Impact of Green Space and Built Environment on Metabolic Syndrome: A Systematic Review with Meta-Analysis

Abstract

Metabolic Syndrome presents a significant public health challenge associated with an increased risk of noncommunicable diseases such as cardiovascular conditions. Evidence shows that green spaces and the built environment may influence metabolic syndrome. We conducted a systematic review and meta-analysis of observational studies published through August 30, 2023, examining the association of green space and built environment with metabolic syndrome. A quality assessment of the included studies was conducted using the Office of Health Assessment and Translation (OHAT) tool. The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) assessment was used to evaluate the overall quality of evidence. Our search retrieved 18 studies that met the inclusion criteria and were included in our review. Most were from China (n=5) and the U.S. (n=5), and most used a cross-sectional study design (n=8). Nine studies (50%) reported only green space exposures, seven (39%) reported only built environment exposures, and two (11%) reported both built environment and green space exposures. Studies reported diverse definitions of green space and the built environment, such as availability, accessibility, and quality, particularly around participants' homes. The outcomes focused on metabolic syndrome; however, studies applied different definitions of metabolic syndrome. Meta-analysis results showed that an increase in normalized difference vegetation index (NDVI) within a 500-m buffer was associated with a lower risk of metabolic syndrome (odds ratio [OR]=0.90, 95%CI=0.87−0.93, I 2 =22.3%, n=4). A substantial number of studies detected bias for exposure classification and residual confounding. Overall, the extant literature shows a 'limited' strength of evidence for green space protecting against metabolic syndrome and an 'inadequate' strength of evidence for the built environment associated with metabolic syndrome. Studies with more robust study designs, better controlled confounding factors, and stronger exposure measures are needed to understand better what types of green spaces and built environment features influence metabolic syndrome.

Full text

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Impact of Green Space and Built Environment on Metabolic Syndrome: A Systematic Review with
Meta-Analysis
Muhammad Mainuddin Patwary1,2* (†), Mohammad Javad Zare Sakhvidi3 (†), Sadia Ashraf2,
Payam Dadvand4,5,6, Matthew H. E. M. Browning7, Md Ashraful Alam8, Michelle L. Bell9, Peter
James10,11, Thomas Astell-Burt12
1 Environment and Sustainability Research Initiative, Khulna, Bangladesh
2 Environmental Science Discipline, Life Science School, Khulna University, Khulna, Bangladesh
3 Department of Occupational Health, School of Public Health, Yazd Shahid Sadoughi University of Medical
Sciences, Yazd, Iran
4 ISGlobal, Barcelona, Spain
5 Universitat Pompeu Fabra (UPF), Barcelona, Spain
6 CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
7 Department of Park, Recreation and Tourism Management, Clemson University, Clemson, SC USA
8 Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital,
Bunkyo-ku, Tokyo
9 Yale School of the Environment, Yale University, New Haven, CT, United States
10 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston,
MA, USA
11 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Harvard
University, Boston, MA, USA
12 School of Health and Society, University of Wollongong, New South Wales, Australia
(†) Joint First Author; *corresponding authors: raju.es111012@gmail.com
Keywords: Cardio-metabolic health; NCD; Greenspace; Built Environment; Environmental Exposure

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Abstract
Metabolic Syndrome presents a significant public health challenge associated with an increased risk of
noncommunicable diseases such as cardiovascular conditions. Evidence shows that green spaces and the
built environment may influence metabolic syndrome. We conducted a systematic review and meta-analysis
of observational studies published through August 30, 2023, examining the association of green space and
built environment with metabolic syndrome. A quality assessment of the included studies was conducted
using the Office of Health Assessment and Translation (OHAT) tool. The Grading of Recommendations,
Assessment, Development, and Evaluations (GRADE) assessment was used to evaluate the overall quality
of evidence. Our search retrieved 18 studies that met the inclusion criteria and were included in our review.
Most were from China (n=5) and the U.S. (n=5), and most used a cross-sectional study design (n=8). Nine
studies (50%) reported only green space exposures, seven (39%) reported only built environment exposures,
and two (11%) reported both built environment and green space exposures. Studies reported diverse
definitions of green space and the built environment, such as availability, accessibility, and quality,
particularly around participants’ homes. The outcomes focused on metabolic syndrome; however, studies
applied different definitions of metabolic syndrome. Meta-analysis results showed that an increase in
normalized difference vegetation index (NDVI) within a 500-m buffer was associated with a lower risk of
metabolic syndrome (odds ratio [OR]=0.90, 95%CI=0.87−0.93, I2=22.3%, n=4). A substantial number of
studies detected bias for exposure classification and residual confounding. Overall, the extant literature
shows a ‘limited’ strength of evidence for green space protecting against metabolic syndrome and an
'inadequate' strength of evidence for the built environment associated with metabolic syndrome. Studies
with more robust study designs, better controlled confounding factors, and stronger exposure measures are
needed to understand better what types of green spaces and built environment features influence metabolic
syndrome.
Graphical Abstract
Highlights
 First review and meta-analysis of green space, built environment and Metabolic Syndrome.
 Meta-analysis found NDVI was negatively associated with Metabolic Syndrome.
 Most built environment studies did not show significant associations with Metabolic Syndrome.
 Most studies probably had high risks of bias for exposure classification detection.
 Evidence is ‘limited’ and ‘inadequate’ for green space and built environment, respectively.

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1. Introduction
The global population is increasingly becoming urbanized, with more than half residing in urban areas by
2020, and this proportion is projected to rise to 68% by 2050 (Zhang et al., 2022; United Nations, 2018;
World Health Organization, 2021). In some regions, this urban transition has coincided with lower physical
activity, higher psychological stress, and unhealthy diet, contributing to a surge in noncommunicable
diseases (NCDs) (World Health Organization, 2013; World Health Organization, 2018; Zhang et al., 2022).
A substantial proportion of premature deaths can be attributed to NCDs, with a projected associated cost
exceeding 5,000 USD for each affected individual (Allen et al., 2017; Chew et al., 2023).
Metabolic syndrome is a cluster of conditions including high blood pressure, abnormal blood sugar levels,
lipid abnormalities, and abdominal obesity, all contributing to elevated risks of NCDs, in particular, type 2
diabetes, cardiovascular diseases (CVDs) and mortality (Day, 2007; O’Neill and O’Driscoll, 2015; Yang et
al., 2020). Metabolic syndrome has emerged as a global public health concern, with 20-30% of adults
affected by metabolic syndrome worldwide (Mohamed et al., 2023; Saklayen, 2018) and could be partly
attributed to the resulting environmental changes and exposures (Leal and Chaix, 2011).
Green space encompasses vegetation, parks and associated natural elements (Frumkin, 2013; Taylor and
Hochuli, 2017)). Increasing urbanization has led to a reduction in green spaces and natural elements that
may limit opportunities for people’s contact with nature (Connelly et al., 2020). Meanwhile, green space
exposure is increasingly understood to improve human health (Yang et al., 2021), including metabolic
syndrome (de Keijzer et al., 2019). Green space exposure may influence health (de Keijzer et al., 2019)
through various mechanisms, including opportunities for physical activity and social engagement (Gong et
al., 2014); reduced stress (Gong et al., 2016); and mitigated exposure to environmental hazards such as air
pollution (Dadvand et al., 2012; Diener and Mudu, 2021), heat (Doick et al., 2014), and noise (Markevych
et al., 2017). More specifically, studies have observed a protective association between residential
greenness and the risk of metabolic syndrome (Chen et al., 2023; de Keijzer et al., 2019; Gong et al., 2014;
Li et al., 2022).
The built environment encompasses human-made alterations to natural surroundings (e.g., residential
structures, roadways, and public spaces) (Lawrence and Low, 1990; Roof and Oleru, 2008; Zhang et al.,
2022). Previous studies have shown that the built environment can influence resident’s metabolic health by
influencing physical activity and sedentary behavior (Dengel et al., 2009; Edwardson et al., 2012; Frank et
al., 2007; Saelens et al., 2003). For example, a study in China reported that higher walkability (accessibility
to nearby amenities from residences or workplaces) was associated with a lower risk of metabolic syndrome
incidence (Zhu et al., 2023). Perceived local land use mix was also negatively associated with metabolic
syndrome (Baldock et al., 2012). Urban built environments, including pedestrian-friendly routes and public
open spaces, are potential influencers of residents’ lifestyle such as physical activity (e.g., walking, cycling)
(Barnett et al., 2017; Colom et al., 2019; Van Cauwenberg et al., 2018) and sedentary behavior (e.g., car
use, television watching) (Koohsari et al., 2015). Individuals having better access to recreational facilities
tend to be more active (Cohen et al., 2006; Dengel et al., 2009; Gordon-Larsen et al., 2006; Jago et al.,
2016), and prolonged sedentary time is associated with poor metabolic health (Carson et al., 2016).
Several previous systematic reviews have explored the connections between various aspects of the green
space and built environment and cardiometabolic health outcomes. These reviews have covered topics such
as the relationship between green space and cardiovascular mortality (Yuan et al., 2021), the impact of the
built environment on cardio-metabolic health (Chandrabose et al., 2019), and cardiovascular disease
outcomes (Malambo et al., 2016). We are unaware of a prior review that has systematically assessed
metabolic syndrome in association with green space and the built environment. We sought to fill this gap
by systematically and quantitatively assessing the association of green spaces and built environments with
metabolic syndrome risk. We believe this approach will contribute valuable insights to the ongoing
discourse surrounding the impact of green spaces and the built environment on cardiovascular health.

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2. Methods
2.1. Study protocol registration
This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) (Page et al., 2021). The review protocol was
preregistered on PROSPERO (CRD42023423940).
2.2. Study question
The research question addressed was as follows: “What are the associations of the green space and built
environment with metabolic syndrome?
2.3. Eligibility criteria
Eligibility criteria were formulated using the Population, Exposure, Comparator, Outcome, and Study
Design (PECOS) framework, a systematic approach that helped ensure the inclusion of relevant articles
while minimizing potential bias in the review process (Hu et al., 2021; Ricciardi et al., 2022; Zare Sakhvidi
et al., 2023). We included articles published by August 30, 2023, with full-texts available in English. The
PECOS criteria applied are detailed in Table S1 and summarized below:
Population: The review considered studies involving human populations. We excluded non-human
research and studies that evaluated the impact of green space through hypothetical scenarios.
Exposure: The review included studies with subjective (e.g., perceived distance) or objective exposure to
green space (e.g., normalized difference vegetation index [NDVI]) in outdoor spaces (e.g., urban green
spaces, parks, forests, and roadside trees). Studies assessing various aspects of the built environment, such
as land use, transportation, walkability, population density, and relevant features, were also included.
However, studies examining the impact of the food environment were excluded.
Comparator: The review considered studies that examined the risk or odds of metabolic syndrome across
varying levels of exposure to the built environment or green space.
Outcome: The included studies focused on metabolic syndrome, adhering to established definitions. For
instance, the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III)
delineated specific criteria for diagnosing metabolic syndrome: meeting three or more of the following
conditions—waist circumference exceeding 40 inches (men) or 35 inches (women), blood pressure
surpassing 130/85 mmHg, fasting triglyceride (TG) levels exceeding 150 mg/dL, fasting high-density
lipoprotein (HDL) cholesterol levels falling below 40 mg/dL (men) or 50 mg/dL (women), and fasting
blood sugar surpassing 100 mg/dL (Huang, 2009).
Study Design: The review encompassed observational studies utilizing cross-sectional, case-control,
cohort, or ecological designs. Both quantitative and mixed-method designs were considered as inclusion
criteria. Experimental studies and those with qualitative measurements were excluded.
2.4. Information sources and literature search
We performed a systematic keyword search across three databases - MEDLINE (through PubMed), Scopus,
and Web of Science - using pre-defined PECOS. The searches were conducted on August 30, 2023. The
search terms encompassed terms extracted from "Medical Subject Headings (MeSH)" in PubMed, along
with title/abstract terms. Our search strategies involved combinations of keywords related to greens pace
(e.g., greenness, green spaces, green area, and greenery), built environment (e.g., built environment, land
use, and walkability) and metabolic syndrome (e.g., metabolic syndrome and cardio-metabolic syndrome).
Language restrictions were applied, limiting the search to English. Additionally, we conducted a manual

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search through the reference lists of pertinent reviews to identify potential articles. Detailed search
strategies and terms are in Supplementary Table S1.
2.5. Study selection
We used Rayyan (https://www.rayyan.ai/) for the management of retrieved articles through systematic
searches. After duplicate removal, two reviewers (MMP and SA) independently assessed the titles and
abstracts of all identified papers. Articles selected during the title and abstract screening were considered
for full-text assessment. Throughout the screening process, studies were included only if they adhered to
the predefined inclusion criteria. Any disagreements were resolved through discussion with a third reviewer
(MHEMB, MJZS, or PD). The full texts of selected articles were examined according to the PECOS and
entered the review if they fulfilled all the eligibility criteria.
2.6. Data extraction
Two researchers (MMP and SA) conducted data extraction. Both reviewers collected information pertaining
to basic information (authors, publication year, country, study design, study population, and sample size),
methodology (metabolic syndrome measurement, green space and built environment assessments,
statistical analyses, and covariates), outcome measurement (odds ratio [OR] or relative risk [RR]) and
hazard ratio [HR] along with their corresponding 95% confidence intervals [CIs] and effect coefficients [β]
accompanied by standard errors [SE] considered in the analysis. In cases of any differences in the extracted
data, consensus was reached through discussion between two co-authors (MMP and SA). In the case of any
missing data, the reviewers contacted the corresponding authors.
2.7. Synthesis methods
Meta-analyses were performed for exposure-outcome pairs that had a minimum of three studies. For
findings with a limited number of studies, we provided narrative descriptions instead of conducting meta-
analyses. When studies reported multiple independent effect estimates for the same exposure-outcome pair,
we selectively extracted the estimate from either the "fully adjusted" or the "main model." Based on the
available data, we performed only one meta-analysis: NDVI at a 500-m buffer and metabolic syndrome.
The following formula was employed to standardize the effect estimates (X. X. Liu et al., 2022):
Given the inherent heterogeneity in the study design and methodology and the small power of our
heterogeneity tests, a conservative random-effects model was chosen for pooling risk estimates (Lin et al.,
2017). The overall heterogeneity was evaluated using Cochran's Q statistic and I2 statistic. Cochran's Q test
determined the presence of statistically significant heterogeneity with a p-value < 0.05, indicating that the
observed variation in effect sizes was unlikely to be due to chance alone (Higgins, 2008). Additionally, the
I2 statistic provided a measure of the proportion of total variation attributable to heterogeneity, with an I2
value greater than 50% indicating substantial heterogeneity (Higgins and Thompson, 2002). The results of
the pooled analysis were visually represented using forest plots.
To assess potential publication bias, we employed a funnel plot and conducted an Egger's regression test.
In cases where we detected significant publication bias, indicated by funnel plot asymmetry or a p-value of
< 0.05 in Egger's regression test, we applied the trim-and-fill method to correct the asymmetry. This resulted
in an adjusted pooled estimate that accounted for the trimmed studies. To identify potentially influential
studies, we conducted a sensitivity analysis by systematically removing one study at a time. This process
allowed us to assess the impact of each specific study on the magnitude, direction, and significance of the
pooled estimates. The meta-analysis was performed using the "metan" command in Stata 14 (StataCorp;
College Station, TX, USA) (Harris et al., 2008).

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2.8. Risk of bias assessment
We evaluated the potential biases in the reviewed studies using the Office of Health Assessment and
Translation (OHAT) tool (Rooney et al., 2014). This tool has been previously employed in reviews of
environmental exposures (including green spaces) and health outcomes (Buczyłowska et al., 2023; Cao et
al., 2023). Adhering to the validated framework for observational human subject studies, our review
focused on three critical elements (bias of exposure, outcome, and confounding) while also assessing five
methodological criteria (selection bias, attrition/exclusion bias, selective reporting bias, conflict of interest
and other). Each domain was categorized as "definitely low," "probably low," "probably high," or
"definitely high" following established guidelines. Detailed guidelines for these adjusted OHAT criteria are
in Table S2. To ensure consistency, two reviewers (MMP and SA) independently assessed the risk of bias
for individual studies. In case of disagreements, a third reviewer (MHEMB) mediated the discussion to
address the disparities in the data.
2.9. Overall quality of evidence assessment
We applied the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool
to classify the overall quality of evidence (Cao et al., 2023; Haddad et al., 2023; Lam et al., 2021).
According to the GRADE, the quality of evidence was classified into four categories: "high," "moderate,"
"low," and "very low." Initially, observational studies are given a moderate rating, which is then further
evaluated with upgrades or downgrades according to GRADE criteria (Woodruff & Sutton, 2014).
Downgrades are made based on factors such as risk of bias, indirectness, inconsistency, imprecision, and
publication bias. Upgrades are made based on factors such as large effect size, dose response, and
minimized confounding (Johnson et al., 2014). Here, 0 was considered for no change in ratings from the
initial quality (i.e., moderate), while -1 or -2 were for downgrade ratings, and +1 or +2 were for upgrade
ratings (Balshem et al., 2011). Two reviewers (MMP and SA) independently rated the evidence, and
disagreements were resolved by a third reviewer (MHEMB). The assessment guidelines with rationale and
judgments are presented in Tables S3 and S4.
2.10. Strength of evidence
Our assessment of the strength of the evidence used the Navigation Guide framework, a systematic
approach for separately evaluating human and non-human studies before combining their overall strength
(Lam et al., 2016). The strength of evidence across studies involved four factors: the quality of the body of
evidence, direction of the effect, confidence in the effect, and other influential attributes of the data. The
resulting ratings reflected the strength of evidence across studies and fell into categories such as sufficient
evidence of benefits (a robust body of evidence supporting beneficial effects), limited evidence of benefits
(the presence of evidence but with limitations), inadequate evidence of benefits (a lack of sufficient data to
draw conclusions about benefits); and evidence of benefits absence (a lack of substantial evidence
supporting benefits) (Uwak et al., 2021). The ratings reflect the strength and reliability of the evidence and
support informed decision-making. Criteria for these adjustments are outlined in Table S5.

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3. Results
3.1. Selection of studies
We identified a total of 1,374 articles through our database searches. After duplicate removal, title, and
abstract screening, we reached 51 articles for full-text assessment. After full-text assessment, 18 articles
were eligible and included in our review (Figure 1).
Figure 1. PRISMA flow chart for the study selection process.
3.2. Characteristics of included studies
Table 1 presents detailed characteristics of the included studies. Publication dates ranged from 2009 to
2023, with 60% (n = 11) published after 2020. The total sample size of the included studies was 156,820,
with individual studies ranging from 75 (Joseph and Vega-lopez, 2020) to 49,893 (Ke et al., 2022) (Table
1). Of the total, 8 (44%) utilized a cross-sectional design, 7 (39%) used a cohort design and 3 (17%) were
based on ecological study design (Figure 2). Most studies (n = 5, 28%) were conducted in China and the
U.S. (n = 5, 28%) followed by Australia (n = 4, 22%). Most studies with a cross-sectional design were
conducted in Australia (n = 4, 50%) and the U.S. (n = 3, 38%). The majority of studies with cohort design
were conducted in China (n = 3, 43%). The ecological studies were conducted in the U.S. (n = 1, 33%),
Greece (n = 1, 33%) and China (n = 1, 33%) (Figure 2). Most targeted adult populations (n = 11, 61%),
followed by elderly individuals (n = 6, 33%) and adolescents (n = 1, 6%).

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Figure 2. Distribution of studies by study design and country.
Thirteen studies identified metabolic syndrome outcomes in terms of the percentage of metabolic syndrome.
Three more reported metabolic syndrome prevalence (Tsiampalis et al., 2021; Voss et al., 2021; Yang et
al., 2019), while two reported a metabolic syndrome cluster score (Tharrey et al., 2023 Dengel et al., 2009).
The majority used expert consensus and statements including the World Health Organization (WHO) (Barr
et al., 2016; de Keijzer et al., 2020); Joint interim statement of the International Diabetes Federation Task
Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart
Association, World Heart Federation, International Atherosclerosis Society, and International Association
for the Study of Obesity (Barnett et al., 2022, Ke et al., 2022, Li et al., 2022, Tharrey et al., 2023, Voss et
al., 2021, Yang et al., 2019); National Cholesterol Education Program Adult Treatment Panel III (NCEP
ATP III) (Joseph and Vega-Lopez, 2020; Letellier et al., 2022; Liu et al., 2022; Tsiampalis et al., 2021);
International Diabetes Federation criteria (Baldock et al. 2012, 2018); National Heart, Lung, and Blood
Institute/American Heart Association (Braun et al., 2016); or criteria of the Metabolic Syndrome Study
Cooperation Group of the Chinese Diabetes Society (CDS2004) (Zhu et al., 2023). Two studies did not
report any source for their definition of metabolic syndrome (Aguilera et al., 2021; Dengel et al., 2009).

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Table 1. Summary characteristics of included studies in the review (n = 18).
Author (s),
Publication
Year,
Country
Sample Size/Unit,
Study Population,
Study design
Type of exposure
Exposure matrices
Outcome definition
Outcome assessment source
Main findings
Aguilera et al.
2021
USA
4,959
Adults
Cross-sectional
Built environment
Distance to the nearest
major arterial road; Street
Length within 500 m;
Distance nearest POE;
Inverse of distance to
nearest POE; Traffic VMT
within 500 m
Metabolic syndrome
(%)
Not Reported
The longer streets within the 500m impact
zone were linked to a higher chance of
developing metabolic syndrome (OR = 1.039,
95%CI= 1.016-1.062, p = 0.001)
Baldock et al.
2012
Australia
1,324
Adults
Cross-sectional
Built environment
Local land-use mix
Metabolic syndrome
(%)
IDF criteria
Local perceived land-use mix was found to
have a lower risk of metabolic syndrome (OR
= 0.87, 95% CI: 0.77-1.00, p = 0.04).
Baldock et al.
2018
Australia
1,491
Adults
Cross-sectional
Green space
Objective distance,
perceived distance,
Overestimated distance to
POS (parks, nature & sports
field)
Metabolic syndrome
(%)
IDF criteria
Objective distance (OR=0.92, 95% CI: 0.73-
1.16, p=0.463), perceived distance (OR=1.08,
95% CI: 0.99-1.18, p=0.097), and
overestimated distance (OR=1.22, 95% CI:
0.97-1.55, p=0.120) to POS were not
associated with metabolic syndrome.
Barnett et al.
2022
Australia
3,681
Adults
Cross-sectional
Built environment;
Green space
Population density
(person/ha), commercial
land use (%), parkland (%),
land-use mix (other), street
intersection density (/km2)
Metabolic syndrome
(%)
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
A one-decile increase in either built
environment and green space factors was not
found to be significantly associated with
metabolic syndrome (Population density
(persons/ha): OR=0.997, 95%CI= (0.989,
1.006); Commercial land use (%): OR=0.998,
95%CI= (0.985, 1.011); Parkland (%):
OR=1.002, 95%CI= (0.996, 1.009); Land use
mix (other): OR=1.392, 95%CI= (0.734,
2.641); Street intersection density (/km2):
OR=1.001, 95%CI= (0.998, 1.004).
Barr et al.
2016
Australia
5,241
Adults
Cross-sectional
Built environment
Public transport
accessibility
Metabolic syndrome
(%)
WHO Definition
High public transport accessibility was not
associated with the occurrence of metabolic
syndrome (OR = 1.09, 95% CI = 0.91-1.31, p
= 0.35).
Braun et al.
2016
USA
3,810
Elderly
Cohort
Built environment
Street Smart Walk Score
Metabolic syndrome
(%)
NHLBI/AHA Definition
Street Smart Walk Score was not associated
with metabolic syndrome in both cross-
sectional (OR (SE)=1.0022 (0.0185)) and

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longitudinal analysis (OR (SE)=0.9885
(0.0583)).
de Keijzer et al.
2019
London
6,076
Adults
Cohort
Green space
NDVI 500m, NDVI 1000m
& NDVI LSOA; VCF
500m, VCF 1000m & VCF
LSOA
Metabolic syndrome
(%)
WHO Definition
Higher residential greenness was associated
with a decreased risk of metabolic syndrome:
(NDVI 500m: HR(95%CI)=0.87 (0.77, 0.99);
VCF 500m: HR(95%CI)=0.86 (0.78, 0.95);
VCF 1000m: HR(95%CI)=0.85 (0.77, 0.94),
VCF LSOA: HR(95%CI)= 0.83 (0.75, 0.92).
Dengel et al.
2009
USA
188
Adolescents
Cross-sectional
Built environment;
Green space
Residential Density,
Distance to Transit, Density
of Transit, Employment
Density, Percent Land Use
–residential, Percent Land
Use –park and recreation,
Percent Land Use –vacant,
Median block size, Number
of access points,
Intersection density
Metabolic syndrome
cluster score
Metabolic syndrome Z-score
followed by (Kelly et al., 2008)
None of the features were significantly
associated with metabolic syndrome, including
residential density (rho=0.1016), distance to
transit (rho=-0.0457), transit density
(rho=0.0172), employment density
(rho=0.0776), percent land use for residential
(rho=0.0749), percent land use for park and
recreation (rho=-0.1320, significant at
p<0.10), percent land use for vacant (rho=-
0.0804), median block size (rho=-0.0338),
number of access points (rho=0.1160), and
intersection density (rho=0.0953).
Joseph and
Vega-lopez
2020
USA
75
Adults
Cross-sectional
Built environment
Walking environment
Metabolic syndrome
(%)
NCEP ATP III Definition
Walking environment was not associated with
the presence of metabolic syndrome (OR =
0.64, 95%CI = 0.34-1.17).
Ke et al.
2022
China
49,893
Adults
Cohort
Green space
NDVI 250m, NDVI 500m
& NDVI 1250m
Metabolic syndrome
(%)
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
Participants lived in the highest quartile of
NDVI 250m, NDVI 500m, and NDVI 1250m
had a 15% (OR = 0.85, 95% CI: 0.80–0.90),
12% (OR = 0.88, 95% CI: 0.83–0.93), and
11% (OR = 0.89, 95% CI: 0.85–0.95) lower
risk of metabolic syndrome.
Letellier et al.
2022
USA
570
Adults
Ecological
Built environment
Transportation
Metabolic syndrome
(%)
NCEP ATP III Definition
Transportation (RR = 0.96, 95%CI = 0.89–
1.04) did not show a significant association
with metabolic syndrome.
Li et al.
2022
China
38,288
Adults
Cross-sectional
Green space
NDVI 500m;
EVI 500m
Metabolic syndrome
(%)
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
Each IQR (0.62) increase in NDVI 500m and
EVI 500m (IQR = 0.74) was associated with a
13% reduced risk of metabolic syndrome
(NDVI 500m: OR = 0.87 [95% CI: 0.83,
0.92]; EVI 500m: OR = 0.87 [0.82, 0.91]),
respectively.

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Liu et al.
2022
China
38,288
Adults
Cohort
Green space
NDVI 500m
Metabolic syndrome
(%)
NCEP ATP III Definition
Higher greenness was not significantly
associated with metabolic syndrome (OR
(95%CI) = 0.93 (0.84, 1.04)).
Tharrey et al.
2023
Luxembourg
1,755
Elderly
Cohort
Green space
SAVI 800m;
TCD 800m
Metabolic syndrome
score (%)
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
An increase in SAVI may significantly prevent
the metabolic syndrome (change SAVI: β
(95% CI) = − 0.05 (− 0.11, 0.00)).
Tsiampalis et
al. 2021
Greece
2,749
Adults
Ecological
Green space
Land covered by urban
green spaces
Metabolic Syndrome
prevalence
NCEP ATP III Definition
Areas with high coverage of urban green
spaces were associated with a reduced
prevalence of metabolic syndrome (IRR =
0.96, 95% CI = 0.95–0.96).
Voss et al.
2021
Germany
2,883
Elderly
Cohort
Green space
NDVI 500m
Metabolic Syndrome
prevalence & incidence
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
High greenness was not associated with
prevalent and incident metabolic syndrome in
both cross-sectional (NDVI 500m: (OR =
0.95, 95%CI = 0.84–1.06) and longitudinal
analyses NDVI 500m : (OR = 0.86, 95%CI:
0.71–1.03).
Yang et al.
2019
China
15,477
Elderly
Ecological
Green space
NDVI 500M, NDVI
1000m; SAVI 500m, SAVI
1000m; VCF 500m, VCF
1000m
Metabolic syndrome
prevalence
Joint Interim Statement of the
IDF, TFEP, NHLBI, AHA,
WHF, IAS, IASO
Higher greenness (NDVI 500m: OR = 0.81,
95%CI = 0.70−0.93; NDVI 1000m: OR =
0.83, 95%CI = 0.72-0.95; SAVI 500m: OR =
0.80, 95% CI = 0.69 − 0.93); SAVI 1000m:
OR = 0.82, 95%CI = 0.71− 0.94; VCF 500m:
OR = 0.91, 95% CI = 0.83 − 1.00; VCF
1000m: OR = 0.95, 95%CI = 0.89−1.00) was
associated with lower rate of metabolic
syndrome.
Zhu et al.
2023
China
17,965
Elderly
Cohort
Built environment
Walkability
Metabolic syndrome
(%)
CDS 2004 criteria
Higher walkability was associated with lower
hazards of metabolic syndrome
(Q2:HR(95%CI) = 0.88(0.81,096)
Q3: HR(95%CI) = 0.91(0.83,0.99)
Q4: HR(95%CI)=0.88(0.80,0.96)).

12
Note: POE = Port of entry; VMT = Vehicle miles traveled; POS = Public open space; NDVI = Normalized Difference Vegetation Index; EVI = Enhanced Vegetation Index; SAVI
= Soil-Adjusted Vegetation Index; VCF = Vegetation Continuous Field; LSOA = Lower Layer Super Output Area; TCD = Tree Canopy Density; HR = Hazard Ratio; OR = Odds
Ratio; 95%CI = 95% Confidence Interval; IDF, International Diabetes Federation; CDS, Chinese Diabetes Society; TFEP, Task Force on Epidemiology and Prevention; NHLBI,
National Heart, Lung, and Blood Institute; AHA, American Heart Association; WHF, World Heart Federation; IAS, International Atherosclerosis Society; IASO, International
Association for the Study of Obesity; NCEP ATP III, National Cholesterol Education Program Adult Treatment Panel III;

13
3.3. Exposure assessment
Nine studies (50%) reported only green space (Baldock et al., 2018; de Keijzer et al., 2019; Ke et al., 2023;
Li et al., 2022; L. Liu et al., 2022; Tharrey et al., 2023; Tsiampalis et al., 2021; Voss et al., 2021; Yang et
al., 2020). Seven studies (39%) reported only built environment factors (Aguilera et al., 2021; Baldock et
al., 2012; Braun et al., 2016; Dengel et al., 2009; Joseph and Vega-López, 2020; Letellier et al., 2022; Zhu
et al., 2023). Two studies (11%) reported both built environment and green space exposures (Barnett et al.,
2022; Dengel et al., 2009) (Table 1).
Exposure to green spaces was assessed through green space availability (n=10) using the NDVI, Enhanced
Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Vegetation Cover Fraction (VCF), Tree
Canopy Density (TCD), or percentage of greenness coverage (Barnett et al., 2022; de keijzer et al., 2019;
Dengel et al., 2009; Ke et al., 2022; Li et al., 2022; Liu et al., 2022; Tharrey et al., 2023; Tsiampalis et al.,
2021; Voss et al., 2021; Yang et al., 2019). Accessibility (n=1) was assessed through distance to green
space, gardens or park facilities (Baldock et al., 2018) (Table 1, Table S9).
The built environment was characterized as traffic-related measures (n = 3), including distance nearest to
major arterial roads (Majart), street length, distance nearest Port of Entry (POE), inverse of distance to
nearest POE and traffic vehicle miles traveled (VMT) (Aguilera et al., 2021), street intersection density
(Barnett et al., 2022), or street pattern (Dengel et al., 2009). Other built environment measures were land-
use mix (n = 3) (Baldock et al., 2012; Barnett et al., 2022; Dengel et al., 2009), walkability (n = 3) (Braun
et al., 2016; Joseph and Vega-lopez, 2020; Zhu et al., 2023), transportations accessibility (n = 3) (Barr et
al., 2016; Dengel et al., 2009; Letellier et al., 2022), population density (n = 1) (Barnett et al., 2022) and
residential density (n = 1) (Dengel et al., 2009) (Table 1, Table S9).
Different data sources were used to estimate exposure, including satellite images, census data, government
data repositories and questionnaires (i.e., perceived proximity and land use) (Table S9). Three studies used
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images with a 250 × 250 m spatial
resolution (de keijzer et al., 2019; Ke et al., 2022; Liu et al., 2022) and four used Landsat satellite images
with a 30 x 30 m resolution (Li et al., 2022; Tharrey et al., 2023; Voss et al., 2021; Yang et al., 2019) to
estimate green space availability using remote sensing-based greenness indices (NDVI, EVI, SAVI, VCF,
TCD). One study used a questionnaire to estimate the perceived distance to green space (Baldock et al.,
2018). In terms of built environment data sources, two studies used government data repositories to estimate
transportation accessibility (Barr et al., 2016) and street distance and density (Aguilera et al., 2021). Two
studies estimated walkability in each participant’s residential location using a free web-based tool available
at https://www.walkscore.com/, an algorithm with scores ranging from 0 to 100 reflecting the degree of
walkability. Studies used a walkability score (Braun et al., 2016; Zhu et al., 2023), and two used
questionnaires to estimate the perceived local land-use mix (Baldock et al., 2012) and perceived walking
environment (Joseph and Vega-lopez, 2020) (Table S9).
Eight studies applied buffers to estimated green space availability (Barnett et al., 2022; de Keijzer et al.,
2019; Ke et al., 2022; Li et al., 2022; Liu et al., 2022; Tharrey et al., 2023; Voss et al., 2021; Yang et al.,
2019) and one study used network buffer to estimate built environment features including transportation
accessibility, percent land-use mix, and street pattern (Dengel et al., 2009). Buffer sizes ranged from 250m
(Kee et al., 2022) to 3000m (Dengel et al., 2009), with the most commonly utilized buffer size being 500m.

14
In most such studies, circular buffers were employed, but one study used a network buffer (1600m) (Dengel
et al., 2009) (Table S9).
Of the reviewed studies, six reported temporality between exposure and outcome, ranging from 2 years
(Tharrey et al., 2023) to 4 years (Liu et al., 2022). (Table S10).
Different levels of adjustment were applied in the studies, ranging from none (Aguilera et al., 2021; Dengel
et al., 2009) to 14 variables (Barr et al., 2016). These adjustments encompassed various categories of
covariates, including demographic factors, behavioral, environmental and area-level factors (Table S8).
The most frequently accounted for variables were demographic (age, sex, income, education, occupation)
and behavioral factors (smoking and alcohol consumption). Neighborhood socioeconomic status (SES)
(Barnett et al., 2022; Barr et al., 2016; Barun et al., 2016; Tsiampalis et al., 2021), area-level income
(Baldock et al., 2012; Baldock et al., 2018; de keijzer et al. 2019), population density (Barnett et al., 2022;
Barr et al., 2016; Yang et al., 2019) and gross domestic product (GDP) (Liu et al., 2022; Yang et al., 2019)
were included as area-level variables in some of the studies (Table S8).
Ten studies employed a mediation, effect modification, or moderation or interaction analysis (Baldock et
al., 2012; Baldock et al., 2018; de Keijzer et al., 2019; Ke et al., 2022; Li et al., 2022; Liu et al., 2022;
Tharrey et al., 2023; Voss et al., 2021; Yang et al., 2019; Zhu et al., 2023). The most frequently tested
mediators were air pollution (de keijzer et al 2019; Li et al., 2022; Liu et al., 2022; Yang et al., 2019, Zhu
et al. 2023) and physical activity (Baldock et al. 2018; de keijzer et al., 2019; Ke et al., 2022; Liu et al.,
2022; Voss et al., 2021, Yang et al., 2019). Few studies reported that physical activity (de keijzer et al.,
2019) and air pollution (de Keijzer et al., 2019; Li et al., 2022; Liu et al., 2022; Yang et al., 2019; Zhu et
al., 2023), significantly mediated the association between greenness and metabolic syndrome, while
walking time (Baldock et al., 2012) mediated the association between built environment and metabolic
syndrome (Table S8).
3.4. Green space and metabolic syndrome associations
The studies that examined associations between green space and metabolic syndrome are summarized in
Figure 3. Eleven studies, including four cross-sectional (Baldock et al., 2018; Barnett et al., 2022; Dengel
et al., 2009; Li et al., 2022), two ecological (Tsiampalis et al., 2021; Yang et al., 2019) and five cohort
studies (de Keijzer et al., 2019; Ke et al., 2022; Liu et al., 2022; Tharrey et al., 2023; Voss et al., 2021;)
reported associations between green space and metabolic syndrome. Of these, one cross-sectional (Li et al.,
2022), one ecological (Yang et al., 2019), and two cohort studies (de Keijzer et al., 2019; Ke et al., 2022)
reported protective associations for green space in terms of NDVI or EVI with metabolic syndrome. One
cohort study reported a protective association for SAVI with metabolic syndrome (Tharrey et al., 2023).
Another ecological study found more urban green space cover was associated with a lower prevalence of
metabolic syndrome (Tsiampalis et al., 2021). However, five studies did not find any significant
associations between green space and metabolic syndrome (Baldock et al., 2018; Barnett et al., 2022;
Dengel et al., 2009; Liu et al., 2022; Voss et al., 2021).

15
Figure 3. Overview of studies examining green space and built environment exposure and their
association with metabolic syndrome. NDVI, Normalized Difference Vegetation Index; EVI, Enhanced
Vegetation Index; SAVI, Soil-Adjusted Vegetation Index; VCF, Vegetation Continuous Field; TCD, Tree
Canopy Density.
We conducted a meta-analysis of the association between green space exposure, using NDVI at 500m
buffer, and metabolic syndrome. The meta-analysis showed that an increase in mean NDVI at 500m buffer
was associated with a lower risk of metabolic syndrome (combined OR = 0.90, 95%CI = 0.87-0.93, I2 =
22.3%, n = 4) (Figure 4).

16
Figure 4. Meta-analysis of increase in mean NDVI with metabolic syndrome.
Sensitivity analyses using a leave-one-out method were not indicative of any influential study (Table S11).
No evidence of publication bias for studies (Egger’s test: p>0.05) with NDVI and metabolic syndrome
outcome was observed (Figure 5).

17
Figure 5. Funnel plot to estimate publication bias for green space and metabolic syndrome association.
3.5. Built environment and metabolic syndrome association
The summary of studies investigating the association between the built environment and metabolic
syndrome is presented in Figure 3 and Table 1. Nine studies, comprising six cross-sectional studies
(Aguilera et al., 2021; Baldock et al., 2012; Barnett et al., 2022; Barr et al., 2016; Dengel et al., 2009;
Joseph and Vega-lopez, 2020), one ecological (Letellier et al., 2022) and two cohort studies (Zhu et al.,
2023; Braun et al., 2016), reported on the association between the built environment and metabolic
syndrome. Due to high heterogeneity in exposure-outcome pairs, a meta-analysis was not possible.
Among the three cross-sectional studies examining local land-use mix (Baldock et al., 2012; Barnett et al.,
2022; Dengel et al., 2009), one study (Baldock et al., 2012) reported that perceived local land-use mix
protective against metabolic syndrome. However, the remaining two studies found no significant
association between them (Barnett et al., 2022; Dengel et al., 2009).
Three cross-sectional studies investigated the association between traffic-related measures and metabolic
syndrome (Aguilera et al., 2021; Barnett et al., 2022; Dengel et al., 2009). Among them, one study reported
a harmful association between longer streets within the 500m impact zone and the risk of metabolic
syndrome (Aguilera et al., 2021). However, the remaining two studies did not find any association between
any type of traffic measures and metabolic syndrome (Barnett et al., 2022; Dengel et al., 2009).
Three studies, including two cross-sectional (Braun et al., 2016; Joseph and Vega-lopez, 2020) and one
cohort study (Zhu et al., 2023), reported associations between walkability and metabolic syndrome. Among
these, the cohort study found that higher walkability was associated with a lower risk of metabolic syndrome
(Zhu et al., 2023). However, the remaining two studies did not identify any significant associations in this
regard (Braun et al., 2016; Joseph and Vega-lopez, 2020).

18
Two cross-sectional studies (Barr et al., 2016; Dengel et al., 2009) and one ecological study (Letellier et
al., 2022) investigated the association between public transportation accessibility and metabolic syndrome.
None of these studies identified significant associations. Furthermore, two cross-sectional studies found no
significant association between population density (Barnett et al., 2022) or residential density (Dengel et
al., 2009) and metabolic syndrome.
3.6. Risk of bias assessment
The details of the risk of bias assessment for individual studies are illustrated in Table S11. The risk of bias
varied dramatically across individual domains (Table 2). Bias in exposure classification reported a 33%
‘Probably low’ risk of bias. Only 6% showed a ‘High’ risk of bias, and the remaining showed a ‘Definitely
low’ risk of bias for outcome measurements. Meanwhile, 17% of the articles reported a ‘High’ risk due to
confounding. Higher shares of ‘Definitely low’ risk of bias were found for selection bias (83%),
attrition/exclusion bias (89%), selective reporting bias (100%), conflict of interest (100%) and other bias
(100%), respectively.
For green space, 54% of studies showed a ‘Probably low’ risk of bias for exposure classification. All studies
showed a ‘Definitely low’ risk of bias in outcome classification, attrition bias, selective reporting bias,
conflict of interest and other biases. Only 36% of studies reported ‘Definitely low’ risk bias due to
confounding, while 92% of studies reported such risk for selection bias domain.
For the built environment, all studies showed a ‘Probably high’ risk of bias for exposure classification. All
studies showed a ‘Definitely low’ risk of bias in selective reporting bias, conflict of interest and other biases.
9 out of 10 studies reported a ‘Definitely low’ risk of bias in outcome classification. Only 22% of studies
reported ‘Definitely low’ risk bias due to confounding, while 67% of studies reported such risk for selection
bias domain.
Table 2. Risk of bias rating for included studies determined by the OHAT tool.
Author (s), Year
Detection bias for exposure
Detection bias for outcome
Confounding bias
Selection bias
Attrition/exclusion bias
Selective reporting bias
Conflict of interest bias
Other bias
Aguilera et al., 2021
Baldock et al., 2012
Baldock et al., 2018
Barnett et al., 2022
Barr et al., 2016
Braun et al., 2016
-
--
--
+
++
++
++
++
-
++
++
++
++
++
++
++
-
++
+
++
++
++
++
++
-
++
+
-
++
++
++
++
-
++
+
++
++
++
++
++
-
++
++
++
-
++
++
++

19
de Keijzer et al., 2019
Dengel et al., 2009
Joseph and Vega-lopez 2020
Ke et al., 2022
Letellier et al., 2022
Li et al., 2022
Liu et al., 2022
Tharrey et al., 2023
Tsiampalis et al., 2021
Voss et al., 2021
Yang et al., 2019
Zhu et al., 2023
Definitely low
Probably low
Probably high
High
3.7. Overall quality of evidence
Our assessments, guided by the criteria for evaluating evidence quality outlined in Table S12, yielded the
following conclusions. For green space, a downgrade of one level was supported for ‘risk of bias’ as most
studies in the review reported a ‘Probably high’ risk of bias in exposure classification detection. Similarly,
a downgrade of one level was applied to the built environment for ‘risk of bias’ as most studies had a
‘Probably high’ risk of bias in exposure classification detection and several studies had a ‘Probably high’
risk bias for confounding variables adjustment. Consequently, the quality of evidence for both exposure-
outcome pairs was low (Table S13).
3.8. Strength of evidence
Following the established criteria for evaluating the strength of evidence as outlined in Table S5, we
conducted the following assessments. For green space-metabolic syndrome, the evidence was classified as
"limited,” indicating that increased green space exposure is associated with a lower risk of metabolic
syndrome. For the built environment-metabolic syndrome association, the evidence was graded as
“inadequate” (Table S12).
4. Discussion
4.1. Summary of the key findings
We present the inaugural systematic review and meta-analysis of associations between green space and the
built environment with metabolic syndrome. Most notably, our meta-analysis of NDVI and metabolic
syndrome supports the potential benefits of green space in playing a protective role in the risk of metabolic
syndrome. This finding is consistent with previous reviews and meta-analyses that indicate beneficial
associations of green space on cardiovascular mortality (X.-X. Liu et al., 2022), blood pressure
levels/hypertension (Zhao et al., 2022) and overweight and obesity status (Luo et al., 2020). However, the
+
++
++
++
++
++
++
++
-
++
--
++
++
++
++
++
-
++
-
+
+
++
++
++
+
++
+
++
++
++
++
++
-
++
--
++
++
++
++
++
+
++
++
++
++
++
++
++
+
++
++
++
++
++
++
++
+
++
+
++
++
++
++
++
-
++
-
++
++
++
++
++
+
++
+
++
++
++
++
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-
++
++
++
++
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-
++
+
++
++
++
++
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++
+
-
--

20
findings of our meta-analysis require cautious interpretation due to several factors. The overall low quality
of evidence across studies, as per GRADE classification, could impact the reliability of the evidence. Thus,
while suggesting potential associations, the evidence falls short of definitiveness.
In terms of the built environment, meta-analysis was not possible due to the high heterogeneity of exposure-
outcome pairs. The reviewed studies presented a mixed view on the connection between the built
environment and metabolic syndrome. Most studies in this domain did not find a significant association,
indicating that traditional measurements of built environment features may not consistently relate to
metabolic syndrome risk. Nevertheless, two studies suggested protective associations, emphasizing the
positive impact of specific built environment aspects like local land-use mix (Baldock et al., 2012) and
walkability (Zhu et al., 2023). This aligns with prior research indicating that areas with diverse land uses
enhance walkability and reduce risks of noncommunicable diseases (Hamer and Chida, 2008). Walkable
neighborhoods can promote physical activity, reduce sedentary behavior, and influence better dietary
choices, potentially benefiting metabolic syndrome (Mackenbach et al., 2014). Furthermore, one study
indicated that longer street distances may increase the risk of metabolic syndrome (Aguilera et al., 2021),
possibly due to the discouragement of physical activity in environments where essential amenities are
farther apart. However, findings should be approached cautiously, given the studies showing probably high
risk of bias for exposure classification detection.
4.2. Potential mechanisms
The potential mechanisms through which green space and the built environment can influence metabolic
syndrome are likely complex and multifaceted. While there is ongoing research in this area, several
potential mechanisms and pathways have been suggested. One prominent mechanism is the positive impact
of green space exposure on mental health. The stress reduction theory suggests that green spaces can induce
a sense of emotional well-being and calming effects (Ulrich, 1983). Thus, exposure to green spaces can
promote relaxation and stress reduction, which may indirectly affect metabolic health. A previous meta-
analysis concluded that individuals with lower stress are less likely to have metabolic syndrome (Kuo et
al., 2019). A second mechanism is the possibility of attention restoration (Kaplan, 1995) to reduce metabolic
syndrome risk. Studies reported that there is a bidirectional association between mental health disorders
and increases in metabolic syndrome risk (Nousen et al., 2014). Attention restoration in green space is
associated with improved psychological well-being, including reduced symptoms of depression and anxiety
(Lei, 2018). Thus, better mental health can lead to healthier lifestyle choices and behaviors that lower
metabolic syndrome risk.
Encouraging physical activity in green spaces and the built environment is another potential pathway to
reduce metabolic syndrome risk (Richardson et al., 2017). The availability of green spaces and recreational
areas can promote activities like walking, jogging, and cycling (Feng et al., 2021). Accessible public
transportation and pedestrian-friendly infrastructure further encourage walking and cycling for daily
activities (Nieuwenhuijsen, 2020). Walkable neighborhoods with well-connected sidewalks and parks are
associated with reduced metabolic syndrome risk (Wan Mohammad et al., 2021). In this review, one study
found higher walkability to be associated with reduced metabolic syndrome risk (Zhu et al., 2023). Regular
physical activity aids in controlling body weight, regulating glucose levels, and enhancing insulin
sensitivity (Syeda et al., 2023). However, the mediating role of physical activity in the relationship between
green space and health is inconsistent (Dzhambov et al., 2018) and may depend on factors like quality and
safety (Klompmaker et al., 2018). Six studies used physical activity as a mediator, and only two mediated
the association (Chen et al., 2023; de Keijzer et al., 2019).

21
Improving personal behaviors and lifestyle, such as avoiding smoking, reducing alcohol consumption, and
eating a balanced diet, can also improve metabolic health (Choi et al., 2019). Studies suggest that green
space may reduce unhealthy consumption behaviors, including smoking and alcohol consumption (Martin
et al., 2019; Zhang et al., 2023). Moreover, sleep quality is a factor in metabolic syndrome risk (Che et al.,
2021), and several studies suggest that green space exposure may improve sleep quality (Shin et al., 2020),
which, in turn, can improve metabolic health.
Access to green spaces and recreational facilities offers alternatives to sedentary behaviors, reducing the
risk of metabolic syndrome associated with prolonged television viewing or excessive screen time
(Squillacioti et al., 2023; Edwardson et al., 2012). Natural settings and well-designed urban spaces can
facilitate social interaction and community engagement (Astell-Burt et al., 2022). Social support and a sense
of belonging can positively influence lifestyle choices, including diet and physical activity, which are key
factors in metabolic syndrome prevention (Jennings and Bamkole, 2019; Joseph and Vega-López, 2020).
Addressing environmental hazards, such as air pollution (Mueller et al., 2020) and heat (Doick et al., 2014),
and promoting a healthier microbiota (Mills et al., 2020) could also be potential mechanisms linking green
space with reduced metabolic syndrome risk. Air pollution, a known contributor to chronic inflammation,
has been linked to the development of metabolic syndrome (Wei et al., 2016). Four studies (Chen et al.,
2023; de Keijzer et al., 2019; Li et al., 2022; Yang et al., 2019) found air pollution mediated the associations
between green space and metabolic syndrome. One reported that lower walkability with high NO2 was
associated with increased metabolic syndrome risk (Zhu et al., 2023). Green spaces can enhance air quality
and reduce heat, mitigating metabolic syndrome risk (Nowak et al., 2014).
4.3. Limitation of existing studies
Several exposure-related factors limit the prevailing literature’s ability to examine green space and
metabolic syndrome outcomes robustly. Temporality is a crucial consideration in epidemiological research
as it helps establish the direction of causality and provides insights into whether exposure precedes or
follows changes in health outcomes (Rothman & Greenland 2005). Most studies in this review were cross-
sectional, limiting the opportunity to establish causality.
Studies in this review primarily focused on green space availability, overlooking essential aspects like
accessibility, quality, and utilization. The limitation lies in the exclusive emphasis on quantity rather than
factors like distance to green spaces or their usability (Sanders et al., 2015). Only one study in the review
considered accessibility through distance to public green spaces (Baldock et al., 2018). None of the studies
accounted for the quality of green space exposure, which is crucial for understanding its impact on health
outcomes (Ye et al., 2022). Enhancing the measurement of green space and built environment exposure is
vital for informed investment and decision-making. Similarly, while studies on built environment exposure
addressed traffic-related measures, walkability, and transportation, they often overlooked neighborhood
open spaces and quality that may play a significant role in physical activity and would translate into a
benefit for metabolic health (Wang et al., 2019).
Exposure accuracy relies on factors like data sources, resolution, and greenspace categorization (Liao et al.,
2021). Most reviewed studies used satellite data for greenspace assessment, though these data may not fully
capture how individuals experience green space in urban areas. When assessing the built environment,
studies predominantly used geospatial information systems (GIS) technology, but varied estimates hindered
comparability. Limited use of perceived measures, such as land-use mix, walkability, and transportation,
also posed exposure measurement risks. Most studies in this review measured green space and the built
environment at the residence level, neglecting exposure at schools or workplaces where substantial time is
spent and crucial for promoting healthy development (Gong et al., 2016). Additionally, the dynamic nature

22
of the urban environment and individual influences on health outcomes were not addressed. The studies
that measured green space availability within buffers around points of interest, such as homes, were
constrained mainly to straight-line measures that might not effectively capture walking or commuting routes
(Labib, Lindley and Huck, 2020; Ye et al., 2022). In contrast, built environment studies often didn't use
buffer sizes, with some arguing that large buffers might overlook finer-scale variations. Street-network
buffers, deemed more effective in capturing local accessibility (James et al., 2014), were employed in two
of the reviewed studies (Barnett et al., 2022; Dengel et al., 2009).
Most studies of this review did not provide detailed explanations for mediation mechanisms. Studies using
traditional statistical mediation analysis methods can have limitations, particularly in cases where multiple
mediating pathways exist (Dzhambov et al., 2020). The requirement for a non-zero total effect larger than
the direct effect can lead to incorrect conclusions. Further, inappropriate adjustment for intermediate
behavioral variables when estimating the total effect of an exposure on an outcome can lead to over-
adjustment and erroneous null findings. Studies did not consider sedentary behavior (e.g., TV viewing, car
driving) as a mediator that might have an impact on metabolic health.
Future research can enhance existing literature by carefully examining multiple measures of SES
(individual- and area-level income, educational achievement, home value) and urbanicity (population
density, residential density) to address residual confounding and moderating effects (Browning et al., 2022;
Browning and Rigolon, 2018; Rigolon et al., 2021). Prioritizing individual-level, quasi-experimental, and
longitudinal designs is crucial, aligning with the need for implementation science in this research field
(Marvier et al., 2023). Diverse green space exposure metrics, including objective (e.g., NDVI, MSAVI,
street view metrics), subjective, and expert assessments of accessibility, availability, and visibility (Labib,
Lindley and Huck, 2020) within network buffers or GPS trajectories, are crucial. Key focal points for future
research should explore the dynamic nature of the built environment, incorporating factors like
neighborhood open spaces and quality within network buffers to understand the built environment's
multifaceted impact on health outcomes. Last, this review underscores the need for more advanced
statistical methods, careful consideration of mediating mechanisms, and a broader exploration of behavioral
factors, including sedentary behavior.
4.6. Limitations and future research needs
The heterogeneity among studies prevented us from conducting meta-analyses for the built environment
and metabolic syndrome. Restricting our search to English keywords limited our ability to capture research
conducted in non-English-speaking countries. This could affect the generalizability of our findings and
overlook important cultural or geographical variations in the relationship between green space, health, and
healthcare costs. Our review summarized information mainly from high-income countries rather than low-
and middle-income countries (LMICs) with substantial healthcare burdens and inequities in access to green
space (Rigolon et al., 2018). Tailoring research to local contexts, considering factors like climate and
culture, would inform whether nature-based solutions can potentially reduce healthcare outcomes globally.
5. Conclusion
This systematic review examined 18 studies of associations between green space, built environment, and
metabolic syndrome risk. While the available evidence is limited, our meta-analysis suggests a potential
benefit of green space in reducing metabolic syndrome risk. Two studies on built environment features
indicated that walkability and land-use mix were also associated with a reduced risk of metabolic syndrome;
however, the strength of evidence for the built environment was inadequate. These findings underscore the
need for more robust research methods, such as longitudinal studies with multiple exposure measurements
and outcome assessments, to better understand these complex relationships. Our review also highlights a

23
significant gap in scientific knowledge concerning the impact of environmental exposures on metabolic
syndrome outcomes in LMICs. These regions have unique climates, economies and cultures. Study findings
from other areas may not directly apply to LMIC contexts. Conducting well-designed longitudinal studies
in diverse urban settings and thoroughly examining the underlying mechanisms can enhance our
understanding of these relationships and their implications.
Author contributions
M.M.P., conceptualized the study, administered the project, developed methodology, conducted data
curation & analysis, wrote the original draft, and created visualizations; M.J.Z.S., developed methodology,
conducted analysis, contributed to reviewing & editing; S.A., conceptualized the study, conducted data
curation, wrote the original draft; P.D., M.H.E.M.B., & M.A.A., conceptualized the study, developed
methodology, contributed to reviewing & editing; M.L.B. & P.J., contributed to methodology development,
contributed to reviewing & editing; T.A.B., contributed to reviewing and editing
All authors review and approve the manuscript.
Conflicts of interest
The authors declare no conflict of interest.
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