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

Miia Halonen; Wnurinham Silva; Susanna Pätsi; Jouko Miettunen; Sylvain Sebert; Justiina Ronkainen; Miia Halonen; Wnurinham Silva; Susanna Pätsi; Jouko Miettunen; Sylvain Sebert; Justiina Ronkainen

doi:10.1093/exposome/osaf016

Authors: Miia Halonen (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland) , Wnurinham Silva (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland) , Susanna Pätsi (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland) , Jouko Miettunen (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland) , Sylvain Sebert (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland) , Justiina Ronkainen (Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland)

Abstract

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.

Keywords: built environment, traffic noise, exposome, air pollution, type 2 diabetes, meta-analysis

How to Cite: Halonen, M. , Silva, W. , Pätsi, S. , Miettunen, J. , Sebert, S. & Ronkainen, J. (2025) “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”, Exposome. 5(1). doi: https://doi.org/10.1093/exposome/osaf016

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 PM_2.5 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 PM_2.5, 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 (PM_2.5) 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 (µ g / m 3) = molecular weight × concentration (ppb) ÷ 24.45

Nitrogen dioxide N O 2 = 46.01 g / mol × 1 ppb ÷ 24.45 = 1.88 µ g / m 3

Ozone O 3 = 48 g / mol × 1 ppb ÷ 24.45 = 1.96 µ g / m 3,

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.

O R standardized = O R original Increment (10) / Increment (original)

C I standardized = C I original Increment (10) /Increment(original)

S E standardized = (CI-uppe r standardized) − (CI-lowe r standardized) / 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; PM_2.5, 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 PM_2.5 (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.

Figure 1.

PRISMA flowchart of the selection of studies.

Air pollution

The relationship between exposure to air pollution and T2D was evaluated for PM_2.5, 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.

Table 1.

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

Author	Country	Study	N	Age	Baseline	Follow-up	Exposure	Units
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	PM_2.5	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	–	PM_2.5	per 10 μg/m³
Su et al., 2023¹²⁸	China	Urban and Rural Elderly Population study	222 179	69.73	2015	–	O₃	per IQR
Bo et al., 2021¹²⁹	Taiwan	MJ Health cohort study	146 789	38.82	2001-2014	5.0 years	PM_2.5	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	PM_2.5	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' Cohort^{^a}	13 669	43	2008	11.5 years	PM_2.5	per 10 μg/m³
Cervantes-Martínez et al., 2022⁵⁵	Mexico	The Mexican Teachers' Cohort^{^a}	13 669	43	2008	11.5 years	NO₂	per 10 ppb
Chen et al., 2013¹³²	Canada	Canadian Community Health Surveys	62 012	54.9	1996	8.32 years	PM_2.5	per 10 μg/m³
Chen et al., 2022¹³³	China	Wuhan Chronic Disease Cohort	10 253	–	2019	–	PM_2.5	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	PM_2.5	per IQR 3.16 μg/m³
Chilian-Herrera et al., 2021¹³⁴	Mexico	National Health and Nutrition Survey Mexico	2297	49.3	2012	–	PM_2.5	per 10 μg/m³
Clark et al., 2017⁵²	Canada	Population Data BC	380 738	58	1994-1998	4.0 years	PM_2.5	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	3992^{^a}	39.35	1995	10.0 years	PM_2.5	per 10 μg/m³
Coogan et al., 2016¹³⁶	US	Black Women’s Health Study	43 003^{^a}	38.7	1995	16.0 years	PM_2.5	per IQR 2.9 μg/m³
Coogan et al., 2016¹³⁶	US	Black Women’s Health Study	43 003^{^a}	38.7	1995	16.0 years	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	–	PM_2.5	per IQR 7.95 μg/m³
Cui et al., 2024⁵⁷	China		79 623	57.14	2017-2020	–	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	–	PM_2.5	per 1 μg/m³
Dzhambov et al., 2016¹³⁹	Bulgaria	Cross-sectional study in Plovdiv city, Bulgaria	513	36.45	2014	–	PM_2.5	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
							PM_2.5	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
Eze et al., 2014⁶⁴	Switzerland	Swiss Study on Air Pollution and Lung Disease in Adults	6392	52	2002	–	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	PM_2.5	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	PM_2.5	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	PM_2.5	per 10 µg/m³
Hansen et al., 2016⁴⁹	Denmark	Danish Nurse Cohort	24 174^{^a}	54	1993	15.3 years	PM₁₀	per 10 μg/ m³
							PM_2.5	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	PM_2.5	per IQR 1.96 μg/m³
Hegelund et al., 2024¹⁴⁵	Denmark	Danish nationwide sample	3 111 988	51.4	2000	15.2 years	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	–	PM_2.5	per 10 µg/m³
Hernandez et al., 2018¹⁴⁶	US		1 158 547	–	2002-2008	–	O₃	per 10 ppb
Honda et al., 2017⁴⁴	US	National Social Life, Health, and Aging Project	916	69.6	2005-2011	–	PM_2.5	per IQR 3.9 μg/m³
Honda et al., 2017⁴⁴	US	National Social Life, Health, and Aging Project	916	69.6	2005-2011	–	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	–	PM_2.5	per 10 μg/m³
Hu et al., 2024¹⁴⁷	China	Shanghai High-Risk Diabetic Screen Project	1128	51.13	2014-2018	–	PM_2.5	per 10 μg/m³
Hu et al., 2023⁵¹	UK	UK Biobank	390 834	56.3	2006-2010	10.9 years	PM_2.5	per IQR 1.3 μg/m³
Hu et al., 2023⁵¹	UK	UK Biobank	390 834	56.3	2006-2010	10.9 years	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
							PM_2.5	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	43 003^{^a}	–	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
							PM_2.5	per 1 μg/m³
							NO₂	per 1 μg/m³
Kang et al., 2023⁷⁹	China	Henan Rural Cohort Study	38 442	55.56	2015-2017	–	PM_2.5	per 1 μg/m³
Kang et al., 2023⁷⁹	China	Henan Rural Cohort Study	38 442	55.56	2015-2017	–	BC	per 1 μg/m³
Klompmaker et al., 2019⁷²	Netherlands	Dutch Public Health Monitor	354 827	Cat.	2012	–	PM_2.5	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	1775^{^a}	54.6	1985-1994	16.0 years	PM₁₀	Per IQR 10.1 μg/m³
Krämer et al., 2010¹⁵¹	Germany		1775^{^a}	54.6	1985-1994	16.0 years	NO₂	per IQR 24.9 μg/m³
Lao et al., 2019¹⁵²	Taiwan	Taiwan MJ Cohort	147 908	38.3	2001-2014	6.7 years	PM_2.5	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	–	PM_2.5	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
Li et al., 2019⁶⁵	China	Chronic Disease Surveillance System of Ningbo	25 130	65.17	2008-2015	–	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
							PM_2.5	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	PM_2.5	per 10μg/m³
Li et al., 2023⁸⁰	China	China Multi-Ethnic Cohort study	69 210	51.8	2018-2019	–	PM_2.5	per SD 20.5 μg/m³
Li et al., 2023⁸⁰	China	China Multi-Ethnic Cohort study	69 210	51.8	2018-2019	–	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	PM_2.5	per 1 μg/m³
Li et al., , 2021⁶³	UK	UK Biobank	449 006	56.46	2006-2010	11.0 years	PM_2.5	per SD increase
Li et al., , 2021⁶³	UK	UK Biobank	449 006	56.46	2006-2010	11.0 years	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
							PM_2.5	per 5µg/m³
							NO₂	per 10 µg/m³
Li et al., 2019¹⁵⁶	Taiwan	–	505 151	42.6	2001	12.0 years	PM_2.5	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	PM_2.5	per 10 μg/m³
Liu et al., 2023¹⁵⁸	China	China Health and Retirement Longitudinal Study (CHARLS)	19 121	57.88	2011	8.0 years	PM_2.5	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
							PM_2.5	per 10 µg/m³
							NO²	per 10 µg/m³
							PM₁₀	per 10 µg/m³
							PM_2.5	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	–	PM_2.5	per IQR 41.1 μg/m³
Liu et al., 2019¹⁶⁰	China	Henan Rural Cohort study	39 191	55.6	2015-2017	–	PM_2.5	per 1 µg/m³
Liu et al., 2019¹⁶⁰	China	Henan Rural Cohort study	39 191	55.6	2015-2017	–	NO₂	per 1 µg/m³
Liu et al., 2019⁴⁵	China	Guangdong Gut Microbiome Project dataset	6627	51.8	2015-2016	–	PM_2.5	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
							PM_2.5	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	PM_2.5	per 10 µg/m³
Mandal et al., 2023¹⁶³	India	Center for Cardiometabolic Risk Reduction in South Asia, Delhi Region	3675	44.6	2010-2012	4.84 years	PM_2.5	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	PM_2.5	per 5 µg/m³
Mei et al., 2023¹⁶⁵	China	Community-based study in China	4235	54.23	2018-2020	–	PM₁₀	per 10 μg/m³
							PM_2.5	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
							PM_2.5	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
							PM_2.5	per 10 μg·m³
							NO₂	per 10 μg·m³
							PM_2.5	per 10 µg/m³
							PM₁₀	per 10 µg/m³
							NO₂	per 10 µg/m³
							PM_2.5	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
							PM_2.5	Na
							NO₂	Na
Orioli et al., 2018⁴⁶	Italy	Italian National Institute of Statistics	376 157	63.5	1999-2013	–	PM₁₀	per 10 μg/m³
							PM_2.5	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	–	PM_2.5	per IQR 2.43 µg/m³
Sung Kyun et al., 2015¹⁶⁷	US	MESA: Multi-Ethnic Study of Atherosclerosis	5135	61.6	2000-2002	9.0 years	PM_2.5	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	PM_2.5	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
Puett et al., 2011⁴¹	US	Nurses' Health Study & Health Professionals Follow-Up Study	89 460	56	1989-2002	13.0 years	PM_2.5	per IQR 4 μg/m³
Qiu et al., 2018¹⁶⁸	Hong Kong	Chinese Elderly Health Services cohort	53 905	72.4	1998	9.8 years	PM_2.5	per IQR 3.2 μg/m³
Qiu et al., 2018¹⁶⁸	Hong Kong	Chinese Elderly Health Services cohort	61 447	72	1998	–	PM_2.5	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
							PM_2.5	per 5-μg/m³
							NO₂	per 10-μg/m³
							O₃	per 10-μg/m³
							PM₁₀	per 10 µg/m³
							PM_2.5	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	–	PM_2.5	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
Riant et al., 2018⁶⁶	France		2797	53	2011-2013	–	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	PM_2.5	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
Shan et al., 2020⁶⁷	China	China Northern 4 Cities Cohort Study	38 529	44.12	1998	12.0 years	NO₂	per 10 µg/m³
Shen et al., 2024⁴²	China	National Free Preconception Health Examination Project in China	20 076 032^{^a}	27.04	2010-2015	–	PM_2.5	per IQR 27 μg/m³
Shin et al., 2023¹⁷⁰	South Korea	Cardiovascular Disease Association Study	14 667	58.6	2005-2011	until 2016	PM_2.5	per 10 μg/m³
Shin et al., 2023¹⁷⁰	South Korea	Cardiovascular Disease Association Study	14 667	58.6	2005-2011	until 2016	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
Sommar et al., 2023¹⁷²	Sweden	The Västerbotten intervention programme	33 766	40	1985-2014	until 2015	PM_2.5	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
							PM_2.5	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	PM_2.5	per SD 15.03 μg/m³
Sun et al., 2025⁵⁸	China		65 824	64.64	2014	8 years	BC	per SD 0.464 μg/m³
Suryadhi et al., 2020¹⁷³	Indonesia	Indonesia Basic Health Research	64 7947	41.9	2013	–	PM_2.5	per 10 µg/m³
Sørensen et al., 2022⁵³	Denmark	Danish Register data	1 922 545	57.5	2005	11.2 years	PM_2.5	per IQR 1.85 µg/m³
Sørensen et al., 2022¹⁷⁴	Denmark	Danish National Health Survey	234 018	52	2010, 2013	until 2017	PM_2.5	per 5 μg/m³
Sørensen et al., 2022¹⁷⁴	Denmark	Danish National Health Survey	234 018	52	2010, 2013	until 2017	NO₂	per 10 μg/m³
Sørensen et al., 2023⁶²	Denmark	Danish Register data	1 843 597	58.9	2005	9.5 years	PM_2.5	per 5 μg/m³
Sørensen et al., 2023⁶²	Denmark	Danish Register data	1 843 597	58.9	2005	9.5 years	NO₂	per 10 μg/m³
Sørensen et al., 2022⁷³	Denmark	Danish Register data	2 631 488	51.7	2005	13.0 years	PM_2.5	per IQR 1.85 µg/m³
Sørensen et al., 2022⁷³	Denmark	Danish Register data	2 631 488	51.7	2005	13.0 years	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	–	PM_2.5	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	PM_2.5	per 37.08 μg/m³
Wang et al., 2024⁸¹	China	Jinchang Cohort	19 884	48.18	2011-2013	2.28 years	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
Weinmayr et al., 2015¹⁷⁷	Germany	Heinz Nixdorf Recall Study	3607	59.65	2000-2003	5.1 years	PM_2.5	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
							PM_2.5	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	PM_2.5	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	PM_2.5	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
							PM_2.5	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	–	PM_2.5	per 10 μg/m³
Ye et al., 2022¹⁸³	China	China Health and Retirement Longitudinal Study (CHARLS)	19 529	62.06	2018	–	PM_2.5	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	PM_2.5	per SD 1.07 μg/m³
Xu et al., 2023⁷⁰	UK	UK Biobank	82 548	55.49	2006-2011	13.76 years	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	PM_2.5	per 10 μg/m³
Zheng et al., 2024⁵⁹	UK	UK Biobank	162 579	55.7	2010	10.1 years	PM_2.5	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	–	PM_2.5	per IQR 27.4 µg/m³
Zhou et al., 2022¹⁸⁷	China	China Health and Retirement Longitudinal Study (CHARLS)	13 589	59.5	2011-2012	–	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	PM_2.5	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
							PM_2.5	per IQR 2.26 μg/m³
							NO₂	per IQR 6.90 μg/m³

Only women in the study population.

Figure 2.

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

Figure 3.

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

Figure 4.

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

Figure 5.

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

Figure 6.

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

Figure 7.

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

Figure 8.

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 PM_2.5 as the main exposure. In the meta-analysis, T2D was positively associated with PM_2.5 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 PM_2.5 and T2D, 26 evaluated joint exposure to air pollution, noise, or the built environment. Three studies found no association between PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5, PM₁₀, NO₂, sulphur dioxide (SO₂), nitrogen oxides (NO_x), and benzene (OR: 1.16, 95% CI: 1.10-1.22) compared to the single-exposure model of PM_2.5 (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 PM_2.5 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 PM_2.5 (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 (PM_2.5, BC, OM, ammonium, sulfate, and nitrate with OR: 1.06 (95% CI: 1.01-1.11) compared to the single-exposure model of PM_2.5 (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 PM_2.5 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 PM_2.5 (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 PM_2.5 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 PM_2.5 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 PM_2.5 exposure and low green space (HR: 1.13, 95% CI: 1.04–1.21). The low PM_2.5 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 PM_2.5 (HR: 1.05, 95% CI: 1.01, 1.10) and joint exposure of road traffic noise, PM_2.5, and NO₂ (HR: 1.06, 95% CI: 1.02-1.09).⁵¹ Li et al. utilized air pollution score (PM_2.5, PM_2.5–10, NO₂, and NO_x) and found similar associations in the single-exposure model of PM_2.5 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 (PM_2.5, 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 PM_2.5 (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 PM_2.5) 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, PM_2.5, 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 PM_2.5 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, NO_x, NO₂, SO₂, PM₁₀, and PM_2.5 (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 (PM_2.5 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 PM_2.5 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, PM_2.5, NO₂, NO_x).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 (PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 level compared to the low PM_2.5 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 PM_2.5, 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 PM_2.5 (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 (PM_2.5, 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: PM_2.5 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.

Table 2.

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

Author, Year	Country	Study	N	Age	Baseline	Follow-up	Outcome	Exposure	Units
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	23 762^{^a}	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
Letellier et al., 2023⁸²	USA	The Community of Mine Study	573 316	58.7	2014-2017	–	Clinical	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
Roswal et al., 2018⁸³	Denmark	Danish Diet, Cancer and Health Cohort	50 534	56.00	1993	15.5 years	Register	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 increase^{^a}
								Railway	per 10 dB increase^{^a}
								Railway	^{^a}Least 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 increase^{^a}
								Railway	per 10 dB increase^{^a}
								Aircraft	^{^a}Grouping: Lden max and Lden min
								Aircraft	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 PM_2.5. 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 PM_2.5.⁸⁵ Thacher et al. also found that the association between railway noise and the risk of T2D was slightly attenuated when further adjusting for PM_2.5, 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, PM_2.5, 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.

Table 3.

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

Author, Year	Country	Study	N	Age	Baseline	Follow-up	Exposure	Confounders	Results
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	11 307^{^a}	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, PM_2.5	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, PM_2.5, 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 PM_2.5 (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 PM_2.5 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 PM_2.5, 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 (PM_2.5, 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, PM_2.5, 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 PM_2.5, PM₁₀, NO₂, O₃, BC, and T2D. Our results for PM_2.5, 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 PM_2.5, 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. PM_2.5 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 PM_2.5 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 PM_2.5 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 studies^57-60^,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 PM_2.5. 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 (PM_2.5, 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.

Data availability

Data will be made available on reasonable request.

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