR = 1.45 [1.38; 1.52]), depression (OR = 2.36 [2.29; 2.43]), and anxiety disorders (OR = 2.08 [2.00; 2.17]). All of these associations were stronger in younger persons, regardless of the nature of childhood trauma. Differences between the sexes were observed only for some of these associations. CONCLUSION Childhood trauma was associated with a higher probability of developing mental as well as somatic illness in adulthood. As childhood trauma is an element of individual history that the victim has little to no control over, and because the illnesses that can arise in adulthood in association with it are a heavy burden on the affected persons and on society, there is a need for research on these associations and for the development of preventive measures.
Collapse
Affiliation(s)
- Johanna Klinger-König
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Angelika Erhardt
- Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Maja P. Völker
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- State Institute of Health I, Bavarian State Office for Health and Food Safety, Erlangen, Germany
| | - Julia Fricke
- Institute of Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Stefanie Castell
- Department of Epidemiology, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
| | - Carolina J. Klett-Tammen
- Department of Epidemiology, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
| | - Tobias Pischon
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Henning Teismann
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Karin B. Michels
- Institute for Prevention and Tumor Epidemiology, Medical Center—University of Freiburg, Medical Faculty, Albert Ludwigs University of Freiburg, Freiburg, Germany
| | - K. Halina Greiser
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heiko Becher
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan Karrasch
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, LMU University Hospital, LMU Munich; Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
| | - Claudia Meinke-Franze
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Sabine Schipf
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rafael Mikolajczyk
- IInstitute of Medical Epidemiology, Biometry and Informatics, Profile Center Health Sciences, Medical School, Martin Luther University Halle-Wittenberg, Halle, Germany
- German Center for Mental Health (DZPG), Jena-Magdeburg-Halle Site, Halle, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Halle, Germany
| | - Amand Führer
- IInstitute of Medical Epidemiology, Biometry and Informatics, Profile Center Health Sciences, Medical School, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Berit Brandes
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
| | - Börge Schmidt
- Institute of Medical Informatics, Biometry and Epidemiology (IMIBE), University Medicine Essen, Essen, Germany
| | - Carina Emmel
- Institute of Medical Informatics, Biometry and Epidemiology (IMIBE), University Medicine Essen, Essen, Germany
| | - Michael Leitzmann
- Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Julian Konzok
- Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Anette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, LMU—Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Mental Health (DZPG), Munich site, Munich, Germany
| | - Nadia Obi
- Central Institute for Occupational Medicine and Maritime Medicine (ZfAM,) University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), Saarbrücken, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Saarbrücken, Germany
| | | | - Ilais Moreno Velásquez
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
| | - Jürgen Deckert
- Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
| | - Bernhard T. Baune
- Department of Psychiatry, University Hospital Münster, Münster, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| |
Collapse
|
7
|
Schuppert C, Rospleszcz S, Hirsch JG, Hoinkiss DC, Köhn A, von Krüchten R, Russe MF, Keil T, Krist L, Schmidt B, Michels KB, Schipf S, Brenner H, Kröncke TJ, Pischon T, Niendorf T, Schulz-Menger J, Forsting M, Völzke H, Hosten N, Bülow R, Zaitsev M, Kauczor HU, Bamberg F, Günther M, Schlett CL. Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study. Sci Rep 2023; 13:22745. [PMID: 38123791 PMCID: PMC10733361 DOI: 10.1038/s41598-023-49569-1] [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: 07/05/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023] Open
Abstract
In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.
Collapse
Affiliation(s)
- Christopher Schuppert
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Susanne Rospleszcz
- Chair of Epidemiology, Institute of Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University, Faculty of Medicine, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Jochen G Hirsch
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | | | - Alexander Köhn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Ricarda von Krüchten
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Maximilian F Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Thomas Keil
- Institute for Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, Erlangen, Germany
| | - Lilian Krist
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Thomas J Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Biobank Technology Platform, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
| | - Norbert Hosten
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
| |
Collapse
|
8
|
Ponce D, Rodríguez F, Miranda JP, Binder AM, Santos JL, Michels KB, Cutler GB, Pereira A, Iñiguez G, Mericq V. Differential methylation pattern in pubertal girls associated with biochemical premature adrenarche. Epigenetics 2023; 18:2200366. [PMID: 37053179 PMCID: PMC10114989 DOI: 10.1080/15592294.2023.2200366] [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: 04/14/2023] Open
Abstract
Biochemical premature adrenarche is defined by elevated serum DHEAS [≥40 μg/dL] before age 8 y in girls. This condition is receiving more attention due to its association with obesity, hyperinsulinemia, dyslipidemia, and polycystic ovary syndrome. Nevertheless, the link between early androgen excess and these risk factors remains unknown. Epigenetic modifications, and specifically DNA methylation, have been associated with the initiation and progression of numerous disorders, including obesity and insulin resistance. The aim of this study was to determine if prepubertal androgen exposure is associated with a different methylation profile in pubertal girls. Eighty-six healthy girls were studied. At age 7 y, anthropometric measurements were begun and DHEAS levels were determined. Girls were classified into Low DHEAS (LD) [<42 μg/dL] and High DHEAS (HD) [≥42 μg/dL] groups. At Tanner stages 2 and 4 a DNA methylation microarray was performed to identify differentially methylated CpG positions (DMPs) between HD and LD groups. We observed a differential methylation pattern between pubertal girls with and without biochemical PA. Moreover, a set of DNA methylation markers, selected by the LASSO method, successfully distinguished between HD and LD girls regardless of Tanner stage. Additionally, a subset of these markers were significantly associated with glucose-related measures such as insulin level, HOMA-IR, and glycaemia. This pilot study provides evidence consistent with the hypothesis that high DHEAS concentration, or its hormonally active metabolites, may induce a unique blood methylation signature in pubertal girls, and that this methylation pattern is associated with altered glucose metabolism.
Collapse
Affiliation(s)
- Diana Ponce
- Institute of Maternal and Child Research, School of Medicine, Universidad de Chile, Santiago, Chile
| | - Fernando Rodríguez
- Institute of Maternal and Child Research, School of Medicine, Universidad de Chile, Santiago, Chile
| | - José P Miranda
- Department of Nutrition, Diabetes, and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Advanced Center for Chronic Diseases (ACCDiS), Pontificia Universidad Católica de Chile & Universidad de Chile, Santiago, Chile
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii, Honolulu, HI, USA
| | - José L Santos
- Department of Nutrition, Diabetes, and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | | | - Ana Pereira
- Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile
| | - Germán Iñiguez
- Institute of Maternal and Child Research, School of Medicine, Universidad de Chile, Santiago, Chile
| | - Verónica Mericq
- Institute of Maternal and Child Research, School of Medicine, Universidad de Chile, Santiago, Chile
| |
Collapse
|
9
|
Welch BM, Keil AP, Buckley JP, Engel SM, James-Todd T, Zota AR, Alshawabkeh AN, Barrett ES, Bloom MS, Bush NR, Cordero JF, Dabelea D, Eskenazi B, Lanphear BP, Padmanabhan V, Sathyanarayana S, Swan SH, Aalborg J, Baird DD, Binder AM, Bradman A, Braun JM, Calafat AM, Cantonwine DE, Christenbury KE, Factor-Litvak P, Harley KG, Hauser R, Herbstman JB, Hertz-Picciotto I, Holland N, Jukic AMZ, McElrath TF, Meeker JD, Messerlian C, Michels KB, Newman RB, Nguyen RH, O’Brien KM, Rauh VA, Redmon B, Rich DQ, Rosen EM, Schmidt RJ, Sparks AE, Starling AP, Wang C, Watkins DJ, Weinberg CR, Weinberger B, Wenzel AG, Wilcox AJ, Yolton K, Zhang Y, Ferguson KK. Racial and Ethnic Disparities in Phthalate Exposure and Preterm Birth: A Pooled Study of Sixteen U.S. Cohorts. Environ Health Perspect 2023; 131:127015. [PMID: 38117586 PMCID: PMC10732302 DOI: 10.1289/ehp12831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 11/17/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
BACKGROUND Phthalate exposures are ubiquitous during pregnancy and may contribute to racial and ethnic disparities in preterm birth. OBJECTIVES We investigated race and ethnicity in the relationship between biomarkers of phthalate exposure and preterm birth by examining: a) how hypothetical reductions in racial and ethnic disparities in phthalate metabolites might reduce the probability of preterm birth; and b) exposure-response models stratified by race and ethnicity. METHODS We pooled individual-level data on 6,045 pregnancies from 16 U.S. cohorts. We investigated covariate-adjusted differences in nine urinary phthalate metabolite concentrations by race and ethnicity [non-Hispanic White (White, 43%), non-Hispanic Black (Black, 13%), Hispanic/Latina (38%), and Asian/Pacific Islander (3%)]. Using g-computation, we estimated changes in the probability of preterm birth under hypothetical interventions to eliminate disparities in levels of urinary phthalate metabolites by proportionally lowering average concentrations in Black and Hispanic/Latina participants to be approximately equal to the averages in White participants. We also used race and ethnicity-stratified logistic regression to characterize associations between phthalate metabolites and preterm birth. RESULTS In comparison with concentrations among White participants, adjusted mean phthalate metabolite concentrations were consistently higher among Black and Hispanic/Latina participants by 23%-148% and 4%-94%, respectively. Asian/Pacific Islander participants had metabolite levels that were similar to those of White participants. Hypothetical interventions to reduce disparities in metabolite mixtures were associated with lower probabilities of preterm birth for Black [13% relative reduction; 95% confidence interval (CI): - 34 % , 8.6%] and Hispanic/Latina (9% relative reduction; 95% CI: - 19 % , 0.8%) participants. Odds ratios for preterm birth in association with phthalate metabolites demonstrated heterogeneity by race and ethnicity for two individual metabolites (mono-n-butyl and monoisobutyl phthalate), with positive associations that were larger in magnitude observed among Black or Hispanic/Latina participants. CONCLUSIONS Phthalate metabolite concentrations differed substantially by race and ethnicity. Our results show hypothetical interventions to reduce population-level racial and ethnic disparities in biomarkers of phthalate exposure could potentially reduce the probability of preterm birth. https://doi.org/10.1289/EHP12831.
Collapse
Affiliation(s)
- Barrett M. Welch
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
- University of Nevada, Reno, Reno, Nevada, USA
| | | | - Jessie P. Buckley
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Stephanie M. Engel
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Tamarra James-Todd
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Ami R. Zota
- Columbia University Mailman School of Public Health, Columbia University, New York, New York, USA
| | | | - Emily S. Barrett
- Rutgers School of Public Health, Rutgers University, Piscataway, New Jersey, USA
| | | | - Nicole R. Bush
- University of California, San Francisco, San Francisco, California, USA
| | | | - Dana Dabelea
- University of Colorado, Anschutz Medical Campus, Aurora, Colorado, USA
| | - Brenda Eskenazi
- Center for Environmental Research and Community Health (CERCH), University of California, Berkeley, Berkeley, California, USA
| | | | | | - Sheela Sathyanarayana
- Seattle Children’s Research Institute, University of Washington, Seattle, Washington, USA
| | - Shanna H. Swan
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jenny Aalborg
- University of Colorado, Anschutz Medical Campus, Aurora, Colorado, USA
| | - Donna D. Baird
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - Asa Bradman
- University of California, Merced, Merced, California, USA
| | | | - Antonia M. Calafat
- National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | | | - Kate E. Christenbury
- Social & Scientific Systems, Inc., a DLH Holdings Company, Durham, North Carolina, USA
| | - Pam Factor-Litvak
- Columbia University Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Kim G. Harley
- Center for Environmental Research and Community Health (CERCH), University of California, Berkeley, Berkeley, California, USA
| | - Russ Hauser
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Julie B. Herbstman
- Columbia University Mailman School of Public Health, Columbia University, New York, New York, USA
| | | | - Nina Holland
- Center for Environmental Research and Community Health (CERCH), University of California, Berkeley, Berkeley, California, USA
| | - Anne Marie Z. Jukic
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | | | - John D. Meeker
- School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Carmen Messerlian
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Karin B. Michels
- University of California, Los Angeles, Los Angeles, California, USA
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Roger B. Newman
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ruby H.N. Nguyen
- University of Minnesota, School of Public Health, Minneapolis, Minnesota, USA
| | - Katie M. O’Brien
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Virginia A. Rauh
- Columbia University Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Bruce Redmon
- University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - David Q. Rich
- University of Rochester Medical Center, Rochester, New York, USA
| | - Emma M. Rosen
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | | | - Anne P. Starling
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christina Wang
- The Lundquist Institute at Harbor, UCLA Medical Center, West Carson, California, USA
| | - Deborah J. Watkins
- School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Clarice R. Weinberg
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Barry Weinberger
- Cohen Children’s Medical Center of New York, Northwell Health, Queens, New York, USA
| | - Abby G. Wenzel
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Allen J. Wilcox
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Kimberly Yolton
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Yu Zhang
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Kelly K. Ferguson
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| |
Collapse
|
10
|
Kim CE, Binder AM, Corvalan C, Pereira A, Shepherd J, Calafat AM, Botelho JC, Hampton JM, Trentham-Dietz A, Michels KB. Time-specific impact of mono-benzyl phthalate (MBzP) and perfluorooctanoic acid (PFOA) on breast density of a Chilean adolescent Cohort. Environ Int 2023; 181:108241. [PMID: 37857187 DOI: 10.1016/j.envint.2023.108241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/13/2023] [Accepted: 09/27/2023] [Indexed: 10/21/2023]
Abstract
INTRODUCTION High mammographic density is among the strongest and most established predictors for breast cancer risk. Puberty, the period during which breasts undergo exponential mammary growth, is considered one of the critical stages of breast development for environmental exposures. Benzylbutyl phthalate (BBP) and perfluorooctanoic acid (PFOA) are pervasive endocrine disrupting chemicals that may increase hormone-sensitive cancers. Evaluating the potential impact of BBP and PFOA exposure on pubertal breast density is important to our understanding of early-life environmental influences on breast cancer etiology. OBJECTIVE To prospectively assess the effect of biomarker concentrations of monobenzyl phthalate (MBzP) and PFOA at specific pubertal window of susceptibility (WOS) on adolescent breast density. METHOD This study included 376 Chilean girls from the Growth and Obesity Cohort Study with data collection at four timepoints: Tanner breast stages 1 (B1) and 4 (B4), 1- year post- menarche (1YPM) and 2-years post-menarche (2YPM). Dual-energy X-ray absorptiometry was used to assess the absolute fibroglandular volume (FGV) and percent breast density (%FGV) at 2YPM. We used concentrations of PFOA in serum and MBzP in urine as an index of exposure to PFOA and BBP, respectively. Parametric G-formula was used to estimate the time-specific effects of MBzP and PFOA on breast density. The models included body fat percentage as a time-varying confounder and age, birthweight, age at menarche, and maternal education as fixed covariates. RESULTS A doubling of serum PFOA concentration at B4 resulted in a non-significant increase in absolute FGV (β:11.25, 95% confidence interval (CI): -0.28, 23.49)), while a doubling of PFOA concentration at 1YPM resulted in a decrease in % FGV (β:-4.61, 95% CI: -7.45, -1.78). We observed no associations between urine MBzP and breast density measures. CONCLUSION In this cohort of Latina girls, PFOA serum concentrations corresponded to a decrease in % FGV. No effect was observed between MBzP and breast density measures across pubertal WOS.
Collapse
Affiliation(s)
- Claire E Kim
- Department of Epidemiology, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexandra M Binder
- Department of Epidemiology, University of California Los Angeles, Los Angeles, CA, USA; Population Sciences in the Pacific Program, University of Hawaii Cancer, Honolulu, HI, USA
| | - Camila Corvalan
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - John Shepherd
- Population Sciences in the Pacific Program, University of Hawaii Cancer, Honolulu, HI, USA
| | - Antonia M Calafat
- National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Julianne C Botelho
- National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin - Madison, Madison, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin - Madison, Madison, USA
| | - Karin B Michels
- Department of Epidemiology, University of California Los Angeles, Los Angeles, CA, USA; Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| |
Collapse
|
11
|
Pereira A, Ferrer P, Binder A, Rojas J, Michels KB, Corvalán C, Mericq V. Association Between Markers of Adiposity During Childhood and Puberty Onset in Latino Girls. J Clin Endocrinol Metab 2023; 108:e1272-e1281. [PMID: 37226986 DOI: 10.1210/clinem/dgad294] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 05/26/2023]
Abstract
CONTEXT Prepubertal adiposity is associated with earlier puberty. It is unclear when this association starts, if all adiposity markers are similarly associated, and whether all pubertal milestones are similarly affected. OBJECTIVE To evaluate the association between different adiposity markers during childhood and the timing of different pubertal milestones in Latino girls. DESIGN, SETTING, AND PARTICIPANTS Longitudinal follow-up of 539 female participants of the Chilean Growth and Obesity Cohort recruited from childcare centers (mean age 3.5 years) from the southeast area of Santiago, Chile. Participants were singletons born between 2002 and 2003 within the normal birthweight range. Since 2006, a trained dietitian measured weight, height, waist circumference (WC) and skinfolds to estimate body mass index (BMI) Centers for Disease Control and Prevention percentiles, central obesity, percentage of fat mass (%FM), and fat mass index (FMI, fat mass/height2). MAIN OUTCOME Since 2009, sexual maturation was assessed every 6 months to assess age at (1) thelarche, (2) pubarche, (3) menarche, and (4) peak height velocity (PHV). RESULTS At thelarche, 12.5% were obese and 2% had central obesity. The median age of pubarche, menarche, and PHV were all associated with markers of adiposity at different time points during childhood whereas thelarche only with %FM and FMI. Adiposity clusters models showed that children with trajectories of high WC, %FM, and FMI during childhood were related with earlier thelarche, pubarche, menarche, and PHV but BMI trajectories only with menarche and PHV. CONCLUSIONS Higher WC, %FM, and FMI were associated with earlier age at thelarche, pubarche, menarche, and PHV. The effect of BMI was less consistent.
Collapse
Affiliation(s)
- Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, 7830490 Santiago, Chile
| | - Pedro Ferrer
- Institute of Nutrition and Food Technology, University of Chile, 7830490 Santiago, Chile
| | - Alexandra Binder
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawai'i Cancer Center, Honolulu, HI 96822, USA
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095-1772, USA
| | - Joanna Rojas
- Department of Nutrition and Dietetics, Faculty of Health Sciences, University of Atacama, 1530000 Copiapó, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095-1772, USA
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79110 Freiburg, Germany
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, 7830490 Santiago, Chile
| | - Verónica Mericq
- Institute of Maternal and Child Research, Faculty of Medicine, University of Chile, 8360160 Santiago, Chile
| |
Collapse
|
12
|
Schropp N, Stanislas V, Michels KB, Thriene K. How Do Prebiotics Affect Human Intestinal Bacteria?-Assessment of Bacterial Growth with Inulin and XOS In Vitro. Int J Mol Sci 2023; 24:12796. [PMID: 37628977 PMCID: PMC10454692 DOI: 10.3390/ijms241612796] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/07/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Prebiotics are believed to exhibit high specificity in stimulating the growth or activity of a limited number of commensal microorganisms, thereby conferring health benefits to the host. However, the mechanism of action of prebiotics depends on multiple factors, including the composition of an individual's gut microbiota, and is therefore difficult to predict. It is known that different bacteria can utilize inulin and xylooligosaccharides (XOS), but an overview of which bacteria in the human gut may be affected is lacking. Detailed knowledge of how bacterial growth is affected by prebiotics is furthermore useful for the development of new synbiotics, which combine a living microorganism with a selective substrate to confer a health benefit to the host. Hence, we developed a statistical model to compare growth in vitro among typical human gut bacteria from different phylogenetic lineages. Based on continuous observation of the optical density (OD600), we compare maximal growth rates (rmax), maximal attained OD600 (ODmax), and area under the growth curve (AUC) of bacteria grown on inulin or XOS. The consideration of these three parameters suggests strain-specific preferences for inulin or XOS and reveals previously unknown preferences such as Streptococcus salivarius growth on XOS.
Collapse
Affiliation(s)
| | | | | | - Kerstin Thriene
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79110 Freiburg, Germany; (N.S.); (V.S.); (K.B.M.)
| |
Collapse
|
13
|
Streit F, Völker MP, Klinger-König J, Zillich L, Frank J, Reinhard I, Foo JC, Witt SH, Sirignano L, Becher H, Obi N, Riedel O, Do S, Castell S, Hassenstein MJ, Karch A, Stang A, Schmidt B, Schikowski T, Stahl-Pehe A, Brenner H, Perna L, Greiser KH, Kaaks R, Michels KB, Franzke CW, Peters A, Fischer B, Konzok J, Mikolajczyk R, Führer A, Keil T, Fricke J, Willich SN, Pischon T, Völzke H, Meinke-Franze C, Loeffler M, Wirkner K, Berger K, Grabe HJ, Rietschel M. The interplay of family history of depression and early trauma: associations with lifetime and current depression in the German national cohort (NAKO). Front Epidemiol 2023; 3:1099235. [PMID: 38523800 PMCID: PMC10959537 DOI: 10.3389/fepid.2023.1099235] [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] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 04/28/2023] [Indexed: 03/26/2024]
Abstract
Introduction Family history of depression and childhood maltreatment are established risk factors for depression. However, how these factors are interrelated and jointly influence depression risk is not well understood. The present study investigated (i) if childhood maltreatment is associated with a family history of depression (ii) if family history and childhood maltreatment are associated with increased lifetime and current depression, and whether both factors interact beyond their main effects, and (iii) if family history affects lifetime and current depression via childhood maltreatment. Methods Analyses were based on a subgroup of the first 100,000 participants of the German National Cohort (NAKO), with complete information (58,703 participants, mean age = 51.2 years, 53% female). Parental family history of depression was assessed via self-report, childhood maltreatment with the Childhood Trauma Screener (CTS), lifetime depression with self-reported physician's diagnosis and the Mini-International Neuropsychiatric Interview (MINI), and current depressive symptoms with the depression scale of the Patient Health Questionnaire (PHQ-9). Generalized linear models were used to test main and interaction effects. Mediation was tested using causal mediation analyses. Results Higher frequencies of the childhood maltreatment measures were found in subjects reporting a positive family history of depression. Family history and childhood maltreatment were independently associated with increased depression. No statistical interactions of family history and childhood maltreatment were found for the lifetime depression measures. For current depressive symptoms (PHQ-9 sum score), an interaction was found, with stronger associations of childhood maltreatment and depression in subjects with a positive family history. Childhood maltreatment was estimated to mediate 7%-12% of the effect of family history on depression, with higher mediated proportions in subjects whose parents had a depression onset below 40 years. Abuse showed stronger associations with family history and depression, and higher mediated proportions of family history effects on depression than neglect. Discussion The present study confirms the association of childhood maltreatment and family history with depression in a large population-based cohort. While analyses provide little evidence for the joint effects of both risk factors on depression beyond their individual effects, results are consistent with family history affecting depression via childhood maltreatment to a small extent.
Collapse
Affiliation(s)
- Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Maja P. Völker
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Johanna Klinger-König
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Iris Reinhard
- Department of Biostatistics, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jerome C. Foo
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heiko Becher
- Institute of Global Health, University Hospital Heidelberg, Heidelberg, Germany
| | - Nadia Obi
- Institute of Medical Biometry and Epidemiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Oliver Riedel
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Bremen, Deutschland
| | - Stefanie Do
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Bremen, Deutschland
| | - Stefanie Castell
- Department for Epidemiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | - Max J. Hassenstein
- Department for Epidemiology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
- PhD Programme “Epidemiology”, Braunschweig-Hannover, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Andreas Stang
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Tamara Schikowski
- IUF—Leibniz Institute for Environmental Medicine, Düsseldorf, Germany
| | - Anna Stahl-Pehe
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, University of Düsseldorf, Düsseldorf, Germany
| | - Hermann Brenner
- Network Ageing Research (NAR), Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology & Ageing Research, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Laura Perna
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Karin Halina Greiser
- German Cancer Research Centre (DKFZ) Heidelberg, Div. of Cancer Epidemiology, Heidelberg, Germany
| | - Rudolf Kaaks
- German Cancer Research Centre (DKFZ) Heidelberg, Div. of Cancer Epidemiology, Heidelberg, Germany
| | - Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Claus-Werner Franzke
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Beate Fischer
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Julian Konzok
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, Halle, Germany
- German Center for Mental Health, Site Jena-Magdeburg-Halle, Jena, Germany
| | - Amand Führer
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for Clinical Epidemiology and Biometry, University of Wuerzburg, Wuerzburg, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, Bad Kissingen, Germany
| | - Julia Fricke
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan N. Willich
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Pischon
- Max-Delbrueck-Centre for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max-Delbrueck-Centre for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Claudia Meinke-Franze
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Kerstin Wirkner
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Klaus Berger
- Institute of Epidemiology & Social Medicine, University of Muenster, Muenster, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| |
Collapse
|
14
|
Huemer MT, Kluttig A, Fischer B, Ahrens W, Castell S, Ebert N, Gastell S, Jöckel KH, Kaaks R, Karch A, Keil T, Kemmling Y, Krist L, Leitzmann M, Lieb W, Meinke-Franze C, Michels KB, Mikolajczyk R, Moreno Velásquez I, Pischon T, Schipf S, Schmidt B, Schöttker B, Schulze MB, Stocker H, Teismann H, Wirkner K, Drey M, Peters A, Thorand B. Grip strength values and cut-off points based on over 200,000 adults of the German National Cohort - a comparison to the EWGSOP2 cut-off points. Age Ageing 2023; 52:6998045. [PMID: 36702514 PMCID: PMC9879709 DOI: 10.1093/ageing/afac324] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The European Working Group on Sarcopenia in Older People (EWGSOP) updated in 2018 the cut-off points for low grip strength to assess sarcopenia based on pooled data from 12 British studies. OBJECTIVE Comparison of the EWGSOP2 cut-off points for low grip strength to those derived from a large German sample. METHODS We assessed the grip strength distribution across age and derived low grip strength cut-off points for men and women (peak mean -2.5 × SD) based on 200,389 German National Cohort (NAKO) participants aged 19-75 years. In 1,012 Cooperative Health Research in the Region of Augsburg (KORA)-Age participants aged 65-93 years, we calculated the age-standardised prevalence of low grip strength and time-dependent sensitivity and specificity for all-cause mortality. RESULTS Grip strength increased in the third and fourth decade of life and declined afterwards. Calculated cut-off points for low grip strength were 29 kg for men and 18 kg for women. In KORA-Age, the age-standardised prevalence of low grip strength was 1.5× higher for NAKO-derived (17.7%) compared to EWGSOP2 (11.7%) cut-off points. NAKO-derived cut-off points yielded a higher sensitivity and lower specificity for all-cause mortality. CONCLUSIONS Cut-off points for low grip strength from German population-based data were 2 kg higher than the EWGSOP2 cut-off points. Higher cut-off points increase the sensitivity, thereby suggesting an intervention for more patients at risk, while other individuals might receive additional diagnostics/treatment without the urgent need. Research on the effectiveness of intervention in patients with low grip strength defined by different cut-off points is needed.
Collapse
Affiliation(s)
- Marie-Theres Huemer
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Alexander Kluttig
- Institute of Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Beate Fischer
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Stefanie Castell
- Department for Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Nina Ebert
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, Dusseldorf, Germany
| | - Sylvia Gastell
- NAKO Study Center South Berlin/Brandenburg, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Karl-Heinz Jöckel
- Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Essen, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany,Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany,State Institute of Health, Bavarian Health and Food Safety Authority, Erlangen, Germany
| | - Yvonne Kemmling
- Department for Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Lilian Krist
- Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, Kiel, Germany
| | - Claudia Meinke-Franze
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Rafael Mikolajczyk
- Institute of Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Ilais Moreno Velásquez
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
| | - Tobias Pischon
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany,Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Biobank, Berlin, Germany,Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Börge Schmidt
- Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Essen, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany,Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany,Network Aging Research, Heidelberg University, Heidelberg, Germany
| | - Henning Teismann
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Kerstin Wirkner
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany,Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Michael Drey
- Department of Medicine IV, University Hospital, LMU Munich, Geriatrics, 80336 Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Barbara Thorand
- Address correspondence to: Prof. Dr. Barbara Thorand. Tel: +49 (0)89/3187-4480.
| |
Collapse
|
15
|
Thriene K, Michels KB. Human Gut Microbiota Plasticity throughout the Life Course. Int J Environ Res Public Health 2023; 20:1463. [PMID: 36674218 PMCID: PMC9860808 DOI: 10.3390/ijerph20021463] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
The role of the gut microbiota in human health and disease has garnered heightened attention over the past decade. A thorough understanding of microbial variation over the life course and possible ways to influence and optimize the microbial pattern is essential to capitalize on the microbiota's potential to influence human health. Here, we review our current understanding of the concept of plasticity of the human gut microbiota throughout the life course. Characterization of the plasticity of the microbiota has emerged through recent research and suggests that the plasticity in the microbiota signature is largest at birth when the microbial colonization of the gut is initiated and mode of birth imprints its mark, then decreases postnatally continuously and becomes less malleable and largely stabilized with advancing age. This continuing loss of plasticity has important implication for the impact of the exposome on the microbiota and health throughout the life course and the identification of susceptible 'windows of opportunity' and methods for interventions.
Collapse
Affiliation(s)
- Kerstin Thriene
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79110 Freiburg, Germany
| | - Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79110 Freiburg, Germany
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| |
Collapse
|
16
|
Jaskulski S, Nuszbaum C, Michels KB. Components, prospects and challenges of personalized prevention. Front Public Health 2023; 11:1075076. [PMID: 36875367 PMCID: PMC9978387 DOI: 10.3389/fpubh.2023.1075076] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/09/2023] [Indexed: 02/18/2023] Open
Abstract
Effective preventive strategies are urgently needed to address the rising burden of non-communicable diseases such as cardiovascular disease and cancer. To date, most prevention efforts to reduce disease incidence have primarily targeted populations using "one size fits all" public health recommendations and strategies. However, the risk for complex heterogeneous diseases is based on a multitude of clinical, genetic, and environmental factors, which translate into individual sets of component causes for every person. Recent advances in genetics and multi-omics enable the use of new technologies to stratify disease risks at an individual level fostering personalized prevention. In this article, we review the main components of personalized prevention, provide examples, and discuss both emerging opportunities and remaining challenges for its implementation. We encourage physicians, health policy makers, and public health professionals to consider and apply the key elements and examples of personalized prevention laid out in this article while overcoming challenges and potential barriers to their implementation.
Collapse
Affiliation(s)
- Stefanie Jaskulski
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Competence Network Preventive Medicine Baden-Württemberg, Competence Area of Personalized Prevention, Freiburg, Germany
| | - Cosima Nuszbaum
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Competence Network Preventive Medicine Baden-Württemberg, Competence Area of Personalized Prevention, Freiburg, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Competence Network Preventive Medicine Baden-Württemberg, Competence Area of Personalized Prevention, Freiburg, Germany.,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
| |
Collapse
|
17
|
van Otterdijk SD, Klett H, Boerries M, Michels KB. The impact of pre-pregnancy folic acid intake on placental DNA methylation in a fortified cohort. FASEB J 2023; 37:e22698. [PMID: 36520012 DOI: 10.1096/fj.202200476rr] [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: 03/29/2022] [Revised: 10/28/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
Folate plays an important role in the modulation of one-carbon metabolism and DNA methylation through a complex biosynthesis pathway. Folate deficiency during pregnancy has been associated with an increased risk for birth defects. This study investigates the extent to which the availability of folate and S-Adenosylmethionine (SAM) affects placental DNA methylation. We hypothesized that maintaining sufficient levels of folate and SAM is particularly important in individuals carrying the MTHFR C677T polymorphism. Maternal- and cord blood was analyzed to genotype the MTHFR rs1801133 SNP. Red blood cell (RBC) folate, vitamin B12, SAM, and S-Adenosylhomocysteine (SAH) were analyzed in cord blood. Epigenome-wide methylation analyses were performed on 90 placenta tissue samples isolated from the fetal side of the placenta; 45 originating from mother-infant dyads homozygous for the MTHFR C677T variant and 45 originating from mother-infant dyads with the homozygous wild type MTHFR677 genotype. Verification of the results was performed using pyrosequencing assays. Genome-wide placental DNA methylation patterns were relatively stable and not significantly affected by levels of one-carbon metabolites. MTHFR genotype was associated with DNA methylation of several loci, including a locus in the MTHFR region. RBC folate and particularly the SAM:SAH ratio did affect overall CpG DNA methylation in some CpG regions when the loci were split according to their CpG island relation. This was most evident in participants carrying the MTHFR C677T variant suggesting a stronger influence of the biosynthesis pathway on the overall placental DNA methylation in MTHFR TT individuals than in MTHFR CC individuals.
Collapse
Affiliation(s)
- Sanne D van Otterdijk
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Freiburg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hagen Klett
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Melanie Boerries
- German Cancer Consortium (DKTK), Freiburg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA
| |
Collapse
|
18
|
Ekwuocha I, Pereira A, Corvalán C, Michels KB, Gaskins AJ. Dietary Iron Intake in Relation to Age at Menarche: A Prospective Cohort Study in Chilean Girls. J Nutr 2023; 153:253-259. [PMID: 36913459 PMCID: PMC10196559 DOI: 10.1016/j.tjnut.2022.10.002] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/30/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Early onset of menarche is considered an important risk factor for a number of diseases in adulthood. Iron intake may be related to pubertal timing because of its role in childhood growth and reproductive function. OBJECTIVE We investigated the relation between dietary iron intake and age at menarche in a prospective cohort of Chilean girls. METHODS Overall, 602 Chilean girls were included in the Growth and Obesity Cohort Study, a longitudinal study that began in 2006 when the girls were 3-4 y old. Starting in 2013, diet was assessed every 6 mo through 24-h recall. The date of menarche was reported every 6 mo. Our analysis included 435 girls with prospective data on diet and age at menarche. We used a multivariable Cox proportional hazards regression model with restricted cubic splines to estimate HRs and 95% CIs for the association between cumulative mean iron intake and age at menarche. RESULTS Most girls (99.5%) attained menarche with a mean (standard deviation) age at menarche of 12.2 (0.9) y. The mean dietary iron intake was 13.5 (range: 4.0-30.6) mg/d. Only 3.7% of girls consumed below 8 mg/d, the RDA. After multivariable adjustment, cumulative mean iron intake had a nonlinear association with menarche (P-for-nonlinearity: 0.02). Iron intakes above the RDA, between 8 and 15 mg/d, were associated with progressively lower probability of earlier menarche. Above 15 mg/d, the HRs were imprecise but tended to approach the null as iron intake increased. This association was attenuated after adjusting for girls' BMI and height before menarche (P-for-nonlinearity: 0.11). CONCLUSION In Chilean girls, iron intake during late childhood, independent of body weight, was not an important determinant of menarche timing.
Collapse
Affiliation(s)
- Ifeoma Ekwuocha
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
| |
Collapse
|
19
|
Yoon LS, Binder AM, Pereira A, Calafat AM, Shepherd J, Corvalán C, Michels KB. Variability in urinary phthalates, phenols, and parabens across childhood and relation to adolescent breast composition in Chilean girls. Environ Int 2022; 170:107586. [PMID: 36302292 PMCID: PMC10517447 DOI: 10.1016/j.envint.2022.107586] [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] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/04/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Epidemiologic evidence suggests that environmental factors acting as endocrine disrupting chemicals (EDCs) are associated with mammographic breast density and the risk of breast cancer. Exposure to EDCs during puberty, a period of rapid breast development, may affect susceptibility to breast carcinogenesis. METHODS In a cohort of 366 Chilean adolescents from the Growth and Obesity Cohort Study, we evaluated the relation between urinary concentrations of 15 suspected EDC biomarkers across three pubertal time points (Tanner breast stage 1 (B1), 4 (B4), and 1-year post-menarche) and breast fibroglandular volume (FGV; percent FGV [%FGV] and absolute FGV [aFGV]) and total breast volume (tBV) at 2-years post-menarche. We used linear mixed models to test differences in creatinine-corrected EDC biomarker concentrations at B4 and 1-year post-menarche compared to B1 and calculated intraclass correlation coefficients (ICC) of EDC concentrations across time points to appraise the consistency of measurements. We fit multivariable generalized estimating equations (GEEs) to evaluate windows of susceptibility for the association between log10-transformed EDCs and log10-transformed breast outcomes. GEEs were adjusted for age, body fat percentage, total caloric intake, and maternal education. RESULTS Urinary EDC biomarker concentrations highly varied across pubertal time points (ICC range 0.01-0.30). For 12 EDCs, biomarker concentrations decreased over time. Triclosan measured at 1-year post-menarche was inversely associated with %FGV at 2-years post-menarche (β = -0.025, 95 % confidence interval = -0.041, -0.008). Mono(2-ethyl-5-carboxypentyl) phthalate and the sum of di(2-ethylhexyl) phthalate metabolite concentrations at B4 were positively associated with aFGV and tBV at 2-years post-menarche. No measured phenols were associated with aFGV and tBV, while no measured parabens were associated with %FGV and aFGV. CONCLUSIONS Our study suggests relatively high variability in EDC biomarker concentrations across the peripubertal time period. We also found evidence to suggest that there may be pubertal windows of susceptibility to select EDCs for the association with adolescent breast density.
Collapse
Affiliation(s)
- Lara S Yoon
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90025, USA.
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90025, USA; Cancer Epidemiology Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813, USA.
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Macul, Santiago 7830490, Chile.
| | - Antonia M Calafat
- Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway, Mailstop F17, Atlanta, GA 30341, USA.
| | - John Shepherd
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813, USA.
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Macul, Santiago 7830490, Chile.
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA 90025, USA; Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Elsässerstraße 2, 79110 Freiburg, Germany.
| |
Collapse
|
20
|
Fernandez-Jimenez N, Fore R, Cilleros-Portet A, Lepeule J, Perron P, Kvist T, Tian FY, Lesseur C, Binder AM, Lozano M, Martorell-Marugán J, Loke YJ, Bakulski KM, Zhu Y, Forhan A, Sammallahti S, Everson TM, Chen J, Michels KB, Belmonte T, Carmona-Sáez P, Halliday J, Daniele Fallin M, LaSalle JM, Tost J, Czamara D, Fernández MF, Gómez-Martín A, Craig JM, Gonzalez-Alzaga B, Schmidt RJ, Dou JF, Muggli E, Lacasaña M, Vrijheid M, Marsit CJ, Karagas MR, Räikkönen K, Bouchard L, Heude B, Santa-Marina L, Bustamante M, Hivert MF, Bilbao JR. A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health. Commun Biol 2022; 5:1313. [PMID: 36446949 PMCID: PMC9709064 DOI: 10.1038/s42003-022-04267-y] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/16/2022] [Indexed: 12/05/2022] Open
Abstract
Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. The placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.
Collapse
Affiliation(s)
- Nora Fernandez-Jimenez
- grid.11480.3c0000000121671098Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU) and Biocruces-Bizkaia Health Research Institute, Leioa, Basque Country Spain
| | - Ruby Fore
- grid.38142.3c000000041936754XDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA USA
| | - Ariadna Cilleros-Portet
- grid.11480.3c0000000121671098Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU) and Biocruces-Bizkaia Health Research Institute, Leioa, Basque Country Spain
| | - Johanna Lepeule
- grid.418110.d0000 0004 0642 0153University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, Grenoble, France
| | - Patrice Perron
- grid.411172.00000 0001 0081 2808Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC Canada
| | - Tuomas Kvist
- grid.7737.40000 0004 0410 2071Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Fu-Ying Tian
- grid.189967.80000 0001 0941 6502Gangarosa Department of Environmental Health, Rollins School of Public Health at Emory University, Atlanta, GA USA
| | - Corina Lesseur
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alexandra M. Binder
- grid.410445.00000 0001 2188 0957Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA ,grid.19006.3e0000 0000 9632 6718Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA USA
| | - Manuel Lozano
- grid.5338.d0000 0001 2173 938XEpidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain ,grid.5338.d0000 0001 2173 938XPreventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Universitat de València, Valencia, Spain
| | - Jordi Martorell-Marugán
- grid.4489.10000000121678994Department of Statistics and Operations Research, University of Granada, Granada, Spain ,grid.4489.10000000121678994Bioinformatics Unit. GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, Spain
| | - Yuk J. Loke
- grid.1058.c0000 0000 9442 535XMurdoch Children’s Research Institute, Parkville, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Paediatrics, University of Melbourne, Parkville, VIC Australia
| | - Kelly M. Bakulski
- grid.214458.e0000000086837370Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Yihui Zhu
- grid.27860.3b0000 0004 1936 9684Department of Medical Microbiology and Immunology, MIND Institute, Genome Center, University of California, Davis, CA USA
| | - Anne Forhan
- grid.508487.60000 0004 7885 7602Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | - Sara Sammallahti
- grid.5645.2000000040459992XDepartment of Child and Adolescent Psychiatry and Psychology, Erasmus MC Rotterdam, The Netherlands
| | - Todd M. Everson
- grid.189967.80000 0001 0941 6502Gangarosa Department of Environmental Health, Rollins School of Public Health at Emory University, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Epidemiology, Rollins School of Public health at Emory University, Atlanta, GA USA
| | - Jia Chen
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Karin B. Michels
- grid.19006.3e0000 0000 9632 6718Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA USA ,grid.5963.9Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Thalia Belmonte
- grid.411342.10000 0004 1771 1175Health Research Institute of Asturias, ISPA and Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit, Puerta del Mar University Hospital, Cadiz, Spain
| | - Pedro Carmona-Sáez
- grid.4489.10000000121678994Department of Statistics and Operations Research, University of Granada, Granada, Spain ,grid.4489.10000000121678994Bioinformatics Unit. GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, Spain
| | - Jane Halliday
- grid.1058.c0000 0000 9442 535XMurdoch Children’s Research Institute, Parkville, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Paediatrics, University of Melbourne, Parkville, VIC Australia
| | - M. Daniele Fallin
- grid.21107.350000 0001 2171 9311Wendy Klag Center for Autism and Developmental Disabilities, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Janine M. LaSalle
- grid.27860.3b0000 0004 1936 9684Department of Medical Microbiology and Immunology, MIND Institute, Genome Center, University of California, Davis, CA USA
| | - Jorg Tost
- grid.418135.a0000 0004 0641 3404Laboratory for Epigenetics & Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, Evry, France
| | - Darina Czamara
- grid.419548.50000 0000 9497 5095Max-Planck-Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany
| | - Mariana F. Fernández
- grid.4489.10000000121678994University of Granada, Center for Biomedical Research (CIBM), Granada, Spain ,grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.466571.70000 0004 1756 6246CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Antonio Gómez-Martín
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.413740.50000 0001 2186 2871Andalusian School of Public Health (EASP), Granada, Spain
| | - Jeffrey M. Craig
- grid.1058.c0000 0000 9442 535XMurdoch Children’s Research Institute, Parkville, VIC Australia ,grid.1021.20000 0001 0526 7079Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Geelong, Australia
| | - Beatriz Gonzalez-Alzaga
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.413740.50000 0001 2186 2871Andalusian School of Public Health (EASP), Granada, Spain
| | - Rebecca J. Schmidt
- grid.27860.3b0000 0004 1936 9684Department of Public Health Sciences and the MIND Institute, University of California Davis School of Medicine, Davis, CA USA
| | - John F. Dou
- grid.214458.e0000000086837370Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Evelyne Muggli
- grid.1058.c0000 0000 9442 535XMurdoch Children’s Research Institute, Parkville, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Paediatrics, University of Melbourne, Parkville, VIC Australia
| | - Marina Lacasaña
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.466571.70000 0004 1756 6246CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain ,grid.413740.50000 0001 2186 2871Andalusian School of Public Health (EASP), Granada, Spain
| | - Martine Vrijheid
- grid.466571.70000 0004 1756 6246CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain ,grid.434607.20000 0004 1763 3517ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carmen J. Marsit
- grid.189967.80000 0001 0941 6502Gangarosa Department of Environmental Health, Rollins School of Public Health at Emory University, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Epidemiology, Rollins School of Public health at Emory University, Atlanta, GA USA
| | - Margaret R. Karagas
- grid.86715.3d0000 0000 9064 6198Department of Biochemistry and Functional Genomics, Universite de Sherbrooke, Sherbrooke, QC Canada
| | - Katri Räikkönen
- grid.7737.40000 0004 0410 2071Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Luigi Bouchard
- grid.86715.3d0000 0000 9064 6198Department of Biochemistry and Functional Genomics, Universite de Sherbrooke, Sherbrooke, QC Canada ,grid.459278.50000 0004 4910 4652Department of Laboratory Medicine, CIUSSS du Saguenay–Lac-St-Jean – Hôpital Universitaire de Chicoutimi, Chicoutimi, QC Canada
| | - Barbara Heude
- grid.508487.60000 0004 7885 7602Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | - Loreto Santa-Marina
- grid.466571.70000 0004 1756 6246CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain ,grid.432380.eBiodonostia, Epidemiology and Public Health Area, Environmental Epidemiology and Child Development Group, 20014 San Sebastian, Basque Country Spain ,Health Department of Basque Government, Sub-directorate of Public Health of Gipuzkoa, San Sebastian, Basque Country Spain
| | - Mariona Bustamante
- grid.466571.70000 0004 1756 6246CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain ,grid.434607.20000 0004 1763 3517ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Marie-France Hivert
- grid.38142.3c000000041936754XDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA USA ,grid.411172.00000 0001 0081 2808Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC Canada ,grid.32224.350000 0004 0386 9924Diabetes Unit, Massachusetts General Hospital, Boston, MA USA
| | - Jose Ramon Bilbao
- grid.11480.3c0000000121671098Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU) and Biocruces-Bizkaia Health Research Institute, Leioa, Basque Country Spain ,grid.512890.7CIBER of diabetes and associated metabolic disorders (CIBERDEM), Madrid, Spain
| |
Collapse
|
21
|
Okubo Y, Nishi A, Michels KB, Nariai H, Kim-Farley RJ, Arah OA, Uda K, Kinoshita N, Miyairi I. The consequence of financial incentives for not prescribing antibiotics: a Japan's nationwide quasi-experiment. Int J Epidemiol 2022; 51:1645-1655. [PMID: 35353127 PMCID: PMC10233477 DOI: 10.1093/ije/dyac057] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 03/16/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND For addressing antibiotic overuse, Japan designed a health care policy in which eligible medical facilities could claim a financial reward when antibiotics were not prescribed for early-stage respiratory and gastrointestinal infections. The policy was introduced in a pilot manner in paediatric clinics in April 2018. METHODS We conducted a quasi-experimental, propensity score-matched, difference-in-differences (DID) design to determine whether the nationwide financial incentives for appropriate non-prescribing of antibiotics as antimicrobial stewardship [800 JPY (≈7.3 US D) per case] were associated with changes in prescription patterns, including antibiotics, and health care use in routine paediatric health care settings at a national level. Data consisted of 9 253 261 cases of infectious diseases in 553 138 patients treated at 10 180 eligible or ineligible facilities. RESULTS A total of 2959 eligible facilities claimed 316 770 cases for financial incentives and earned 253 million JPY (≈2.29 million USD). Compared with ineligible facilities, the introduction of financial incentives in the eligible facilities was associated with an excess reduction in antibiotic prescriptions [DID estimate, -228.6 days of therapy (DOTs) per 1000 cases (95% CI, -272.4 to -184.9), which corresponded to a relative reduction of 17.8% (95% CI, 14.8 to 20.7)]. The introduction was also associated with excess reductions in drugs for respiratory symptoms [DID estimates, -256.9 DOTs per 1000 cases (95% CI, -379.3 to -134.5)] and antihistamines [DID estimate, -198.5 DOTs per 1000 cases (95% CI, -282.1 to -114.9)]. There was no excess in out-of-hour visits [DID estimate, -4.43 events per 1000 cases (95% CI, -12.8 to 3.97)] or hospitalizations [DID estimate, -0.08 events per 1000 cases (95% CI, -0.48 to 0.31)]. CONCLUSIONS Our findings suggest that financial incentives to medical facilities for not prescribing antibiotics were associated with reductions in prescriptions for antibiotics without adverse health care consequences. Japan's new health policy provided us with policy options for immediately reducing inappropriate antibiotic prescriptions by relatively small financial incentives.
Collapse
Affiliation(s)
- Yusuke Okubo
- Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Akihiro Nishi
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Hiroki Nariai
- Department of Pediatrics, UCLA Mattel Children's Hospital, University of California Los Angeles, Los Angeles, CA, USA
| | - Robert J Kim-Farley
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Onyebuchi A Arah
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Kazuhiro Uda
- Department of Pediatrics, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
- Division of Infectious Diseases, Department of Medical Subspecialties, National Center for Child Health and Development, Tokyo, Japan
| | - Noriko Kinoshita
- Division of Infectious Diseases, Department of Medical Subspecialties, National Center for Child Health and Development, Tokyo, Japan
- Department of Infectious Diseases, National Center for Global Health and Medicine, Tokyo, Japan
| | - Isao Miyairi
- Division of Infectious Diseases, Department of Medical Subspecialties, National Center for Child Health and Development, Tokyo, Japan
- Department of Microbiology, Immunology, and Biochemistry, University of Tennessee Health Science Center, Knoxville, Tennessee, USA
- Department of Pediatrics, Hamamatsu University School of Medicine, Hamamatsu, Japan
| |
Collapse
|
22
|
Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, Jöckel KH, Greiser KH, Michels KB, Krause G, Albrecht S, Öztürk I, Kuss O, Berger K, Lampl BMJ, Leitzmann M, Zeeb H, Starke KR, Schipf S, Meinke-Franze C, Ahrens W, Seidler A, Klee B, Pischon T, Andreas Deckert AD, Schmidt B, Mikolajczyk R, Karch A, Bohn B, Brenner H, Holleczek B, Dragano N. Authors' response: Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias. Scand J Work Environ Health 2022; 48:588-590. [PMID: 36153787 PMCID: PMC10539105 DOI: 10.5271/sjweh.4061] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
We thank van Tongeren et al for responding to our study on occupational disparities in SARS-CoV-2 infection risks during the first pandemic wave in Germany (1). The authors address the potential for bias resulting from differential testing between occupational groups and propose an alternative analytical strategy for dealing with selective testing. In the following, we want to discuss two aspects of this issue, namely (i) the extent and reasons of differential testing in our cohort and (ii) the advantages and disadvantages of different analytical approaches to study risk factors for SARS-CoV-2 infection. Our study relied on nationwide prospective cohort data including more than 100 000 workers in order to compare the incidence of infections between different occupations and occupational status positions. We found elevated infection risks in personal services and business administration, in essential occupations (including health care) and among people in higher occupational status positions (ie, managers and highly skilled workers) during the first pandemic wave in Germany (2). Van Tongeren's et al main concern is that the correlations found could be affected by a systematic bias because people in healthcare professions get tested more often than employees in other professions. A second argument is that better-off people could be more likely to use testing as they are less affected by direct costs (prices for testing) and the economic hardship associated with a positive test result (eg, loss of earnings in the event of sick leave). We share the authors' view that differential testing must be considered when analysing and interpreting the data. Thus, in our study, we examined the proportion of tests conducted in each occupational group as part of the sensitivity analyses (see supplementary figure S1, accessible at www.sjweh.fi/article/4037). As expected, testing proportions were exceptionally high in medical occupations (due to employer requirements). However, we did not observe systematic differences among non-medical occupations or when categorising by skill-level or managerial responsibility. This might be explained by several reasons. First, SARS-CoV-2 testing was free of charge during the first pandemic wave in Germany, but reporting a risk contact or having symptoms was a necessary condition for testing ( https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html (accessed 5 September 2022). The newspaper article cited by van Tongeren et al is misleading as it refers to a calendar date after our study period. Second, different motivation for testing due to economic hardship in case of a positive test result is an unlikely explanation, because Germany has a universal healthcare system, including paid sick leave and sickness benefits for all workers (3). Self-employed people carry greater financial risks in case of sickness. We therefore included self-employment in the multivariable analyses to address this potential source of bias. While the observed inverse social gradient may be surprising, it actually matches with findings of ecological studies from Germany (4, 5), the United States (6, 7) as well as Spain, Portugal, Sweden, The Netherlands, Israel, and Hong Kong (8), all of which observed higher infection rates in wealthier neighbourhoods during the initial outbreak phase of the pandemic. One possible explanation is the higher mobility of managers and better educated workers, who are more likely to participate in meetings and engage in business travel and holiday trips like skiing. Given the increasing number of studies providing evidence for this hypothesis, we conclude that the inverse social gradient in our study likely reflects different exposure probabilities and is not a result of systematic bias. This also holds true for the elevated infection risks in essential workers, which is actually corroborated by a large body of research (9-11). Regarding differential likelihood of testing, van Tongeren et al state that "[i]t is relatively simple to address this problem by using a test-negative design" (1). As van Tongeren et al describe, this is a case-control approach only including individuals who were tested (without considering those who were not tested). However, the proposed analytical strategy can lead to another (more serious) selection bias if testing proportions and/or testing criteria differ between groups (12). This can be easily illustrated when comparing the results based on a time-incidence design with those obtained by a test-negative design as shown in table 1 (see PDF). Both approaches show similar results in terms of vertical occupational differences. Infection was more common if individuals had a high skill level or had a managerial position, but associations were stronger in the time-incidence design and did not reach statistical significance in the test-negative design (as indicated by the confidence intervals overlapping "1"). Unfortunately, the test-negative approach relies on a strongly reduced sample size and thus results in greater statistical uncertainty and loss of statistical power (13). In contrast, the test-negative design yields a different picture when estimating the association between essential occupation and infection risk: In this analysis, essential workers did not differ from non-essential workers in their chance of being infected with SARS-CoV-2 (the test-negative design even exhibits a lower chance for essential workers). This is rather counter-intuitive and is not in accordance with what we know about the occupational hazards of healthcare workers during the pandemic (14). The main problem is that proportions of positive tests are highly unreliable when testing proportions and/or testing criteria differ between groups. As essential workers were tested more often without being symptomatic (due to employer requirements), a lower proportion of positive tests in this group does not necessarily correspond to a lower risk of infection. Consequently, we are not convinced that the test-negative design should be the 'gold standard' for studying risk factors for SARS-CoV-2 infections (15). Especially problematic is the loss of statistical power (increasing the probability of a type II error) and the low validity of the test-positivity when test criteria and/or test proportions differ between groups. References 1. van Tongeren M, Rhodes S, Pearce N. Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias. Scand J Work Environ Health 2022;48(7):586-587. https://doi.org/10.5271/sjweh.4052. 2. Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, et al. Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany. Scand J Work Environ Health 2022;48:446-56. https://doi.org/10.5271/sjweh.4037. 3. Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017;390:882-97. https://doi.org/10.1016/S0140-6736(17)31280-1. 4. Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener C, et al. Socioeconomic inequalities in the risk of SARS-CoV-2 infection - First results from an analysis of surveillance data from Germany. J Heal Monit 2020;5:18-29. https://doi.org/10.25646/7057. 5. Plümper T, Neumayer E. The pandemic predominantly hits poor neighbourhoods? SARS-CoV-2 infections and COVID-19 fatalities in German districts. Eur J Public Health 2020;30:1176-80. https://doi.org/10.1093/eurpub/ckaa168. 6. Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. Racial, Economic, and Health Inequality and COVID-19 Infection in the United States. J Racial Ethn Heal Disparities 2021;8:732-42. https://doi.org/10.1007/s40615-020-00833-4. 7. Mukherji N. The Social and Economic Factors Underlying the Incidence of COVID-19 Cases and Deaths in US Counties During the Initial Outbreak Phase. Rev Reg Stud 2022;52. https://doi.org/10.52324/001c.35255. 8. Beese F, Waldhauer J, Wollgast L, Pförtner T, Wahrendorf M, Haller S, et al. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic-A Scoping Review. Int J Public Health 2022;67:1-14. https://doi.org/10.3389/ijph.2022.1605128. 9. Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Heal 2020;5:e475-83. https://doi.org/10.1016/S2468-2667(20)30164-X. 10. Chou R, Dana T, Buckley DI, Selph S, Fu R, Totten AM. Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann Intern Med 2020;173:120-36. https://doi.org/10.7326/M20-1632. 11. Stringhini S, Zaballa M-E, Pullen N, de Mestral C, Perez-Saez J, Dumont R, et al. Large variation in anti-SARS-CoV-2 antibody prevalence among essential workers in Geneva, Switzerland. Nat Commun 2021;12:3455. https://doi.org/10.1038/s41467-021-23796-4. 12. Accorsi EK, Qiu X, Rumpler E, Kennedy-Shaffer L, Kahn R, Joshi K, et al. How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. Eur J Epidemiol 2021;36:179-96. https://doi.org/10.1007/s10654-021-00727-7. 13. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Editio. New York: Routledge; 2013. https://doi.org/10.4324/9780203771587. 14. The Lancet. The plight of essential workers during the COVID-19 pandemic. Lancet 2020;395:1587. https://doi.org/10.1016/S0140-6736(20)31200-9. 15. Vandenbroucke JP, Brickley EB, Pearce N, Vandenbroucke-Grauls CMJE. The Evolving Usefulness of the Test-negative Design in Studying Risk Factors for COVID-19. Epidemiology 2022;33:e7-8. https://doi.org/10.1097/EDE.0000000000001438.
Collapse
Affiliation(s)
- Marvin Reuter
- Dr. Marvin Reuter, Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, Heinrich Heine University Duesseldorf Moorenstrasse 5, 40225 Düsseldorf,
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
23
|
Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, Jöckel KH, Greiser KH, Michels KB, Krause G, Albrecht S, Öztürk I, Kuss O, Berger K, Lampl BMJ, Leitzmann M, Zeeb H, Starke KR, Schipf S, Meinke-Franze C, Ahrens W, Seidler A, Klee B, Pischon T, Deckert A, Schmidt B, Mikolajczyk R, Karch A, Bohn B, Brenner H, Holleczek B, Dragano N. Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany. Scand J Work Environ Health 2022; 48:446-456. [PMID: 35670286 PMCID: PMC9888438 DOI: 10.5271/sjweh.4037] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVE The aim of this study was to identify the occupational risk for a SARS-CoV-2 infection in a nationwide sample of German workers during the first wave of the COVID-19 pandemic (1 February-31 August 2020). METHODS We used the data of 108 960 workers who participated in a COVID follow-up survey of the German National Cohort (NAKO). Occupational characteristics were derived from the German Classification of Occupations 2010 (Klassifikation der Berufe 2010). PCR-confirmed SARS-CoV-2 infections were assessed from self-reports. Incidence rates (IR) and incidence rate ratios (IRR) were estimated using robust Poisson regression, adjusted for person-time at risk, age, sex, migration background, study center, working hours, and employment relationship. RESULTS The IR was 3.7 infections per 1000 workers [95% confidence interval (CI) 3.3-4.1]. IR differed by occupational sector, with the highest rates observed in personal (IR 4.8, 95% CI 4.0-5.6) and business administration (IR 3.4, 95% CI 2.8-3.9) services and the lowest rates in occupations related to the production of goods (IR 2.0, 95% CI 1.5-2.6). Infections were more frequent among essential workers compared with workers in non-essential occupations (IRR 1.95, 95% CI 1.59-2.40) and among highly skilled compared with skilled professions (IRR 1.36, 95% CI 1.07-1.72). CONCLUSIONS The results emphasize higher infection risks in essential occupations and personal-related services, especially in the healthcare sector. Additionally, we found evidence that infections were more common in higher occupational status positions at the beginning of the pandemic.
Collapse
Affiliation(s)
- Marvin Reuter
- Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, University of Düsseldorf, Dusseldorf, Germany,
Correspondence to: Dr. Marvin Reuter, Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, Heinrich Heine University Duesseldorf Moorenstrasse 5, 40225 Düsseldorf, Germany. [E-Mail: ]
| | - Mariann Rigó
- Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, University of Düsseldorf, Dusseldorf, Germany
| | - Maren Formazin
- Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany
| | - Falk Liebers
- Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany
| | - Ute Latza
- Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany
| | | | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Germany
| | - Karin Halina Greiser
- German Cancer Research Centre (DKFZ) Heidelberg, Div. of Cancer Epidemiology, Heidelberg, Germany
| | - Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Gérard Krause
- Helmholtz Centre for Infection Research, Braunschweig, Germany,Institute for Infectious Disease Epidemiology, TWINCORE, Hannover, Germany,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Stefan Albrecht
- Robert Koch Institute, Department for Epidemiology and Health Monitoring, Germany
| | - Ilter Öztürk
- Robert Koch Institute, Department for Epidemiology and Health Monitoring, Germany
| | - Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Benedikt MJ Lampl
- Regensburg Department of Public Health, Germany,Department of Epidemiology and Preventive Medicine, University of Regensburg, Germany
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Germany
| | - Hajo Zeeb
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Karla Romero Starke
- Institute and Policlinic for Occupational and Social Medicine, Faculty of Medicine, Technische Universität Dresden, Germany
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Claudia Meinke-Franze
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Andreas Seidler
- Institute and Policlinic for Occupational and Social Medicine, Faculty of Medicine, Technische Universität Dresden, Germany
| | - Bianca Klee
- Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Tobias Pischon
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Germany
| | - Andreas Deckert
- Heidelberg Institute of Global Health, Heidelberg University,Heidelberg, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | | | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Nico Dragano
- Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, University of Düsseldorf, Dusseldorf, Germany
| |
Collapse
|
24
|
Tanoey J, Baechle C, Brenner H, Deckert A, Fricke J, Günther K, Karch A, Keil T, Kluttig A, Leitzmann M, Mikolajczyk R, Obi N, Pischon T, Schikowski T, Schipf SM, Schulze MB, Sedlmeier A, Moreno Velásquez I, Weber KS, Völzke H, Ahrens W, Gastell S, Holleczek B, Jöckel KH, Katzke V, Lieb W, Michels KB, Schmidt B, Teismann H, Becher H. Birth Order, Caesarean Section, or Daycare Attendance in Relation to Child- and Adult-Onset Type 1 Diabetes: Results from the German National Cohort. Int J Environ Res Public Health 2022; 19:10880. [PMID: 36078596 PMCID: PMC9517906 DOI: 10.3390/ijerph191710880] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/25/2022] [Accepted: 08/27/2022] [Indexed: 06/15/2023]
Abstract
(1) Background: Global incidence of type 1 diabetes (T1D) is rising and nearly half occurred in adults. However, it is unclear if certain early-life childhood T1D risk factors were also associated with adult-onset T1D. This study aimed to assess associations between birth order, delivery mode or daycare attendance and type 1 diabetes (T1D) risk in a population-based cohort and whether these were similar for childhood- and adult-onset T1D (cut-off age 15); (2) Methods: Data were obtained from the German National Cohort (NAKO Gesundheitsstudie) baseline assessment. Self-reported diabetes was classified as T1D if: diagnosis age ≤ 40 years and has been receiving insulin treatment since less than one year after diagnosis. Cox regression was applied for T1D risk analysis; (3) Results: Analyses included 101,411 participants (100 childhood- and 271 adult-onset T1D cases). Compared to "only-children", HRs for second- or later-born individuals were 0.70 (95% CI = 0.50-0.96) and 0.65 (95% CI = 0.45-0.94), respectively, regardless of parental diabetes, migration background, birth year and perinatal factors. In further analyses, higher birth order reduced T1D risk in children and adults born in recent decades. Caesarean section and daycare attendance showed no clear associations with T1D risk; (4) Conclusions: Birth order should be considered in both children and adults' T1D risk assessment for early detection.
Collapse
Affiliation(s)
- Justine Tanoey
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christina Baechle
- Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ), Leibniz Institute for Diabetes Research, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Hermann Brenner
- Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Andreas Deckert
- Heidelberg Institute of Global Health, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Julia Fricke
- Institute of Social Medicine, Epidemiology and Health Economics, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Kathrin Günther
- Leibniz Institute for Prevention Research and Epidemiology—BIPS, 28359 Bremen, Germany
| | - André Karch
- Institute for Epidemiology and Social Medicine, Albert-Schweitzer-Campus 1, Building D3, 48149 Münster, Germany
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, 91058 Erlangen, Germany
| | - Alexander Kluttig
- Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Martin Luther University Halle-Wittenberg, 06112 Halle (Saale), Germany
| | - Michael Leitzmann
- Department for Epidemiology and Preventive Medicine, Regensburg University Medical Center, 93053 Regensburg, Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Martin Luther University Halle-Wittenberg, 06112 Halle (Saale), Germany
| | - Nadia Obi
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Tobias Pischon
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Molecular Epidemiology Research Group, 13125 Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Biobank Technology Platform, 13125 Berlin, Germany
- Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Tamara Schikowski
- Leibniz Research Institute for Environmental Medicine—IUF, 40225 Düsseldorf, Germany
| | - Sabine M. Schipf
- Institute for Community Medicine, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Matthias B. Schulze
- German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, 14558 Nuthetal, Germany
| | - Anja Sedlmeier
- Department for Epidemiology and Preventive Medicine, Regensburg University Medical Center, 93053 Regensburg, Germany
| | - Ilais Moreno Velásquez
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Molecular Epidemiology Research Group, 13125 Berlin, Germany
| | | | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology—BIPS, 28359 Bremen, Germany
| | - Sylvia Gastell
- German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany
| | - Bernd Holleczek
- Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Karl-Heinz Jöckel
- Institute of Medical Informatics, Biometry und Epidemiology, Essen University Hospital, 45147 Essen, Germany
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, 24105 Kiel, Germany
| | - Karin B. Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79110 Freiburg, Germany
| | - Börge Schmidt
- Institute of Medical Informatics, Biometry und Epidemiology, Essen University Hospital, 45147 Essen, Germany
| | - Henning Teismann
- Institute for Epidemiology and Social Medicine, Albert-Schweitzer-Campus 1, Building D3, 48149 Münster, Germany
| | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| |
Collapse
|
25
|
Lesser C, Mericq V, Reyes M, Garmendia ML, Shepherd JA, Michels KB, Corvalán C, Pereira A. Habitual Phytoestrogen Intake Is Associated with Breast Composition in Girls at 2 Years after Menarche Onset. Cancer Epidemiol Biomarkers Prev 2022; 31:1334-1340. [PMID: 35477112 PMCID: PMC9250624 DOI: 10.1158/1055-9965.epi-22-0016] [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: 01/20/2022] [Revised: 03/23/2022] [Accepted: 04/15/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND High phytoestrogen intake during adolescence is associated with a reduced risk of breast cancer. Breast density (BD) is a strong predictor of breast cancer and can be considered an early marker. We aim to assess the association between the mean habitual intake of isoflavones, lignans, and total phytoestrogens intake during puberty until 2 years after menarche onset and absolute fibroglandular volume (AFGV) and percentage of fibroglandular volume (%FGV) in Hispanic girls at the end of puberty. METHODS Longitudinal study set up in the Growth and Obesity Chilean Cohort Study (GOCS). We included 329 girls with dietary data (multiple 24-hours recalls) from puberty until 2 years after menarche onset (81% had 2-4 recalls). Two international datasets were used to estimate isoflavones, lignans, and total phytoestrogens in the diet. Breast composition was measured by dual energy X-ray absorptiometry at 2 years after menarche. Multiple linear regression models were used to assess the association between isoflavones, lignans, and total phytoestrogens intake and AFGV and %FGV. RESULTS The average total phytoestrogen intake was 1 mg/day and %FGV was 50.7% (SD = 15.2) and AFGV 218.8 cm3 (SD = 79.3). An inverse association was found between consumption of isoflavones and AFGV, as well as, with total phytoestrogens [Q4 vs. Q1 adjusted model ß = -49.2 cm3; 95% CI (-85.5 to -13.0)]. CONCLUSIONS Girls with a higher intake of total phytoestrogens and isoflavones during puberty until 2 years after menarche onset had significantly lower AFGV. IMPACT Although the intake of phytoestrogens is low in Western populations, higher consumption of them during a critical period of life like puberty could be beneficial to reduce breast cancer during adulthood.
Collapse
Affiliation(s)
- Constanza Lesser
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Verónica Mericq
- Institute of Maternal and Child Research (IDIMI), Faculty of Medicine, University of Chile, Santiago, Chile
| | - Marcela Reyes
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - John A Shepherd
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaiʻi Cancer Center, University of Hawaiʻi, Honolulu, HI, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, USA.,Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| |
Collapse
|
26
|
Yoon L, Corvalán C, Pereira A, Shepherd J, Michels KB. Sugar-sweetened beverage consumption and breast composition in a longitudinal study of Chilean girls. Breast Cancer Res 2022; 24:3. [PMID: 34998441 PMCID: PMC8742361 DOI: 10.1186/s13058-021-01495-8] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/08/2021] [Indexed: 12/05/2022] Open
Abstract
Background Frequent sugar-sweetened beverage (SSB) intake has been associated with indirect markers of breast cancer risk, such as weight gain in adolescents and early menarche. How SSB intake relates to breast composition in adolescent girls has not been explored. Methods We evaluated the association between prospective intake of SSB and breast density in a cohort of 374 adolescent girls participating in the Growth and Obesity Cohort Study in Santiago, Chile. Multivariable linear regression models were used to analyze the association between average daily SSB intake quartiles and breast composition (absolute fibroglandular volume [aFGV], percent fibroglandular volume [%FGV], total breast volume [tBV]). Models were adjusted for potential confounding by BMI Z-score, age, daily energy intake (g/day), maternal education, hours of daily television watching after school, dairy intake (g/day), meat intake (g/day), waist circumference, and menarche. To examine the sensitivity of the association to the number of dietary recalls for each girl, analyses were further stratified by girls with one dietary recall and girls with > one dietary recall. Results A total of 881 dietary recalls were available for 374 girls prior to the breast density assessment. More than 60% of the cohort had > one dietary recall available. In multivariable analyses, we found no association between SSB intake quartile and aFGV (Q2 vs Q1 β: − 5.4, 95% CI − 15.1, 4.4; Q3 vs Q1 β: 1.3, 95% CI − 8.6, 11.3; Q4 vs Q1 β: 3.0, 95% CI − 7.1, 13). No associations were noted for %FGV and tBV. Among girls with at least one dietary recall, we found no significant associations between SSB intake quartiles and %FGV, aFGV, or tBV. Conclusion Overall, we observed no evidence that SSB intake was associated with breast density in adolescent Chilean girls. Supplementary Information The online version contains supplementary material available at 10.1186/s13058-021-01495-8.
Collapse
Affiliation(s)
- Lara Yoon
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 650 Charles Young Drive South, Los Angeles, CA, 90095, USA
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - John Shepherd
- Epidemiology and Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 650 Charles Young Drive South, Los Angeles, CA, 90095, USA. .,Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| |
Collapse
|
27
|
Yoon LS, Jacobs JP, Hoehner J, Pereira A, Gana JC, Corvalán C, Michels KB. The Association Between Breast Density and Gut Microbiota Composition at 2 Years Post-Menarche: A Cross-Sectional Study of Adolescents in Santiago, Chile. Front Cell Infect Microbiol 2022; 11:794610. [PMID: 34976871 PMCID: PMC8718921 DOI: 10.3389/fcimb.2021.794610] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/22/2021] [Indexed: 01/04/2023] Open
Abstract
The gut microbiome has been linked to breast cancer via immune, inflammatory, and hormonal mechanisms. We examined the relation between adolescent breast density and gut microbial composition and function in a cohort of Chilean girls. This cross-sectional study included 218 female participants in the Growth and Obesity Cohort Study who were 2 years post-menarche. We measured absolute breast fibroglandular volume (aFGV) and derived percent FGV (%FGV) using dual energy X-ray absorptiometry. All participants provided a fecal sample. The gut microbiome was characterized using 16S ribosomal RNA sequencing of the V3-V4 hypervariable region. We examined alpha diversity and beta diversity across terciles of %FGV and aFGV. We used MaAsLin2 for multivariable general linear modeling to assess differential taxa and predicted metabolic pathway abundance (MetaCyc) between %FGV and aFGV terciles. All models were adjusted for potential confounding variables and corrected for multiple comparisons. The mean %FGV and aFGV was 49.5% and 217.0 cm3, respectively, among study participants. Similar median alpha diversity levels were found across %FGV and aFGV terciles when measured by the Shannon diversity index (%FGV T1: 4.0, T2: 3.9, T3: 4.1; aFGV T1: 4.0, T2: 4.0, T3: 4.1). %FGV was associated with differences in beta diversity (R2 =0.012, p=0.02). No genera were differentially abundant when comparing %FGV nor aFGV terciles after adjusting for potential confounders (q > 0.56 for all genera). We found no associations between predicted MetaCyc pathway abundance and %FGV and aFGV. Overall, breast density measured at 2 years post-menarche was not associated with composition and predicted function of the gut microbiome among adolescent Chilean girls.
Collapse
Affiliation(s)
- Lara S Yoon
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, United States
| | - Jonathan P Jacobs
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, United States.,Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.,Division of Gastroenterology, Hepatology and Parenteral Nutrition, Veterans Administration Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | | | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Juan Cristóbal Gana
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, United States.,Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| |
Collapse
|
28
|
Okubo Y, Nariai H, Michels KB, Kim-Farley RJ, Nishi A, Arah OA, Kinoshita N, Uda K, Miyairi I. Change in clinical practice variations for antibiotic prescriptions across different pediatric clinics: A Japan's nationwide observational study. J Infect Chemother 2021; 27:1621-1625. [PMID: 34376349 DOI: 10.1016/j.jiac.2021.07.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 04/21/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND In 2016, the Japanese government set the National Action Plan on antimicrobial resistance to reduce antibiotic prescriptions. However, the trends and variations of antibiotic prescription patterns in a routine healthcare setting during the fiscal year 2013-2018 across different clinics at a national level are unclear. METHODS This retrospective cohort study included all clinics with >100 pediatric outpatients with infectious diseases per month during the fiscal year 2013-2018 using a national database in Japan. We investigated the trends in antibiotic prescription rates and their patterns and variations across different clinics over the six years following the 2019 World Health Organization Access, Watch, Reserve antibiotic groups, and Amoxicillin Index. RESULTS A total of 2278 clinics with 94,414,170 infectious disease-related visits were eligible for the study. Most clinics showed higher Watch percentages (median 85.4%; IQR, 68.5-95.1) than Access percentages (median, 13.8%; IQR, 4.2-30.7) and Amoxicillin Index (median, 13.3%; IQR, 3.9-30.4). The introduction of the Action Plan changed annual absolute reductions in the antibiotic prescription rates from -16.0 DOTs/1000 visitors (95%CI, -16.4-15.6) to -239.3 per 1000 visitors (95%CI, -240.0-238.6). However, these impacts were heterogeneous across clinics. From 2013 to 2018, 41.4% reduced the antibiotic prescription rates by >33.3% (median, -1035.5 DOTs/1000 visitors; IQR, -1519.4-680.2), 18.7% did not change the rates (median, -40.3 DOTs/1000 visitors; IQR, -168.4-68.6), and 7.3% increased the rates by >10% (499.5 DOTs per 1000 visitors; IQR, 232.6-837.5). CONCLUSIONS We observed the National Action Plan's impacts and extensive prescription variations across different pediatric clinics. However, one-fourth of clinics did not improve antibiotic prescription patterns even after introducing the Action Plan.
Collapse
Affiliation(s)
- Yusuke Okubo
- Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA.
| | - Hiroki Nariai
- Department of Pediatrics, UCLA Mattel Children's Hospital, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA
| | - Robert J Kim-Farley
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA
| | - Akihiro Nishi
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA
| | - Onyebuchi A Arah
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, (UCLA), Los Angeles, CA, USA
| | - Noriko Kinoshita
- Department of Infectious Diseases, National Center for Global Health and Medicine, Tokyo, Japan
| | - Kazuhiro Uda
- Division of Infectious Diseases, Department of Pediatrics, Tokyo Metropolitan Children's Medical Center, Tokyo, Japan
| | - Isao Miyairi
- Division of Infectious Diseases, Department of Medical Subspecialties, National Center for Child Health and Development, Tokyo, Japan; Department of Microbiology, Immunology, and Biochemistry, University of Tennessee Health Science Center, USA
| |
Collapse
|
29
|
Binder N, Lederer AK, Michels KB, Binder H. Assessing mediating effects of high-dimensional microbiome measurements in dietary intervention studies. Biom J 2021; 63:1366-1374. [PMID: 33960007 DOI: 10.1002/bimj.201900373] [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: 11/30/2019] [Revised: 12/12/2020] [Accepted: 03/24/2021] [Indexed: 11/08/2022]
Abstract
Habitual diet can influence health-related outcomes directly, but such effects may also be modulated indirectly by gut microbiota. We consider randomized trials and the question to what extent the effect of diet on an outcome of interest is mediated through the gut microbiome or whether there is a diet-microbiome interaction identifying subgroups of individuals who are more susceptible to specific dietary effects. The baseline microbiome by itself may be a modifier of the effects of diet on health. Yet, the high dimensionality of microbiome data requires innovative statistical approaches to identify potential mediating or moderating effects. To motivate our proposal for an appropriate analysis workflow, we consider a randomized trial that investigates the effect of a 4-week vegan diet on the diversity of gut microbiota and branched-chain amino acid metabolism in healthy omnivorous volunteers. To address the challenge of compositional microbiome data, we consider an adaptation of the lasso for penalized estimation of multivariable regression models with a large number of microbiotic taxa. This is plugged into a classical regression mediation effect analysis strategy. The interaction effects are obtained via an approach that can directly estimate them without having to deal with main effects. As a result we obtain signatures comprised of microbiotic taxa with potential mediating and moderating effects. Some taxa no longer show up as mediating, when taking moderating effects into account. Thus, the proposed analysis strategy allows to identify specific mediating effects, while avoiding potential erroneous conclusions, where moderating effects might have believed to be mediating effects.
Collapse
Affiliation(s)
- Nadine Binder
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Ann-Kathrin Lederer
- Center for Complementary Medicine, Institute for Infection Prevention and Hospital Epidemiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| |
Collapse
|
30
|
Reimers LL, Goldberg M, Tehranifar P, Michels KB, Cohn BA, Flom JD, Wei Y, Cirillo P, Terry MB. Benign breast disease and changes in mammographic breast density. Breast Cancer Res 2021; 23:49. [PMID: 33902651 PMCID: PMC8074418 DOI: 10.1186/s13058-021-01426-7] [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] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 04/01/2021] [Indexed: 11/18/2022] Open
Abstract
Background Mammographic breast density (MBD) and benign breast disease (BBD) are two of the strongest risk factors for breast cancer. Understanding trends in MBD by age and parity in women with BBD is essential to the clinical management and prevention of breast cancer. Methods Using data from the Early Determinants of Mammographic Density (EDMD) study, a prospective follow-up study of women born in 1959–1967, we evaluated MBD in 676 women. We used linear regression with generalized estimating equations to examine associations between self-reported BBD and MBD (percent density, dense area, and non-dense area), assessed through a computer-assisted method. Results A prior BBD diagnosis (median age at diagnosis 32 years) was reported by 18% of our cohort. The median time from BBD diagnosis to first available study mammogram was 9.4 years (range 1.1–27.6 years). Women with BBD had a 3.44% higher percent MBD (standard error (SE) = 1.56, p-value = 0.03) on their first available mammogram than women without BBD. Compared with parous women without BBD, nulliparous women with BBD and women with a BBD diagnosis prior to first birth had 7–8% higher percent MBD (β = 7.25, SE = 2.43, p-value< 0.01 and β = 7.84, SE = 2.98, p-value = 0.01, respectively), while there was no difference in MBD in women with a BBD diagnosis after the first birth (β = −0.22, SE = 2.40, p-value = 0.93). Conclusion Women with self-reported BBD had higher mammographic breast density than women without BBD; the association was limited to women with BBD diagnosed before their first birth. Supplementary Information The online version contains supplementary material available at 10.1186/s13058-021-01426-7.
Collapse
Affiliation(s)
- Laura L Reimers
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Mandy Goldberg
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Parisa Tehranifar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.,Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Barbara A Cohn
- Child Health and Development Studies, Public Health Institute, Berkeley, CA, USA
| | - Julie D Flom
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Ying Wei
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Piera Cirillo
- Child Health and Development Studies, Public Health Institute, Berkeley, CA, USA
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA. .,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA. .,The Imprints Center for Genetic and Environmental Lifecourse Studies, Columbia University Mailman School of Public Health and the New York State Psychiatric Institute, New York, NY, USA.
| |
Collapse
|
31
|
Schmid D, Willett WC, Forman MR, Ding M, Michels KB. TV viewing during childhood and adult type 2 diabetes mellitus. Sci Rep 2021; 11:5157. [PMID: 33664288 PMCID: PMC7933176 DOI: 10.1038/s41598-021-83746-4] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/05/2021] [Indexed: 11/28/2022] Open
Abstract
We examined whether regular television (TV) viewing at ages 3–5 and 5–10 years is related to the incidence of type 2 diabetes mellitus (T2D) in adult women. We used data from 34,512 mother-nurse daughter dyads in the Nurses’ Health Study (NHS) II and the Nurses’ Mothers’ Cohort Study. Mothers of NHS II participants completed a questionnaire on their pregnancy with the nurse and her early life experience. During 391,442 person-years of follow-up from 2001 to 2013, 1515 nurses developed T2D. Increasing levels of TV viewing at 3–5 years of age retrospectively reported by the mothers were related to a greater risk of T2D in adulthood: multivariable-adjusted hazard ratios (HRs) for ≤ 1, 2, and ≥ 3 h/day vs. no TV viewing were 1.11 [95% confidence interval (CI) 0.96–1.28], 1.20 (95% CI 1.02–1.41), and 1.35 (95% CI 1.11–1.65), p trend = 0.002, respectively, after adjustment for early life variables, including childhood physical activity and adiposity. Retrospectively reported TV viewing for ≥ 3 h/day at 5–10 years of age was associated with a 34% greater risk of adult T2D (HR 1.34, 95% CI 1.05–1.70, p trend < 0.001). Additional adjustments for adult variables, including adult TV viewing and current BMI attenuated the effect estimates (≥ 3 h/day TV viewing at 3–5 years: HR 1.22, 95% CI 0.99–1.49, p trend = 0.07; TV viewing at 5–10 years: 1.16, 95% CI 0.91–1.49, p trend = 0.09). The present study suggests that TV viewing during early childhood increases risk of T2D in adult women; adult BMI explains part of this association. Further research is required to confirm this observation and understand the mediating pathways.
Collapse
Affiliation(s)
- Daniela Schmid
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Elsässerstr. 2, 79110, Freiburg, Germany.,Division for Quantitative Methods in Public Health and Health Services Research, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
| | - Walter C Willett
- Department of Nutrition, 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, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michele R Forman
- Department of Nutrition Science, College of Health and Human Science, Purdue Center for Cancer Research, Purdue University, West Lafayette, IN, USA
| | - Ming Ding
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Elsässerstr. 2, 79110, Freiburg, Germany. .,Department for Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
| |
Collapse
|
32
|
Michels KB, Willett WC, Vaidya R, Zhang X, Giovannucci E. Yogurt consumption and colorectal cancer incidence and mortality in the Nurses' Health Study and the Health Professionals Follow-Up Study. Am J Clin Nutr 2020; 112:1566-1575. [PMID: 33022694 PMCID: PMC7727484 DOI: 10.1093/ajcn/nqaa244] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 03/05/2020] [Accepted: 08/04/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Yogurt is a commonly consumed fermented food. Regular yogurt consumption may contribute to a favorable gut microbiome and gut health, but few epidemiologic studies have considered the relation between regular yogurt consumption and the incidence of and mortality from colorectal cancer. OBJECTIVES We used data from 2 large, prospective cohort studies, the Nurses' Health Study and the Health Professionals Follow-Up Study, to examine the role of yogurt consumption on colorectal cancer incidence and mortality. METHODS During 32 years of follow-up in 83,054 women (mean age at baseline, 45.7 years) and 26 years of follow-up in 43,269 men (mean age at baseline, 52.3 years), we documented a total of 2666 newly diagnosed cases of colorectal cancer in these cohorts. We modeled yogurt consumption at baseline and cumulatively updated it throughout follow-up. Results: Baseline yogurt consumption was associated with a reduced risk of colon cancer in age-adjusted analyses (P for trend < 0.001). Associations remained statistically significant after adjusting for potential confounders, including calcium and fiber intake (P for trend = 0.03), and were restricted to proximal colon cancer. The consumption of 1 + servings per week of yogurt at baseline, compared to no yogurt consumption, was associated with a multivariable HR of 0.84 (95% CI, 0.70-0.99; P trend = 0.04) for the proximal colon cancer incidence. Latency analyses suggested that the most important window of opportunity for regular yogurt consumption to prevent colorectal cancer was 16-20 years in the past. When yogurt consumption was cumulatively updated, associations attenuated and were no longer significant. No statistically significant inverse trend was observed between yogurt consumption and the colorectal cancer mortality. CONCLUSIONS In these large cohorts, the frequency of yogurt consumption was associated with a reduced risk of proximal colon cancer with a long latency period. No significant inverse trend was observed for colorectal cancer mortality.
Collapse
Affiliation(s)
- Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Walter C Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts,USA
| | - Rita Vaidya
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA
| | - Xuehong Zhang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts,USA
| | - Edward Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts,USA
| |
Collapse
|
33
|
Leseva MN, Binder AM, Ponsonby AL, Vuillermin P, Saffery R, Michels KB. Differential gene expression and limited epigenetic dysregulation at the materno-fetal interface in preeclampsia. Hum Mol Genet 2020; 29:335-350. [PMID: 31868881 DOI: 10.1093/hmg/ddz287] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 11/26/2019] [Indexed: 12/31/2022] Open
Abstract
Despite the many advances made in the diagnosis and management of preeclampsia, this syndrome remains a leading cause of maternal mortality and life-long morbidity, as well as adverse fetal outcomes. Successful prediction and therapeutic intervention require an improved understanding of the molecular mechanisms, which underlie preeclampsia pathophysiology. We have used an integrated approach to discover placental genetic and epigenetic markers of preeclampsia and validated our findings in an independent cohort of women. We observed the microRNA, MIR138, to be upregulated in singleton preeclamptic placentas; however, this appears to be a female infant sex-specific effect. We did not identify any significant differentially methylated positions (DMPs) in singleton pregnancies, indicating that DNA methylation changes in mild forms of the disease are likely limited. However, we identified infant sex-specific preeclampsia-associated differentially methylated regions among singletons. Disease-associated DMPs were more obvious in a limited sampling of twin pregnancies. Interestingly, 2 out of the 10 most significant changes in methylation over larger regions overlap between singletons and twins and correspond to NAPRT1 and ZNF417.
Collapse
Affiliation(s)
- Milena N Leseva
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg 79110, Germany
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Anne-Louise Ponsonby
- Discovery Theme, Florey Institute of Neuroscience and Mental Health, Parkville, Victoria 3052, Australia.,Cell Biology Theme, The Murdoch Children's Research Institute, Royal Children's Hospital, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Peter Vuillermin
- Cell Biology Theme, The Murdoch Children's Research Institute, Royal Children's Hospital, University of Melbourne, Parkville, Victoria 3052, Australia.,School of Medicine, Deakin University, Geelong, Victoria 3220, Australia.,Child Health Research Unit, Barwon Health, Geelong, Victoria 3220, Australia
| | - Richard Saffery
- Cell Biology Theme, The Murdoch Children's Research Institute, Royal Children's Hospital, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg 79110, Germany.,Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
| | | |
Collapse
|
34
|
Michels KB, Keller K, Pereira A, Kim CE, Santos JL, Shepherd J, Corvalan C, Binder AM. Association between indicators of systemic inflammation biomarkers during puberty with breast density and onset of menarche. Breast Cancer Res 2020; 22:104. [PMID: 33004039 PMCID: PMC7531086 DOI: 10.1186/s13058-020-01338-y] [Citation(s) in RCA: 8] [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: 02/03/2020] [Accepted: 09/07/2020] [Indexed: 12/31/2022] Open
Abstract
Background Systemic inflammation may play a role in shaping breast composition, one of the strongest risk factors for breast cancer. Pubertal development presents a critical window of breast tissue susceptibility to exogenous and endogenous factors, including pro-inflammatory markers. However, little is known about the role of systemic inflammation on adolescent breast composition and pubertal development among girls. Methods We investigated associations between circulating levels of inflammatory markers (e.g., interleukin-6 (IL-6), tumor necrosis factor receptor 2 (TNFR2), and C-reactive protein (CRP)) at Tanner stages 2 and 4 and breast composition at Tanner stage 4 in a cohort of 397 adolescent girls in Santiago, Chile (Growth and Obesity Cohort Study, 2006–2018). Multivariable linear models were used to examine the association between breast composition and each inflammatory marker, stratifying by Tanner stage at inflammatory marker measurement. Accelerated failure time models were used to evaluate the association between inflammatory markers concentrations at each Tanner stage and time to menarche. Results In age-adjusted linear regression models, a doubling of TNFR2 at Tanner 2 was associated with a 26% (95% CI 7–48%) increase in total breast volume at Tanner 4 and a 22% (95% CI 10–32%) decrease of fibroglandular volume at Tanner 4. In multivariable models further adjusted for body fatness and other covariates, these associations were attenuated to the null. The time to menarche was 3% (95% CI 1–5%) shorter among those in the highest quartile of IL-6 at Tanner 2 relative to those in the lowest quartile in fully adjusted models. Compared to those in the lowest quartile of CRP at Tanner 4, those in the highest quartile experienced 2% (95% CI 0–3%) longer time to menarche in multivariable models. Conclusions Systemic inflammation during puberty was not associated with breast volume or breast density at the conclusion of breast development among pubertal girls after adjusting for body fatness; however, these circulating inflammation biomarkers, specifically CRP and IL-6, may affect the timing of menarche onset.
Collapse
Affiliation(s)
- Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, 650 Charles Young Drive South, Room 71-264 CHS, Los Angeles, CA, 90095, USA. .,Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany.
| | - Kristen Keller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Claire E Kim
- Department of Epidemiology, Fielding School of Public Health, University of California, 650 Charles Young Drive South, Room 71-264 CHS, Los Angeles, CA, 90095, USA
| | - José L Santos
- Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - John Shepherd
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawai'i Cancer Center, University of Hawai'i, Honolulu, HI, USA
| | - Camila Corvalan
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, 650 Charles Young Drive South, Room 71-264 CHS, Los Angeles, CA, 90095, USA.,Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawai'i Cancer Center, University of Hawai'i, Honolulu, HI, USA
| |
Collapse
|
35
|
Stiemsma LT, Nakamura RE, Nguyen JG, Michels KB. Does Consumption of Fermented Foods Modify the Human Gut Microbiota? J Nutr 2020; 150:1680-1692. [PMID: 32232406 PMCID: PMC7330458 DOI: 10.1093/jn/nxaa077] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [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: 12/10/2019] [Revised: 01/21/2020] [Accepted: 03/02/2020] [Indexed: 01/08/2023] Open
Abstract
The human microbiota is a key contributor to many aspects of human health and its composition is largely influenced by diet. There is a growing body of scientific evidence to suggest that gut dysbiosis (microbial imbalance of the intestine) is associated with inflammatory and immune-mediated diseases (e.g., inflammatory bowel disease and asthma). Regular consumption of fermented foods (e.g., kimchi, kefir, etc.) may represent a potential avenue to counter the proinflammatory effects of gut dysbiosis. However, an assessment of the available literature in this research area is lacking. Here we provide a critical review of current human intervention studies that analyzed the effect of fermented foods on the composition and/or function of the human gut microbiota. A total of 19 human intervention studies were identified that met this search criteria. In this review, we discuss evidence that consumption of fermented foods may modify the gut microbiota in humans. Further, there is cursory evidence to suggest that gut microbiota compositional changes mediate associations between fermented food consumption and human health outcomes. Although promising, there remains considerable heterogeneity in the human populations targeted in the intervention studies we identified. Larger longitudinal feeding studies with longer follow-up are necessary to confirm and enhance the current data. Further, future studies should consider analyzing microbiota function as a means to elucidate the mechanism linking fermented food consumption with human health. This review highlights methodologic considerations for intervention trials, emphasizing an expanse of research opportunities related to fermented food consumption in humans.
Collapse
Affiliation(s)
- Leah T Stiemsma
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Reine E Nakamura
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Jennifer G Nguyen
- Department of Biology, University of California, Los Angeles, CA, USA
| | | |
Collapse
|
36
|
Gabel J, Hoskinson C, Kump A, Michels KB, Marino N, Stiemsma LT. Abstract B23: The mammary tissue microbiome in breast cancer development. Cancer Res 2020. [DOI: 10.1158/1538-7445.mvc2020-b23] [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
Introduction: Breast cancer is the most prevalent type of cancer among women (other than some nonmelanoma skin cancers), with approximately one in eight women diagnosed with breast cancer during their lifetime. The mammary tissue microbiome has recently been characterized, and shifts in the abundance of mammary tissue bacterial taxa have been associated with benign and malignant breast tumors. This suggests particular bacterial taxa as oncogenic and key in driving breast cancer development. However, these studies utilized mammary tissue obtained from breast reduction or enhancement surgery as healthy control tissue, which has significant histologic abnormalities compared to tissue from normal healthy donors. The Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (IUSCC) is a unique and precious repository of normal breast tissue, ideal for compositional and functional analyses of the mammary tissue microbiome. We have also identified a subset of women who donated healthy tissue to the KTB and later developed breast cancer. This provides us with a rare opportunity to study shifts in the mammary microbiome composition that may preclude breast cancer development. Our research objective is to compare the composition of the mammary microbiome in healthy and prediagnostic tissue to that of cancerous tissue.
Methods: Our cohort comprises four tissue subsets: healthy (n = 50), prediagnostic (n = 15), adjacent normal (n = 50), and tumor (n = 50) selected from the KTB and IUSCC Tissue Banks. DNA was isolated from all 165 tissue samples. DNA from three of these samples, two positive controls, and two negative controls was submitted for Illumina Miseq paired-end sequencing of the V4 region of the 16S gene at the University of California, Davis Host Microbe Systems Core Lab.
Results/Conclusions: The breast tissue samples display microbiome compositions dominated by three phyla: Proteobacteria, Firmicutes, and Bacteroidetes. Negative controls displayed fewer than 10 reads and positive controls yielded accurate percentages of several difficult-to-lyse bacteria. In January 2020, all 165 DNA samples will be submitted for 16S rRNA metagenomic sequencing for analysis of the microbiome composition among the four tissue subsets. We will use multivariate linear modeling to adjust for clinical factors such as age, BMI, and menopausal status and to determine if microbiome composition is modified by cancer status. This work will be fundamental in characterizing the composition of the healthy mammary tissue microbiome and identifying microbial determinants of breast cancer.
Citation Format: Jaelyn Gabel, Courtney Hoskinson, Annie Kump, Karin B. Michels, Natascia Marino, Leah T. Stiemsma. The mammary tissue microbiome in breast cancer development [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B23.
Collapse
Affiliation(s)
- Jaelyn Gabel
- 1Natural Science Division, Seaver College, Pepperdine University, Malibu, CA,
| | - Courtney Hoskinson
- 1Natural Science Division, Seaver College, Pepperdine University, Malibu, CA,
| | - Annie Kump
- 1Natural Science Division, Seaver College, Pepperdine University, Malibu, CA,
| | - Karin B. Michels
- 2Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA,
- 3Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany, Breisgau, Germany,
| | - Natascia Marino
- 4Division of Hematology/Oncology, Department of Medicine, Indiana University, Bloomington, IN,
- 5Susan G. Komen Tissue Bank at Indiana University Simon Cancer Center, Bloomington, IN
| | - Leah T. Stiemsma
- 1Natural Science Division, Seaver College, Pepperdine University, Malibu, CA,
| |
Collapse
|
37
|
Langer S, Horn J, Kluttig A, Mikolajczyk R, Karrasch S, Schulz H, Wichmann HE, Linseisen J, Jaeschke L, Pischon T, Fricke J, Keil T, Ahrens W, Günther K, Kuß O, Schikowski T, Schmidt B, Jöckel KH, Michels KB, Franzke CW, Becher H, Jagodzinski A, Castell S, Kemmling Y, Lieb W, Waniek S, Wirkner K, Löffler M, Kaaks R, Greiser KH, Berger K, Legath N, Meinke-Franze C, Schipf S, Leitzmann M, Baurecht H, Weigl K, Amitay E, Gottschick C. [Occurrence of bronchial asthma and age at initial asthma diagnosis-first results of the German National Cohort]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:397-403. [PMID: 32125462 DOI: 10.1007/s00103-020-03105-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND Asthma is one of the most common chronic diseases in both children and adults. Asthma first occurring in adulthood (adult-onset asthma, AOA) is associated with poorer prognosis compared to childhood-onset asthma (COA), which urgently calls for more research in this area. The aim of this work was to analyze the data on asthma collected in the German National Cohort and compare it with the German Health Interview and Examination Survey for Adults (DEGS), in particular regarding AOA. MATERIAL AND METHODS Our analysis was based on the dataset of the main questionnaire at mid-term of the German National Cohort baseline examination, comprising 101,723 participants. Variables considered in the analyses were self-reported diagnosis of asthma, age at first diagnosis, asthma treatment in the past 12 months, age, and sex. RESULTS In the midterm dataset, 8.7% of women and 7.0% of men in the German National Cohort reported that they had ever been diagnosed with asthma. Approximately one third of participants with asthma received their initial diagnosis before their 18th birthday. COA affected 2.2% of women and 2.8% of men, whereas AOA affected 6.5% of women and 4.2% of men. During the previous 12 months, 33% of COA cases and 60% of AOA cases were medically treated. CONCLUSION The proportion of persons affected by asthma in the German National Cohort, as well as observed patterns regarding age and gender, corresponds to other data sources such as DEGS. However, in our analysis, the proportion of individuals with AOA was higher than described in the literature. The increase in cumulative asthma diagnoses with age is markedly steeper in younger participants, indicating a rising trend over time.
Collapse
Affiliation(s)
- Susan Langer
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Johannes Horn
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Alexander Kluttig
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Rafael Mikolajczyk
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Stefan Karrasch
- Institut für Epidemiologie, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, München, Deutschland.,Institut und Poliklinik für Arbeits‑, Sozial- und Umweltmedizin, Klinikum der Universität München, München, Deutschland.,Comprehensive Pneumology Center Munich (CPC-M), Mitglied des Deutschen Zentrums für Lungenforschung (DZL), München, Deutschland
| | - Holger Schulz
- Institut für Epidemiologie, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, München, Deutschland.,Comprehensive Pneumology Center Munich (CPC-M), Mitglied des Deutschen Zentrums für Lungenforschung (DZL), München, Deutschland
| | - Heinz-Erich Wichmann
- Institut für Epidemiologie, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, München, Deutschland
| | - Jakob Linseisen
- Ludwig-Maximilians-Universität München, Lehrstuhl für Epidemiologie, UNIKA-T Augsburg, Augsburg, Deutschland.,Klinische Epidemiologie, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, München, Deutschland
| | - Lina Jaeschke
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
| | - Tobias Pischon
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland.,Charité - Universitätsmedizin Berlin, Berlin, Deutschland.,Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partnerstandort Berlin, Berlin, Deutschland.,MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC) und Berlin Institute of Health (BIH), Berlin, Deutschland
| | - Julia Fricke
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité Universitätsmedizin Berlin, Berlin, Deutschland
| | - Thomas Keil
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité Universitätsmedizin Berlin, Berlin, Deutschland.,Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Würzburg, Deutschland.,Landesinstitut für Gesundheit, Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Bad Kissingen, Deutschland
| | - Wolfgang Ahrens
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland.,Institut für Statistik, Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
| | - Kathrin Günther
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland
| | - Oliver Kuß
- Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität Düsseldorf, Institut für Biometrie und Epidemiologie, Deutsches Diabetes-Zentrum (DDZ), Düsseldorf, Deutschland
| | - Tamara Schikowski
- IUF - Leibniz-Institut für umweltmedizinische Forschung gGmbH, Düsseldorf, Deutschland
| | - Börge Schmidt
- Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Essen, Deutschland
| | - Karl-Heinz Jöckel
- Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Essen, Deutschland
| | - Karin B Michels
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Claus-Werner Franzke
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Heiko Becher
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Annika Jagodzinski
- Klinik für Allgemeine und Interventionelle Kardiologie, Universitäres Herz- und Gefäßzentrum Hamburg - Eppendorf, Hamburg, Deutschland.,Deutsches Zentrum für Herzkreislaufforschung, Hamburg, Deutschland.,Epidemiologisches Studienzentrum, Universitätsklinikum Hamburg - Eppendorf, Hamburg, Deutschland
| | - Stefanie Castell
- Helmholtz-Zentrum für Infektionsforschung, Braunschweig, Deutschland
| | - Yvonne Kemmling
- Helmholtz-Zentrum für Infektionsforschung, Braunschweig, Deutschland
| | - Wolfgang Lieb
- Institut für Epidemiologie, Christian-Albrechts-Universität Kiel, Kiel, Deutschland
| | - Sabina Waniek
- Institut für Epidemiologie, Christian-Albrechts-Universität Kiel, Kiel, Deutschland
| | - Kerstin Wirkner
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland.,LIFE-Forschungszentrum für Zivilisationskrankheiten, Universität Leipzig, Leipzig, Deutschland
| | - Markus Löffler
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland.,LIFE-Forschungszentrum für Zivilisationskrankheiten, Universität Leipzig, Leipzig, Deutschland
| | - Rudolf Kaaks
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Karin Halina Greiser
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Westfälische Wilhelms-Universität Münster, Münster, Deutschland
| | - Nicole Legath
- Institut für Epidemiologie und Sozialmedizin, Westfälische Wilhelms-Universität Münster, Münster, Deutschland
| | - Claudia Meinke-Franze
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Sabine Schipf
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Michael Leitzmann
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Hansjörg Baurecht
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Korbinian Weigl
- Abt. Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Efrat Amitay
- Abt. Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Cornelia Gottschick
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland.
| |
Collapse
|
38
|
Chavarro JE, Martín-Calvo N, Yuan C, Arvizu M, Rich-Edwards JW, Michels KB, Sun Q. Association of Birth by Cesarean Delivery With Obesity and Type 2 Diabetes Among Adult Women. JAMA Netw Open 2020; 3:e202605. [PMID: 32282045 PMCID: PMC7154804 DOI: 10.1001/jamanetworkopen.2020.2605] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
IMPORTANCE Cesarean delivery is associated with an increased risk of childhood obesity in offspring. However, whether this increased risk also includes obesity-associated conditions remains unclear. OBJECTIVE To evaluate the association of birth by cesarean delivery with offspring's risks of obesity and type 2 diabetes in adulthood. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study compared the incidence of obesity and type 2 diabetes between birth by cesarean delivery and vaginal delivery among 33 226 women participating in the Nurses' Health Study II who were born between 1946 and 1964, with follow-up through the end of the 2013-2015 follow-up cycle. Participants' mothers provided information on mode of delivery and pregnancy characteristics. Participants provided information every 2 years on weight and diagnosis of type 2 diabetes. Relative risks of obesity and type 2 diabetes were estimated using log-binomial and proportional hazards regression accounting for maternal body mass index and other confounding factors. Statistical analysis was performed from June 2017 to December 2019. EXPOSURE Birth by cesarean delivery compared with birth by vaginal delivery. MAIN OUTCOMES AND MEASURES Risk of obesity and incidence of type 2 diabetes. RESULTS At baseline, the participants' mean (SD) age was 33.8 (4.6) years (range, 24.0-44.0 years). A total of 1089 of the 33 226 participants (3.3%) were born by cesarean delivery. After 1 913 978 person-years of follow-up, 12 156 (36.6%) women were obese and 2014 (6.1%) had received a diagnosis of type 2 diabetes. Women born by cesarean delivery were more likely to be classified as obese and to have received a diagnosis of type 2 diabetes during follow-up. The multivariable-adjusted relative risk of obesity among women born by cesarean vs vaginal delivery was 1.11 (95% CI, 1.03-1.19). The multivariable-adjusted hazard ratio for type 2 diabetes among women born by cesarean vs vaginal delivery was 1.46 (95% CI, 1.18-1.81); this association remained significant after additional adjustment for participant's own body mass index (relative risk, 1.34 [95% CI, 1.08-1.67]). These associations persisted when analyses were restricted to women at low risk of cesarean delivery based on maternal characteristics. CONCLUSIONS AND RELEVANCE This study suggests that women born by cesarean delivery may have a higher risk than women born by vaginal delivery of being obese and developing type 2 diabetes during adult life.
Collapse
Affiliation(s)
- Jorge E. Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Nerea Martín-Calvo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Physiopathology of Obesity and Nutrition, Carlos III Institute of Health, Madrid, Spain
- Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Changzheng Yuan
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Mariel Arvizu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Janet W. Rich-Edwards
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Connors Center for Women’s Health and Gender Biology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Karin B. Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
39
|
Nimptsch K, Jaeschke L, Chang-Claude J, Kaaks R, Katzke V, Michels KB, Franzke CW, Obi N, Becher H, Kuß O, Schikowski T, Schulze MB, Gastell S, Hoffmann W, Schipf S, Ahrens W, Günther K, Krist L, Keil T, Jöckel KH, Schmidt B, Brenner H, Holleczek B, Fischer B, Leitzmann M, Lieb W, Berger K, Krause G, Löffler M, Schmidt-Pokrzywniak A, Mikolajczyk R, Linseisen J, Greiser KH, Pischon T. [Self-reported cancer in the German National Cohort (NAKO Gesundheitsstudie): assessment methods and first results]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:385-396. [PMID: 32179962 DOI: 10.1007/s00103-020-03113-y] [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] [Indexed: 11/30/2022]
Abstract
BACKGROUND In the German National Cohort (NAKO Gesundheitsstudie), the largest prospective cohort study in Germany, data on self-reported cancer diagnoses are now available for the first half of participants. OBJECTIVES Description of the methods to assess self-reported cancer diagnoses and type of cancer in the NAKO and presentation of first results. MATERIALS AND METHODS In a computer-assisted, standardized personal interview, 101,787 participants (54,526 women, 47,261 men) were asked whether they had ever been diagnosed with cancer (malignant tumors including in situ) by a physician and how many cancer diagnoses they had. The type of cancer was classified with a list. Absolute and relative frequencies of self-reported cancer diagnoses and types of cancer were calculated and compared with cancer registry data. RESULTS A physician-diagnosed cancer was reported by 9.4% of women and 7.0% of men. Of the participants who reported a cancer diagnosis, 88.3% reported to have had only one cancer diagnosis. In women, the most frequent malignancies were breast cancer, cervical cancer, and melanoma. In men, the most frequent malignancies were prostate cancer, melanoma, and colorectal cancer. Comparing the frequencies of cancer diagnoses reported by 45- to 74-year-old NAKO participants within the last five years to cancer registry-based 5‑year prevalences, most types of cancer were less frequent in the NAKO, with the exception of melanoma in men and women, cervical cancer and liver cancer in women, and bladder cancer and breast cancer in men. CONCLUSIONS The NAKO is a rich data basis for future investigations of incident cancer.
Collapse
Affiliation(s)
- Katharina Nimptsch
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Deutschland.
| | - Lina Jaeschke
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Deutschland
| | - Jenny Chang-Claude
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Rudolf Kaaks
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Verena Katzke
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Karin B Michels
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Claus-Werner Franzke
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Nadia Obi
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Heiko Becher
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Oliver Kuß
- Deutsches Diabetes-Zentrum (DDZ), Institut für Biometrie und Epidemiologie, Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Tamara Schikowski
- Deutsches Diabetes-Zentrum (DDZ), Institut für Biometrie und Epidemiologie, Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Matthias B Schulze
- Abteilung Molekulare Epidemiologie, Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal, Deutschland.,Institut für Ernährungswissenschaft, Universität Potsdam, Nuthetal, Deutschland
| | - Sylvia Gastell
- NAKO-Studienzentrum Berlin-Süd/Brandenburg, Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal, Deutschland
| | - Wolfgang Hoffmann
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Sabine Schipf
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Wolfgang Ahrens
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland.,Institut für Statistik, Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
| | - Kathrin Günther
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland
| | - Lilian Krist
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Thomas Keil
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland.,Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Würzburg, Deutschland.,Landesinstitut für Gesundheit, Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Bad Kissingen, Deutschland
| | - Karl-Heinz Jöckel
- Universitätsklinikum Essen, Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Essen, Deutschland
| | - Börge Schmidt
- Universitätsklinikum Essen, Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Essen, Deutschland
| | - Hermann Brenner
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland.,Abteilung Präventive Onkologie, Deutsches Krebsforschungszentrum (DKFZ) und Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland.,Deutsches Konsortium für Translationale Krebsforschung (DKTK), Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Bernd Holleczek
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland.,Krebsregister Saarland, Saarbrücken, Deutschland
| | - Beate Fischer
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Michael Leitzmann
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Wolfgang Lieb
- Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Westfälische Wilhelms-Universität Münster, Münster, Deutschland
| | - Gérard Krause
- Abteilung Epidemiologie, Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Markus Löffler
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Medizinische Fakultät, Universität Leipzig, Leipzig, Deutschland
| | - Andrea Schmidt-Pokrzywniak
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Rafael Mikolajczyk
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Jakob Linseisen
- Lehrstuhl für Epidemiologie, UNIKA-T, Ludwig-Maximilians-Universität (LMU) München, Augsburg, Deutschland.,Selbständige Forschungsgruppe Klinische Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Karin Halina Greiser
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Tobias Pischon
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Deutschland.,Charité - Universitätsmedizin Berlin, Berlin, Deutschland.,Partnerstandort Berlin, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Berlin, Deutschland.,MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
| |
Collapse
|
40
|
Holtfreter B, Samietz S, Hertrampf K, Aarabi G, Hagenfeld D, Kim TS, Kocher T, Koos B, Schmitter M, Ahrens W, Alwers E, Becher H, Berger K, Brenner H, Damms-Machado A, Ebert N, Fischer B, Franzke CW, Frölich S, Greiser H, Gies A, Günther K, Hassan L, Hoffmann W, Jaeschke L, Keil T, Kemmling Y, Krause G, Krist L, Legath N, Lieb W, Leitzmann M, Linseisen J, Loeffler M, Meinke-Franze C, Michels KB, Mikolajczyk R, Obi N, Peters A, Pischon T, Schipf S, Schmidt B, Völzke H, Waniek S, Wigmann C, Wirkner K, Schmidt CO, Kühnisch J, Rupf S. Design und Qualitätskontrolle der zahnmedizinischen Untersuchung in der NAKO Gesundheitsstudie. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:426-438. [DOI: 10.1007/s00103-020-03107-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Zusammenfassung
Hintergrund
Karies und Parodontitis sind weltweit hoch prävalente Erkrankungen. Durch ihre Erfassung im Rahmen der NAKO Gesundheitsstudie können Assoziationen zwischen oralen und systemischen Erkrankungen untersucht werden.
Fragestellung
In einer ersten Qualitätsanalyse zur Halbzeit der NAKO-Basiserhebung wird die Plausibilität der zahnmedizinischen Ergebnisse überprüft. Es werden Maßnahmen zur Verbesserung der Datenqualität vorgeschlagen.
Material und Methoden
Ein zahnmedizinisches Interview, eine Speichelprobengewinnung und eine Befunderhebung wurden durchgeführt. Im Rahmen der Level-1-Untersuchung wurden Zahn- und Prothesenanzahl erfasst. In der Level-2-Untersuchung wurden detaillierte parodontologische, kariologische und funktionelle Befunde erhoben. Alle Untersuchungen wurden von geschultem nichtzahnmedizinischen Personal durchgeführt. Es wurden Plausibilitätsprüfungen durchgeführt sowie Verteilungen deskriptiv dargestellt.
Ergebnisse
In die Analysen gingen Daten von 57.967 Interviewteilnehmer*innen, 56.913 Level-1- und 6295 Level-2-Teilnehmer*innen ein. Der Anteil fehlender Werte lag für die einzelnen Parameter der Level-1- und Level-2-Untersuchungen zwischen 0,02 % und 3,9 %. Die Parameter zeigten eine plausible Verteilung; vereinzelt wurden unplausible Werte beobachtet, z. B. beim horizontalen und vertikalen Überbiss (Overjet und Overbite). Anhand der Intraklassenkorrelationskoeffizienten wurden für die einzelnen Parameter Unterschiede zwischen regionalen Clustern, den Studienzentren und verschiedenen Untersucher*innen nachgewiesen.
Diskussion
Die bisherigen Ergebnisse bestätigten die Umsetzbarkeit des Studienprotokolls durch nichtzahnmedizinisches Personal und die erfolgreiche Integration in das Untersuchungsprogramm der NAKO Gesundheitsstudie. Die Studienzentren benötigen eine intensive zahnmedizinische Betreuung für das Qualitätsmanagement.
Collapse
|
41
|
Wolf K, Kraus U, Dzolan M, Bolte G, Lakes T, Schikowski T, Greiser KH, Kuß O, Ahrens W, Bamberg F, Becher H, Berger K, Brenner H, Castell S, Damms-Machado A, Fischer B, Franzke CW, Gastell S, Günther K, Holleczek B, Jaeschke L, Kaaks R, Keil T, Kemmling Y, Krist L, Legath N, Leitzmann M, Lieb W, Loeffler M, Meinke-Franze C, Michels KB, Mikolajczyk R, Moebus S, Mueller U, Obi N, Pischon T, Rathmann W, Schipf S, Schmidt B, Schulze M, Thiele I, Thierry S, Waniek S, Wigmann C, Wirkner K, Zschocke J, Peters A, Schneider A. [Nighttime transportation noise annoyance in Germany: personal and regional differences in the German National Cohort Study]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:332-343. [PMID: 32047975 DOI: 10.1007/s00103-020-03094-y] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Noise annoyance is associated with adverse health-related conditions and reduced wellbeing. Thereby, subjective noise annoyance depends on the objective noise exposure and is modified by personal and regional factors. OBJECTIVE How many participants of the German National Cohort Study (GNC; NAKO Gesundheitsstudie) were annoyed by transportation noise during nighttime and what factors were associated with noise annoyance? MATERIALS AND METHODS This cross-sectional analysis included 86,080 participants from 18 study centers, examined from 2014 to 2017. We used multinomial logistic regression to investigate associations of personal and regional factors to noise annoyance (slightly/moderately or strongly/extremely annoyed vs. not annoyed) mutually adjusting for all factors in the model. RESULTS Two thirds of participants were not annoyed by transportation noise during nighttime and one in ten reported strong/extreme annoyance with highest percentages for the study centers Berlin-Mitte and Leipzig. The strongest associations were seen for factors related to the individual housing situation like the bedroom being positioned towards a major road (OR of being slightly/moderately annoyed: 4.26 [95% CI: 4.01;4.52]; OR of being strongly/extremely annoyed: 13.36 [95% CI: 12.47;14.32]) compared to a garden/inner courtyard. Participants aged 40-60 years and those in low- and medium-income groups reported greater noise annoyance compared to younger or older ones and those in the high-income group. CONCLUSION In this study from Germany, transportation noise annoyance during nighttime varied by personal and regional factors.
Collapse
Affiliation(s)
- Kathrin Wolf
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland.
| | - Ute Kraus
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
| | - Mihovil Dzolan
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
- Fakultät für Sport- und Gesundheitswissenschaften, Technische Universität München, München, Deutschland
| | - Gabriele Bolte
- Institut für Public Health und Pflegeforschung, Abteilung Sozialepidemiologie, Universität Bremen, Bremen, Deutschland
| | - Tobia Lakes
- Geographisches Institut, Humboldt-Universität zu Berlin, Berlin, Deutschland
| | - Tamara Schikowski
- IUF - Leibniz-Institut für Umweltmedizinische Forschung, Düsseldorf, Deutschland
| | - Karin Halina Greiser
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Oliver Kuß
- Deutsches Diabetes-Zentrum (DDZ), Institut für Biometrie und Epidemiologie, Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität, Düsseldorf, Deutschland
| | - Wolfgang Ahrens
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland
- Institut für Statistik, Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
| | - Fabian Bamberg
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Heiko Becher
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Hermann Brenner
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Stefanie Castell
- Abteilung für Epidemiologie, Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Antje Damms-Machado
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Beate Fischer
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Claus-Werner Franzke
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Sylvia Gastell
- Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, NAKO Studienzentrum, Nuthetal, Deutschland
| | - Kathrin Günther
- Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland
| | | | - Lina Jaeschke
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
| | - Rudolf Kaaks
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Thomas Keil
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Würzburg, Deutschland
- Landesinstitut für Gesundheit, Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Bad Kissingen, Deutschland
| | - Yvonne Kemmling
- Abteilung für Epidemiologie, Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Lilian Krist
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Nicole Legath
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Michael Leitzmann
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Wolfgang Lieb
- Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland
| | - Markus Loeffler
- Leipziger Forschungszentrum für Zivilisationserkrankungen (LIFE), Universität Leipzig, Leipzig, Deutschland
- Institut für Medizinische Informatik, Statistik, und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Claudia Meinke-Franze
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Karin B Michels
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Rafael Mikolajczyk
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Susanne Moebus
- Institut für medizinische Informatik, Biometrie und Epidemiologie, Universität Duisburg-Essen, Essen, Deutschland
| | - Ulrich Mueller
- Bundesinstitut für Bevölkerungsforschung, Wiesbaden, Deutschland
| | - Nadia Obi
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Tobias Pischon
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
- Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partnerstandort Berlin, Berlin, Deutschland
- MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC) und Berlin Institute of Health (BIH), Berlin, Deutschland
| | - Wolfgang Rathmann
- Deutsches Diabetes-Zentrum (DDZ), Institut für Biometrie und Epidemiologie, Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität, Düsseldorf, Deutschland
| | - Sabine Schipf
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Börge Schmidt
- Institut für medizinische Informatik, Biometrie und Epidemiologie, Universität Duisburg-Essen, Essen, Deutschland
| | - Matthias Schulze
- Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal, Deutschland
| | - Inke Thiele
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
| | - Sigrid Thierry
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
- NAKO Studienzentrum, Universitätsklinikum Augsburg, Augsburg, Deutschland
| | - Sabina Waniek
- Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland
| | - Claudia Wigmann
- IUF - Leibniz-Institut für Umweltmedizinische Forschung, Düsseldorf, Deutschland
| | - Kerstin Wirkner
- Leipziger Forschungszentrum für Zivilisationserkrankungen (LIFE), Universität Leipzig, Leipzig, Deutschland
- Institut für Medizinische Informatik, Statistik, und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Johannes Zschocke
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Annette Peters
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
- Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Ludwig-Maximilians-Universität München, München, Deutschland
| | - Alexandra Schneider
- Institut für Epidemiologie, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ingolstädter Landstr. 1, 85764, Neuherberg, Deutschland
| |
Collapse
|
42
|
Michels KB, De Vivo I, Calafat AM, Binder AM. In utero exposure to endocrine-disrupting chemicals and telomere length at birth. Environ Res 2020; 182:109053. [PMID: 31923847 PMCID: PMC8667573 DOI: 10.1016/j.envres.2019.109053] [Citation(s) in RCA: 2] [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] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 12/13/2019] [Accepted: 12/16/2019] [Indexed: 05/04/2023]
Abstract
Telomere length correlates with morbidity and mortality. While telomere length appears to be influenced by hormone levels, the potential impact of exposure to endocrine-disrupting chemicals (EDCs) has not been studied. We examined the association between maternal gestational concentrations of biomarkers of EDC exposure and telomere length at birth in the Harvard Epigenetic Birth Cohort. EDC (phenols and phthalates) biomarker concentrations were measured in maternal spot urine samples during the first trimester and telomere length in maternal and cord blood collected at delivery among 181 mother-newborn singleton dyads. Maternal and newborn telomere length exhibited a positive correlation (Spearman ρ = 0.20 (p-value< 0.01). Infant telomere length was associated with maternal biomarker concentrations of specific EDCs, and most of these associations were observed to be infant sex-specific. Prenatal exposure to triclosan, a non-paraben phenol with antimicrobial properties, was one of the most strongly associated EDCs with telomere length; telomere length was 20% (95% CI 5%-33%) shorter among boys in the highest quartile of maternal biomarker concentrations compared to the lowest quartile. In contrast, we observed longer telomere length associated with increased gestational concentrations of mono-isobutyl phthalate, and among boys, with increased concentrations of mono-2-ethylhexyl phthalate. In this birth cohort, we observed associations between maternal gestational exposure to select EDC biomarkers and telomere length, most of which were sex-specific. These findings need to be confirmed in future studies.
Collapse
Affiliation(s)
- Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA; Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany.
| | - Immaculata De Vivo
- Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Antonia M Calafat
- Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| |
Collapse
|
43
|
Schmid D, Song M, Zhang X, Willett WC, Vaidya R, Giovannucci EL, Michels KB. Yogurt consumption in relation to mortality from cardiovascular disease, cancer, and all causes: a prospective investigation in 2 cohorts of US women and men. Am J Clin Nutr 2020; 111:689-697. [PMID: 31968071 PMCID: PMC7049530 DOI: 10.1093/ajcn/nqz345] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 07/25/2019] [Accepted: 12/23/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Although a link between regular yogurt consumption and mortality appears plausible, data are sparse and have yielded inconsistent results. OBJECTIVES We examined the association between regular yogurt consumption and risk of all-cause and cause-specific mortality among US women and men. METHODS A total of 82,348 women in the Nurses' Health Study and 40,278 men in the Health Professionals Follow-Up Study without a history of cardiovascular disease (CVD) and cancer in 1980 (women) or 1986 (men) were followed up until 2012. Yogurt consumption was assessed by updated validated FFQs. RESULTS During 3,354,957 person-years of follow-up, 20,831 women and 12,397 men died. Compared with no yogurt consumption, the multivariable-adjusted HRs (95% CIs) of mortality were 0.89 (0.86, 0.93), 0.85 (0.81, 0.89), 0.88 (0.84, 0.91), and 0.91 (0.85, 0.98) for ≤1-3 servings/mo, 1 serving/wk, 2-4 servings/wk, and >4 servings/wk in women (P-trend = 0.34), respectively. For men, the corresponding HRs (95% CIs) were 0.99 (0.94, 1.03), 0.98 (0.91, 1.05), 1.04 (0.98, 1.10), and 1.05 (0.95, 1.16), respectively. We further noted inverse associations for cancer mortality (multivariable-adjusted HR comparing extreme categories: 0.87; 95% CI: 0.78, 0.98; P-trend = 0.04) and CVD mortality (HR: 0.92; 95% CI: 0.79, 1.08; P-trend = 0.41) in women, although the latter was attenuated in the multivariable-adjusted model. Replacement of 1 serving/d of yogurt with 1 serving/d of nuts (women and men) or whole grains (women) was associated with a lower risk of all-cause mortality, whereas replacement of yogurt with red meat, processed meat (women and men), and milk or other dairy foods (women) was associated with a greater mortality. CONCLUSIONS In our study, regular yogurt consumption was related to lower mortality risk among women. Given that no clear dose-response relation was apparent, this result must be interpreted with caution.
Collapse
Affiliation(s)
- Daniela Schmid
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany,Division for Quantitative Methods in Public Health and Health Services Research, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT—Private University for Health Sciences, Medical Informatics and Technology, Hall in Tiol, Austria
| | - Mingyang Song
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA,Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Walter C Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rita Vaidya
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA,Address correspondence to KBM (e-mail: )
| |
Collapse
|
44
|
Kluttig A, Zschocke J, Haerting J, Schmermund A, Gastell S, Steindorf K, Herbolsheimer F, Hillreiner A, Jochem C, Baumeister S, Sprengeler O, Pischon T, Jaeschke L, Michels KB, Krist L, Greiser H, Schmidt G, Lieb W, Waniek S, Becher H, Jagodzinski A, Schipf S, Völzke H, Ahrens W, Günther K, Castell S, Kemmling Y, Legath N, Berger K, Keil T, Fricke J, Schulze MB, Loeffler M, Wirkner K, Kuß O, Schikowski T, Kalinowski S, Stang A, Kaaks R, Damms Machado A, Hoffmeister M, Weber B, Franzke CW, Thierry S, Peters A, Kartschmit N, Mikolajczyk R, Fischer B, Leitzmann M, Brandes M. [Measuring physical fitness in the German National Cohort-methods, quality assurance, and first descriptive results]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:312-321. [PMID: 32072217 DOI: 10.1007/s00103-020-03100-3] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Physical fitness is defined as an individual's ability to be physically active. The main components are cardiorespiratory fitness (CRF), muscle strength, and flexibility. Regardless of physical activity level, physical fitness is an important determinant of morbidity and mortality.The aim of the current study was to describe the physical fitness assessment methodology in the German National Cohort (NAKO) and to present initial descriptive results in a subsample of the cohort.In the NAKO, hand grip strength (GS) and CRF as physical fitness components were assessed at baseline using a hand dynamometer and a submaximal bicycle ergometer test, respectively. Maximum oxygen uptake (VO2max) was estimated as a result of the bicycle ergometer test. The results of a total of 99,068 GS measurements and 3094 CRF measurements are based on a data set at halftime of the NAKO baseline survey (age 20-73 years, 47% men).Males showed higher values of physical fitness compared to women (males: GS = 47.8 kg, VO2max = 36.4 ml·min-1 · kg-1; females: GS = 29.9 kg, VO2max = 32.3 ml · min-1 · kg-1). GS declined from the age of 50 onwards, whereas VO2max levels decreased continuously between the age groups of 20-29 and ≥60 years. GS and VO2max showed a linear positive association after adjustment for body weight (males β = 0.21; females β = 0.35).These results indicate that the physical fitness measured in the NAKO are comparable to other population-based studies. Future analyses in this study will focus on examining the independent relations of GS and CRF with risk of morbidity and mortality.
Collapse
Affiliation(s)
- Alexander Kluttig
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland.
| | - Johannes Zschocke
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland.,Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Johannes Haerting
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | | | - Sylvia Gastell
- NAKO Studienzentrum, Deutsches Institut für Ernährungsforschung, Potsdam-Rehbrücke, Deutschland
| | - Karen Steindorf
- Abteilung Bewegung, Präventionsforschung und Krebs, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Florian Herbolsheimer
- Abteilung Bewegung, Präventionsforschung und Krebs, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Andrea Hillreiner
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Carmen Jochem
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Sebastian Baumeister
- Lehrstuhl für Epidemiologie der LMU München, UNIKA-T, Augsburg, Deutschland.,Selbstständige Forschungsgruppe Klinische Epidemiologie, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, München, Deutschland
| | - Ole Sprengeler
- BIPS, Leibniz Institut für Präventionsforschung und Epidemiologie, Bremen, Deutschland
| | - Tobias Pischon
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland.,Charité - Universitätsmedizin Berlin, Berlin, Deutschland.,MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC) und Berlin Institute of Health (BIH), Berlin, Deutschland.,Partnerstandort Berlin, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Berlin, Deutschland
| | - Lina Jaeschke
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
| | - Karin B Michels
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Lilian Krist
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Halina Greiser
- Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | | | - Wolfgang Lieb
- Institut für Epidemiologie, Christian-Albrechts-Universität Kiel, Kiel, Deutschland
| | - Sabina Waniek
- Institut für Epidemiologie, Christian-Albrechts-Universität Kiel, Kiel, Deutschland
| | - Heiko Becher
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Annika Jagodzinski
- Epidemiologisches Studienzentrum, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland.,Partnerstandort Hamburg, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Hamburg, Deutschland
| | - Sabine Schipf
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Henry Völzke
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland.,Partnerstandort Greifswald, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Greifswald, Deutschland
| | - Wolfgang Ahrens
- BIPS, Leibniz Institut für Präventionsforschung und Epidemiologie, Bremen, Deutschland.,Institut für Statistik, Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
| | - Kathrin Günther
- BIPS, Leibniz Institut für Präventionsforschung und Epidemiologie, Bremen, Deutschland
| | - Stefanie Castell
- Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Yvonne Kemmling
- Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Nicole Legath
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Thomas Keil
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland.,Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Würzburg, Deutschland.,Landesinstitut für Gesundheit, Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Bad Kissingen, Deutschland
| | - Julia Fricke
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Matthias B Schulze
- Abteilung Molekulare Epidemiologie, Deutsches Institut für Ernährungsforschung, (DIfE), Nuthetal, Deutschland
| | - Markus Loeffler
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Kerstin Wirkner
- LIFE - Leipziger Forschungszentrum für Zivilisationserkrankungen, Universität Leipzig, Leipzig, Deutschland
| | - Oliver Kuß
- Institut für Biometrie und Epidemiologie, Deutsches Diabetes-Zentrum (DDZ), Leibniz-Zentrum für Diabetes-Forschung an der Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Tamara Schikowski
- IUF - Leibniz-Institut für umweltmedizinische Forschung, Düsseldorf, Deutschland
| | - Sonja Kalinowski
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Universitätsklinikum Essen, Essen, Deutschland
| | - Andreas Stang
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Universitätsklinikum Essen, Essen, Deutschland
| | - Rudolf Kaaks
- Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | | | - Michael Hoffmeister
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | | | - Claus-Werner Franzke
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Sigrid Thierry
- Institut für Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Anette Peters
- Institut für Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Nadja Kartschmit
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Rafael Mikolajczyk
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Str. 8, 06112, Halle (Saale), Deutschland
| | - Beate Fischer
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Michael Leitzmann
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland
| | - Mirko Brandes
- BIPS, Leibniz Institut für Präventionsforschung und Epidemiologie, Bremen, Deutschland
| |
Collapse
|
45
|
Schmid D, Willett WC, Ding M, Michels KB. Maternal and Infant Anthropometric Characteristics and Breast Cancer Incidence in the Daughter. Sci Rep 2020; 10:2550. [PMID: 32054969 PMCID: PMC7018761 DOI: 10.1038/s41598-020-59527-w] [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: 09/03/2019] [Accepted: 01/28/2020] [Indexed: 11/13/2022] Open
Abstract
The intrauterine and early life environments have been linked to the etiology of breast cancer in prior studies. We prospectively examined whether maternal and newborn anthropometric factors as reported by the mother are related to an increased incidence of adult breast cancer in the daughter. We used data from 35,133 mother-daughter dyads of the Nurses’ Health Study (NHS) II and the Nurses’ Mothers’ Cohort Study. In 2001, living mothers of NHS II participants who were free of cancer completed a questionnaire on their pregnancy with the nurse and their nurse daughter’s early life experience. During 403,786 years of follow-up, 865 daughters developed incident cases of invasive breast cancer. Nurses with a birthweight of ≥4000 g had a 32% greater risk for breast cancer (multivariable-adjusted hazard ratio (HR) = 1.32, 95% confidence interval (CI) = 1.02–1.71, p-trend = 0.09) compared with those with birthweights of 3000–3499 g. Higher birth length tended to increase risk of premenopausal breast cancer (p for trend = 0.05). We further noted a modest U-shaped relation between maternal weight gain during pregnancy and premenopausal breast cancer incidence in the daughter. Fetal growth may contribute to shaping later life risk for breast cancer, especially prior to menopause.
Collapse
Affiliation(s)
- Daniela Schmid
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Division for Quantitative Methods in Public Health and Health Services Research, Department of Public Health, Health Services Research, and Health Technology Assessment, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
| | - Walter C Willett
- Department of Nutrition, 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, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ming Ding
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany. .,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
| |
Collapse
|
46
|
Fischer B, Sedlmeier AM, Hartwig S, Schlett CL, Ahrens W, Bamberg F, Baurecht H, Becher H, Berger K, Binder H, Bohn B, Carr PR, Castell S, Franzke CW, Fricke J, Gastell S, Greiser KH, Günther K, Jaeschke L, Kaaks R, Kemmling Y, Krist L, Kuß O, Legath N, Lieb W, Linseisen J, Löffler M, Michels KB, Mikolajczyk R, Niedermaier T, Norman K, Obi N, Peters A, Pischon T, Schikowski T, Schipf S, Schmidt B, Schulze MB, Stang A, Stojicic J, Tiller D, Völzke H, Waniek S, Leitzmann MF. [Anthropometric measures in the German National Cohort-more than weight and height]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:290-300. [PMID: 32020361 DOI: 10.1007/s00103-020-03096-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
High levels of adiposity in the population have a major impact on various diseases, but previous epidemiologic studies have largely been restricted to simple anthropometric measures such as the body mass index (BMI), an imperfect predictor of disease risk. There is a critical need for the use of improved measures of relative weight and body composition in large-scale, population-based research.The current article presents initial descriptive results of body composition and fat distribution based on the midterm baseline dataset of the German National Cohort, which included 101,817 participants who were examined in 18 study centers in Germany between March 2014 and March 2017. The anthropometric measures encompassed body weight, height, waist and hip circumference, bioelectrical impedance analysis (BIA), sonography of abdominal adipose tissue, 3D-body scanning, and magnetic resonance imaging.BMI analyses showed that 46.2% of men and 29.7% of women were overweight and 23.5% of men and 21.2% of women were obese. On average, women in almost all age groups demonstrated more subcutaneous adipose tissue layer thickness than men. The mean values of visceral adipose tissue layer thickness, on the other hand, were higher among men than among women in all age groups and increased continuously across age groups in both sexes.The comprehensive assessment of body composition and fat distribution provides novel future opportunities for detailed epidemiologic analyses of overweight and adiposity in relation to the development of chronic diseases.
Collapse
Affiliation(s)
- Beate Fischer
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Deutschland.
| | - Anja M Sedlmeier
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Deutschland
| | - Saskia Hartwig
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Christopher L Schlett
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Wolfgang Ahrens
- Leibniz Institut für Präventionsforschung und Epidemiologie, BIPS, Bremen, Deutschland
- Institut für Statistik, Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
| | - Fabian Bamberg
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Hansjörg Baurecht
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Deutschland
| | - Heiko Becher
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Klaus Berger
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Hans Binder
- Interdisziplinäres Zentrum für Bioinformatik (IZBI), Universität Leipzig, Leipzig, Deutschland
| | | | - Prudence R Carr
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Stefanie Castell
- Abteilung Epidemiologie, Helmholtz Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Claus-Werner Franzke
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Julia Fricke
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Sylvia Gastell
- NAKO Studienzentrum, Deutsches Institut für Ernährungsforschung (DIfE), Nuthetal, Deutschland
| | - Karin Halina Greiser
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Kathrin Günther
- Leibniz Institut für Präventionsforschung und Epidemiologie, BIPS, Bremen, Deutschland
| | - Lina Jaeschke
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
| | - Rudolf Kaaks
- Abteilung Epidemiologie von Krebserkrankungen, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Yvonne Kemmling
- Abteilung Epidemiologie, Helmholtz Zentrum für Infektionsforschung (HZI), Braunschweig, Deutschland
| | - Lilian Krist
- Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Oliver Kuß
- Institut für Biometrie und Epidemiologie, Deutsches Diabetes-Zentrum (DDZ), Leibniz-Zentrum für Diabetes-Forschung, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Nicole Legath
- Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland
| | - Wolfgang Lieb
- Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland
| | - Jakob Linseisen
- Lehrstuhl für Epidemiologie, UNIKA-T Augsburg, LMU München, Augsburg, Deutschland
- SFG Klinische Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Markus Löffler
- Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Universität Leipzig, Leipzig, Deutschland
| | - Karin B Michels
- Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Rafael Mikolajczyk
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Tobias Niedermaier
- Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - Kristina Norman
- AG Ernährung und Körperzusammensetzung, Forschungsgruppe Geriatrie, Charité - Universitätsmedizin, Berlin, Deutschland
- Abteilung Ernährung und Gerontologie, Deutsches Institut für Ernährungsforschung (DIfE), Nuthetal, Deutschland
| | - Nadia Obi
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Annette Peters
- Institut für Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Tobias Pischon
- Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland
- Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Partnerstandort Berlin, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Berlin, Deutschland
- MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC) und Berlin Institute of Health (BIH), Berlin, Deutschland
| | - Tamara Schikowski
- IUF - Leibniz Institut für umweltmedizinische Forschung, Düsseldorf, Deutschland
| | - Sabine Schipf
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Börge Schmidt
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Universitätsklinikum Essen, Essen, Deutschland
| | - Matthias B Schulze
- Abteilung Molekulare Epidemiologie, Deutsches Institut für Ernährungsforschung (DIfE), Nuthetal, Deutschland
| | - Andreas Stang
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Universitätsklinikum Essen, Essen, Deutschland
| | - Jelena Stojicic
- Institut für Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland
| | - Daniel Tiller
- Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland
| | - Henry Völzke
- Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Sabina Waniek
- Institut für Epidemiologie, Christian-Albrechts-Universität zu Kiel, Kiel, Deutschland
| | - Michael F Leitzmann
- Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Deutschland
| |
Collapse
|
47
|
Jaeschke L, Steinbrecher A, Boeing H, Gastell S, Ahrens W, Berger K, Brenner H, Ebert N, Fischer B, Greiser KH, Hoffmann W, Jöckel KH, Kaaks R, Keil T, Kemmling Y, Kluttig A, Krist L, Leitzmann M, Lieb W, Linseisen J, Löffler M, Michels KB, Obi N, Peters A, Schipf S, Schmidt B, Zinkhan M, Pischon T. Factors associated with habitual time spent in different physical activity intensities using multiday accelerometry. Sci Rep 2020; 10:774. [PMID: 31964962 PMCID: PMC6972881 DOI: 10.1038/s41598-020-57648-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 01/10/2019] [Accepted: 12/22/2019] [Indexed: 01/21/2023] Open
Abstract
To investigate factors associated with time in physical activity intensities, we assessed physical activity of 249 men and women (mean age 51.3 years) by 7-day 24h-accelerometry (ActiGraph GT3X+). Triaxial vector magnitude counts/minute were extracted to determine time in inactivity, in low-intensity, moderate, and vigorous-to-very-vigorous activity. Cross-sectional associations with sex, age, body mass index, waist circumference, smoking, alcohol consumption, education, employment, income, marital status, diabetes, and dyslipidaemia were investigated in multivariable regression analyses. Higher age was associated with more time in low-intensity (mean difference, 7.3 min/d per 5 years; 95% confidence interval 2.0,12.7) and less time in vigorous-to-very-vigorous activity (−0.8 min/d; −1.4, −0.2), while higher BMI was related to less time in low-intensity activity (−3.7 min/d; −6.3, −1.2). Current versus never smoking was associated with more time in low-intensity (29.2 min/d; 7.5, 50.9) and less time in vigorous-to-very-vigorous activity (−3.9 min/d; −6.3, −1.5). Finally, having versus not having a university entrance qualification and being not versus full time employed were associated with more inactivity time (35.9 min/d; 13.0, 58.8, and 66.2 min/d; 34.7, 97.7, respectively) and less time in low-intensity activity (−31.7 min/d; −49.9, −13.4, and −50.7; −76.6, −24.8, respectively). The assessed factors show distinct associations with activity intensities, providing targets for public health measures aiming to increase activity.
Collapse
Affiliation(s)
- Lina Jaeschke
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
| | - Astrid Steinbrecher
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Heiner Boeing
- Division of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Potsdam-Rehbruecke, Germany
| | - Sylvia Gastell
- Division of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Potsdam-Rehbruecke, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.,Institute of Statistics, Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, INF 581, Heidelberg, Germany
| | - Nina Ebert
- German Diabetes Center (DDZ), Leibniz Center for Diabetes Research, Heinrich Heine University, Institute for Biometrics and Epidemiology, Düsseldorf, Germany
| | - Beate Fischer
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | | | - Wolfgang Hoffmann
- Section Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Rudolf Kaaks
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Keil
- Institute for Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Yvonne Kemmling
- Department of Epidemiology, Helmholtz-Centre for Infection Research, Braunschweig, Germany
| | - Alexander Kluttig
- Institute of Medical Epidemiology, Biometry and Informatics, Martin-Luther-University, Halle (Saale), Germany
| | - Lilian Krist
- Institute for Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, Kiel, Germany
| | - Jakob Linseisen
- Chair of Epidemiology, LMU Munich at UNIKA-T, Augsburg, Germany.,Helmholtz Zentrum München, IRG Clinical Epidemiology, Neuherberg, Germany
| | - Markus Löffler
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
| | - Karin B Michels
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Nadia Obi
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Center for Health and Environment, Neuherberg, Germany
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Zinkhan
- Institute of Medical Epidemiology, Biometry and Informatics, Martin-Luther-University, Halle (Saale), Germany
| | - Tobias Pischon
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.,Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany.,MDC/BIH Biobank, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| |
Collapse
|
48
|
Goldberg M, Cohn BA, Houghton LC, Flom JD, Wei Y, Cirillo P, Michels KB, Terry MB. Early-Life Growth and Benign Breast Disease. Am J Epidemiol 2019; 188:1646-1654. [PMID: 31107507 PMCID: PMC6736448 DOI: 10.1093/aje/kwz126] [Citation(s) in RCA: 5] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 05/08/2019] [Accepted: 05/13/2019] [Indexed: 12/22/2022] Open
Abstract
Using prospective data from the Early Determinants of Mammographic Density study (United States, 1959-2008, n = 1121), we examined the associations between maternal body size, birth size, and infant and early childhood growth during 3 time periods (0-4 months, 4-12 months, and 1-4 years) and benign breast disease (BBD) using multivariable logistic regression with generalized estimating equations. A total of 197 women (17.6%) reported receiving a diagnosis of BBD by a physician. Higher body mass index at age 7 years was inversely associated with BBD risk. Rapid weight gain from age 1 year to 4 years, defined as an increase of least 2 major percentiles (e.g., 5th, 10th, 25th, 50th, 75th, and 95th) relative to stable growth, defined as remaining within 2 percentiles, was also inversely associated with BBD (odds ratio (OR) = 0.51, 95% confidence interval (CI): 0.23, 1.15). In contrast, rapid weight gain in infancy was positively associated with BBD relative to stable growth (from 0 to 4 months, OR = 1.65, 95% CI: 1.04, 2.62; from 4 to 12 months, 1.85, 95% CI: 0.89, 3.85), independent of birth weight, which was not associated with BBD. Our results suggest that patterns of early-life weight gain are important to BBD risk. Thus, susceptibility to BBD, like susceptibility to breast cancer, might start in early life.
Collapse
Affiliation(s)
- Mandy Goldberg
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Barbara A Cohn
- Child Health and Development Studies, Public Health Institute, Berkeley, California
| | - Lauren C Houghton
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Julie D Flom
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Ying Wei
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
| | - Piera Cirillo
- Child Health and Development Studies, Public Health Institute, Berkeley, California
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
- Herbert Irving Comprehensive Cancer Center, Irving Medical Center, Columbia University, New York, New York
- Imprints Center for Genetic and Environmental Lifecourse Studies, Mailman School of Public Health, Columbia University, New York, New York
| |
Collapse
|
49
|
Terry MB, Michels KB, Brody JG, Byrne C, Chen S, Jerry DJ, Malecki KMC, Martin MB, Miller RL, Neuhausen SL, Silk K, Trentham-Dietz A. Environmental exposures during windows of susceptibility for breast cancer: a framework for prevention research. Breast Cancer Res 2019; 21:96. [PMID: 31429809 PMCID: PMC6701090 DOI: 10.1186/s13058-019-1168-2] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [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: 02/08/2023] Open
Abstract
Background The long time from exposure to potentially harmful chemicals until breast cancer occurrence poses challenges for designing etiologic studies and for implementing successful prevention programs. Growing evidence from animal and human studies indicates that distinct time periods of heightened susceptibility to endocrine disruptors exist throughout the life course. The influence of environmental chemicals on breast cancer risk may be greater during several windows of susceptibility (WOS) in a woman’s life, including prenatal development, puberty, pregnancy, and the menopausal transition. These time windows are considered as specific periods of susceptibility for breast cancer because significant structural and functional changes occur in the mammary gland, as well as alterations in the mammary micro-environment and hormone signaling that may influence risk. Breast cancer research focused on these breast cancer WOS will accelerate understanding of disease etiology and prevention. Main text Despite the plausible heightened mechanistic influences of environmental chemicals on breast cancer risk during time periods of change in the mammary gland’s structure and function, most human studies of environmental chemicals are not focused on specific WOS. This article reviews studies conducted over the past few decades that have specifically addressed the effect of environmental chemicals and metals on breast cancer risk during at least one of these WOS. In addition to summarizing the broader evidence-base specific to WOS, we include discussion of the NIH-funded Breast Cancer and the Environment Research Program (BCERP) which included population-based and basic science research focused on specific WOS to evaluate associations between breast cancer risk and particular classes of endocrine-disrupting chemicals—including polycyclic aromatic hydrocarbons, perfluorinated compounds, polybrominated diphenyl ethers, and phenols—and metals. We outline ways in which ongoing transdisciplinary BCERP projects incorporate animal research and human epidemiologic studies in close partnership with community organizations and communication scientists to identify research priorities and effectively translate evidence-based findings to the public and policy makers. Conclusions An integrative model of breast cancer research is needed to determine the impact and mechanisms of action of endocrine disruptors at different WOS. By focusing on environmental chemical exposure during specific WOS, scientists and their community partners may identify when prevention efforts are likely to be most effective.
Collapse
Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, Room 1611, New York, NY, 10032, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, 650 Charles E. Young Drive South, CHS 71-254, Los Angeles, CA, 90095, USA
| | | | - Celia Byrne
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road A-1039F, Bethesda, MD, 20814, USA
| | - Shiuan Chen
- Department of Cancer Biology, Beckman Research Institute of City of Hope, 1450 E. Duarte Road, Duarte, CA, 91010, USA
| | - D Joseph Jerry
- Pioneer Valley Life Sciences Institute and Department of Veterinary & Animal Sciences, University of Massachusetts Amherst, 661 North Pleasant St., Amherst, MA, 01003, USA
| | - Kristen M C Malecki
- Department of Population Health Sciences and the Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St., WARF Room 605, Madison, WI, 53726, USA
| | - Mary Beth Martin
- Departments of Oncology and Biochemistry & Molecular Biology, Georgetown University Medical Center, E411 New Research Building, Washington, DC, 20057, USA
| | - Rachel L Miller
- Departments of Medicine, Pediatrics, Environmental Health Sciences; Vagelos College of Physicians and Surgeons, Mailman School of Public Health, Columbia University, PH8E-101B, 630 W. 168th St, New York, NY, 10032, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, 1450 E. Duarte Road, 1500 E. Duarte Road, Duarte, CA, 91010, USA
| | - Kami Silk
- Department of Communication, University of Delaware, 250 Pearson Hall, 125 Academy St, Newark, DE, 19716, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St., WARF Room 307, Madison, WI, 53726, USA.
| | | |
Collapse
|
50
|
Yuan V, Price EM, Del Gobbo G, Mostafavi S, Cox B, Binder AM, Michels KB, Marsit C, Robinson WP. Accurate ethnicity prediction from placental DNA methylation data. Epigenetics Chromatin 2019; 12:51. [PMID: 31399127 PMCID: PMC6688210 DOI: 10.1186/s13072-019-0296-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [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: 04/25/2019] [Accepted: 07/22/2019] [Indexed: 12/19/2022] Open
Abstract
Background The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. Results Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers (n = 50 SNPs). PlaNET’s ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms (n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable. Electronic supplementary material The online version of this article (10.1186/s13072-019-0296-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Victor Yuan
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - E Magda Price
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - Giulia Del Gobbo
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada
| | - Sara Mostafavi
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada.,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada.,Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Brian Cox
- Department of Physiology, University of Toronto, Medical Sciences Building, 3rd Floor, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Carmen Marsit
- Department of Environmental Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Wendy P Robinson
- Department of Medical Genetics, University of British Columbia, C201-4500 Oak Street, Vancouver, BC, V6H 3N1, Canada. .,BC Children's Hospital Research Institute, 938 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada.
| |
Collapse
|