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Niedermayer F, Wolf K, Zhang S, Dallavalle M, Nikolaou N, Schwettmann L, Selsam P, Hoffmann B, Schneider A, Peters A. Sex-specific associations of environmental exposures with prevalent diabetes and obesity - Results from the KORA Fit study. Environ Res 2024; 252:118965. [PMID: 38642640 DOI: 10.1016/j.envres.2024.118965] [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] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/25/2024] [Accepted: 04/16/2024] [Indexed: 04/22/2024]
Abstract
Promising evidence suggests a link between environmental factors, particularly air pollution, and diabetes and obesity. However, it is still unclear whether men and women are equally susceptible to environmental exposures. Therefore, we aimed to assess sex-specific long-term effects of environmental exposures on metabolic diseases. We analyzed cross-sectional data from 3,034 participants (53.7% female, aged 53-74 years) from the KORA Fit study (2018/19), a German population-based cohort. Environmental exposures, including annual averages of air pollutants [nitrogen oxides (NO2, NOx), ozone, particulate matter of different diameters (PM10, PMcoarse, PM2.5), PM2.5abs, particle number concentration], air temperature and surrounding greenness, were assessed at participants' residences. We evaluated sex-specific associations of environmental exposures with prevalent diabetes, obesity, body-mass-index (BMI) and waist circumference using logistic or linear regression models with an interaction term for sex, adjusted for age, lifestyle factors and education. Further effect modification, in particular by urbanization, was assessed in sex-stratified analyses. Higher annual averages of air pollution, air temperature and greenness at residence were associated with diabetes prevalence in men (NO2: Odds Ratio (OR) per interquartile range increase in exposure: 1.49 [95% confidence interval (CI): 1.13, 1.95], air temperature: OR: 1.48 [95%-CI: 1.15, 1.90]; greenness: OR: 0.78 [95%-CI: 0.59, 1.01]) but not in women. Conversely, higher levels of air pollution, temperature and lack of greenness were associated with lower obesity prevalence and BMI in women. After including an interaction term for urbanization, only higher greenness was associated with higher BMI in rural women, whereas higher air pollution was associated with higher BMI in urban men. To conclude, we observed sex-specific associations of environmental exposures with metabolic diseases. An additional interaction between environmental exposures and urbanization on obesity suggests a higher susceptibility to air pollution among urban men, and higher susceptibility to greenness among rural women, which needs corroboration in future studies.
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Affiliation(s)
- Fiona Niedermayer
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Siqi Zhang
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, United States
| | - Marco Dallavalle
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nikolaos Nikolaou
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lars Schwettmann
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Peter Selsam
- Department Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany
| | - Barbara Hoffmann
- Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Neuherberg, Germany
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Bussalleu A, Hoek G, Kloog I, Probst-Hensch N, Röösli M, de Hoogh K. Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning. Sci Total Environ 2024; 928:172454. [PMID: 38636867 DOI: 10.1016/j.scitotenv.2024.172454] [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] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.
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Affiliation(s)
- Alonso Bussalleu
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
| | - Gerard Hoek
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Martin Röösli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Kees de Hoogh
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
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Badpa M, Schneider A, Schwettmann L, Thorand B, Wolf K, Peters A. Air pollution, traffic noise, greenness, and temperature and the risk of incident type 2 diabetes: Results from the KORA cohort study. Environ Epidemiol 2024; 8:e302. [PMID: 38617422 PMCID: PMC11008658 DOI: 10.1097/ee9.0000000000000302] [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: 10/17/2023] [Accepted: 02/12/2024] [Indexed: 04/16/2024] Open
Abstract
Introduction Type 2 diabetes (T2D) is a major public health concern, and various environmental factors have been associated with the development of this disease. This study aimed to investigate the longitudinal effects of multiple environmental exposures on the risk of incident T2D in a German population-based cohort. Methods We used data from the KORA cohort study (Augsburg, Germany) and assessed exposure to air pollutants, traffic noise, greenness, and temperature at the participants' residencies. Cox proportional hazard models were used to analyze the associations with incident T2D, adjusting for potential confounders. Results Of 7736 participants included in the analyses, 10.5% developed T2D during follow-up (mean: 15.0 years). We found weak or no association between environmental factors and the risk of T2D, with sex and education level significantly modifying the effects of air pollutants. Conclusion Our study contributes to the growing body of literature investigating the impact of environmental factors on T2D risks and suggests that the impact of environmental factors may be small.
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Affiliation(s)
- Mahnaz Badpa
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Lars Schwettmann
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
- Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
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Ni W, Breitner S, Nikolaou N, Wolf K, Zhang S, Peters A, Herder C, Schneider A. Effects of Short- And Medium-Term Exposures to Lower Air Temperature on 71 Novel Biomarkers of Subclinical Inflammation: Results from the KORA F4 Study. Environ Sci Technol 2023; 57:12210-12221. [PMID: 37552838 PMCID: PMC10448716 DOI: 10.1021/acs.est.3c00302] [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: 01/13/2023] [Revised: 07/26/2023] [Accepted: 07/26/2023] [Indexed: 08/10/2023]
Abstract
Increasing evidence has revealed that exposure to low temperatures is linked to a higher risk of chronic diseases and death; however, the mechanisms underlying the observed associations are still poorly understood. We performed a cross-sectional analysis with 1115 participants from the population-based KORA F4 study, which was conducted in Augsburg, Germany, from 2006 to 2008. Seventy-one inflammation-related protein biomarkers were analyzed in serum using proximity extension assay technology. We employed generalized additive models to explore short- and medium-term effects of air temperature on biomarkers of subclinical inflammation at cumulative lags of 0-1 days, 2-6 days, 0-13 days, 0-27 days, and 0-55 days. We found that short- and medium-term exposures to lower air temperature were associated with higher levels in 64 biomarkers of subclinical inflammation, such as Protein S100-A12 (EN-RAGE), Interleukin-6 (IL-6), Interleukin-10 (IL-10), C-C motif chemokine 28 (CCL28), and Neurotrophin-3 (NT-3). More pronounced associations between lower air temperature and higher biomarker of subclinical inflammation were observed among older participants, people with cardiovascular disease or prediabetes/diabetes, and people exposed to higher levels of air pollution (PM2.5, NO2, and O3). Our findings provide intriguing insight into how low air temperature may cause adverse health effects by activating inflammatory pathways.
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Affiliation(s)
- Wenli Ni
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
- Institute
for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer
School of Public Health, LMU Munich, Munich 81377, Germany
| | - Susanne Breitner
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
- Institute
for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer
School of Public Health, LMU Munich, Munich 81377, Germany
| | - Nikolaos Nikolaou
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
- Institute
for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer
School of Public Health, LMU Munich, Munich 81377, Germany
| | - Kathrin Wolf
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
| | - Siqi Zhang
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
| | - Annette Peters
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
- Institute
for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer
School of Public Health, LMU Munich, Munich 81377, Germany
- German
Center for Diabetes Research (DZD), München-Neuherberg, Munich D-85764, Germany
- German Centre
for Cardiovascular Research (DZHK), Partner
Site Munich Heart Alliance, Munich 80802, Germany
| | - Christian Herder
- Institute
for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University
Düsseldorf, Düsseldorf 40225, Germany
- Division
of Endocrinology and Diabetology, Medical Faculty and University Hospital
Düsseldorf, Heinrich Heine University
Düsseldorf, Düsseldorf 40204, Germany
- German
Center for Diabetes Research (DZD), München-Neuherberg, Munich D-85764, Germany
| | - Alexandra Schneider
- Institute
of Epidemiology, Helmholtz Zentrum München
- German Research Center for Environmental Health (GmbH), Neuherberg D-85764, Germany
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Hael MA. Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis. Environ Sci Pollut Res Int 2023; 30:50067-50095. [PMID: 36790700 PMCID: PMC9930045 DOI: 10.1007/s11356-023-25790-3] [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] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/03/2023] [Indexed: 04/16/2023]
Abstract
The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the underlying/hidden dynamic patterns of spatiotemporal air pollutants concentration. According to the results, CO levels increased 25% from 1990 to 1996, peaking in the cities of Taiz, Sana'a, and Ibb before decreasing. Also, PM2.5 pollution reached a peak in 2018, increasing 30% with severe concentrations in Hodeidah, Marib, and Mocha. Moreover, O3 pollution fluctuated with peaks in 2014-2015, 2% increase and pollution rate of 265 Dobson. Besides, SO2 pollution rose from 1997 to 2010, reaching a peak before stabilizing. Thus, these findings provide insights into the structure of the spatiotemporal air pollutants cycle and can assist policymakers in identifying sources and suggesting measures to reduce them. As a result, the study's findings are promising and may guide future research on predicting multivariate air pollution statistics over the analyzed area.
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Affiliation(s)
- Mohanned Abduljabbar Hael
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
- Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen.
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