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Published on in Vol 5, No 2 (2021): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31316, first published .
Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices

Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices

Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices

Authors of this article:

Lin Yang1 Author Orcid Image ;   Ka Long Chan2 Author Orcid Image ;   John W M Yuen1 Author Orcid Image ;   Frances K Y Wong1 Author Orcid Image ;   Lefei Han1, 3 Author Orcid Image ;   Hung Chak Ho4 Author Orcid Image ;   Katherine K P Chang1 Author Orcid Image ;   Yuen Shan Ho1 Author Orcid Image ;   Judy Yuen-Man Siu5 Author Orcid Image ;   Linwei Tian6 Author Orcid Image ;   Man Sing Wong2 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

    Original Paper

    1School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    3School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    4Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, Hong Kong

    5Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    6School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong

    Corresponding Author:

    Frances K Y Wong, PhD

    School of Nursing

    The Hong Kong Polytechnic University

    Hung Hom Campus

    GH519

    Hong Kong

    Hong Kong

    Phone: 852 2766 6419

    Fax:852 2364 9663

    Email: frances.wong@polyu.edu.hk


    Background: The health benefits of urban green space have been widely reported in the literature; however, the biological mechanisms remain unexplored, and a causal relationship cannot be established between green space exposure and cardiorespiratory health.

    Objective: Our aim was to conduct a panel study using personal tracking devices to continuously collect individual exposure data from healthy Chinese adults aged 50 to 64 years living in Hong Kong.

    Methods: A panel of cardiorespiratory biomarkers was tested each week for a period of 5 consecutive weeks. Data on weekly exposure to green space, air pollution, and the physical activities of individual participants were collected by personal tracking devices. The effects of green space exposure measured by the normalized difference vegetation index (NDVI) at buffer zones of 100, 250, and 500 meters on a panel of cardiorespiratory biomarkers were estimated by a generalized linear mixed-effects model, with adjustment for confounding variables of sociodemographic characteristics, exposure to air pollutants and noise, exercise, and nutrient intake.

    Results: A total of 39 participants (mean age 56.4 years, range 50-63 years) were recruited and followed up for 5 consecutive weeks. After adjustment for sex, income, occupation, physical activities, dietary intake, noise, and air pollution, significant negative associations with the NDVI for the 250-meter buffer zone were found in total cholesterol (–21.6% per IQR increase in NDVI, 95% CI –32.7% to –10.6%), low-density lipoprotein (–14.9%, 95% CI –23.4% to –6.4%), glucose (–11.2%, 95% CI –21.9% to –0.5%), and high-sensitivity C-reactive protein (–41.3%, 95% CI –81.7% to –0.9%). Similar effect estimates were found for the 100-meter and 250-meter buffer zones. After adjustment for multiple testing, the effect estimates of glucose and high-sensitivity C-reactive protein were no longer significant.

    Conclusions: The health benefits of green space can be found in some metabolic and inflammatory biomarkers. Further studies are warranted to establish the causal relationship between green space and cardiorespiratory health.

    JMIR Cardio 2021;5(2):e31316

    doi:10.2196/31316

    Keywords

    green space; biomarker; cardiovascular disease; respiratory disease 


    Background

    Previous studies have demonstrated the health benefits of the natural environment and urban green space on mental health [1,2], perceived stress [3-5], sleep quality [6,7], and cardiovascular and respiratory health [8]. The health benefits of green space in neighborhoods may be due to increased physical activity, reduced air pollution exposure, and relief of stress from work and life [9-12]. A prospective cohort study in the United States also showed that people living in communities with higher green space coverage had a lower mortality rate, and this association was likely mediated by physical activity and air pollution [13]. However, to date, the underlying mechanism remains unexplored in the literature, which hinders the establishment of a causal relationship between green space exposure and cardiorespiratory health. In environmental health studies, the panel study design has often been adopted to investigate the short-term impacts of environmental factors on cardiovascular and respiratory health by comparing the levels of metabolic, inflammatory, and oxidative biomarkers of the same cohort of volunteers at different time points [14-16]. The panel study design features repeated collection of samples from the same individuals at different times, with the aim to demonstrate the changes in biomarkers at various exposure levels. As each individual serves as their own control, this study design minimizes the confounding of time-invariant factors (such as demographics and health-seeking behavior) but is subject to the confounding of other temporal factors, such as air pollution [17]. In addition, a panel study requires good-quality personal exposure data, which are often absent in many regions. Fortunately, this obstacle has been diminished by the recent research development of combining personal tracking devices with satellite images to estimate individual exposures to air pollution and actual access to green space [18].

    Objective

    With the aim to explore the effects of green space on respiratory and cardiovascular biomarkers to provide evidence for the underlying biological pathways, we conducted a panel study using personal tracking devices to continuously collect the individual exposure data in healthy Chinese adults aged 50 to 64 years living in Hong Kong. Using these data, we estimated the independent effects of green space exposure on different metabolic, inflammatory, and oxidative biomarkers for cardiovascular and respiratory health.


    Study Design and Participants

    The target population was Chinese adults aged 50 to 64 years who had been living in Hong Kong for the past 2 years. This age group was chosen because of their high risk of preclinical chronic conditions [19]. Inclusion criteria were nonsmoker or no exposure to secondhand smoke at home or work; generally healthy without any diagnosed chronic diseases or regular taking of medicines; no need of walking assistance; and staying at the same residential address during the whole study period. We recruited participants using convenience sampling via posters and social media and through snowball sampling by inviting participants to refer their friends to us.

    Using the formula for mixed models with repeated measurements [20], we calculated the sample based on the results from one previous study on indoor and outdoor air pollution [21], which used a similar study design. We assumed that the mean concentration of 8-hydroxy-2'-deoxyguanosine (8-OHdG) measurements was 4.0 ng/mL, with a standard deviation of 2.3. The sample size of 30 could achieve 90% power to detect an effect size of 0.8, under the assumption that the autocorrelation of repeated measurements was 0.9. Given the 20% attrition rate during the repeated measurements, we decided to recruit 38 to 40 participants for this study.

    The participants were recruited and followed up for 5 consecutive weeks during October to December 2017. On the same weekday of each week during the study period, each individual participant was invited to visit the Integrative Health Clinic (IHC) of the Hong Kong Polytechnic University for blood sample collection and physical examination. Each participant had a total of 6 visits during the whole study period (1 at enrollment and 5 at follow-up). These visits were scheduled on early mornings (8 AM-10 AM) of the same weekday to reduce the bias caused by the diurnal change of biomarkers. If participants took any medication, experienced allergies in the preceding 7 days, or worked a night shift on the day before their scheduled visit, these visits would be postponed for 1 week.

    A summary of the data collection procedure at the clinic visits is shown in Table S1 in Multimedia Appendix 1. At recruitment (the first visit to the IHC), each participant signed a consent form and took a self-administered questionnaire, with the support of research assistants if needed. The questionnaire collected their demographic information (age, gender, years living in Hong Kong), socioeconomic characteristics (education, household income, occupation, housing type), lifestyle information (alcohol drinking, physical activity), and residential address.

    Personal Exposure Assessment

    Access to Green Space

    At the first visit, a GPS device (BT-Q1000XT, Qstarz International Co Ltd) was distributed to each participant. Research assistants provided the participants with both verbal and paper instructions on device use. The GPS device logged the participant’s location coordinates, date, and time at 1-minute epochs, and the real-time data were automatically collected by a server on the university campus. Raw GPS data were first screened in ArcGIS software (Esri) for missing or suspicious data by comparing them with the daily activity log. Days with less than 600 minutes of GPS data recorded were regarded as missing and excluded from subsequent analysis. The research assistants regularly checked the GPS data collected on the server to ensure the completeness of these data. The participants showed good compliance and yielded no missing data.

    We matched cleaned GPS data to a map of the normalized difference vegetation index (NDVI) in the entire territory of Hong Kong to calculate the urban greenness within a 100-, 250-, and 500-meter–radius buffer zone of individual GPS coordinates, based on a Satellite Pour l’Observation de la Terre (SPOT) 6 image obtained in 2016. The NDVI is a normalized ratio of infrared and red bands ranging between –1 and 1, with higher numbers indicating more green vegetation [22]. The NDVI has been widely used in previous environmental studies on the health impacts of green space [23,24]. The average NDVI of individuals was weighted by the daily hours spent in and outside the home.

    Exposure to Air Pollution and Traffic Noise

    We also estimated individual exposure to two ambient air pollutants, nitrogen dioxide (NO2) and particulate matter with a diameter less than 2.5 μm (PM2.5), by matching the raw GPS coordinates with the hourly maps of PM2.5 and NO2 at 10-meter spatial resolution. These hourly maps were remodeled from temporal information reported by the Kwai Chung Monitoring Station and adjusted by the spatial patterns of air pollutants reported in previous local studies [25,26]. Weekly average concentrations of NDVI, PM2.5, and NO2 were calculated from raw data estimated at 1-minute epochs.

    The annual average household exposure to road traffic noise was estimated by matching the residential addresses with a 3D-built environment database in Hong Kong that was validated using real-time field data in a local study [27].

    Physical Activity and Dietary Intake Measurements

    At the first visit, an accelerometer data logger (ActiGraph GT3X, ActiGraph LLC) was also distributed to each participant, and the research assistants reminded them to wear the accelerometer on the wrist of their nondominant hand all the time during the study period to continuously record their physical activities. Raw accelerometer data were collected at 1-minute epochs. Individual daily physical activities were calculated from the vector magnitudes of the cleaned data. The cutoff points of light, moderate, and vigorous physical activity were determined according to a formula validated in a Chinese population [28].

    We asked each participant to record their daily dietary intakes in weeks 2 and 5 using a standard dietary journal widely used in nutritional studies [29]. Food items and consumed amounts collected from the dietary journals were analyzed with the software Food Processor, version 11.3 (ESHA). The Food Processor database is composed of over 72,000 food items from sources including the United States Department of Agriculture Standard Reference database, Food and Nutrient Database for Dietary Studies, and different manufacturers’ data. Food items, especially local and Chinese food items, which could not be found in the Food Processor database were searched using the Nutrient Information Inquiry System developed by the Centre for Food Safety, the Taiwan Food and Nutrient Database developed by the Food and Drug Administration of Taiwan, or the manufacturers’ webpages. If the nutrition information of the food items still could not be found, the most appropriate food items available were chosen for the analysis. The daily amounts of energy and nutrients consumed, including total calories, calories from fat and saturated fat, protein, carbohydrates, and total fiber, were calculated for individual participants using the Food Processor software.

    The above collected data were converted into weekly averages by taking the arithmetic mean—with the exception of the weekly average access to green space, which was calculated using the geometric mean of the NDVI—in the corresponding period between two visits of each participant.

    Outcome Measurements

    At each visit, participants first measured their body weight and height, systolic blood pressure, diastolic blood pressure, and heart rate (HR). Lung function tests were conducted using a spirometer (microQuark, COSMED), following the guidelines of the American Thoracic Society [30]. Individual data pertaining to the forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) were included in the analysis. Each participant donated 5 ml fasting blood at each visit, which was collected in a heparinized tube by a qualified phlebotomist following a standard venipuncture procedure. Specimens were maintained on ice and transported to the laboratory, where blood samples were immediately centrifuged (3000 rpm for 10 minutes at 4 oC) and stored in two 1.0 to 1.5 ml aliquots at –80 oC for batch testing later. The above procedure was completed within 30 minutes of the participant’s arrival at the laboratory to minimize damage to the biomarkers.

    We chose a panel of biomarkers used in previous environmental studies on the cardiovascular health effects of green space and air pollution [21,31-34]: (1) metabolic biomarkers: glucose, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL-C), total cholesterol (TC), and triglyceride (TG); (2) inflammatory biomarkers: high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), and soluble platelet selectin (sP-selectin); and 3) oxidative stress biomarkers: 8-hydroxy-2'-deoxyguanosine (8-OHdG), malondialdehyde (MDA), copper-zinc superoxide dismutase (Cu,Zn-SOD), and glutathione peroxidase 1 (GPx-1). The metabolic biomarkers and hs-CRP were measured by an AU480 chemistry analyzer (Beckman Coulter) with corresponding assay kits. The remaining biomarkers were measured using enzyme-linked immunosorbent assay (ELISA) kits purchased from various commercial sources.

    Data Analysis

    Spearman coefficients were calculated to assess the correlations between variables. A generalized linear mixed-effects model (GLME), which includes two components, namely, a fixed effect (green space effects and confounding) and a random effect (within-subject variations), was used to estimate the association between green space exposure and biomarker levels or lung functions. The potential confounding factors, including sex, total household income, occupation, physical activities, and household traffic noise exposure, were included as fixed effects in the model. Because there were categorical confounding factors, the generalized version of the variance inflation factor (VIF) was used to detect multicollinearity [35]. A generalized VIF of more than 10 would indicate multicollinearity, and the confounding variables would be removed from the models [36]. Subject numbers were fitted as a random effect variable to account for within-subject variations of repeated measurements.

    We fit 4 different models to the weekly measurements of each biomarker to estimate the independent effects of green space. In model 1, the NDVI was added as the only explanatory factor; in model 2, variables of sex, physical activity (moderate to vigorous physical activity levels), occupation, household income, and traffic noise exposure were added to model 1 as confounding factors; in models 3 and 4, PM2.5 and NO2 were respectively added to model 2 to assess the independent effects of green space exposure with adjustment for ambient air pollutants; and in model 5, daily consumption amounts of protein and carbohydrates were added to model 2 to adjust for the confounding effects of dietary intake. The effects of green space were quantified by the percentage changes of biomarker concentrations associated with a per interquartile range increase of the NDVI. The goodness of fit was evaluated by the Akaike information criterion (AIC), with the minimum indicating the best fit. The likelihood ratio tests of full and partial models were used to show the statistical significance of the variables. All data analysis was conducted using R software, version 3.6.2 (R Foundation for Statistical Computing). The statistical significance was set to P<.05. Multiple testing of biomarkers was controlled by the Bonferroni correction.

    Ethical Statement

    This project was approved by the Human Subjects Ethics Subcommittee of the Hong Kong Polytechnic University. All participants signed a consent form, and no personal information except residential address was collected in this study.


    Participant Characteristics

    We recruited 40 participants during October to December 2017. One participant withdrew from the study after the first week due to unforeseen family issues. The remaining 39 participants finished all the scheduled visits. The 39 participants had a mean age of 56.4 years (range 50-63 years); 27 (69%) were women, 31 (80%) were married, 35 (90%) were living with family members (family size range 1-6 people), and 14 (36%) received postsecondary education (Table 1).

    Table 1. Baseline characteristics of the participants (N=39).
    Characteristics
    		Value
    Age (years), mean (SD)56.4 (3.5)
    Sex, n (%)


    		Male12 (31)

    		Female27 (69)
    Housing type, n (%)

    		Self-owned25 (64)

    		Rent14 (36)
    Living condition, n (%)

    		Alone4 (10)

    		With family member35 (90)
    Household size, mean (SD)3.1 (1.2)
    Education level, n (%)

    		Secondary25 (64)

    		Postsecondary14 (36)
    Occupation, n (%)

    		Working21 (54)

    		Homemaker and retired18 (41)
    Household income (HK $)a, n (%)

    		Low (10,500 or below)21 (54)

    		Medium (10,501-23,000)13 (33)

    		High (23,001 or above)5 (13)
    Marital status, n (%)

    		Married31 (80)

    		Single4 (10)

    		Widowed/divorced4 (10)
    BMI, mean (SD)23.0 (3.4)

    aHK $1=US $0.13.

    Outcome Measurements

    The weekly average NDVIs at the 100-, 250-, and 500-meter buffer zones were –0.54 (IQR 0.34), –0.61 (IQR 0.25), and –0.61 (IQR 0.25), respectively (Table 2). The weekly average concentrations of PM2.5 at the 100-, 250-, and 500-meter buffer zones were 25.57 μg/m3 (SD 1.28), 29.26 μg/m3 (SD 1.12), and 29.25 μg/m3 (SD 1.01). The weekly average concentrations of NO2 were 23.46 µg/m3 (SD 12.89), 39.73 µg/m3 (SD 5.30), and 39.78 μg/m3 (SD 5.21) at the 100-, 250-, and 500-meter buffer zones, respectively.

    Table 2. Weekly average of green space exposure and air pollution for the 3 buffer zones, as well as BMI and physical activity at the 250-meter buffer zone, in the 5-week follow-up period.
    VariableValue, mean (SD)

    		OverallWeek 1Week 2Week 3Week 4Week 5

    NDVIa

    		100-m buffer zone–0.54 (0.33)–0.67 (0.35)–0.53 (0.34)–0.53 (0.29)–0.46 (0.35)–0.51 (0.33)

    		250-m buffer zone–0.61 (0.22)–0.70 (0.29)–0.61 (0.19)–0.59 (0.17)–0.57 (0.21)–0.61 (0.21)

    		500-m buffer zone–0.61 (0.22)–0.70 (0.29)–0.60 (0.19)–0.59 (0.17)–0.57 (0.21)–0.60 (0.21)
    PM2.5b (µg/m3)

    		100-m buffer zone25.57 (1.28)25.60 (1.62)25.68 (1.20)25.46 (1.22)25.67 (1.42)25.46 (1.07)

    		250-m buffer zone29.26 (1.12)29.07 (1.47)29.25 (1.00)29.17 (1.05)29.44 (1.07)29.35 (1.02)

    		500-m buffer zone29.25 (1.01)29.04 (1.44)29.25 (0.82)29.18 (0.95)29.41 (0.89)29.33 (0.93)
    NO2c (µg/m3)

    		100-m buffer zone23.46 (12.89)23.58 (14.84)23.48 (13.26)23.63 (12.38)22.78 (11.82)23.84 (13.36)

    		250-m buffer zone39.73 (5.30)40.43 (8.19)39.78 (4.00)39.44 (4.64)39.08 (4.19)40.00 (5.20)

    		500-m buffer zone39.78 (5.21)40.47 (7.81)39.83 (4.09)39.52 (4.56)39.12 (4.34)40.03 (5.09)
    BMI (kg/m2)22.83 (3.26)22.85 (3.30)22.78 (3.35)22.89 (3.30)22.91 (3.29)22.73 (3.25)
    MVPAd (minutes)177.61 (76.64180.77 (79.75)184.02 (79.01)176.64 (77.24)172.96 (77.39)173.91 (73.37)

    aNDVI: normalized difference vegetation index.

    bPM2.5: particulate matter with a diameter less than 2.5 μm.

    cNO2: nitrogen dioxide.

    dMVPA: moderate to vigorous physical activity.

    Table 3 shows the weekly means and variations of the outcome measures. Overall, these biomarkers show small variations across visits and between individuals, except for the systemic inflammatory biomarkers and some oxidative stress biomarkers. The biomarkers had low to moderate correlations except for the metabolic biomarkers, which had high correlations (Figure S1, Multimedia Appendix 1).

    Table 3. Summary statistics of the outcome measurements at the weekly visits.
    VariableOutcome, mean (SD)


    		Week 1Week 2Week 3Week 4Week 5
    Lung functions

    		FVCa (%)100.69 (9.65)99.85 (8.39)99.67 (10.26)100.66 (10.06)98.84 (9.54)

    		FEV1b (%)103.76 (10.43)102.11 (9.48)102.01 (11.19)103.01 (10.62)101.17 (10.30)

    		FEV1/FVC ratio103.11 (5.02)102.33 (5.63)102.37 (5.29)102.44 (5.90)102.44 (5.46)

    		Heart rate (beats per minute)71.67 (10.13)69.46 (9.17)69.44 (9.25)71.05 (9.68)70.28 (9.12)
    Blood pressure (mm Hg)

    		Systolic127.64 (15.78)123.59 (16.28)124.08 (14.24)122.77 (16.98)122.92 (13.02)

    		Diastolic79.90 (12.30)77.46 (9.60)77.46 (10.50)76.18 (10.18)75.82 (9.07)
    Metabolism biomarkers

    		Total cholesterol (mmol/L)5.86 (0.91)5.72 (1.02)5.77 (1.07)5.78 (1.09)5.52 (1.05)

    		Triglycerides (mmol/L)1.48 (0.82)1.43 (0.80)1.44 (0.72)1.53 (0.86)1.45 (0.74)

    		LDL-Cc (mmol/L)3.83 (0.84)3.73 (0.92)3.76 (0.97)3.73 (0.95)3.57 (0.92)

    		HDL-Cd (mmol/L)1.47 (0.34)1.44 (0.34)1.45 (0.33)1.45 (0.36)1.38 (0.32)

    		Total cholesterol/HDL-C ratio4.19 (1.12)4.17 (1.15)4.16 (1.10)4.18 (1.10)4.17 (1.11)

    		Triglycerides/HDL-C ratio1.15 (0.88)1.14 (0.91)1.12 (0.76)1.20 (0.86)1.18 (0.85)

    		LDL-C/HDL-C ratio2.77 (0.90)2.75 (0.95)2.74 (0.92)2.72 (0.89)2.72 (0.89)

    		Glucose (mmol/L)5.75 (0.82)5.60 (0.78)5.59 (0.72)5.63 (0.68)5.45 (0.74)
    Systemic inflammatory biomarkers

    		hs-CRPe (mg/L)1.98 (3.11)1.56 (3.20)1.01 (0.76)1.07 (1.20)1.05 (0.82)

    		IL-6f (pg/mL)1.72 (2.40)1.78 (2.58)1.31 (1.66)1.74 (2.26)1.25 (1.75)

    		TNF-αg (pg/mL)1.6 (1.22)1.65 (1.25)1.49 (1.22)1.73 (1.45)1.50 (1.27)

    		sP-selectinh (ng/ml)3.51 (2.62)3.20 (2.14)2.90 (1.98)3.27 (1.89)3.24 (1.92)
    Oxidative stress biomarkers

    		MDAi (μM)40.85 (22.26)31.83 (21.24)30.11 (18.88)36.35 (23.69)31.75 (19.76)

    		8-OHdGj (ng/mL)1.32 (0.54)1.41 (0.54)1.22 (0.39)1.37 (0.49)1.47 (0.55)

    		SODk (U/μL)0.77 (0.38)0.74 (0.35)0.77 (0.32)0.79 (0.36)0.80 (0.50)

    		GPx-1l (ng/mL)3.25 (7.06)3.17 (6.91)2.79 (6.31)2.54 (5.84)4.25 (10.8)

    aFVC: forced vital capacity.

    bFEV1: forced expiratory volume in the first second.

    cLDL-C: low-density lipoprotein cholesterol.

    dHDL-C: high-density lipoprotein cholesterol.

    ehs-CRP: high-sensitivity C-reactive protein.

    fIL-6: interleukin-6.

    gTNF-α: tumor necrosis factor-α.

    hsP-selectin: soluble platelet selectin.

    iMDA: malondialdehyde.

    j8-OHdG: 8-hydroxy-2\'-deoxyguanosine.

    kSOD: superoxide dismutase.

    lGPx-1: glutathione peroxidase 1.

    We compared the AICs of the different GLME models and found that model 3 with the 100-meter NDVI, model 1 with the 250-meter NDVI, and model 1 with the 500-meter NDVI generally returned smaller AICs for most biomarkers than the rest of the models (Table S2, Multimedia Appendix 1). The majority of the generalized VIF values of the variables for all the models was below 2, indicating the absence of collinearity (data not shown). The effect estimates of the NDVI for the 250-meter and 500-meter buffer zones were more consistent between different models, with a few exceptions (TC/HDL ratio and 8-OHdG) (Figures 1 and 2, Tables S3-S5 in Multimedia Appendix 1). Positive associations with NDVI were found for the FEV1/FVC ratio, IL-6, TNF-α, MDA, and SOD, whereas negative associations were found for TC, TG, LDL-C, HDL-C, TG/HDL, LDL/HDL, glucose, and hs-CRP. However, only some estimates reached statistical significance (P<.05) at the 250-meter buffer zone, including TC (–21.6% per IQR increase in NDVI, 95% CI –32.7% to –10.6%), LDL-C (–14.9%, 95% CI –23.4% to –6.4%), HDL-C (–4.9%, 95% CI –8.0% to –1.9%), glucose (–11.2%, 95% CI –21.9% to –0.5%), hs-CRP (–41.3%, 95% CI –81.7% to –0.9%), and TNF-α (41.5%, 95% CI 20.9% to 62.0%). The effect estimates of the 100- and 500-meter buffer zones were similar to those of the 250-meter buffer zone except for glucose and hs-CRP, which did not reach statistical significance (P>.05) at the 100-meter buffer zone. After adjustment for multiple testing by Bonferroni correction, the estimates remained statistically significant for TC, LDL-C, HDL-C, and TNF-α at the 250- and 500-meter buffer zones (P<.002).

    ‎
    Figure 1. Percentage change in metabolic biomarker concentrations associated with per IQR increase in normalized difference vegetation index (NDVI) at (A) the 100-meter buffer zone, (B) the 250-meter buffer zone, and (C) the 500-meter buffer zone. Vertical bars are 95% CI. Note: model 1: outcome ~ NDVI; model 2: outcome ~ NDVI + covariates (sex, income, occupation, moderate to vigorous physical activity, noise); model 3: outcome ~ NDVI + covariates + PM2.5; model 4: outcome ~ NDVI + covariates + NO2; model 5: outcome ~ NDVI + covariates + protein + carbohydrates. hs-CRP: high-sensitivity C-reactive protein, IL-6: interleukin-6, TNF-α: tumor necrosis factor α, 8-OHdG: 8-hydroxy-2'-deoxyguanosine, SOD: superoxide dismutase, GPx-1: glutathione peroxidase.
    View this figure
    ‎
    Figure 2. Percentage change in inflammatory and oxidative biomarker concentrations associated with per-IQR increase in normalized difference vegetation index (NDVI) at (A) the 100-meter buffer zone, (B) the 250-meter buffer zone, and (C) the 500-meter buffer zone. Vertical bars are 95% CIs. Note: model 1: outcome ~ NDVI; model 2: outcome ~ NDVI + covariates (sex, income, occupation, moderate to vigorous physical activity, noise); model 3: outcome ~ NDVI + covariates + PM2.5; model 4: outcome ~ NDVI + covariates + NO2; model 5: outcome ~ NDVI + covariates + protein + carbohydrates. LDL-C: low-density lipoprotein, HDL-C: high-density lipoprotein, TC: total cholesterol, TG: triglyceride.
    View this figure

    Principal Findings

    In this panel study, we found a negative association of NDVI exposure with different metabolic and inflammatory biomarkers. The findings were generally consistent with the beneficial effects of neighborhood green space on biomarkers of respiratory and cardiovascular health in the literature [32], but our study was among the first to use personal tracking devices to objectively and continuously collect individual data of weekly exposure to green space. Different from previous studies using residential greenness as long-term exposure, we adopted a longitudinal study design to explore the effects of weekly exposure to green space on biomarkers of cardiorespiratory health in healthy adults. This reflects subclinical signs for the beneficial or detrimental effects of green space, which could shed light on underlying biological mechanisms that remain unexplored in the literature.

    After adjustment for air pollution, physical activities, and dietary intake, we found that lower levels of TC, LDL-C, and fasting glucose were associated with higher green space exposure. Our findings are consistent with 2 cross-sectional studies and 1 cohort study in Chinese populations, which reported that a larger amount of green space in working places or residential areas was associated with lower levels of TC, TG, LDL-C, and fasting glucose [15,37,38]. Our effect estimates are also similar to those reported in a large sample of 15,477 adults in China [37]. However, we found that HDL-C also slightly increased in those with higher green space exposure; this is in contrast to the findings of these two studies, although the LDL/HDL, TG/HDL, and TC/HDL ratios remained unchanged. The simultaneous increase of HDL-C and LDL-C could be due to an intake of high-cholesterol food in some participants, as some previous studies have observed [39]. Hence, the negative associations of all lipid biomarkers may be due to inadequate adjustment for physical activities and dietary intake. Further studies with a larger sample size are needed to elucidate the controversial results of the lipid profile.

    Compared to the metabolic biomarkers, the associations of green space and proinflammatory biomarkers were less significant and inconsistent in our study. We found negative associations between green space exposure and hs-CRP, although they were not statistically significant. This echoes the findings of a cohort study of school-aged children in Portugal [40]. Our findings are consistent with a previous study conducted in deprived communities in the United Kingdom that demonstrated an association of residential greenness with hs-CRP [41]. Mao et al [42] conducted an intervention study in 24 older patients with essential hypertension and found that those who stayed in a forest for 7 days and nights had significant reductions in IL-6 but no significant changes in TNF-α. Similar findings were reported in a trial among 20 patients with chronic obstructive pulmonary disease (COPD) after a 1-day forest trip [43]. However, we did not observe any significant effects of green space on IL-6, whereas TNF- levels were consistently higher among those with more green space exposure. The reliability and validity of inflammatory biomarkers to reflect the risk of cardiovascular diseases associated with green space require further study. Other inflammatory biomarkers, such as interleukin-1β, interleukin-8, vascular endothelial growth factor, and soluble tumor necrosis factor-α receptor II [44], could be considered as alternatives in future studies on the health effects of green space.

    Elevated MDA and lower SOD levels have been linked to increased risks of coronary artery disease, heart failure, and other chronic diseases [45,46]. It has been proposed that residential green space can reduce oxidative stress and increase angiogenic capacity in patients with cardiovascular diseases [47]. Several experimental studies in patients with hypertension or COPD or in college students found that forest bathing could significantly elevate SOD and lower MDA, despite small sample sizes [48,49]. Our study, however, did not observe significant associations of daily exposure to green space with these oxidative biomarkers. This discrepancy may be due to differences in the study designs and sampling populations.

    We did not find any significant effects of green space on lung functions, except FEV1/FVC. This could be due to the fact that the participants were relatively healthy, without any pre-existing chronic respiratory conditions. Sinharay et al [50] conducted a randomized crossover study and found a significant reduction in FEV1 and FVC among patients with COPD, but not among healthy volunteers. Future studies may consider using more sensitive biomarkers for airway inflammation, such as fractional exhaled nitric oxide and differential frequency-dependent respiratory resistance at 5 Hz and 20 Hz, which have used in recent environmental epidemiology studies [50].

    Similar to previous green space studies [51,52], we calculated 100-, 250- and 500-meter buffer zones to obtain a comprehensive view of the green space exposure. It is interesting to note that the effect estimates of the 250- and 500-meter buffer zones tend to have consistent patterns but differ from those of the 100-meter buffer zone. By contrast, in a study in Mexican American children in the United States, it was found that children living in residential areas with a higher NDVI had lower odds of dry cough and asthma, and such associations were consistently found across the 3 buffer zones [51]. Other similar studies in children also reported significant associations in the 100-meter buffer zone but not in the larger buffer zones [53]. The daily activity zones of adults are much wider than those of children, which may also explain the lack of significant association of greenness with biomarkers in the 100-meter buffer zone in our adult participants. We speculate that this could also be due to the highly crowded living conditions of Hong Kong, with numerous high-rise residential buildings. Hence, the 250- and 500-meter buffer zones probably better represent community greenness exposure among adults in a metropolitan city such as Hong Kong.

    Limitations

    Our study had several limitations. First, the small sample size may render low statistical power, which could explain the few significant effect estimates and wide confidence intervals. Therefore, additional studies with a larger sample size are needed to further elucidate the inconsistent findings. Second, green space exposure was measured by daily average NDVI, which could not reflect the activities performed by participants around the green spaces. Nevertheless, we simultaneously collected the physical activities by personal trackers, which can reduce the confounding effect of these activities. Third, due to a limited budget, we only tested a selected panel of biomarkers, although we attempted to cover a wide range of biomarkers for metabolism, respiratory functions, oxidative stress, and proinflammation. Future studies could adopt more biomarkers to gain a comprehensive understanding of the pathways involved in health effects of green space. Last but not least, sampling bias may exist due to the convenience sampling approach we used in this study. The volunteers tend to be healthier and more educated than the general population, as shown in Table 1. However, the time-invariant characteristics of the participants (such as demographic, lifestyle, and socioeconomic factors) have been well adjusted for in the panel study, because each participant served as their own control. Nevertheless, the weekly green space exposure of these participants had enough variations (as shown in Table 2) to allow us to investigate the effects of green space on different biomarkers. In the future, a large-scale study with a more representative sample of participants could provide more evidence for the health benefits of green space exposure in urban settings.

    Conclusions

    By combining data collected via personal tracking devices with green space GIS data, we were able to demonstrate that higher exposure to green space was associated with a better lipid profile and lower inflammatory biomarkers; however, no significant associations were found with respiratory and oxidative biomarkers. The findings of this study provide more clues to the potential biological pathways for the health benefits of green space. From the public health perspective, the health effects of green space identified from this study will also aid the design of future intervention programs to improve the quality of life of the general public.

    Acknowledgments

    We thank Fiona Y Wong and Shi Zhao for their assistance in data analysis.

    This work was supported by the Faculty of Health and Social Science Dean’s Reserve Fund from the Hong Kong Polytechnic University.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Supplementary materials.

    DOCX File , 3779 KB
    1. Francis J, Wood LJ, Knuiman M, Giles-Corti B. Quality or quantity? Exploring the relationship between Public Open Space attributes and mental health in Perth, Western Australia. Soc Sci Med 2012 May;74(10):1570-1577. [CrossRef] [Medline]
    2. Astell-Burt T, Mitchell R, Hartig T. The association between green space and mental health varies across the lifecourse. A longitudinal study. J Epidemiol Community Health 2014 Jun;68(6):578-583. [CrossRef] [Medline]
    3. van den Berg AE, Maas J, Verheij RA, Groenewegen PP. Green space as a buffer between stressful life events and health. Soc Sci Med 2010 Apr;70(8):1203-1210. [CrossRef] [Medline]
    4. Stigsdotter UK, Ekholm O, Schipperijn J, Toftager M, Kamper-Jørgensen F, Randrup TB. Health promoting outdoor environments--associations between green space, and health, health-related quality of life and stress based on a Danish national representative survey. Scand J Public Health 2010 Jun 22;38(4):411-417. [CrossRef] [Medline]
    5. Ulrich RS, Simons RF, Losito BD, Fiorito E, Miles MA, Zelson M. Stress recovery during exposure to natural and urban environments. J Environ Psychol 1991 Sep 21;11(3):201-230. [CrossRef]
    6. Astell-Burt T, Feng X, Kolt GS. Does access to neighbourhood green space promote a healthy duration of sleep? Novel findings from a cross-sectional study of 259 319 Australians. BMJ Open 2013 Aug 13;3(8):e003094 [FREE Full text] [CrossRef] [Medline]
    7. Yang L, Ho JY, Wong FK, Chang KK, Chan KL, Wong MS, et al. Neighbourhood green space, perceived stress and sleep quality in an urban population. Urban Forestry & Urban Greening 2020 Oct;54:126763. [CrossRef]
    8. Tischer C, Gascon M, Fernández-Somoano A, Tardón A, Lertxundi Materola A, Ibarluzea J, et al. Urban green and grey space in relation to respiratory health in children. Eur Respir J 2017 Jun;49(6):1502112 [FREE Full text] [CrossRef] [Medline]
    9. Gatrell J, Jensen R. Growth through greening: developing and assessing alternative economic development programmes. Appl Geogr 2002 Oct;22(4):331-350. [CrossRef]
    10. Liu H, Shen Y. The impact of green space changes on air pollution and microclimates: a case study of the Taipei Metropolitan Area. Sustainability 2014 Dec 03;6(12):8827-8855. [CrossRef]
    11. Kabisch N, Qureshi S, Haase D. Human–environment interactions in urban green spaces — A systematic review of contemporary issues and prospects for future research. Environmental Impact Assessment Review 2015 Jan;50:25-34. [CrossRef]
    12. Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environ Res 2017 Oct;158:301-317. [CrossRef] [Medline]
    13. James P, Hart JE, Banay RF, Laden F. Exposure to greenness and mortality in a nationwide prospective cohort study of women. Environ Health Perspect 2016 Sep;124(9):1344-1352 [FREE Full text] [CrossRef] [Medline]
    14. Egorov AI, Griffin SM, Converse RR, Styles JN, Sams EA, Wilson A, et al. Vegetated land cover near residence is associated with reduced allostatic load and improved biomarkers of neuroendocrine, metabolic and immune functions. Environ Res 2017 Oct;158:508-521 [FREE Full text] [CrossRef] [Medline]
    15. Tsao T, Tsai M, Wang Y, Lin H, Wu C, Hwang J, et al. The health effects of a forest environment on subclinical cardiovascular disease and heath-related quality of life. PLoS One 2014;9(7):e103231 [FREE Full text] [CrossRef] [Medline]
    16. Mena C, Fuentes E, Ormazábal Y, Palomo-Vélez G, Palomo I. Effect of straight-line and road network distances to parks and markets on anthropometric measurements, biochemical markers, and a healthy lifestyle in adult people. Sport Sci Health 2015 Dec 18;12(1):55-61. [CrossRef]
    17. Møller P, Loft S. Oxidative damage to DNA and lipids as biomarkers of exposure to air pollution. Environ Health Perspect 2010 Aug;118(8):1126-1136 [FREE Full text] [CrossRef] [Medline]
    18. Wong CM, Lai HK, Tsang H, Thach TQ, Thomas GN, Lam KBH, et al. Satellite-based estimates of long-term exposure to fine particles and association with mortality in elderly Hong Kong residents. Environ Health Perspect 2015 Nov;123(11):1167-1172 [FREE Full text] [CrossRef] [Medline]
    19. Madsen C, Rosland P, Hoff DA, Nystad W, Nafstad P, Naess OE. The short-term effect of 24-h average and peak air pollution on mortality in Oslo, Norway. Eur J Epidemiol 2012 Sep;27(9):717-727. [CrossRef] [Medline]
    20. Davis C. Statistical Methods for the Analysis of Repeated Measurements. New York, NY: Springer; 2002.
    21. Day DB, Xiang J, Mo J, Li F, Chung M, Gong J, et al. Association of ozone exposure with cardiorespiratory pathophysiologic mechanisms in healthy adults. JAMA Intern Med 2017 Sep 01;177(9):1344-1353 [FREE Full text] [CrossRef] [Medline]
    22. Goward SN, Markham B, Dye DG, Dulaney W, Yang J. Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sensing of Environment 1991 Feb;35(2-3):257-277. [CrossRef]
    23. Ho HC, Lau KK, Yu R, Wang D, Woo J, Kwok TCY, et al. Spatial variability of geriatric depression risk in a high-density city: a data-driven socio-environmental vulnerability mapping approach. Int J Environ Res Public Health 2017 Aug 31;14(9):994 [FREE Full text] [CrossRef] [Medline]
    24. Rugel EJ, Henderson SB, Carpiano RM, Brauer M. Beyond the Normalized Difference Vegetation Index (NDVI): Developing a Natural Space Index for population-level health research. Environ Res 2017 Nov;159:474-483. [CrossRef] [Medline]
    25. Lee M, Brauer M, Wong P, Tang R, Tsui TH, Choi C, et al. Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong. Sci Total Environ 2017 Aug 15;592:306-315. [CrossRef] [Medline]
    26. Barratt B, Lee M, Wong P, Tang R, Tsui T, Cheng W. A dynamic three-dimensional air pollution exposure model for Hong Kong, Research Report 194. Health Effects Institute 2018 Feb:1-65 [FREE Full text]
    27. Guo M, Huang J, Yuxuan Ni M. 3D built-environment attributes and household road traffic noise exposure in Hong Kong. In: IOP Conference Series: Earth and Environmental Science. 2019 Sep 24 Presented at: Sustainable Built Environment Conference 2019 Wales: Policy to Practice; September 24-25, 2019; Cardiff, Wales p. 012012. [CrossRef]
    28. Lee P, Tse CY. Calibration of wrist-worn ActiWatch 2 and ActiGraph wGT3X for assessment of physical activity in young adults. Gait Posture 2019 Feb;68:141-149. [CrossRef] [Medline]
    29. Lee R, Nieman D. Nutritional Assessment, 7th Edition. Boston, MA: McGraw-Hill Education; 2019.
    30. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J 2005 Aug;26(2):319-338 [FREE Full text] [CrossRef] [Medline]
    31. de Oliveira BFA, Chacra APM, Frauches TS, Vallochi A, Hacon S. A curated review of recent literature of biomarkers used for assessing air pollution exposures and effects in humans. J Toxicol Environ Health B Crit Rev 2014 Dec 12;17(7-8):369-410. [CrossRef] [Medline]
    32. Twohig-Bennett C, Jones A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res 2018 Oct;166:628-637 [FREE Full text] [CrossRef] [Medline]
    33. Day DB, Xiang J, Mo J, Clyde MA, Weschler CJ, Li F, et al. Combined use of an electrostatic precipitator and a high-efficiency particulate air filter in building ventilation systems: Effects on cardiorespiratory health indicators in healthy adults. Indoor Air 2018 May;28(3):360-372 [FREE Full text] [CrossRef] [Medline]
    34. Huang W, Wang G, Lu S, Kipen H, Wang Y, Hu M, et al. Inflammatory and oxidative stress responses of healthy young adults to changes in air quality during the Beijing Olympics. Am J Respir Crit Care Med 2012 Dec 01;186(11):1150-1159 [FREE Full text] [CrossRef] [Medline]
    35. Fox J, Monette G. Generalized collinearity diagnostics. J Am Stat Assoc 1992 Mar;87(417):178. [CrossRef]
    36. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2012 May 18;36(1):27-46. [CrossRef]
    37. Yang B, Markevych I, Heinrich J, Bloom MS, Qian Z, Geiger SD, et al. Residential greenness and blood lipids in urban-dwelling adults: The 33 Communities Chinese Health Study. Environ Pollut 2019 Jul;250:14-22. [CrossRef] [Medline]
    38. Lin B, Yen Y, Lao XQ, Chen Y, Chan T. Association between neighborhood greenspace and fasting plasma glucose from a large cohort study in Taiwan. Urban Forestry & Urban Greening 2019 Aug;44:126439. [CrossRef]
    39. Greene CM, Zern TL, Wood RJ, Shrestha S, Aggarwal D, Sharman MJ, et al. Maintenance of the LDL cholesterol:HDL cholesterol ratio in an elderly population given a dietary cholesterol challenge. J Nutr 2005 Dec;135(12):2793-2798. [CrossRef] [Medline]
    40. Ribeiro AI, Tavares C, Guttentag A, Barros H. Association between neighbourhood green space and biological markers in school-aged children. Findings from the Generation XXI birth cohort. Environ Int 2019 Nov;132:105070 [FREE Full text] [CrossRef] [Medline]
    41. Chaparro MP, Benzeval M, Richardson E, Mitchell R. Neighborhood deprivation and biomarkers of health in Britain: the mediating role of the physical environment. BMC Public Health 2018 Jun 27;18(1):801 [FREE Full text] [CrossRef] [Medline]
    42. Mao G, Cao Y, Lan X, He Z, Chen Z, Wang Y, et al. Therapeutic effect of forest bathing on human hypertension in the elderly. J Cardiol 2012 Dec;60(6):495-502 [FREE Full text] [CrossRef] [Medline]
    43. Jia BB, Yang ZX, Mao GX, Lyu YD, Wen XL, Xu WH, et al. Health effect of forest bathing trip on elderly patients with chronic obstructive pulmonary disease. Biomed Environ Sci 2016 Mar;29(3):212-218 [FREE Full text] [CrossRef] [Medline]
    44. Fang SC, Mehta AJ, Alexeeff SE, Gryparis A, Coull B, Vokonas P, et al. Residential black carbon exposure and circulating markers of systemic inflammation in elderly males: the normative aging study. Environ Health Perspect 2012 May;120(5):674-680 [FREE Full text] [CrossRef] [Medline]
    45. Kotur-Stevuljevic J, Memon L, Stefanovic A, Spasic S, Spasojevic-Kalimanovska V, Bogavac-Stanojevic N, et al. Correlation of oxidative stress parameters and inflammatory markers in coronary artery disease patients. Clin Biochem 2007 Feb;40(3-4):181-187. [CrossRef] [Medline]
    46. Landmesser U, Merten R, Spiekermann S, Büttner K, Drexler H, Hornig B. Vascular extracellular superoxide dismutase activity in patients with coronary artery disease: relation to endothelium-dependent vasodilation. Circulation 2000 May 16;101(19):2264-2270. [CrossRef] [Medline]
    47. Yeager R, Riggs DW, DeJarnett N, Tollerud DJ, Wilson J, Conklin DJ, et al. Association between residential greenness and cardiovascular disease risk. J Am Heart Assoc 2018 Dec 18;7(24):e009117 [FREE Full text] [CrossRef] [Medline]
    48. Im SG, Choi H, Jeon Y, Song M, Kim W, Woo J. Comparison of effect of two-hour exposure to forest and urban environments on cytokine, anti-oxidant, and stress levels in young adults. Int J Environ Res Public Health 2016 Jun 23;13(7):625 [FREE Full text] [CrossRef] [Medline]
    49. Mao GX, Lan XG, Cao YB, Chen ZM, He ZH, Lv YD, et al. Effects of short-term forest bathing on human health in a broad-leaved evergreen forest in Zhejiang Province, China. Biomed Environ Sci 2012 Jun;25(3):317-324 [FREE Full text] [CrossRef] [Medline]
    50. Sinharay R, Gong J, Barratt B, Ohman-Strickland P, Ernst S, Kelly FJ, et al. Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. The Lancet 2018 Jan;391(10118):339-349. [CrossRef]
    51. Eldeirawi K, Kunzweiler C, Zenk S, Finn P, Nyenhuis S, Rosenberg N, et al. Associations of urban greenness with asthma and respiratory symptoms in Mexican American children. Ann Allergy Asthma Immunol 2019 Mar;122(3):289-295. [CrossRef] [Medline]
    52. Hartley K, Ryan P, Brokamp C, Gillespie GL. Effect of greenness on asthma in children: A systematic review. Public Health Nurs 2020 May 03;37(3):453-460. [CrossRef] [Medline]
    53. Lambert KA, Bowatte G, Tham R, Lodge C, Prendergast L, Heinrich J, et al. Residential greenness and allergic respiratory diseases in children and adolescents - A systematic review and meta-analysis. Environ Res 2017 Nov;159:212-221. [CrossRef] [Medline]

    ‎
    8-OHdG: 8-hydroxy-2'-deoxyguanosine
    AIC: Akaike information criterion
    COPD: chronic obstructive pulmonary disease
    Cu,Zn-SOD: copper–zinc superoxide dismutase
    ELISA: enzyme-linked immunosorbent assay
    FEV1: forced expiratory volume in the first second
    FVC: forced vital capacity
    GLME: generalized linear mixed-effects model
    GPx-1: glutathione peroxidase
    HDL-C: high-density lipoprotein
    HR: heart rate
    hs-CRP: high-sensitivity C-reactive protein
    IHC: Integrative Health Clinic
    IL-6: interleukin-6
    LDL-C: low-density lipoprotein
    MDA: malondialdehyde
    NDVI: normalized difference vegetation index
    NO2: nitrogen dioxide
    PM2.5: particulate matter with a diameter less than 2.5 μm
    sP-selectin: soluble platelet selectin
    SPOT: Satellite Pour l’Observation de la Terre
    TC: total cholesterol
    TG: triglyceride
    TNF-α: tumor necrosis factor α
    VIF: variance inflation factor

    Edited by G Eysenbach; submitted 17.06.21; peer-reviewed by Z Xu, A Guttentag; comments to author 25.08.21; revised version received 28.10.21; accepted 16.11.21; published 30.12.21

    Copyright

    ©Lin Yang, Ka Long Chan, John W M Yuen, Frances K Y Wong, Lefei Han, Hung Chak Ho, Katherine K P Chang, Yuen Shan Ho, Judy Yuen-Man Siu, Linwei Tian, Man Sing Wong. Originally published in JMIR Cardio (https://cardio.jmir.org), 30.12.2021.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included.