1
|
Persson Å, Pyko A, Stucki L, Ögren M, Åkesson A, Oudin A, Tjønneland A, Rosengren A, Segersson D, Rizzuto D, Helte E, Andersson EM, Aasvang GM, Gudjonsdottir H, Selander J, Christensen JH, Leander K, Mattisson K, Eneroth K, Barregard L, Stockfelt L, Albin M, Simonsen MK, Spanne M, Roswall N, Tiittanen P, Molnár P, Ljungman PLS, Männistö S, Yli-Tuomi T, Cole-Hunter T, Lanki T, Lim YH, Andersen ZJ, Sørensen M, Pershagen G, Eriksson C. Long-term exposure to transportation noise and obesity: A pooled analysis of eleven Nordic cohorts. Environ Epidemiol 2024; 8:e319. [PMID: 38983882 PMCID: PMC11233097 DOI: 10.1097/ee9.0000000000000319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/11/2024] [Indexed: 07/11/2024] Open
Abstract
Background Available evidence suggests a link between exposure to transportation noise and an increased risk of obesity. We aimed to assess exposure-response functions for long-term residential exposure to road traffic, railway and aircraft noise, and markers of obesity. Methods Our cross-sectional study is based on pooled data from 11 Nordic cohorts, including up to 162,639 individuals with either measured (69.2%) or self-reported obesity data. Residential exposure to transportation noise was estimated as a time-weighted average Lden 5 years before recruitment. Adjusted linear and logistic regression models were fitted to assess beta coefficients and odds ratios (OR) with 95% confidence intervals (CI) for body mass index, overweight, and obesity, as well as for waist circumference and central obesity. Furthermore, natural splines were fitted to assess the shape of the exposure-response functions. Results For road traffic noise, the OR for obesity was 1.06 (95% CI = 1.03, 1.08) and for central obesity 1.03 (95% CI = 1.01, 1.05) per 10 dB Lden. Thresholds were observed at around 50-55 and 55-60 dB Lden, respectively, above which there was an approximate 10% risk increase per 10 dB Lden increment for both outcomes. However, linear associations only occurred in participants with measured obesity markers and were strongly influenced by the largest cohort. Similar risk estimates as for road traffic noise were found for railway noise, with no clear thresholds. For aircraft noise, results were uncertain due to the low number of exposed participants. Conclusion Our results support an association between road traffic and railway noise and obesity.
Collapse
Affiliation(s)
- Åsa Persson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Andrei Pyko
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Center for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Lara Stucki
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Ögren
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Agneta Åkesson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anna Oudin
- Occupational and Environmental Medicine, Lund University, Lund, Sweden
| | - Anne Tjønneland
- Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Annika Rosengren
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Department of Medicine Geriatrics and Emergency Medicine, Sahlgrenska University Hospital Östra Hospital, Gothenburg, Sweden
| | - David Segersson
- Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
| | - Debora Rizzuto
- Aging Research Center, Department of Neurobiology Care Science and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Emilie Helte
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eva M Andersson
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Gunn Marit Aasvang
- Department of Air Quality and Noise, Norwegian Institute of Public Health, Oslo, Norway
| | - Hrafnhildur Gudjonsdottir
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Jenny Selander
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Karin Leander
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Kristoffer Mattisson
- Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden
| | | | - Lars Barregard
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Leo Stockfelt
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Maria Albin
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden
| | - Mette K Simonsen
- Department of Neurology and the Parker Institute, Frederiksberg Hospital, Frederiksberg, Denmark
| | - Mårten Spanne
- Environment Department, City of Malmö, Malmö, Sweden
| | - Nina Roswall
- Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark
| | - Pekka Tiittanen
- Department of Health Security, Finnish Institute for Health and Welfare, Kuopio, Finland
| | - Peter Molnár
- Department of Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Petter L S Ljungman
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Danderyd Hospital, Stockholm, Sweden
| | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tarja Yli-Tuomi
- Department of Health Security, Finnish Institute for Health and Welfare, Kuopio, Finland
| | - Thomas Cole-Hunter
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Timo Lanki
- Department of Health Security, Finnish Institute for Health and Welfare, Kuopio, Finland
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland
| | - Youn-Hee Lim
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Zorana J Andersen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Mette Sørensen
- Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark
- Department of Natural Science and Environment, Roskilde University, Denmark
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Charlotta Eriksson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Center for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| |
Collapse
|
2
|
Gudjonsdottir H, Tynelius P, Stattin NS, Méndez DY, Lager A, Brynedal B. Undiagnosed type 2 diabetes is common - intensified screening of established risk groups is imperative in Sweden: the SDPP cohort. BMC Med 2024; 22:168. [PMID: 38637767 PMCID: PMC11027361 DOI: 10.1186/s12916-024-03393-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Undiagnosed type 2 diabetes (T2D) is a global problem. Current strategies for diagnosis in Sweden include screening individuals within primary healthcare who are of high risk, such as those with hypertension, obesity, prediabetes, family history of diabetes, or those who smoke daily. In this study, we aimed to estimate the proportion of individuals with undiagnosed T2D in Stockholm County and factors associated with T2D being diagnosed by healthcare. This information could improve strategies for detection. METHODS We used data from the Stockholm Diabetes Prevention Programme (SDPP) cohort together with information from national and regional registers. Individuals without T2D aged 35-56 years at baseline were followed up after two ten-year periods. The proportion of diagnosed T2D was based on register information for 7664 individuals during period 1 and for 5148 during period 2. Undiagnosed T2D was assessed by oral glucose tolerance tests at the end of each period. With logistic regression, we analysed factors associated with being diagnosed among individuals with T2D. RESULTS At the end of the first period, the proportion of individuals with T2D who had been diagnosed with T2D or not was similar (54.0% undiagnosed). At the end of the second period, the proportion of individuals with T2D was generally higher, but they were less likely to be undiagnosed (43.5%). The likelihood of being diagnosed was in adjusted analyses associated with overweight (OR=1.85; 95% CI 1.22-2.80), obesity (OR=2.73; 95% CI 1.76-4.23), higher fasting blood glucose (OR=2.11; 95% CI 1.67-2.66), and self-estimated poor general health (OR=2.42; 95% CI 1.07-5.45). Socioeconomic factors were not associated with being diagnosed among individuals with T2D. Most individuals (>71%) who developed T2D belonged to risk groups defined by having at least two of the prominent risk factors obesity, hypertension, daily smoking, prediabetes, or family history of T2D, including individuals with T2D who had not been diagnosed by healthcare. CONCLUSIONS Nearly half of individuals who develop T2D during 10 years in Stockholm County are undiagnosed, emphasizing a need for intensified screening of T2D within primary healthcare. Screening can be targeted to individuals who have at least two prominent risk factors.
Collapse
Affiliation(s)
- Hrafnhildur Gudjonsdottir
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden.
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
| | - Per Tynelius
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Nouha Saleh Stattin
- Academic Primary Healthcare Centre, Stockholm, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden
| | - Diego Yacamán Méndez
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Anton Lager
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Boel Brynedal
- Centre for Epidemiology and Community Medicine, Region Stockholm, Stockholm, Sweden
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
4
|
Yacamán Méndez D, Zhou M, Trolle Lagerros Y, Gómez Velasco DV, Tynelius P, Gudjonsdottir H, Ponce de Leon A, Eeg-Olofsson K, Östenson CG, Brynedal B, Aguilar Salinas CA, Ebbevi D, Lager A. Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes. BMC Med 2022; 20:356. [PMID: 36253773 PMCID: PMC9578256 DOI: 10.1186/s12916-022-02551-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes. METHODS Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes. RESULTS Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years. CONCLUSIONS Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.
Collapse
Affiliation(s)
- Diego Yacamán Méndez
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden. .,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden. .,Obesity Center, Academic Specialist Center, Stockholm Health Care Services, Stockholm, Sweden.
| | - Minhao Zhou
- Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Ylva Trolle Lagerros
- Obesity Center, Academic Specialist Center, Stockholm Health Care Services, Stockholm, Sweden.,Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Donaji V Gómez Velasco
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Per Tynelius
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Hrafnhildur Gudjonsdottir
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Antonio Ponce de Leon
- Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Katarina Eeg-Olofsson
- Department of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Claes-Göran Östenson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Boel Brynedal
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Carlos A Aguilar Salinas
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - David Ebbevi
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| | - Anton Lager
- Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden.,Center for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden
| |
Collapse
|