1
|
Schwedhelm C, Nimptsch K, Ahrens W, Hasselhorn HM, Jöckel KH, Katzke V, Kluttig A, Linkohr B, Mikolajczyk R, Nöthlings U, Perrar I, Peters A, Schmidt CO, Schmidt B, Schulze MB, Stang A, Zeeb H, Pischon T. Chronic disease outcome metadata from German observational studies - public availability and FAIR principles. Sci Data 2023; 10:868. [PMID: 38052810 PMCID: PMC10698176 DOI: 10.1038/s41597-023-02726-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 11/07/2023] [Indexed: 12/07/2023] Open
Abstract
Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators' main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.
Collapse
Affiliation(s)
- Carolina Schwedhelm
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany.
| | - Katharina Nimptsch
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, 28359, Germany
- Institute of Statistics, Faculty of Mathematics and Computer Science, University of Bremen, Bremen, 28334, Germany
| | - Hans Martin Hasselhorn
- Department of Occupational Health Science, University of Wuppertal, Wuppertal, 42119, Germany
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, 45122, Germany
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, 69120, 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), 06112, Germany
| | - Birgit Linkohr
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, 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), 06112, Germany
- DZPG (German Center for Mental Health), partner site Halle-Jena-Magdeburg, 07743, Jena, Germany
| | - Ute Nöthlings
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, 53115, Germany
| | - Ines Perrar
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, 53115, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Department of Epidemiology, Medical Faculty of the Ludwig-Maximilians-Universität München, Munich, 81377, Germany
| | - Carsten O Schmidt
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17489, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, 45122, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, 14558, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, 14558, Germany
| | - Andreas Stang
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, 45122, Germany
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, 02118, USA
| | - Hajo Zeeb
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, 28359, Germany
- Faculty 11 - Human and Health Sciences, University of Bremen, Bremen, 28359, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany
- Biobank Technology Platform, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany
- Core Facility Biobank, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, 13125, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, 10117, Germany
| |
Collapse
|
2
|
Della Corte K, Jalo E, Kaartinen NE, Simpson L, Taylor MA, Muirhead R, Raben A, Macdonald IA, Fogelholm M, Brand-Miller J. Longitudinal Associations of Dietary Sugars and Glycaemic Index with Indices of Glucose Metabolism and Body Fatness during 3-Year Weight Loss Maintenance: A PREVIEW Sub-Study. Nutrients 2023; 15:2083. [PMID: 37432216 DOI: 10.3390/nu15092083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/21/2023] [Accepted: 04/22/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Dietary sugars are often linked to the development of overweight and type 2 diabetes (T2D) but inconsistencies remain. OBJECTIVE We investigated associations of added, free, and total sugars, and glycaemic index (GI) with indices of glucose metabolism (IGM) and indices of body fatness (IBF) during a 3-year weight loss maintenance intervention. DESIGN The PREVIEW (PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World) study was a randomised controlled trial designed to test the effects of four diet and physical activity interventions, after an 8-week weight-loss period, on the incidence of T2D. This secondary observational analysis included pooled data assessed at baseline (8), 26, 52, 104 and 156 weeks from 514 participants with overweight/obesity (age 25-70 year; BMI ≥ 25 kg⋅m-2) and with/without prediabetes in centres that provided data on added sugars (Sydney and Helsinki) or free sugars (Nottingham). Linear mixed models with repeated measures were applied for IBF (total body fat, BMI, waist circumference) and for IGM (fasting insulin, HbA1c, fasting glucose, C-peptide). Model A was adjusted for age and intervention centre and Model B additionally adjusted for energy, protein, fibre, and saturated fat. RESULTS Total sugars were inversely associated with fasting insulin and C-peptide in all centres, and free sugars were inversely associated with fasting glucose and HbA1c (Model B: all p < 0.05). Positive associations were observed between GI and IGM (Model B: fasting insulin, HbA1c, and C-peptide: (all p < 0.01), but not for added sugars. Added sugar was positively associated with body fat percentage and BMI, and GI was associated with waist circumference (Model B: all p < 0.01), while free sugars showed no associations (Model B: p > 0.05). CONCLUSIONS Our findings suggest that added sugars and GI were independently associated with 3-y weight regain, but only GI was associated with 3-y changes in glucose metabolism in individuals at high risk of T2D.
Collapse
Affiliation(s)
- Karen Della Corte
- School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - Elli Jalo
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
| | - Niina E Kaartinen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, 00271 Helsinki, Finland
| | - Liz Simpson
- Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Queen's Medical Centre, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham NG7 2RD, UK
| | - Moira A Taylor
- Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Queen's Medical Centre, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham NG7 2RD, UK
| | - Roslyn Muirhead
- School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - Anne Raben
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958 Copenhagen, Denmark
- Clinical Research, Copenhagen University Hospital-Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark
| | - Ian A Macdonald
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - Mikael Fogelholm
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
| | - Jennie Brand-Miller
- School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| |
Collapse
|
3
|
Freedman LS, Kipnis V, Midthune D, Commins J, Barrett B, Sagi-Kiss V, Palma-Duran SA, Johnston CS, O'Brien DM, Tasevska N. Establishing 24-Hour Urinary Sucrose Plus Fructose as a Predictive Biomarker for Total Sugars Intake. Cancer Epidemiol Biomarkers Prev 2022; 31:1227-1232. [PMID: 35314857 DOI: 10.1158/1055-9965.epi-21-1293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/21/2021] [Accepted: 03/02/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Twenty-four-hour urinary sucrose and fructose (24uSF) has been studied as a biomarker of total sugars intake in two feeding studies conducted in the United Kingdom (UK) and Arizona (AZ). We compare the biomarker performance in these populations, testing whether it meets the criteria for a predictive biomarker. METHODS The UK and AZ feeding studies included 13 and 98 participants, respectively, aged 18 to 70 years, consuming their usual diet under controlled conditions. Linear mixed models relating 24uSF to total sugars and personal characteristics were developed in each study and compared. The AZ calibrated biomarker equation was applied to generate biomarker-estimated total sugars intake in UK participants. Stability of the model across AZ study subpopulations was also examined. RESULTS Model coefficients were similar between the two studies [e.g., log(total sugars): UK 0.99, AZ 1.03, P = 0.67], as was the ratio of calibrated biomarker person-specific bias to between-person variance (UK 0.32, AZ 0.25, P = 0.68). The AZ equation estimated UK log(total sugar intakes) with mean squared prediction error of 0.27, similar to the AZ study estimate (0.28). Within the AZ study, the regression coefficients of log(total sugars) were similar across age, gender, and body mass index subpopulations. CONCLUSIONS Similar model coefficients in the two studies and good prediction of UK sugar intakes by the AZ equation suggest that 24uSF meets the criteria for a predictive biomarker. Testing the biomarker performance in other populations is advisable. IMPACT Applications of the 24uSF biomarker will enable improved assessment of the role of sugars intake in risk of chronic disease, including cancer. See related commentary by Prentice, p. 1151.
Collapse
Affiliation(s)
- Laurence S Freedman
- Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel
| | - Victor Kipnis
- Division of Cancer Prevention, NCI, Bethesda, Maryland
| | | | - John Commins
- Information Management Services, Inc., Rockville, Maryland
| | - Brian Barrett
- Information Management Services, Inc., Rockville, Maryland
| | - Virag Sagi-Kiss
- College of Health Solutions, Arizona State University, Phoenix, Arizona
| | | | - Carol S Johnston
- College of Health Solutions, Arizona State University, Phoenix, Arizona
| | - Diane M O'Brien
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska
| | - Natasha Tasevska
- College of Health Solutions, Arizona State University, Phoenix, Arizona
| |
Collapse
|