exposome Exposome 2635-2265 Oxford University Press 10.1093/exposome/osaf016 osaf016 Review AcademicSubjects/MED00305 AcademicSubjects/MED00860 AcademicSubjects/SCI01040 Type 2 diabetes and the urban exposome: role of air pollution, noise, and built environment in the risk of type 2 diabetes: systematic review and meta-analysis https://orcid.org/0000-0001-9631-9451 HalonenMiia Conceptualization Data curation Formal analysis Methodology Project administration Validation Visualization Writing - original draft Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland miia.halonen@oulu.fi SilvaWnurinham Conceptualization Data curation Investigation Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland PätsiSusanna Methodology Validation Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland MiettunenJouko Methodology Validation Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland SebertSylvain Conceptualization Funding acquisition Supervision Validation Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland RonkainenJustiina Conceptualization Methodology Supervision Validation Writing - review & editing Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland Corresponding author: Miia Halonen, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Aapistie 5, 90220, Oulu, Finland (miia.halonen@oulu.fi). 2025 22012025 26112025 51 osaf016 21082025 31102025 20112025 13122025 © The Author(s) 2025. Published by Oxford University Press. 2025 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract

Air pollution, noise, and built environment are associated with the epidemics of type 2 diabetes (T2D). The extent to which these have independent and/or joint effects on T2D and whether some components of the urban exposome have stronger effects remains unclear. We conducted a systematic review of the associations of 11 environmental exposures of urban exposome with the risk of T2D. We searched PubMed and Scopus since 2005 until January 2025 for studies on association of T2D in adults with air pollution; particles with a diameter of less than 2.5 (PM2.5) and 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3) and black carbon (BC), noise; traffic-, railway-, and aircraft noise, and built environment; greenness, walkability, and population density. We included 151 articles, one study referring to exposome approach. Air pollutants were associated with T2D risk in meta-analyses, BC showing strongest association, OR: 1.32, 95% CI: 1.15-1.50 (n = 8). Subgroup analyses and meta-regression for PM2.5, PM10, NO2, and O3 by study characteristics highlighted variations in risk estimates but didn’t explain considerable heterogeneity. Traffic noise was associated with T2D (OR: 1.06, 95% CI: 1.03, 1.08, n = 11). In qualitative synthesis, living environment with higher walkability and greenness showed inverse association with T2D. Results indicate that air pollution and traffic noise are associated with increased risk of T2D. Greener and walkable living environment can potentially reduce risk of T2D. It remained unclear whether the effects were independent. Future studies should consider environmental joint exposures. Advancing use of exposome approach can help understand T2D risk comprehensively.

 exposometype 2 diabetesair pollutiontraffic noisebuilt environmentmeta-analysis European Union’s Horizon 2020 research and innovation programme 874739 Introduction

Despite existing research knowledge and the implementation of public health interventions addressing lifestyle factors associated with type 2 diabetes (T2D), such as smoking, unhealthy diet, and sedentary behavior,1 the global prevalence of diabetes continues to increase.2 It is forecasted that 853 million people will be living with diabetes by 2050, 90% of whom will consist of adults with T2D.2 Urban living environments are increasingly recognized as risk factors for T2D, and urban design-based interventions have been proposed as a promising approach to public health.

Air pollution, in particular, has been a key focus in diabetes epidemiology, accounting for approximately 40% of studies on environmental determinants of diabetes.3 It is estimated that approximately a fifth of the global burden of T2D is attributable to air pollution, 13.4% from ambient PM2.5 and 6.5% from household air pollution.4 Previous reviews have shown a relationship between air pollution and the risk of T2D but have focused on single pollutants.5-9 Many studies consider mainly PM2.5, and the evidence for less studied air pollutants such as Ozone (O3) or Black Carbon (BC) is still scarce.3 Environmental noise exposure refers to any unwanted noise created by human activities that are harmful to human health and quality of life.10 Dzhambov et al. found that people exposed to high noise levels at home might be at higher risk (19% - 22%) for developing T2D.11 Sakhvidi et al. reported an association between aircraft noise (OR: 1.17, 95% CI: 1.06, 1.29, n = 4) and road traffic noise (OR: 1.07, 95% CI: 1.02-, 1.12, n = 3) with T2D, but no association was observed for railway noise exposure.12 The number of studies in these reviews is small, and the research evidence on the relationship between source-specific noise exposures and T2D is still limited.

Green space is a common measure of the built environment, which refers to any land covered with grass, trees, plants, or other vegetation.13 Walkability is another common measure of the built environment. It can be composed of several indexes such as population density, street connectivity, or the number of walkable destinations such as access to parks, public transport, or food outlets.14 The possible relationship between green space and T2D has been reviewed in a systematic review by De la Fuente et al.15 who found seven studies assessing the risk of T2D in adult populations and a systematic review and meta-analysis of three studies by Sharifi et al.13 Both supported the hypothesis that people exposed to more green spaces have a reduced risk of T2D.

There is robust evidence that urban environmental exposures influencing the risk of T2D in a population cannot be reduced to only one of its components. These exposures rarely exist in isolation and influence the risk of chronic diseases like T2D within the broader context as a complex system. The concept of exposome incorporates these characteristics. It was introduced in 2005 to synthesize ideas brought forward by scientific frameworks, such as The Human Genome Project,16 The Social Determinants of Health,17 and the Environmental Cause of Diseases.18 Exposome refers to the totality of environmental exposures, a compilation of all physical, chemical, biological, and (psycho) social influences that “impact biology”.19 The concept of exposome has since played a key role in shaping a more holistic approach to environmental influences on health, enabling a more comprehensive understanding of disease etiology.20 The holistic hypothesis posits that individual environmental factors are interconnected, and their effects on health can only be fully understood in the context of the whole system. Review articles analyzing the exposome approach in the context of cardiometabolic health in general have been published.21-24 Yet, to date, there is a lack of systematic reviews and meta-analyses examining both the individual and combined effects of urban exposome components on T2D in the post-exposome era.

Adopting the holistic hypothesis, the aim of the current study was to systematically review and analyze existing research on the association between the physical environment constituting the urban exposome and its association with T2D. We identified and included three distinct environmental exposure groups that may influence the risk of T2D: air pollution, noise, and the built environment. We utilized the PECOS framework to create the search strategy and research question. It defines the Population, Exposure, Comparator, Outcomes, and Study Design as pillars of the review question and is increasingly used in the field of environmental health.25 The PECOS framework for this review is available in Table S1. The defined PECOS research question is as follows: Among the general adult population (P) what is the effect of environmental exposures (air pollution, noise, and built environment) and their joint effect (E/C) on the risk of type 2 diabetes (O) in observational studies (S)?

 Methods

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines.26 The protocol was registered to the International Prospective Register of Systematic Reviews (PROSPERO) with registration number CRD42021264893.27

Search strategy

The search strategy (Table S2) was developed in cooperation with a health science librarian and conducted in two electronic databases, PubMed and Scopus. We used keywords (MeSH-terms), a set of synonyms for the keywords, and text words (titles and abstracts) combined and truncated where appropriate. In addition to selected electronic databases the reference lists of selected studies and relevant review articles were scanned for additional studies. Search results were transferred into the Covidence software28 for the selection process. The selection of articles to be included was performed independently by two evaluators (MH, WS). Evaluators first screened titles and abstracts, and in the second stage, full texts were evaluated based on the pre-defined selection criteria. Discrepancies between the evaluators were resolved by discussion.

 Selection criteria

Original research articles published in English as full publications in peer-reviewed journals between January 2005 and January 2025 were included. Only studies in human populations examining the role of one or multiple environmental exposures as independent variables in relation to T2D were included. Adult populations, all nationalities, and ethnicities were included to provide a broad overview. Eligible studies had to report quantitative measures of the association between environmental exposure and T2D. Studies excluded in the full-text review (n = 156) are listed in Table S3.

 Definition of the exposure

Environmental exposures were defined as air pollution, noise, and the built environment. For air pollution particles with a diameter of less than 2.5 (PM2.5) and 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), and black carbon (BC) were included. For noise: traffic noise, aircraft noise, and railway noise were included, and for built environment: greenness, walkability, and population density.

 Definition of the outcome

Articles were included if T2D cases were identified through register-based, clinical, or self-reported diagnostics. Register-based diagnostics were derived from hospital and patient registers, national registers, or medication reimbursement registers. Clinical diagnostics for T2D was defined by clinical cut-off values, including measurements of fasting plasma glucose, glycated hemoglobin, and oral glucose tolerance test results collected during a clinical examination performed by health professionals. Self-reported diagnostics refer to T2D status obtained from surveys and questionnaires.

 Data extraction

For all records, the following characteristics were recorded: title, author(s), publication year, country of study, study design, number of participants, mean age, sex ratio, definition- and assessment method of environmental exposure(s), outcome(s), T2D definition and assessment method, statistical methods, covariates, effect size measure, results as a measure of association and statistical significance, and direction of the association. Information on whether an exposome method was used in each study was extracted as a categorical variable (yes/no). In case of missing or incomplete data from the selected study, an attempt to retrieve information was made by contacting the authors. If the studies reported the effect estimates for both continuous and categorical environmental exposure, the results from continuous exposure were extracted. For categorical exposures (tertiles, quartiles, quintiles) the results were recorded as high versus low; the highest- and the lowest study-specific category. For studies using time-series analysis, the most recent results were included.

 Statistical analyses

We extracted and pooled effect estimates from the single-pollutant models reported as main or fully adjusted model. Joint exposure models were extracted and reported separately to describe the combined effect of multiple environmental exposures on the risk of T2D. To be able to compare the effect estimates between the single-exposure and joint exposure models, we calculated the absolute risk differences between the fully adjusted single-exposure and joint exposure models. To allow a comparison between the studies, air pollution effect measures were pooled for a fixed increment of 10 μg/m3, except for BC which was standardized to a 5 μg/m³ increment due to lower exposure units used in included studies. Noise exposures were pooled for a fixed increment of 10 dB.

NO2 and O3 exposures that were expressed in parts per billion (ppb) were first converted to μg/m3 using the general formulas described below.

Concentration (µg/m3)=molecular weight×concentration (ppb) ÷ 24.45 Nitrogen dioxide NO2=46.01 g/mol×1 ppb ÷ 24.45=1.88 µg/m3 Ozone O3=48 g/mol×1 ppb ÷ 24.45=1.96 µg/m3,

where the value of 24.45 is the volume (liters) of a mole (gram molecular weight) of a gas when the temperature is at 25°C and the pressure is at 1 atmosphere (1 atm = 1.01325 bar). 25°C and pressure of 1 atmosphere are what is normally assumed for the conversion factors.

The following formulas were then used for standardization and obtaining standard errors as described in Yang et al.29 Similar standardization was conducted for noise exposure as a fixed increment per 10 dB.

ORstandardized=ORoriginal Increment(10)/Increment(original) CIstandardized=CIoriginalIncrement(10)/Increment(original) SEstandardized=(CI-upperstandardized)−(CI-lowerstandardized)/3.92

Meta-analyses were performed separately for each exposure when available. When the same study population was represented in multiple articles, we included the one that had the most relevance to the meta-analysis in terms of the number of participants, exposure measurement, and/or year published. There is no guideline for the minimum number of studies needed for a meta-analysis. We considered four risk estimates for each exposure as a minimum to justify running a meta-analysis. We pooled the risk estimates reported as hazard ratio (HR), odds ratios (OR), or risk ratio (RR), and 95% confidence intervals (95% CI), based on guidelines indicating that HR, OR and RR may be combined when the outcome of interest is common, and the effect size is small.8,30,31 The random effects model with the DerSimonian and Laird method (inverse-variance method) was used to incorporate an assumption that the different studies are estimating different but related effects.32

Subgroup analyses were used to consider possible modification of effects by study characteristics: study design (longitudinal and cross-sectional), geographic region (Europe, Asia, North America, South-America, Oceania, and Africa), T2D definition (self-reported and register or clinical measures), adjustment for relevant factors related to T2D (yes: 5 or more included versus no: less than 5), adjustment for other environmental risk factors; PM2.5, PM10, NO2, O3, BC, traffic-, railway- or aircraft noise, walkability, greenness or population density (yes or no), and risk of bias score (low, moderate or high). Meta-regression was performed to identify potential sources of heterogeneity within these study characteristics. Statistical analyses were conducted using IBM SPSS Statistics (29.0).

 Risk of bias

We assessed the risk of bias (ROB) of all included studies using a self-developed tool. We integrated relevant components from the Newcastle–Ottawa Scale (NOS)33 and the WHO Risk of Bias Assessment Instrument for Systematic Reviews Informing the WHO Global Air Quality Guidelines.34 From the NOS, we incorporated key concepts related to selection of participants, ascertainment of exposure and outcomes, and control for confounding. From the WHO instrument, we drew on its domain-based approach focusing on study design, exposure assessment methods, outcome validity, and adjustment for key confounders and co-exposures. The evaluation included four main domains: (1) Study design, (2) Exposure measurement, (3) Outcome measurement, (4) testing for confounding, and had six questions:

Is the study design longitudinal?

Was the exposure measured before the outcome?

Was the exposure measured/modeled from the participant’s home address?

Was the outcome measured by clinical measure or using register-based data, or self-reported data combined with clinical and/or register data?

Did the study adjust for T2D risk factors and potential confounders related to environmental exposures (at least 5 out of 8 equals yes: age, sex, socioeconomic status [SES, at least one of the following: socioeconomic position or status, education, employment or income status, deprivation], body mass index (BMI), smoking, physical activity, family history of diabetes, measure of nutrition/diet)?

Was the model adjusted for one or more environmental risk factors (air pollution, noise, green space)?

Each question was answered by the reviewer either yes, no, or information not available, yes giving one point and other options zero points. The results from the six questions were then rated as high ROB (0 to 2 points), moderate ROB (3-4), or low ROB (5-6). The evaluation was conducted by one reviewer (MH). We did not exclude any studies based on their ROB assessment, but we utilized the results in sensitivity analyses to identify how different sources of heterogeneity may affect the meta-analysis results.

The potential presence of publication bias and its impact on the results of the meta-analyses were assessed using statistical tests such as funnel plots and Egger’s test. The I2 statistic (I2) and Tau-squared (τ2) values were calculated as a measure of heterogeneity across studies. τ2 measures the variance among the studies, and I2 describes the percentage of the total variability (from 0 to 100%) in effect estimates that is due to heterogeneity rather than sampling error. We used the following guide to interpret the I2 values in the context of meta-analyses: 0% to 40%: might not be important, 30% to 60%: may represent moderate heterogeneity, 50% to 90%: may represent substantial heterogeneity, and 75% to 100%: considerable heterogeneity.35

 Results

The systematic literature searches yielded a total of 13 629 records. After removing the duplicate records and screening according to the set selection criteria, 151 articles were identified as eligible for this review. The process is described in Figure 1 as a PRISMA flow diagram. The included studies had different combinations of environmental exposures; 133 studies used air pollution as the main exposure, 20 used noise, and 39 built environment. The most studied pollutant was PM2.5 (n = 90) and the least population density (n = 1) followed by aircraft noise (n = 5). Only one of the studies included by Ohanyan and colleagues, used an exposome method.36 In the risk of bias (ROB) assessment, 37 studies were evaluated as having high ROB, 57 studies with moderate ROB, and 57 with low ROB. The ROB assessment for each included study is available in the supplementary materials Table S4.

PRISMA flowchart of the selection of studies.

 Air pollution

The relationship between exposure to air pollution and T2D was evaluated for PM2.5, PM10, NO2, O3, and BC. Study characteristics are described in Table 1. Separate meta-analyses were conducted for each pollutant, and the results are shown in Figures 2-8. Reasons for exclusion from meta-analyses are described in Table S5.

Characteristics of included air pollution articles (n = 133). The table is organized in alphabetical ascending order of the first author.

AuthorCountryStudyNAgeBaselineFollow-upExposureUnits
Anderson et al., 2012127 Denmark The Danish Diet, Cancer, and Health Cohort 51 818 56.1 1993/1997 9.7 years NO2 per IQR 4.9 μg/m3
Badpa et al., 2024111 Germany Cooperative Health Research in the Region of Augsburg KORA-Study 7736 49.2 1994-1995, 1999-2001 15.0 years PM2.5 per IQR 1.3 μg/m3
PM10 per IQR 2.2 μg/m3
NO2 per IQR 7.0 μg/m3
O3 per IQR 3.6 μg/m3
Bai et al., 201871 Canada Ontario Population Health and Environment Cohort 1 056 012 51.1 1996 17.0 years NO2 per IQR 4.0 μg/m3
Su et al., 2023128 China Urban and Rural Elderly Population study 222 179 69.73 2015 – PM2.5 per 10 μg/m3
O3 per IQR
Bo et al., 2021129 Taiwan MJ Health cohort study 146 789  38.82 2001-2014 5.0 years PM2.5 tertiles: −31.99 to −0.99; −0.99 to 0.27; 0.27–32.7 μg/m3
Bowe et al., 2018130 US A cohort of US veterans 1 729 108 61.2 2003 8.5 years PM2.5 per 10 μg/m3
Brook et al., 2008131 Canada Register-based cohort 7634 49.85 1992-1999 - NO2 per 1 ppb
Cervantes-Martínez et al., 202255 Mexico The Mexican Teachers' Cohorta 13 669 43 2008 11.5 years PM2.5 per 10 μg/m3
NO2 per 10 ppb
Chen et al., 2013132 Canada Canadian Community Health Surveys 62 012 54.9 1996 8.32 years PM2.5 per 10 μg/m3
Chen et al., 2022133 China Wuhan Chronic Disease Cohort 10 253 – 2019 – PM2.5 per 1 μg/m3
PM10 per 1 μg/m3
NO2 per 1 μg/m3
Chen et al., 202437 China Anhui Cohort Study of Older People Health 2766 71.68 2001-2003 5.55 years PM2.5 per IQR 3.16 μg/m3
Chilian-Herrera et al., 2021134 Mexico National Health and Nutrition Survey Mexico 2297 49.3 2012 – PM2.5 per 10 μg/m3
Clark et al., 201752 Canada Population Data BC 380 738 58 1994-1998 4.0 years PM2.5 per IQR 1.6 μg/m3
NO2 per IQR 8.4 μg/m3
BC per IQR 0.9 10−5/m
Coogan et al., 2012135 US Black Women’s Health Study 3992a 39.35 1995 10.0 years PM2.5 per 10 μg/m3
Coogan et al., 2016136 US Black Women’s Health Study 43 003a 38.7 1995 16.0 years PM2.5 per IQR 2.9 μg/m3
NO2 per IQR 9.7 ppb
Cui et al., 202457 China Chronic disease surveillance project: middle-aged and elderly individuals in Anhui Province, China 79 623 57.14 2017-2020 – PM2.5 per IQR 7.95 μg/m3
BC per IQR 0.30 μg/m3
Dijkema et al., 2011137 Netherlands Hoorn Screening Study for type 2 diabetes 8018 58 1998-2000 – NO2 quartiles: 8.8 to 14.2; 14.2 to 15.2; 15.2 to 16.5; 16.5-36.0 μg/m3
Dimakakou et al., 2020138 UK UK Biobank 502 504 NA 2006-2010 – PM2.5 per 1 μg/m3
Dzhambov et al., 2016139 Bulgaria Cross-sectional study in Plovdiv city, Bulgaria 513 36.45 2014 – PM2.5 categories: 0.0 to 17.5; 17.5 to 20.3; 20.3 to 25.0; 25.0 to 40; 40.0 to 66.8 μg/m3
Dzhambov et al., 202569 Bulgaria Cross-sectional study in 5 Bulgarian cities 4640 49.0 2023 – NO2 per 5 μg/m3
Elbarbary et al., 2020140 China Study on global AGEing and adult health (SAGE) 8179 62.9 2007-2010 – PM10 per 10 µg/m3
PM2.5 per 10 µg/m3
NO2 per 10 µg/m3
Eze et al., 201464 Switzerland Swiss Study on Air Pollution and Lung Disease in Adults 6392 52 2002 – PM10 per 10 μg/m3
NO2 per 10 µg/m3
Eze et al., 201768 Switzerland Swiss Study on Air Pollution and Lung Disease in Adults. 2631 59.2 2002 8.3 years NO2 per IQR 15 μg/m3
Fan et al., 2024141 UK UK Biobank 78 230 cat. 40-70 2010 12.19 years PM2.5 per IQR 1.26 μg/m3
PM10 per IQR 1.79 μg/m3
NO2 per IQR 10.32 μg/m3
Fan et al., 2025142 UK UK Biobank 77 278 cat. 40-70 2006-2010 12.19 years PM2.5 per IQR 1.26 μg/m3
PM10 per IQR 1.79 μg/m3
NO2 per IQR 10.32 μg/m3
Guo et al., 2021143 Taiwan MJ Health cohort study 156 314 40.7 2001 5.2 years PM2.5 per 10 µg/m3
Hansen et al., 201649 Denmark Danish Nurse Cohort 24 174a 54 1993 15.3 years PM10 per 10 μg/ m3
PM2.5 per IQR 3.1 μg/m3
NO2 per IQR 7.5 μg/m3
Hassanvand et al., 2018144 Iran National surveillance of risk factors of noncommunicable diseases 2903 55.31  2011 – PM10 Unclear
Hegelund et al., 2024145 Denmark Danish nationwide sample 3 111 988 51.4 2000 15.2 years PM2.5 per IQR 1.96 μg/m3
NO2 per IQR 10.23 μg/m3
Hernandez et al., 2018146 US Selected Metropolitan/ Micropolitan Area Risk Trends from Behavioral Risk Factor Surveillance System 1 158 547 – 2002-2008 – PM2.5 per 10 µg/m3
O3 per 10 ppb
Honda et al., 201744 US National Social Life, Health, and Aging Project 916 69.6 2005-2011 – PM2.5 per IQR 3.9 μg/m3
NO2 per IQR 8.6 ppb
Howell et al., 201974 Canada CANHEART-Cohort: The Cardiovascular Health in Ambulatory Care Research Team 2 496 458 53.2 2008 – NO2 per 10 ppb
Hu et al., 2024147 China Shanghai High-Risk Diabetic Screen Project  9371 52.92 2002-2013 – PM2.5 per 10 μg/m3
1128 51.13 2014-2018 – PM2.5 per 10 μg/m3
Hu et al., 202351 UK UK Biobank 390 834  56.3 2006-2010 10.9 years PM2.5 per IQR 1.3 μg/m3
NO2 per IQR 9.8 μg/m3
Huo et al., 2022148 China Henan Rural Cohort Study  11 640 – 2015-2017 – PM10 per 1 μg/m3
PM2.5 per 1 μg/m3
NO2 per 1 μg/m3
Jabbari et al., 2020149 Iran Tehran Cardiometabolic Genetic Study 2428 45.4 2009 9.0 years PM10 per 10 μg/ m3
Jerret et al., 201775 US Black Women’s Health Study 43 003a – 1995 8.0 years O3 per 6.7 ppb
Kang et al., 2022150 China Henan Rural Cohort study 38 841 55.56 2015-2017 – PM10 per 1 μg/m3
PM2.5 per 1 μg/m3
NO2 per 1 μg/m3
Kang et al., 202379 China Henan Rural Cohort Study 38 442 55.56 2015-2017 – PM2.5 per 1 μg/m3
BC per 1 μg/m3
Klompmaker et al., 201972 Netherlands Dutch Public Health Monitor 354 827 Cat. 2012 – PM2.5 per IQR 0.83 μg/m3
PM10 per IQR 1.24 μg/m3
NO2 per IQR 7.85 μg/m3
Krämer et al., 2010151 Germany SALIA- Study on the influence of Air pollution on Lung function, Inflammation and Aging 1775a 54.6 1985-1994 16.0 years PM10 Per IQR 10.1 μg/m3
NO2 per IQR 24.9 μg/m3
Lao et al., 2019152 Taiwan Taiwan MJ Cohort 147 908 38.3 2001-2014 6.7 years PM2.5 per quartiles: < 21.7; 21.7–<24.1; 24.1–<28.0; ≥ 28.0 μg/m3
Lee et al., 2021153 Japan Center for Preventive Medicine, St. Luke’s International HospitaL - database 66 885 46 2005-2019 – PM2.5 per 1 µg/m3
Li et al., 201965 China Chronic Disease Surveillance System of Ningbo 25 130 65.17 2008-2015 – PM10 per 10 μg/m3
O3 per 10 μg/m3
Li et al., 202176 Taiwan National Health Insurance Research Database 6 426 802 39.84  2005 11.0 years O3 per IQR 3.30 ppb
Li et al., 202256 UK UK Biobank 263 733 56.48 2006-2010 11.94 years PM10 quintiles (5) low to high
PM2.5 quintiles (5) low to high
NO2 quintiles (5) low to high
Li et al., 2024154 China China-PAR sub-cohorts: China Multi-Center Collaborative Study of Cardiovascular Epidemiology; International Collaborative Study of Cardiovascular Disease in Asia; Community Intervention of Metabolic Syndrome in China Chinese Family Health Study 71 689 51.28 2000 5.93 years PM2.5 per 10μg/m3
Li et al., 202380 China China Multi-Ethnic Cohort study 69 210 51.8 2018-2019 – PM2.5 per SD 20.5 μg/m3
BC per SD 1.1 μg/m3
Li et al., 202448 China Prospective Cohort Study in China 124 204 39 2005-2020 8.47 years PM2.5 per 1 μg/m3
Li et al., , 202163 UK UK Biobank 449 006 56.46 2006-2010 11.0 years PM2.5 per SD increase
NO2 per SD increase
Li et al., 2022155 UK UK Biobank 359 153 56.3 2006-2010 8.9 years PM10 per 10 µg/m3
PM2.5 per 5µg/m3
NO2 per 10 µg/m3
Li et al., 2019156 Taiwan – 505 151 42.6 2001 12.0 years PM2.5 per 10 μg/m3
Liang et al., 2019157 China Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China PAR) 88 397 51.7 1992-1994, 1998, 2000-2001, 2007-2008 2012-2015 PM2.5 per 10 μg/m3
Liu et al., 2023158 China China Health and Retirement Longitudinal Study (CHARLS) 19 121 57.88 2011 8.0 years PM2.5 per 10 μg/m3
Liu et al., 202243 China China Health and Retirement Longitudinal Study (CHARLS)

9638

3510

60.3

59.3

2011

2015

–

5.0 years

 		PM10 per 10 µg/m3
PM2.5 per 10 µg/m3
NO2 per 10 µg/m3
PM10 per 10 µg/m3
PM2.5 per 10 µg/m3
NO2 per 10 µg/m3
Liu et al., 202277 China Henan Rural Cohort Study 39 192 57.7 2015-2017 – O3 per IQR 4.04 μg/m3
Liu et al., 2016159 China China Health and Retirement Longitudinal Study (CHARLS) 11 847 59 2011-2012 – PM2.5 per IQR 41.1 μg/m3
Liu et al., 2019160 China Henan Rural Cohort study 39 191 55.6 2015-2017 – PM2.5 per 1 µg/m3
NO2 per 1 µg/m3
Liu et al., 201945 China Guangdong Gut Microbiome Project dataset 6627 51.8 2015-2016 – PM2.5 per IQR 8.03 μg/m3
Lucht et al., 2020161 Germany Heinz Nixdorf recall study 2451 58.2 2000-2003 10.0 years PM10 per IQR 3.8 µg/m3
PM2.5 per 1 µg/m3
NO2 per 1 µg/m3
Ma et al., 2024162 China Study in the national program Guangxi Zhuang Autonomous Region 12 426 54.22 2018-2019 – O3 per IQR 1.18 μg/m3
Mandal et al., 2023163 India Center for Cardiometabolic Risk Reduction in South Asia, Chennai Region 5118 40.1 2010-2012 4.84 years PM2.5 per 10 µg/m3
Center for Cardiometabolic Risk Reduction in South Asia, Delhi Region 3675 44.6 2010-2012 4.84 years PM2.5 per 10 µg/m3
McAlexander et al., 2022164 US REGARDS: REasons for Geographic and Racial Differences in Stroke 11 208 62.7 2003-2007 2013-2016 PM2.5 per 5 µg/m3
Mei et al., 2023165 China Community-based study in China 4235 54.23 2018-2020 – PM10 per 10 μg/m3
PM2.5 per 10 μg/m3
NO2 per 10 μg/m3
O3 per 10 μg/m3
Niedermayer et al., 202438 Germany Cooperative Health Research in the Region of Augsburg (KORA) FIT-Study 3034 63.2 2018-2019 – NO2 per IQR 6.3 μg/m3
PM2.5 per IQR 1.4 μg/m3
PM10 per IQR 2.0 μg/m3
O3 per IQR 3.5 μg/m3
O’Donovan et al., 2017166 UK

ADDITION-Leicester

Let’s prevent

Walking away

6171

3442

830

56.2

63.2

63.1

2004-2009

2009-2011

2010

–

–

–

 		PM10 per 10 μg·m3
PM2.5 per 10 μg·m3
NO2 per 10 μg·m3
PM2.5 per 10 µg/m3
PM10 per 10 µg/m3
NO2 per 10 µg/m3
PM2.5 per 10 µg/m3
PM10 per 10 µg/m3
NO2 per 10 µg/m3
Ohanyan et al., 202236 Netherlands Population-based Occupational and Environmental Health Cohort Study (AMIGO) 14 829 50.7 2011-2012 – PM10 Na
PM2.5 Na
NO2 Na
Orioli et al., 201846 Italy Italian National Institute of Statistics 376 157 63.5 1999-2013 – PM10 per 10 μg/m3
PM2.5 per 10 μg/m3
NO2 per 10 μg/m3
O3 per 10 μg/m3
Sung Kyun et al., 2015167 US MESA: Multi-Ethnic Study of Atherosclerosis 5839 64.3 2000-2002 – PM2.5 per IQR 2.43 µg/m3
5135 61.6 2000-2002 9.0 years PM2.5 per IQR 2.43 µg/m3
Paul et al., 202050 Canada Ontario Population Health and Environment Cohort 790 461 55.5 2001 15.0 years PM2.5 per IQR 3.5µg/m3
NO2 per IQR 13.8ppb
O3 per IQR 6.3ppb
Puett et al., 201141 US Nurses' Health Study & Health Professionals Follow-Up Study 89 460 56 1989-2002 13.0 years PM10 per IQR 7 μg/m3
PM2.5 per IQR 4 μg/m3
Qiu et al., 2018168 Hong Kong Chinese Elderly Health Services cohort  53 905 72.4 1998 9.8 years PM2.5 per IQR 3.2 μg/m3
61 447 72 1998 – PM2.5 per IQR 3.2 μg/m3
Renzi et al., 201839 Italy Rome Longitudinal Study 

65 955

106 387

–

–

2008

2008

6.0 years

–

 		PM10 per 10 µg/m3
PM2.5 per 5-μg/m3
NO2 per 10-μg/m3
O3 per 10-μg/m3
PM10 per 10 µg/m3
PM2.5 per 5-μg/m3
NO2 per 10-μg/m3
O3 per 10-μg/m3
Requia et al., 2017169 Canada Canadian community health survey data 5 570 326 – 2007-2014 – PM2.5 per 10 μg/m3
Riant et al., 201866 France ELISABET: Prevalence and underdiagnosis of airway obstruction among middle-aged adults in northern France 2797 53 2011-2013 – PM10 per 2 μg/m3
NO2 per 5-μg/m3
Sade et al., 202347 US Medicare enrollees 65-y and older in the fee-for-service program, part A and part B, in the US 41 780 637 75.97 2000 until 2016 PM2.5 per 5 μg/m3
NO2 per 5 ppb
O3 per 5 ppb
Shan et al., 202067 China China Northern 4 Cities Cohort Study 38 529 44.12 1998 12.0 years PM10 per 10 µg/m3
NO2 per 10 µg/m3
Shen et al., 202442 China National Free Preconception Health Examination Project in China  20 076 032a 27.04 2010-2015 – PM2.5 per IQR 27 μg/m3
Shin et al., 2023170 South Korea Cardiovascular Disease Association Study 14 667 58.6 2005-2011 until 2016 PM2.5 per 10 μg/m3
NO2 per 10-ppb
Sohn et al., 2017171 South-Korea Korea Community Health Survey 52 127 46.7 2012 – PM10 per 1000 ppm
Sommar et al., 2023172 Sweden The Västerbotten intervention programme 33 766 40 1985-2014 until 2015 PM10 per 10 μg/m3
PM2.5 per 5 μg/m3
Strak et al., 201740 Netherlands Dutch national health survey  289 703 Cat. ≥ 19 2012 – PM10 per IQR 1.20 μg/m3
PM2.5 per IQR 0.81 μg/m3
NO2 per IQR 7.76 μg/m3
Sun et al., 202558 China Participants with annual check-ups at 37 community hospitals in Tianjin Binhai New Area 65 824 64.64 2014 8 years PM2.5 per SD 15.03 μg/m3
BC per SD 0.464 μg/m3
Suryadhi et al., 2020173 Indonesia Indonesia Basic Health Research 64 7947 41.9 2013 – PM2.5 per 10 µg/m3
Sørensen et al., 202253 Denmark Danish Register data 1 922 545 57.5 2005 11.2 years PM2.5 per IQR 1.85 µg/m3
Sørensen et al., 2022174 Denmark Danish National Health Survey 234 018 52 2010, 2013 until 2017 PM2.5 per 5 μg/m3
NO2 per 10 μg/m3
Sørensen et al., 202362 Denmark Danish Register data 1 843 597 58.9 2005 9.5 years PM2.5 per 5 μg/m3
NO2 per 10 μg/m3
Sørensen et al., 202273 Denmark Danish Register data 2 631 488 51.7 2005 13.0 years PM2.5 per IQR 1.85 µg/m3
NO2 per IQR 7.15 µg/m3
Tani et al., 2023175 Japan Individuals enrolled in health checkups in Okayama, Japan 75 553 69.9 2006-2008 – PM2.5 per IQR 2.1 μg/m3
Wang et al., 2020176 China Jinchang Cohort 19 884 48.18 2011 2.28 years PM10 per 10 μg/m3
Wang et al., 202278 China China Health and Retirement Longitudinal Study (CHARLS) 13 548 59 2011 7.0 years O3 per 10 μg/m3
Wang et al., 202481 China Jinchang Cohort 19 884 48.18 2011-2013 2.28 years PM2.5 per 37.08 μg/m3
BC per 1.48 μg/m3
Weinmayr et al., 2015177 Germany Heinz Nixdorf Recall Study  3607 59.65 2000-2003 5.1 years PM10 per IQR 3.78 μg/m3
PM2.5 per IQR 2.29 μg/m3
Wong et al., 2020178 Malaysia Malaysian National Health and Morbidity Surveys 29 460 – 2015 – PM10 per IQR 10.34 μg/m3
NO2 per IQR 9.57 μg/m3
O3 per IQR 7.83 μg/m3
Wu et al., 202254 UK UK Biobank 398 993 55.49 2006-2010 12.0 years PM10 per IQR 3.25 μg/m3
PM2.5 per IQR 2.31 μg/m3
NO2 per IQR 7.08 μg/m3
Wu et al., 2023179 UK UK Biobank 162 334 53.99 2006-2010 11.7 years PM2.5 per IQR 1.29 μg/m3
PM10 per IQR 1.77 μg/m3
NO2 per IQR 10.20 μg/m3
Wu et al., 2024180 China Cohort in Yinzhou District, Ningbo, China. 24 147 62.9 2015-2018 until 2021 PM2.5 per IQR 5.64 μg/m3
PM10 per IQR 7.91μg/m3
NO2 per IQR 8.75 μg/m3
Yang et al., 2018181 China 33 Communities Chinese Health Study 15 477 45 2009 – PM10 per IQR 19 μg/m3
PM2.5 per IQR 26 μg/m3
NO2 per IQR 9 μg/m3
O3 per IQR 22 μg/m3
Yang et al., 2018182 China Study on global AGEing and adult health (SAGE) 11 504 62.7 2007-2010 – PM2.5 per 10 μg/m3
Ye et al., 2022183 China China Health and Retirement Longitudinal Study (CHARLS) 19 529 62.06 2018 – PM2.5 per IQR 16.2 μg/m3
Yu et al., 2021184 US The Sacramento Area Latino Study on Aging 1090 70.5 1998 10.0 years O3 per 10-ppb
Xu et al., 202370 UK UK Biobank  82 548  55.49 2006-2011 13.76 years PM2.5 per SD 1.07 μg/m3
NO2 per SD 9.23 μg/m3
Zadeh et al., 2023185 Iran Tehran Lipid and Glucose Study 5024 40.6 2001 12.2 years PM10 per 10 μg/m3
NO2 per 10 μg/m3
O3 per 10 μg/m3
Zhang et al., 2021186 China China Health and Retirement Longitudinal Study (CHARLS) 13 013 61.88 2015 – NO2 per IQR (12.39 μg/m3)
Zhang et al., 202461 China China Health and Retirement Longitudinal Study (CHARLS) 9242 59.0 2011-2012 until 2018 PM2.5 per 10 μg/m3
Zheng et al., 202459 UK UK Biobank 162 579 55.7 2010 10.1 years PM2.5 per IQR 2.249 μg/m3
PM10 per IQR 3.163 μg/m3
NO2 per IQR 7.353 μg/m3
Zhou et al., 2022187 China China Health and Retirement Longitudinal Study (CHARLS) 13 589 59.5 2011-2012 – PM2.5 per IQR 27.4 µg/m³
BC per IQR 2.2 µg/m³
Zhou et al., 202460 China Sub-cohort of the China Multi-Ethnic Cohort 17 566 51.4 2018 4.2 years PM2.5 per IQR 8.21 μg/m3
NO2 per IQR 15.75 μg/m3
O3 per IQR 1.96 μg/m3
BC per IQR 1.51μg/m3
Zou et al., 2023188 UK UK Biobank 372 530 55.7 2006-2010 12.6 years PM10 per IQR 3.15 μg/m3
PM2.5 per IQR 2.26 μg/m3
NO2 per IQR 6.90 μg/m3

Only women in the study population.

Results of the random effects meta-analysis of the association between PM2.5 per 10 μg/m3 and T2D (OR with 95% CI).

Results of the random effects meta-analysis of the association between PM10 per 10 μg/m3 and T2D (OR with 95% CI).

Results of the random effects meta-analysis of the association between NO2 per 10 μg/m3 and T2D (OR with 95% CI).

Results of the random effects meta-analysis of the association between O3 per 10 μg/m3 and T2D (OR with 95% CI).

Results of the random effects meta-analysis of the association between T2D and BC per 5 μg/m3 (OR with 95% CI).

Results of the random effects meta-analyses of the association between traffic noise per 10 dB and T2D (OR with 95% CI).

Results of the random effects meta-analyses of the association between railway noise per 10 dB and T2D (OR with 95% CI).

 Particles with a diameter of less than 2.5 µm

Altogether, 90 studies used PM2.5 as the main exposure. In the meta-analysis, T2D was positively associated with PM2.5 with an OR of 1.19 (95% CI: 1.16-1.22, n = 57, Figure 2). The meta-analysis showed considerable heterogeneity between the studies (I2= 96.8%). The funnel plot analysis (Figure S1) showed studies concentrating on the right side of the funnel, and Egger’s test indicated a risk of publication bias (P-value <0.001). The Trim-and-Fill analysis imputed 15 studies (Figure S2), and the overall effect estimate of observed and imputed studies decreased to OR: 1.14 (95% CI: 1.10-1.17). Standardization of two studies, Chen et al.37 and Niedermayer et al.38 increased their risk estimates substantially, from HR: 1.35 (95% CI: 0.83-2.18) to 2.58 (95% CI: −3.03 - 8.20) and from OR: 1.2, 95% CI: 0.99-1.46 to 3.68, 95% CI: −3.32-10.68, respectively. We performed a leave-one-out analysis, but excluding either one of these studies did not change the overall effect estimate from the meta-analysis.

Subgroup analysis highlighted some variation by study characteristics (Figure S7) but did not explain the considerable heterogeneity between the studies. Meta-regression analysis (Table S6) showed that studies with a moderate risk of bias had a smaller risk of T2D compared to studies with a low risk of bias (estimate: −0.10, 95% CI: −0.18, −0.02, P-value: 0.019). Studies with a high risk of bias also indicated a smaller risk of T2D compared to low risk of bias group, but the result was not significant (estimate: −0.12, 95% CI: −0.24, 0.01, P-value: 0.063). When comparing the study regions, the European and Asian region showed higher risk of T2D with estimates 0.12 (95% CI: 0.00-0.23, P-value: 0.054) and estimate: 0.09 (95% CI: 0.00-0.18, P-value: 0.054) respectively when compared with the North America as the reference region. Longitudinal studies had a higher risk estimate (0.10, 95% CI: 0.02-0.18, P-value: 0.020) when compared to cross-sectional studies.

 Particles with a diameter of less than 2.5 µ and joint exposure

Of the 90 studies considering the association between PM2.5 and T2D, 26 evaluated joint exposure to air pollution, noise, or the built environment. Three studies found no association between PM2.5 and T2D in either single-exposure or joint-exposure models adjusted for air pollution (PM10, NO2, or O3).39-41 In seven studies, adjustment for other air pollutants did not considerably change the association (risk differences between single- and joint exposure models ranged between −0.01 and 0.04).42-48 In three studies, the adjustment for air pollution (NO2, PM10, or O3) decreased the association between PM2.5 and T2D to nonsignificant.49-51 For Clark et al. the association was independent of covarying noise exposure (OR: 1.03, 95% CI: 1.02-1.05), but further adjustment for greenness and walkability attenuated the estimate to nonsignificant (OR: 1.01, 95% CI: 1.00 - 1.03).52 Sorensen et al. reported that the association between PM2.5 and T2D (HR: 1.05, 95% CI: 1.03- 1.06) was reduced to unity or below in two-, three- and four-pollutant models when ultrafine particles, elemental carbon, and/or NO2 were included.53 In three studies, the association between PM2.5 and T2D changed considerably when adjusting for NO2; Wu et al.54 reported a 14.1% relative decrease in risk estimate, Cervantes-Martinez et al.55 observed a 19.8% decrease, whereas Li et al.56 found a 7.27% relative increase in the risk estimate.

Quantile g-computing (QGC) method was used in four studies.57-60 Zheng et al. reported higher risk estimate for joint exposure model when using the QGC method for air pollutants PM2.5, PM10, NO2, sulphur dioxide (SO2), nitrogen oxides (NOx), and benzene (OR: 1.16, 95% CI: 1.10-1.22) compared to the single-exposure model of PM2.5 (OR: 1.08, 95% CI: 1.03-1.14).59 In contrast, Zhou et al. reported a lower risk estimate from joint exposure of air pollutants PM2.5 mass, NO2, O3, nitrate, ammonium, organic matter (OM), BC, chloride, and sulfate (HR: 1.48, 95% CI: 1.26-1.73) compared to the single-exposure model of PM2.5 (HR: 1.75, 95% CI: 1.42-2.16).60 Cui et al. also reported a lower risk estimate for joint exposure of six air pollutants (PM2.5, BC, OM, ammonium, sulfate, and nitrate with OR: 1.06 (95% CI: 1.01-1.11) compared to the single-exposure model of PM2.5 (OR: 1.18, 95% CI: 1.11-1.25).57 Furthermore, the observed association of joint exposure using QGM was influenced by the stratification of green space exposure (measured as tree and grass cover). The risk of T2D was higher in the group exposed to low levels of green space (OR: 1.51, 95% CI: 1.38-1.64) compared to high exposure group, where a potential protective effect of green space was reported (OR: 0.85, 95% CI: 0.79-0.90).57

The potential modification effect of green space on the association between PM2.5 and T2D was also assessed in three other studies. Zhang et al. found a 6% increase in the risk of T2D in a single-exposure model of PM2.5 (HR: 1.06, 95% CI: 1.02-1.10). They further tested for the potential interactive effect of air pollution and greenness using the relative excess risk due to interaction (RERI) but did not detect a strong interaction effect between PM2.5 and normalized difference vegetation index (NDVI) on diabetes, with RERI of −0.092 (95% CI: −0.551, 0.287).61 Sørensen et al. assessed the effect modification by population density, road traffic noise, and surrounding green space, but no consistent indications of effect modification were found.62 Sun et al. examined how green space (NDVI) influences the association between air pollutants and the risk of T2D. In subgroup analyses, participants with high PM2.5 exposure had a greater risk of T2D in areas with low green space (HR: 2.39, 95% CI: 2.25–2.53) than those in areas with high green space (HR: 2.33, 95% CI: 2.18–2.48). The risk was considerably lower among participants with both low PM2.5 exposure and low green space (HR: 1.13, 95% CI: 1.04–1.21). The low PM2.5 with high green space was used as the reference group.58 Hu et al. utilized the cumulative risk index (CRI) method and reported similar association between the single exposure model of PM2.5 (HR: 1.05, 95% CI: 1.01, 1.10) and joint exposure of road traffic noise, PM2.5, and NO2 (HR: 1.06, 95% CI: 1.02-1.09).51 Li et al. utilized air pollution score (PM2.5, PM2.5–10, NO2, and NOx) and found similar associations in the single-exposure model of PM2.5 and the joint exposure of air pollutant score (HR: 1.04, 95% CI: 1.02-1.06).63

 Particles with a diameter of less than 10 µm

PM10 was used as the main exposure in 40 studies, from which 27 were included in the meta-analysis. Every 10 μg/m3 increase in PM10 was associated with an increased risk of T2D OR: 1.23 (95% CI: 1.13-1.34, [I2 = 97.1%, Figure 3]). Results from Egger’s test suggest the presence of publication bias (P-value <0.001), and in funnel plot analysis, the studies were concentrated on the right side of the funnel (Figure S3). The Trim-and-Fill analysis imputed one study, but the observed plus imputed study effect estimate did not differ from the original effect estimate. In subgroup- or meta-regression analyses the study characteristics did not explain the considerable heterogeneity between the studies (Figure S7 and Table S6).

 Particles with a diameter of less than 10 µm and joint exposure

Of these 40 studies, 12 used multi-exposure models by adjusting for air pollution, walkability, railway-, or traffic noise.39-41,43,46,49,54,56,64-67 Four studies did not find an association between PM10 and T2D in eiher single-exposure or joint-exposure model adjusted for air pollution (PM2.5, NO2 or O3).39,41,49,66 In six studies, the risk estimate remained similar or showed slight attenuation after adjustment, with the difference between single- and multi-exposure models ranging from −0.01 to 0.05.43,46,54,56,64,67 The most pronounced difference was reported by Li et al. where further adjustment of air pollutants (SO2, NO2, and O3) substantially attenuated the association from RR: 1.62 (95% CI: 1.16-2.28) to RR: 1.10 (95% CI: 0.15-8.32).65 Strak et al. reported a similar association when adjusting for PM2.5 (OR: 1.05, 95% CI: 1.03-1.07), but adjusting for NO2, the association attenuated and lost significance (from OR: 1.04, 95% CI: 1.02-1.06 to OR: 1.00, 95% CI: 0.97-1.02).40 Zheng et al. utilized the QGC method for joint exposure of air pollutants (benzene, NO2, SO2, PM10, and PM2.5) and reported a higher risk estimate from the joint model (HR: 1.16, 95% CI: 1.10-1.22) compared to the single-exposure model of PM10 (HR: 1.06, 95% CI: 1.01-1.120).59

Nitrogen dioxide: NO2 was used as the main exposure in 59 studies, from which 36 were included in the meta-analysis. Every 10 μg/m3 increase in NO2 was significantly associated with an increased risk of T2D with an overall effect estimate OR: 1.13 (95% CI: 1.10-1.16, I2 = 96.5%, Figure 4). The funnel plot analysis appeared asymmetric with a scattered plot (Figure S4), and Egger′s test was statistically significant (P-value <0.001), indicating a possible risk of bias. The Trim-and-Fill analysis did not impute any additional studies. Subgroup analyses (Figure S7) or meta-regression analyses (Table S6) per study characteristics were not able to explain the considerable heterogeneity between the studies. Only the study region, the Asian region, compared to North America, had a significant difference; studies conducted in the Asian region had a higher risk compared to studies conducted in North America (Estimate: 0.12, 95% CI: 0.004-0.05, P-value: 0.04).

 Nitrogen dioxide and joint exposure

The joint exposure of environmental exposures was assessed in 23 of the 59 studies. Three studies found no association between NO2 and T2D in either the single-exposure or joint-exposure model49,68,69 and in three studies56,64,70 association lost significance when adjusted for air pollution or noise exposures. The risk remained similar in six studies39,40,46,47,50,71 and attenuated in three studies44,54,55 where risk differences between single- and joint exposure models were between 0.10 and 0.32. The risk increased in one study, which showed the most pronounced difference when adjusting the single-exposure model for PM10, the HR of T2D increased from 1.47 (95% CI: 1.42- 1.53) to HR: 2.23 (95% CI: 2.13-2.33). However, adjusting for SO2 resulted in a more modest increase (HR: 1.61, 95%CI: 1.55-1.67).67

Three studies51,53,72 utilized the CRI method to assess the joint exposure of environmental variables. Klompmaker et al. found the risk from the cumulative index (NO2, traffic noise, NDVI) higher than the risk estimate of the single-exposure models of NO2 exposure.72 A similar result was reported by Sørensen et al. with single-exposure of NO2 HR: 1.06 (95% CI: 1.05- 1.07) and CRI: of 1.13 (95% CI: 1.11-1.15) including total ultrafine particles (UFP), NO2, noise, and green space.73 Hu et al. reported similar association from the single exposure model (HR: 1.07, 95% CI: 1.02, 1.11) and the cumulative risk index of road traffic noise, PM2.5, and NO2 (HR: 1.06 (95% CI: 1.02-1.09).51

Zhou et al. used the QGC method and reported lower risk in the joint exposure model of air pollutants PM2.5 mass, NO2, O3, OM, BC, nitrate, ammonium, chloride, and sulfate (HR: 1.48, 95% CI: 1.26—1.73) compared to single-exposure model of NO2 (HR: 1.58, 95% CI: 1.25-1.99).60 Whereas Zheng et al. reported a higher risk of T2D when using QGC method for joint exposure of air pollutants benzene, NOx, NO2, SO2, PM10, and PM2.5 (HR: 1.16, 95% CI: 1.10-1.22) compared to the single-exposure model of NO2 (HR: 1.07, 95% CI: 1.02-1.12).59 Sørensen et al. assessed the effect modification by population density, road traffic noise, and surrounding green space, but no consistent indications of effect modification were found.62 Howell et al. reported an increased risk of T2D when further adjusting NO2 for walkability (from OR: 1.11, 95% CI: 1.10-1.13 to OR: 1.16, 95% CI: 1.14-1.17). Significant interaction was found between NO2 and walkability, indicating that at low levels of NO2, the likelihood of T2D was higher among those living in less walkable neighborhoods. However, the probability of T2D rose in highly walkable neighborhoods and became comparable across all levels of walkability.74

 Ozone

O3 was used as the main exposure in 20 studies, all included in the meta-analysis. For every 10 μg/m3 increase in O3, the risk of T2D was OR: 1.05 (95% CI: 1.02-1.08, I2 = 96.6%, Figure 5). The funnel plot analysis appeared asymmetric (Figure S6), and Egger’s test was statistically significant (P-value < 0.001). The Trim-and-fill analysis imputed 3 studies, but the estimate of the corrected combined effect size did not change from the original meta-analysis estimate. Subgroup analyses per study characteristics (Figure S7) showed some variation, but adjustment and ROB-score were the only significant covariates in meta-regression analyses (Table S6). Studies that adjusted for less than five out of eight T2D risk factors (age, sex, SES, BMI, smoking, physical activity, family history of diabetes, measure of nutrition/diet) showed lower risk compared to studies that did adjust at least for 5 of these covariates (estimate: −0.10, 95% CI: −0.16, −0.03, P-value: 0.006). Studies with high ROB showed a smaller risk compared to studies with low ROB (estimate: −0.12, 95% CI: −0.023, −0.01, P-value: 0.03). Other study characteristics did not explain the considerable heterogeneity that was observed between the studies.

 Ozone and joint exposure

Joint effects of O3 and environmental exposures were assessed in eight studies.39,50,60,65,75-78 The most pronounced difference was observed by Zhou et al. where the single exposure model of O3 was not significant (HR: 1.11, 95% CI: 0.99-1.24) but when using the QGC method for joint exposure to air pollutants (PM2.5 mass, NO2, O3, OM, BC, nitrate, ammonium, chloride, and sulfate) the risk of T2D increased to HR: 1.48 (95% CI: 1.26-1.73).60 Li et al. found differing results when adjusting for co-pollutants. The association between O3 and T2D in the single-pollutant model was protective (RR: 0.78, 95% CI: 0.68-0.90), but after adjustment for co-pollutants (PM10, NO2, SO2), the association became nonsignificant (RR: 1.07, 95% CI: 0.35-6.81).65

Li et al. found a slightly stronger association when adjusting for SO2 and PM2.5 HR: 1.09 (95% CI: 1.09-1.10) or SO2 and PM10 HR: 1.08 (95% CI: 1.07-1.08) compared to the single-exposure model of O3 HR: 1.06 (95% CI: 1.05-1.06).76 In single-exposure models, Renzi et al. found a modest association between O3 and T2D for incident cases (HR: 1.01, 95% CI: 1.00 - 1.02) but not for prevalent cases. (OR: 1.00, 95% CI: 0.99-1.01). The results stayed similar when adjusting for noise (day-evening-night level, Lden) and green space (NDVI) or in two-pollutant models adjusting for another pollutant (PM10, PM2.5, NO2, NOx).39 Similarly, Paul et al. found a modest association from the single-exposure model (HR: 1.01, 95% CI: 1.00–1.01) which remained when adjusting for other air pollutants (PM2.5 and NO2).50 Liu et al. reported that compared to the single-exposure model (OR: 1.50, 95% CI: 1.40-1.62) adjustment for PM2.5 in a two-pollutant model resulted in a slightly higher risk estimate (OR: 1.52, 95% CI: 1.35-1.72), whereas adjustment for PM10 (OR: 1.40, 95% CI: 1.25-1.57) or NO2 (OR: 1.48, 95% CI: 1.31-1.68) yielded lower estimates.77

In a study by Jerret et al. adjustment for PM2.5 and NO2, the risk from the single-exposure model (HR: 1.18, 95% CI: 1.04-1.34) attenuated to a non-significant (HR: 1.13, 95% CI: 0.97-1.31). They also explored the possible modification effect but found no interaction between PM2.5 and O3, but borderline evidence of an interaction between O3 and NO2, where the HRs for O3 levels were larger in areas of lower NO2 (interaction P-value: 0.09).75 For Wang et al. adjustment for PM2.5 did not change the result of the single-exposure model (HR: 1.06, 95% CI: 1.00-1.11) but the effect estimate was slightly stronger in the high PM2.5 level compared to the low PM2.5 level (HR = 1.07, 95% CI: 1.01-1.12) with no between-group significance.78

 Black carbon

BC was used as the main exposure in eight studies. The meta-analysis, including all these studies, showed a significant association between T2D and BC per 5 μg/m3 increase OR: 1.32 (95% CI: 1.15-1.50, I2 = 98.6%, Figure 6). The funnel plot was asymmetric (Figure S6), and Egger’s test was significant (P-value: <0.001). The Trim-and-fill analysis did not impute any studies. Subgroup- or meta-regression analyses were not conducted due to a small number of studies.

 Black carbon and joint exposure

Joint effects of BC and other environmental exposures were examined in seven studies.52,57,58,60,79-81 Clark et al. was the only study using only adjustment, reporting that the association observed in the single-exposure model (OR: 1.03, 95% CI: 1.01-1.04) attenuated after adjusting for transportation noise, greenness, and walkability (OR: 1.00, 95% CI: 0.98-1.02).52 Li et al. reported smaller risk estimate in the joint exposure model (OR: 1.04, 95% CI: 1.01-1.07) using weighted quantile sum (WQS) method for a score of PM2.5, BC, nitrate, organic matter, soil particles, ammonium, sulfate compared to the single exposure of BC (OR: 1.07, 95% CI: 1.01-1.15).80 Kang et al. utilized proportion and residual analyses to specify the most responsible constituents of PM2.5 (BC, OM, ammonium, nitrate, inorganic sulfate, soil particles, and sea salt) showing that BC was the most responsible constituent, in which 1% increase in the proportion of BC corresponded with 1.51-fold risk (95% CI: 1.29-1.77) for T2D.79

The QGC method was used in four studies.57,58,60,81 Cui et al. assessed the joint exposure of air pollutants (PM2.5, BC, organic matter, ammonium, nitrate, sulfate) and the risk of diabetes was higher in single exposure models (for BC OR: 1.13, 95% CI: 1.07-1.20) compared to the joint exposure (OR 1.06, 95% CI: 1.01-1.11).57 Sun et al. assessed the joint exposure of BC, OM, ammonium salt, nitrate, sulfate, and chloride which showed a stronger association (HR: 1.46, 95% CI: 1.43-1.49) compared to single-exposure model of BC (HR: 1.40, 95% CI: 1.38-1.42). In stratification analyses the participants with high exposure to BC had higher risk of T2D in areas with low green space (low NDVI) (HR: 2.18, 95% CI: 2.05-2.31) compared to areas with high NDVI (HR: 1.95; 95% CI: 1.83-2.08), using the low BC/high NDVI group as the reference group.58 Wang et al. reported that in joint exposure model of BC, sulfate, nitrate, ammonium, and organic matter the risk of T2D was lower but more precise (HR: 1.27, 95% CI: 1.09-1.49) than in single-exposure model of BC (3.80, 95% CI: 1.83-7.16). Further adjustment of the joint exposure model by NO2, PM10, and SO2 increased the risk to HR: 1.35 (95% CI: 1.15-1.60). Of the five constituents, BC had the greatest positive contribution (32.7%) to the mixing effect on the risk of T2D.81 Zhou et al. reported a higher risk in the joint exposure model of air pollutants: PM2.5 mass, NO2, O3, nitrate, ammonium, organic matter, BC, chloride, and sulfate (HR: 1.48, 95% CI: 1.26—1.73) compared to single-exposure model of BC (HR: 1.45, 95% CI: 1.18-1.78).60

 Noise exposure

The characteristics of the 20 studies that used noise as main exposure are shown in Table 2 Of those, 18 studied road traffic noise, 6 railway noise, and 5 aircraft noise exposure. Of the included studies, 16 were conducted in Europe, two in Canada, one in North America, and one in the Asian region. Regarding the study design, 13 studies used a longitudinal study design and seven cross-sectional design.

Characteristics of included noise exposure articles (n = 20). The table is organized in alphabetical ascending order of the first author.

Author, YearCountryStudyNAgeBaselineFollow-upOutcomeExposureUnits
Badpa et al., 2024111 Germany Cooperative Health Research in the Region of Augsburg (KORA-Study) 7736 49.2 1994-1995, 1999-2001 15.0 years Self-reported, Register Traffic per IQR 7.9 dB during night
Clark et al., 201752 Canada Population Data BC 380 738 58.00 1994 4.0 years Register Traffic Lden per 1 IQR (6.8dBa)
Dzhambov et al., 2016139 Bulgaria Cross-sectional study in Plovdiv city, Bulgaria 513 36.45 2014 – Self-reported Traffic Categories: Lden 71-80 dB with ref. category 51-70 dB
Dzhambov et al., 202569 Bulgaria Cross-sectional study in 5 Bulgarian cities 4640 49 2023 – Self-reported Traffic per 5 dB
Railway per 5 dB
Aircraft per 5 dB
Eriksson et al., 201489 Sweden Stockholm Diabetes Prevention Program 5156 47.00 1992 8.9 years Self-reported, Clinical Aircraft Categories: ≥ 50 versus < 50 dB(A)
Eze et al., 201768 Switzerland Swiss study on Air Pollution and Respiratory Diseases in Adults 2631 59.20 2002 8.3 years Self-reported Clinical Traffic, Lden per 1 IQR (10 dB)
Aircraft Lden per 1 IQR (12dB)
Railway Lden per 1 IQR (11dB)
Hu et al., 202351 UK UK Biobank 390 834 56.3 2006-2010 10.9 years Register Traffic per IQR 3.5
Jørgensen et al., 201988 Denmark The Danish Nurse Cohort 23 762a 54.00 1993 15.2 years Register Traffic Lden per 10 dB increase
Klompmaker et al., 201972 Netherlands Dutch Public Health Monitor 354 827 Cat. 2012 – Self-reported Traffic Lden Per 5 dB
Letellier et al., 202382 USA The Community of Mine Study

573

316

 		58.7 2014-2017 – Clinical Aircraft Static and dynamic exposure; ≥45 dB(A), ≥ median (0.10) and as continuous exposure
Traffic Static; ≥53 dB(A) census tract level, ≥55 dB(A) buffer around home, continuous, dynamic; ≥ median (0.13) and continuous exposure
Ohanyan et al., 202236 Netherlands UK Biobank 14 410 50.70 2011-2012 – Self-reported Traffic Categories: Lden>55 dB versus <55dB
Roswal et al., 201883 Denmark Danish Diet, Cancer and Health Cohort 50 534 56.00 1993 15.5 years Register Traffic Lden per 10 dB
Railway Lden per 10 dB
Shin et al., 202084 Canada Ontario Population Health and Environment Cohort 914 607 55.30 2001 15.0 years Register Traffic per 1 IQR (10 dB)
Sørensen et al., 2013189 Denmark Danish Diet, Cancer and Health Cohort  50 187 56.10 1993 9.6 years Register Traffic per 10 dB
Sørensen et al., 202253 Denmark Danish National Register data 1 922 545 57.5 2005 11.2 years Register Traffic per IQR: 10.6 dB in most exposed facade and per IQR 9.5dB least exposed facade
Sørensen et al., 202385 Denmark Danish National Health Survey 286 151 55.20 2010 6.2 years Register Traffic per 10dB increasea
Railway per 10 dB increasea
aLeast and most exposed facades
Thacher et al., 202186 Denmark Danish National Register data 3 563 991 50.75 2000 12.9 years Register Traffic per 10 dB increasea
Railway per 10 dB increasea
Aircraft aGrouping: Lden max and Lden min
Cat. (5): <45, 45-49 50-54, 55-59, ≥60
Vincens et al., 2022190 Sweden Swedish register data  5381 Cat. 2017 – Register Railway per 10 dB
Yu et al., 2024191 China Data from 480 community residents in China 480 54 2017-2018 – Clinical Traffic Q1 (<51.5 dB), Q2 (51.5-<53.9 dB), Q3 (53.9-<58.0 dB), Q4 (≥58.0 dB) and as continuous exposure
Zuo et al., 202287 UK UK Biobank 305 969 57.10 2006-2010 11.9 years Register Traffic per 10 dB

Study population only women.

 Traffic noise

Traffic noise was used as the main exposure in 18 studies, of which 11 were included in the meta-analysis. We found a 6% increase (OR: 1.06, 95% CI: 1.03-1.08; I2 = 92.8%) in the risk of T2D associated with a 10 dB increase in exposure to traffic noise (Figure 7). The funnel plot analysis (Figure S8) and Egger’s test (P-value > 0.001) indicated the potential presence of publication bias. The Trim-and-Fill method did not impute additional studies. Due to the similarity of study characteristics, we did not perform any further subgroup analysis.

 Traffic noise and joint exposure

Of these 18 studies, 13 assessed the joint exposure of the selected environmental exposures.51-53,68,69,72,82-88 Nine studies accounted for air pollution, green space, walkability, railway- or aircraft noise as potential confounders.52,68,69,82-87 In five of these, the risk estimates remained or slightly attenuated after adjusting for one or more co-environmental exposures (risk differences between single- and joint exposure models between 0.00 and 0.02).52,83,84,86,87 Dzhambov et al. did not find an association between traffic noise and T2D in either single- or joint exposure models.69 Letellier et al. did not report separately the results from single-exposure model, but in their two-exposure model adjusted for NO2, they did not find a significant association between traffic noise and the risk of T2D (OR: 1.02, 95% CI: 0.84-1.24).82 Eze et al. showed the most pronounced difference between single- and multi-exposure models. Specifically, adjusting the single-exposure model for green space, NO2, aircraft- and railway noise increased the RR of T2D from 1.17 (95% CI: 0.88-1.53) to RR: 1.35 (95% CI: 1.02-1.78).68 Sørensen et al. reported that the association observed in the single-exposure models of traffic noise exposure (least- and most-exposed facades) weakened after adjusting for railway noise and PM2.5. In modification analyses, the CIs were overlapping, but the association of traffic noise exposure with T2D seemed strongest among people living in suburban areas (population density of 101–2000 persons per km2).85

Three studies utilized a CRI method to assess the joint exposure of environmental variables.51,53,72 In all three, the risk from the cumulative index was higher than the risk estimate of the single-exposure models of traffic noise exposure. Klompmaker et al. also tested for the potential interaction effect of green space but did not find a significant interaction between NDVI and road traffic noise on the risk of T2D. 72 Three studies further tested for the potential effect modification of air pollution on the association between road traffic noise and T2D but didn’t find a difference in the associations.68,85,88

 Railway noise

The meta-analysis for railway noise exposure included all six extracted studies, all conducted in the European region. The results (Figure 8) did not show an association between exposure to railway noise and T2D (OR: 1.01, 95% CI: 0.95-1.06; I2 = 69.8%) with an indication of publication bias (Egger’s test P-value: <0.001). The Trim-and-Fill method imputed one additional study and increased the estimated overall effect estimate (observed plus imputed) to OR: 1.03, 95% CI: 0.96-1.09 (Figure S9). Due to the small number of studies and the similarity of study characteristics, we did not perform any further subgroup analysis.

Railway noise and joint exposure: Joint effects of railway noise and other environmental exposures were examined in four studies.68,83,85,86 Eze et al. found no evidence of association between railway noise and the risk of T2D either in single exposure model or joint-exposure model adjusting for green space, NO2, aircraft noise, and road traffic noise.68 Similarly, Roswall et al. observed no association in either single or two exposure models, the latter adjusted for road traffic noise. They further investigated the potential effect modification by road traffic noise exposure but found no interaction.83 Sørensen et al. reported that the association observed in the single-exposure model weakened and lost significance after adjusting for road traffic noise and PM2.5.85 Thacher et al. also found that the association between railway noise and the risk of T2D was slightly attenuated when further adjusting for PM2.5, green space, road-, and aircraft noise.86

 Aircraft noise

Five studies assessed aircraft noise exposure and the risk of T2D.68,69,83,86,89 Only one of the studies found a significant association between aircraft noise and T2D, where independent of the other transportation noise sources and NO2, the risk of incident diabetes was RR: 1.92, 95% CI: 1.04-3.55.68 Meta-analysis was not performed due to the low number of studies that could be standardized for the analysis.

 Aircraft noise and joint exposure

Joint exposures were examined in three of the five studies. Eze et al. observed an increase in the multiexposure model adjusting for NO2, road- and railway noise exposure (from RR: 1.92, 95% CI: 1.04-3.55 to RR: 1.96, 95% CI: 1.00-3.81). However, further adjustment for green space attenuated the results to non-significant (RR: 1.87, 95% CI: 0.96-3.62).68 In the study by Thacher et al. further adjustment for green space, PM2.5, road traffic- and railway noise slightly increased the risk of T2D, but being significant only in the category of 50–54 decibels (HR: 1.04, 95% CI: 1.01-1.07), under 45 decibels was used as the reference category.86 Letellier et al. did not report results from the single-exposure model, but in their two-exposure model adjusted for NO2, they did not find an association between aircraft noise exposure and the risk of T2D (OR: 1.58, 95% CI: 0.85-2.93).82

 Built environment

The characteristics and main results of the 39 studies exploring the built environment (green space, walkability, and population density) are presented in Table 3 Due to the high variation in exposure assessment methods, we provide here a narrative synthesis of the results without performing meta-analyses for the association of the built environment exposures with T2D.

Characteristics of included built environment articles (n = 38). The table is organized in alphabetical ascending order of the first author.

Author, YearCountryStudyNAgeBaselineFollow-upExposureConfoundersResults
Albers et al., 2024110 Netherlands The Maastricht Study 6695 60.0 2010-2020 6.2 years Walkability Age, sex Walkability index 0-100 (1650 m radius) per IQR , 52.23-22.87 HR: 1.23, 95% CI: 0.95-1.58 for incident diabetes.
Anza-Ramirez et al., 2022102 Latin America SALURBAL project (Salud Urbana en America Latina/Urban Health in Latin America) 122 211 42.6 2002-2013 – Green space Age, sex, education, population educational attainment at sub-city level % of urban area, country, sub-city intersection density and population density, city isolation- and fragmentation Per 1 SD (0.2) of sub-city greenness (median NDVI) OR: 0.98, 95% CI: 0.94; 1.02.
– Population density Age, sex, education, population educational attainment at sub-city level % of urban area, country, sub-city intersection density, greenness, city isolation- and fragmentation Per 1 SD of sub-city population density (4.876/km2) OR: 0.96, 95% CI: 0.92-1.00.
Astell-Burt et al., 2014101 Australia 45 and Up Study 267 072 cat 2006-2009 – Green space Age, sex, couple status, ancestry, country of birth, language spoken at home, weight, risk of psychological distress, smoking, hypertension, diet, active lifestyle, employment status, annual income, education

Green space categories (4) in 21-40% OR: 0.99, 95% CI: 0.96-1.03.

41-60%: OR 0.90; 95% CI 0.85-0.96. 61-80%: OR: 91, 95% CI: 0.84-0.99. 81%+: OR: 0.94, 95% CI: 0.85-1.03.
Badpa et al., 2024111 Germany KORA-study: Cooperative Health Research in the Region Augsburg 7736 49.2 KORA S3: 1994-1995 KORA S4: 1999-2001 until 2016 Green space Age, sex, sub-cohort indicator, BMI, smoking status, alcohol consumption, education level, physical activity, and dietary score. Green space (NDVI 1000m buffer) per IQR 0.14 HR: 0.98, 95% CI: 0.88-1.09.
Bodicoat et al., 201491 UK ADDITION-Leicester, Let’s Prevent Diabetes, Walking Away from Diabetes 10 476 59 2004-2011 – Green space Age, sex, urban/rural location, area level social deprivation Green space (3km buffer) quartiles (4) highest vs lowest quartile OR: 0.53, 95% CI: 0.35-0.82.
Booth et al., 2019117 Canada National register data, Canada 958 567 48.5 2002 9.2 years Walkability Age, CVD, hypertension, SES, Ethnicity, immigration status, city/town of residency Walkability per categories, low versus highest category HR: 0.85, 95% CI: 0.78-0.93.
Booth et al., 2013113 Canada National register data, Canada 1,239,262 45 2005 5.0 years Walkability Age, sex, income (area level poverty used as a surrogate) Most walkable quintile (5) versus least walkable quintile (1) RR: 1.32, 95% CI: 1.26-1.38 for men and RR: 1.24, 95% CI: 1.18-1.31 for women.
Clark et al., 201752 Canada Population Data British Columbia 380 738 58.0 1994 4.0 years Green space Age, gender, and area-level household income NDVI 100m buffer per IQR 0.12, OR: 0.90, 95% CI: 0.87-0.92.
Walkability Age, gender, and area-level household income Neighborhood walkability index per IQR 4.3, OR: 1.01, 95% CI: 0.98-1.04.
Dalton et al., 2016105 UK European Prospective Investigation of Cancer Norfolk 23 865 59.1 1993-1997 until 2007 Green space Sex, age, BMI, parental DM, SES Greenspace 800m buffer per quantiles (4) ref 1. least green versus 4 most green HR: 0.81, 95% CI: 0.65-0.99.
Dendup et al., 201990 Australia 45 and Up Study 46 786 cat 2006-2009 – Green space Age, sex, household income, education, economic status, couple status Per 1% increase in total green space OR: 0.993, 95% CI: 0.988 to 0.998.
43 137 cat 2006-2009 2012-2015 Green space See above Per 1% increase in total green space OR: 0.99, 95% CI: 0.99-1.00.
Doubleday et al., 2022106 US MESA: Multi-Ethnic Study of Atherosclerosis 5574 – 2000 15.8 years Green space Age, sex, ethnicity, education, income, employment status, neighborhood deprivation, neighborhood social cohesion-, walkability- and safety, urbanicity, site, family history of DM, BMI, physical activity, chronic stress, smoking, drinking Green space per IQR (55) increase annual median NDVI with 1 km buffer HR: 0.79, 95% CI: 0.63-0.99.
Dzhambov et al., 202569 Bulgaria Cross-sectional study in 5 Bulgarian cities 4640 49.0 2023 – Green space Age, sex, ethnicity, education, income adequacy, employment, city, and urbanicity NDVI (300m buffer) per 0.1 increase; categories (4) 1:ref. 0.19-0.32: 1.00 2: 0.33-0.37 OR: 0.89, 95% CI: 0.64-1.26 3: 0.38-0.43 OR: 1.32, 95% CI:0.95, 1.85 4: 0.42-0.75 OR: 0.84, 95% CI: 0.59, 1.20.
4369 Walkability Age, sex, ethnicity, education, income adequacy, employment, city, and urbanicity Walkability (300m buffer) OR: 1.04, 95% CI: 0.87-1.23.
Fan et al., 201992 China Cross-sectional study in Kashgar city, China 4670  47.2 2016 – Green space Age, sex, education, marital status, physical activity Green space (NDVI 1 km buffer) per one IQR (0.6) increase: OR: 0.92, 95% CI: 0.86, −0.99.
Frank et al., 2022115 Canada My Health My Community 22 418 45.58 2013-2014 – Walkability Age, gender, income. ethnicity, regional accessibility, years in the neighborhood Walkability per quintiles (5) ref. car dependent (Q1) versus walkable category (Q5) OR: 0.62, 95% CI: 0.45-0.85.
Glazier et al., 2014116 Canada Canada census survey, national health survey, and diabetes database 2 446 029 Between 30-64 2003-2009 – Walkability Age, sex Walkability (800m buffer) per quintiles (5) Q1:Q5 RR: 1.33, 95% CI: 1.33-1.33.
Howell et al., 201974 Canada Cardiovascular Health in Ambulatory Care Research Team CANHEART-Study 2,496,458 53 – Walkability Age, sex, ethnicity, immigration history, neighborhood COPD, comorbidity burden Walkability per quintiles (5) lowest vs. highest walkability OR: 1.25, 95% CI: 1.22.
Hu et al., 202398 China Chinese Longitudinal Healthy Longevity Survey 3924 84.6 2017-2018 – Green space marital status, education level, household income level, smoking status, and drinking status. NDVI (500m buffer) per quartiles (4) Q1: ≤0.14, Q2: >0.14-0.17), Q3: >0.17-0.21, Q4: >0.21. OR 0.55, 95% CI: 0.43-0.71.
Hua et al., 2024121 US The New York University Women’s Health Study 11 307a 50.4 1985-1991 25.6 years Walkability Age, race, education level, smoking, alcohol use and parity. Model 3 further included neighborhood poverty level, moving Walkability as the neighborhood walkability score (residential density, destination accessibility, street connectivity, and rail transit density) per SD 0.9 HR: 0.89, 95% CI 0.86-0.92.
Ihlebæk et al., 2018103 Norway Oslo Health Study (HUBRO) 8638 Cat. 1. 29-39; 2. 40-59; 3. 60 2000-2001 – Green space Age, ethnicity, education, civil status, smoking, PA, occupation, negative life events, social support, stability in neighborhood, income, % living in owned house, education Green space per quintiles (5) least (1) versus highest quintile (5) for women OR: 1.91, 95% CI: 0.49-7.43 for men OR: 0.96, 95% CI: 0.27-3.44.
Jian et al., 202499 China Population-based survey: members of Third Division of the Xinjiang Production and Construction Corps 9723 18y and older: 7,718 ≤ 50y (79.4%) & 2,005 >50y (20.6%) 2016 – Green space Age, sex, education level, marital status NDVI (500m buffer) per IQR (value na) OR: 0.85, 95% CI: 0.74-0.97.
Kartschmit et al., 2020118 Germany Heinz Nixdorf Recall Study, Dortmund Health Study, KORA, CARLA, Study of Health in Pomerania 16,008 58.7 1997-2006 – Walkability Sex, age, education, cohort, BMI  Walkability (impedance) per 1 SD (291.3) RR: 1.05, 95% CI: 0.99-1.11.
12 105 55.95 1997-2006 9.2 years Walkability Sex, age, education, cohort, BMI Walkability (impedance) per 1 SD (286.8) RR: 1.01, 95% CI: 0.95-1.08.
Khan et al., 202193 Bangladesh Bangladesh Demographic and Health Survey 2367.00 49.3 2011 – Green space Age, sex, education, working status, marital status Per 1 SD increase in green space exposure (EVI) OR: 0.81, 95% CI: 0.69-0.94.
Klompmaker et al., 201972 Netherlands Dutch Public Health Monitor 2012 354 827 NA 2012 – Green space Age, sex, marital status, region of origin, education, work, income, smoking, alcohol consumption, PA, BMI, neighborhood SES Green space (NDVI 300 m buffer) per IQR 0.13 OR: 0.91, 95% CI: 0.89-0.93.
Li et al., 202194 China Henan Rural Cohort Study 39 019 55.58 2015-2017 – Green space Age, sex, BMI, income, PA, education, marital status, smoking, drinking, diet, family history of DM  Green space (NDVI 500 m buffer) per IQR OR: 0.87, 95% CI: 0.83-0.90.
Makhlouf et al., 2023119 US Population Level Analysis and Community Estimates Data US, American Community Survey Data 315 221 353 NI 2021 – Walkability Age, sex, race, social vulnerability index Walkability across quartiles Q1 (least walkable) through Q4 (most walkable) multivariable linear regression model: β = −0.243 (0.002) p-value=<0.001.
Makramet al., 2025100 US Houston Methodist Learning Health System Outpatient Registry 1 077 181 52.0 2016-2022 – Green space Age, sex, race/ethnicity, area deprivation index, WalkScore Green space per NatureScore™ (0-100) Nature Adequate OR: 1.00 (0.98-1.02), nature Rich OR: 0.98 (0.96-1.00), Nature Utopia 0.92 (0.90-0.94).
Müller et al., 201895 Germany Dortmund Health Study 1312 52.6 2003-2004 – Green space Age, sex, education, income, living with/without a partner, migration background, unemployment rate Proportion of green space Ref T1 (5.13-23.16) to T2 (23.37-30.95) OR:1.89, 95% CI: 1.07-3.33.
Müller-Riemenschneider et al., 2013112 Australia Western Australian Health and Wellbeing Surveillance System 5970 cat 2003-2006 – Walkability Age, sex education, marital status, household income, diet, PA, sedentary behavior Walkability per less walkable neighborhood (ref.) versus high walkable neighborhoods OR: 1.08, 95% CI: 0.72-1.62.
Niedermayer et al., 202438 Germany KORA-FIT (Part of the Cooperative Health Research in the Region of Augsburg, KORA) 3034 63.2 2018-2019 – Green space Age, sex, alcohol, smoking, physical activity, education Green space (NDVI 500 m buffer) per IQR: 0.1, OR: 0.90, 95% CI: 0.74-1.09.
Ohanyan et al., 202236 Netherlands AMIGO: Population-based Occupational and Environmental Health Cohort Study 14 829 50.7 2011-2012 – Green space Age, sex, duration of living at the current address, participants-, mother’s, and father’s country of birth, civil state, education, employment, smoking Green space (NDVI 100 m buffer) B-estimate: 0.0663 SE:0.0429.
Plans et al., 2022104 Spain Heart Healthy Hoods cohort study  1625 56 2017 – Green space Age, sex, migration status, SES, population density Green space density (500m buffer) per quantiles (4) ref. Q1 high versus Q4 low OR: 1.44, 95% CI: 0.82-2.52.
Sun et al., 202558 China Prospective cohort study in Tianjin, China 65 824 64.64 2014 until 2021 Green space Age, gender, BMI, exercise frequency, smoking status, and alcohol frequency NDVI (500m buffer) per SD 0.045 HR: 0.90, 95% CI: 0.88-0.92.
Sundquist et al., 2015120 Sweden National register and healthcare data, Sweden 512,061 49.0 2006 2007-2010 Walkability Age, sex, household income, education Walkability per 10 quintiles ref. Q10 (5.27) to Q1 (−3.44) OR: 1.16, 95% CI: 1.00-1.34.
Sørensen et al., 202253 Denmark National Register Data 1 922 545 57.5 2005 11.2 years Green space Age, sex, calendar-year, civil status, individual and family income, country of origin, occupational status, education, neighborhood-level % of population with: low income, only basic education, unemployed, manual labor, non-Western background, criminal record, sole-providers, live in social housing.

NonGreen150m: % of areas within 150 m not classified as agricultural areas, household gardens, recreational areas, forests, open nature areas, HR: 1.05, 95% CI: 1.04-1.05.

NonGreen1000m: % of areas with 1000 m buffer that are not publicly accessible green areas, HR: 1.03, 95% CI: 1.02-1.04.
Tsai et al., 2020107 Taiwan National Health Insurance Research Database 429 504 42.0 2001 11.0 years Green space Age, sex, SES, insurance amount, occupational type, comorbidities Green space per IQR (0.11 465) OR: 0.80, 95% CI: 0.71-0.90.
Yang et al., 201997 China The 33 Communities Chinese Health Study 15 477 45.0 2009 – Green space Age, sex, ethnicity, education, family income Green space (NDVI 500 m buffer) per 0.1-unit increase OR: 0.88, 95% CI: 0.82-0.94.
Yang et al., 2023108 UK UK Biobank 379 238 56.4 2006-2020 12.4 years Green space Age, sex, ethnicity, assessment center, deprivation, education, economic status, smoking, alcohol intake, diet, sedentary time, family history of DM Green space (300m buffer) per 10 unit increase in the percentage of green space HR: 0.98, 95% CI: 0.98-0.99.
Yu et al., 2022109 China Yinzhou cohort 22 535 61.47 2015-2018 3.8 years  Green space Age, sex, marital status, education, income, BMI, smoking, alcohol consumption, PDI, PA, history of hypertension- and dyslipidemia, PM2.5 Green space (NDVI 250 m buffer) HR: 0.56, 95% CI: 0.51-0.61.
Yu et al., 202396 China  Fujian Behavior and Disease Surveillance Cohort 50 593 53.8 2018 – Green space Age, sex, marital status, education, occupation, smoking, drinking status, sleep quality, diet, temperature, humidity Green space (NDVI 500m buffer) per 0.1-unit increase OR: 0.81, 95% CI: 0.79-0.83.
Zhang et al., 202461 China China Health and Retirement Longitudinal Study (CHARLS) 9242 59.0 2011-2012 Follow-ups 2013, 2015, 2018.  Green space Age, gender, education level, marriage, residence, region, cash at home, smoking status, drinking status, BMI, sleep duration, social activity, health status in youth, hypertension and dyslipidemia NDVI high-level (≥ 0.2726) compared with low-level group (< 0.2726) HR: 0.80, 95% CI: 0.69-0.93.

PA: physical activity, DM: diabetes mellitus, SES: socioeconomic status. cat: categories.

Only women in the study population.

 Green space

A total of 30 studies used green space as the main exposure. The studies were conducted in various geographic locations utilizing different assessment methods of exposure to green spaces. The most commonly used measure of green space was NDVI, which indicates the amount of green vegetation in the environment. NDVI was measured with buffers varying from 100 meters to 3 kilometers. Out of 19 cross-sectional studies, 13 reported an inverse association between exposure to green space and T2D36,72,90-100 and five did not find a significant association.38,69,101-103 Plans et al. found a significant association for women only, while the OR for a model including both women and men was 1.44 CI: 0.82-2.52 in high (quartile 1) versus low (quartile 4) green space density.104

Eight studies with a longitudinal study design reported a lower risk of T2D with higher greenness.52,58,61,105-109 Yu et al. showed the strongest association, where IQR increase in the cumulative average of NDVI in the 250-meter buffer was associated with a 44% (HR: 0.56, CI: 0.51, 0.61) reduction in risk of T2D in China. Results remained similar with 500  and 1000 m buffers. Furthermore, living in the highest quartile of cumulative average NDVI within a 250 m buffer was associated with a 57% (HR = 0.43, 95% CI: 0.36, 0.52) reduction in diabetes risk compared with the lowest quartile.109 Dendup et al. studied prevalent and incident cases of diabetes separately and found an association only in categories ≥30% compared with 0%-4% total green space (OR: 0.70, CI: 0.51-0.96) in Australia.90

Albers et al. examined the standardized proportion of greenspace within a 1650 m radius in the Netherlands and found non-significant trends towards decreased risk for both prevalent and incident T2D.110 Badpa et al. studied the association between NDVI and T2D in Germany. Their result with a wider buffer size (1000 m per IQR 0.14) did not reach significance but was in the expected direction, showing inverse association with the risk of T2D: HR: 0.98, 95% CI: 0.88- 1.09. Using the smaller buffer size (300 m per IQR 0.12), the risk changed in an unexpected direction, but remained statistically non-significant (HR: 1.04, 95% CI: 0.94-1.14).111 Sørensen et al. explored the association of green space and T2D using variables for a non-green living environment. NonGreen150m, which measured the percentage of areas within 150 meters of the residential address, not classified as agricultural areas, household gardens, recreational areas, forests and open nature areas, was associated with higher risk of T2D (HR: 1.05, 95% CI: 1.04-1.05. NonGreen1000m measured the percentage of areas within 1000 meters of the residential address that are not publicly accessible green areas (ie, not classified as recreational areas, forests and open nature areas) and was also associated with a higher risk of T2D (HR 1.03, 95% CI: 1.02-1.04).53

 Green space and joint exposure

The joint environmental exposure was considered in nine articles.36,52,53,58,61,72,98,102,108 The protective association of residential greenness remained in the study conducted by Clark et al. where the strongest association was found in a model further adjusting for traffic noise, PM2.5, and walkability (OR: 0.89, 95% CI: 0.86-0.92).52 For Anza-Ramirez et al. further adjustment for all built environment exposures (sub-city intersection- and population density, city isolation- and fragmentation) did not change the result for green space exposure (NDVI) and the risk of T2D considerably (from OR: 0.97, 95% CI: 0.93-1.01 to OR: 0.98, 95% CI: 0.94-1.02).102

The mediating role of air pollution was assessed in three studies.72,98,108 Hu et al. reported that the estimated associations between green space (NDVI) and diabetes were mediated by PM2.5 (5.0%, 95% CI: 0.6%-12%), NO2 (41.0%, 95% CI: 6.4%- 76.0%), and O3 (10.7%, 95% CI: 3.7%-23.0%).98 Klompmaker et al. reported that the proportion mediated by NO2 depended on the buffer size of green space; NDVI with a 300 m buffer proportion mediated by NO2 was 0.20 (95% CI: 0.08-0.33) and with a larger buffer, 1000 m: 0.34, 95% CI: 0.15-0.53.72 Yang et al. found evidence of the mediating role of PM2.5 in the estimated effect between green space and T2D, with a mediation proportion of 37.0%. When exploring the modification effect, they found no evidence for PM2.5, but for NO2 they observed a protective effect of green space in low levels of NO2 (HR: 0.97, 95% CI: 0.96-0.99) but not in higher quartiles (P-value for interaction: 0.098). Sun et al.58 categorised air pollutants (PM2.5, BC, OM, ammonium salt, nitrate, sulfate, and chloride) and NDVI into high and low groups based on their medians. Using low pollutant concentration and high NDVI as the reference, all the other combinations showed a higher risk of diabetes, indicating that green space can offer a degree of protective effect when exposed to pollutants and NDVI concurrently.108

Two studies utilized a CRI method to assess the joint exposure of environmental variables.53,72 Sorensen et al. used two green space variables (NonGreen 150 m buffer and NonGreen 1000 m buffer) that were associated with the risk of T2D in single- and two-pollutant models (adjusting for both NonGreen exposures). In a multi-pollutant model including ultrafine particles, NO2, road traffic, NonGreen150m and NonGreen1000m, the risk was slightly attenuated but remained associated with T2D (NonGreen150m HR: 1.04, 95% CI: 1.03-1.04) and NonGreen1000m HR: 1.02, 95% CI: 1.01-1.03). To quantify the cumulative burden of these environmental exposures the CRI method was used, increasing the risk of T2D to HR: 1.12 (95% CI: 1.11-1.13).53 Similarly, in the study by Klompmaker et al. exposure to green space (NDVI 300 m) was associated with T2D in both single- and two-pollutant models (adjusting for traffic noise). When using the CRI method for the joint exposure of NO2, NDVI, traffic noise, and oxidative potential metric with dithiothreitol assay, the combined exposure was larger than in the single exposure models.72 Ohanyan et al. utilized a multi-exposure Random Forest analysis (RF) where green space (NDVI 1 km) reached statistical significance, whereas it was not significantly associated with T2D in the single-exposure model for either NDVI 100 m or NDVI 1 km.36

 Walkability

Altogether 13 studies explored walkability and its association with T2D.52,69,110,112-121 Of the longitudinal studies, two from Canada found that high walkability was associated with lower T2D incidence among the Canadian adult population.113,114 Hua et al. studied walkability and risk of T2D in the New York University Women’s Health Study and found that women living in the most walkable neighborhood had 25%-33% reduced risk of diabetes.121 A study conducted in the Netherlands by Albers et al. used a walkability index (0-100 per IQR 52.23-22.87) with both prevalent and incident T2D and reported evidence for an inverse association only with the prevalent cases of T2D.110 Kartschmit et al.118 and Clark et al.52 did not find a significant association with walkability and T2D in German or Canadian adult populations. Four cross-sectional studies reported an inverse association between high walkability and T2D.74,113,115,119 Sundquist et al.120 and Müller-Riemenschneider et al.112 also observed an inverse association between high walkability and T2D in the crude models, but adjusting for individual-level factors diminished the association. Dzhambov et al. didn’t find an association between walkability and T2D in their study in five Bulgarian cities (OR: 1.04, 95% CI: 0.87-1.23).69

 Walkability and joint exposure

Of these 13 studies, three considered joint environmental exposure. For Dzhambov et al., similar to their single-exposure model, there was no association between walkability and risk of T2D when adjusted for environmental co-exposures (OR: 1.12, 95% CI: 0.91-1.38).69 The protective association of higher walkability and T2D became significant in the study conducted by Clark et al. when further adjusting for traffic noise, PM2.5, and greenness (from OR: 1.01, 95% CI: 0.98-1.04 to OR: 0.95, 95% CI: 0.91-0.99).52 Howell et al. adjusted for NO2, which changed the risk of T2D in the lowest quintile of walkability (versus the highest) from OR: 1.16, 95% CI: 1.13-1.19 to OR: 1.25, 95% CI: 1.22-1.29. Further interaction analysis identified significant interaction effects between walkability and NO2, indicating that at low levels of NO2, the likelihood of diabetes was higher among those living in less walkable neighborhoods. When the levels of NO2 increased, the probability of diabetes rose in highly walkable neighborhoods and became comparable across all levels of walkability.74

 Population density

One study by Anza-Ramirez et al. studied the role of population density on the risk of T2D. They analysed data from 122 211 individuals from 10 Latin American countries. In a single exposure model (adjusted for age, sex, education, population educational attainment at sub-city level, percentage of urban area, and country as a fixed effect) sub-city population density showed no clear association with T2D, as the OR per 1 SD increase (4.876/km2) was near null (OR: 0.99, 95% CI: 0.94-1.03). When further adjusting for all built environment exposures (sub-city intersection density and greenness, city isolation- and fragmentation) the risk of T2D slightly attenuated to OR: 0.96, 95% CI: 0.92-1.00.102

 Heterogeneity analysis

To understand the reasons for high heterogeneity, subgroup analyses and univariate meta-regression analyses were used to explore the influence of specific study characteristics on observed effect estimates. The study characteristics considered were study design, geographic region, outcome measurement method, adjustment for relevant factors related to T2D, adjustment for other environmental risk factors, and risk of bias score. These additional analyses were not able to explain the high heterogeneity between studies (Figure S7 and Table S6).

 Discussion

This systematic review and meta-analysis included 151 studies related to the Urban Exposome of T2D. The research knowledge of these studies was synthesized with narrative syntheses per exposure group (air pollution, noise, and built environment) and, when feasible, meta-analysed with possible subgroup analyses to evaluate whether the associations varied by individual study characteristics.

The results of the meta-analyses suggested a positive association between air pollutants PM2.5, PM10, NO2, O3, BC, and T2D. Our results for PM2.5, PM10, and NO2 were similar to previous systematic reviews and meta-analyses.5,7,8 For instance, compared with the results of Yang et al. for air pollution and risk of T2D, our meta-analyses showed stronger associations. For PM2.5, they reported the HR of 1.10 (95% CI: 1.04-1.17) and for incident cases OR: 1.08 (95% CI: 1.04-1.12) compared to our result of OR 1.19 (95% CI: 1.16-1.22), which combines both the incident and prevalent cases. For PM10, they found HR of 1.11 (95% CI: 1.00-1.22) and OR: 1.10 (95% CI: 1.03-1.17), whereas our result was OR: 1.23 (95% CI: 1.13-1.34). For NO2 the HR was 1.01 (95% CI: 0.99-1.02) and OR: 1.07 (95% CI: 1.04-1.11) compared to ours OR: 1.13 (95% CI: 1.10-1.16). Similar to our results, they found a high between-study heterogeneity for the meta-analyses.5 The result for a positive association between ozone exposure and risk of T2D was in line with a recent systematic review and meta-analysis by Yu et al. which included fewer studies but had a similar effect size of 1.06 (95% CI: 1.02–1.11, n = 5) to ours OR: 1.05 (95% CI: 1.02-1.08, n = 20).9 We were not able to identify prior meta-analyses on BC and the risk of T2D. Therefore, our result on BC and risk of T2D (OR: 1.32, CI: 1.15-1.50, n = 8) brings new knowledge to this field of research. Of the air pollutants included in this review, BC showed the strongest association with T2D, while being the least studied air pollutant. PM2.5 was the most studied exposure, considered as the main exposure in 90 studies, while showing the highest risk of publication bias. The high number of imputed studies (n = 15) in Trim-and-Fill analysis and a decrease in the effect estimate (from OR: 1.19, 95% CI: 1.16-1.22 to OR: 1.14, 95% CI: 1.10-1.17) suggest that the publication bias might have led to an overestimation of the effect size in the observed studies for PM2.5. Utilizing air pollution scores with methods such as Weighted Quantile Sum (WQS) regression or Quantile g-computation (QGC) method can help to understand the cumulative burden of air pollution, providing more insights into the association with T2D instead of focusing on PM2.5 or other air pollutants alone.

Differences in the overall effect sizes of the observed associations per sub-groups were small but can still provide important information to highlight which factors might contribute to a higher risk of T2D. From meta-regression analyses, we found that a study region was a significant covariate for PM2.5 in both European and Asian regions, showing a higher risk of T2D compared to North America. Also, NO2 studies conducted in the Asian region showed a higher risk of T2D compared to studies conducted in North America. However, the study regions included in this review did not cover any African countries, and only two studies were from South America and four from Australia. Furthermore, only one of the studies assessing noise exposure was conducted in Asia. Between 2021 and 2045, the most significant relative increase in the prevalence of diabetes is expected to occur in middle-income countries (21.1%) compared to high- (12.2%) and low-income countries (11.9%).4 Together, these findings call for more research focused on low- and middle-income countries.

Results from the road traffic- and railway noise meta-analyses were similar to the systematic review and meta-analysis from 2018 by Sakhvidi et al. where traffic noise was positively associated with T2D, and no evidence for association was observed for railway noise exposure.12 More research is needed to assess the role of noise exposure, especially the role of aircraft noise, which had conflicting results in narrative synthesis. The qualitative synthesis of 38 studies on built environment exposures (green space, walkability, and population density) indicated that the living environment with higher walkability and greenness has an inverse association with T2D. We identified only one study that directly examined the relationship between population density and the risk of T2D. However, population density is frequently included in walkability indexes and was studied in that context in four of the included studies.74,113,116,117 Our narrative review was in line with previous reviews; Sharifi et al. reported from a meta-analysis that more access to green space was associated with lower odds of diabetes OR: 0.79 (95% CI: 0.67-0.90).13 Similarly, De la Fuente et al. reviewed studies on green space exposure between 2009 and 2020 and found evidence supporting the protective role of green spaces in the urban context against T2D and other chronic health conditions, such as obesity and sedentary behaviors.15

In this review, 55 studies assessed the joint exposure of environmental exposures (air pollution, noise, or built environment) on the risk of T2D. The noise exposure studies were more consistent in considering other environmental co-exposures than the built environment or air pollution studies, the latter often considering only the other air pollutants. The joint exposures were commonly considered as confounders, but in recent years, the use of more complex statistical methods has increased. Three studies51,53,72 utilized the Cumulative Risk Index (CRI) method to understand the cumulative burden of environmental exposures on the risk of T2D, and five studies57-60,81 used Quantile g-computation (QGC) method to assess the joint effect of mixtures of air pollutants. One study utilized the penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches to study the risk of T2D comprehensively.36 As a whole, the difference to single exposure models was modest but signaled slightly smaller effect sizes in joint exposure models. This could potentially lead to overestimation in effect sizes in single-pollutant models, especially when considering the co-exposures only as confounding factors.

Only one of the included studies utilized the exposome approach, indicating that it is not yet widely understood in environmental epidemiology to assess the risk of T2D.36 The exposome approach was developed to address more accurate and comprehensive environmental exposure data, including the selection, harmonization, description, and analysis of a large set of exposures, making it complex in many respects. This might explain its still scarce use in assessing the risks of T2D. Longitudinal studies that combine data from different sources (biological samples, physical examinations, questionnaires, national registers, and geospatial models) can provide adequate resources for successful exposome studies. In order to analyse various exposures simultaneously, statistical methods that consider the potential correlation or interaction between exposures are needed, such as the CRI and QGS methods. Within the exposome framework, advanced methods have been developed to study the individual and joint effects of multiple environmental exposures and have been reviewed extensively elsewhere.122-126

Strengths and limitations

This is the largest systematic review and meta-analysis to date to assess the relationship between T2D and several different exposures of the Urban Exposome while simultaneously mapping the use of the exposome approach. This work provides a comprehensive synthesis of evidence, and by pooling the results into various meta-analyses, we were able to provide precise effect estimates for various environmental exposures related to the risk of T2D. By combining different study designs, settings, and populations, we were able to have greater generalizability of findings.

Our review also has some limitations. We were able to include studies only in English, and therefore, possible studies that would have met the inclusion criteria from other languages are missing. Not all studies distinguished the type of diabetes, especially in many register-based studies there was no distinction between type 1 and type 2 diabetes. However, approximately 90% of diabetes diagnoses among adult populations are T2D, and therefore this is unlikely to affect the results substantially. In future studies, it is highly important to distinguish the types of diabetes to understand the specific risk factors of each type. We did not use a validated tool for ROB assessment, which could be a possible limitation for our study. We decided to use the self-developed tool due to the variety of study designs and settings included in this review. The developed tool gives an overview of the possible sources of bias, and we did not exclude any studies based on it, but utilized a ROB score to understand the differences between the studies and ROB domains.

In our review, we have utilized a broad scope of observational studies to gain an extensive understanding of the role of environmental exposures on the risk of T2D. This resulted in many study designs with different exposure measures, which can create a potential risk of bias. Publication bias affected the investigated associations, and considerable heterogeneity was present in most of the meta-analytic estimates, partly preventing us from drawing very firm conclusions. However, we did various sensitivity analyses to strengthen the generalizability of our findings and to assess whether individual study characteristics affected the overall estimates. While we recognize these challenges that prevent us from making conclusions that the results would be causal, this review can still provide a better understanding of the current state of research and the possible role of environmental exposures in the risk of T2D. Future studies should utilize the more comprehensive approaches to understand both the harmful and beneficial exposures in the urban exposome and not solely focus on specific exposures such as PM2.5. Translating research information to policymakers allows them to design policies on those conditions that can be modified. When successful, healthcare workers can implement new health interventions or programs to decrease the risks of adverse living environment.

 Conclusion

We conclude that exposure to air pollution (PM2.5, PM10, NO2, O3, and BC) and road traffic noise are associated with an increased risk of T2D. Furthermore, a greener and more walkable living environment can potentially reduce the risk of T2D. The knowledge of their joint effect and the mechanism of action in the population remains unclear. Future studies should consider joint exposures, as well as the standardization of the exposure and outcome assessment methods. The exposome approach was used only in one research article in the reviewed research. Advancing the use of the exposome approach and terminology can help in understanding the T2D risk comprehensively by enabling a holistic assessment of cumulative environmental exposures.

 Author contributions

Miia Halonen(Conceptualization [Equal], Data curation [Equal], Formal analysis [Lead], Methodology [Lead], Project administration [Lead], Validation [Equal], Visualization [Lead], Writing—original draft [Lead], Writing—review & editing [Lead]), Wnurinham Silva(Conceptualization [Equal], Data curation [Equal], Investigation [Equal], Writing—review & editing [Equal]), Susanna Pätsi(Methodology [Supporting], Validation [Equal], Writing—review & editing [Equal]), Jouko Miettunen(Methodology [Supporting], Validation [Equal], Writing—review & editing [Equal]), and Sylvain Sebert(Conceptualization [Equal], Funding acquisition [Lead], Supervision [Equal], Validation [Equal], Writing—review & editing [Equal]), Justiina Ronkainen(Conceptualization [Equal], Methodology [Supporting], Supervision [Equal], Validation [Equal], Writing—review & editing [Equal])

 Supplementary material

Supplementary material is available at Exposome online.

 Funding

This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 874739 (LongITools).

 Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

 Data availability

Data will be made available on reasonable request.

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