Egorov AI, Griffin SM, Converse RR, 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;158(March):508-521.
This cross-sectional epidemiological study compares green space exposure (GSE), as identified by the US Environmental Protection Agency (EPA) EnviroAtlas LiDAR land-cover dataset using 1m2 resolution, with objective physiological biomarker data. Ten concentric circles of 50 m radius each (ie, 0-50 m, 50-100 m, to 450-500 m) were analyzed from participants’ geocoded home addresses to determine various GSEs. Seven different models were used to assess GSE based on varying radii from the homes (eg, 0-50 m only, weighted exponential decay proportional to distance <500 m, all 10 concentric 50 m circles weighted equally, mean GSE of 0-500 m).
Participants (N=206) were residents of the Durham-Chapel Hill, North Carolina metro region. Mean participant age was 37.4 years (range, 18-85), and 66% of participants were female. Mean BMI and smoking status rates mirrored local population averages. Mean education level was slightly greater than local average.
To determine if there is a relationship between green space in the surrounding environment and allostatic load as measured by 18 biomarkers.
The principle outcome measure in this study is allostatic load. Allostatic load is a quantitative measure of the “wear and tear” on the body caused by stress. It is calculated from a combination of biomarkers.1 This study uses 18 biomarkers, dichotomized to 90th percentile or 10th percentile cut-off levels, to determine healthy/unhealthy status in the following categories:
- Metabolic: C-reactive protein (CRP), fibrinogen, serum amyloid A (SAA), high-density lipoprotein (HDL), low-density lipoprotein (LDL), uric acid, BMI
- Neuroendocrine: epinephrine, norepinephrine, dopamine, dehydroepiandrosterone (DHEA), α-amylase
- Immune: interleukin (IL)-1B, IL-6, IL-8, tumor necrosis factor (TNF)-alpha, vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), myeloperoxidase (MPO)
Other biomarkers typically included in allostatic load calculations (eg, systolic/diastolic blood pressure, salivary cortisol) were not available because they were not collected in the parent project, the source of this data.
Data was calculated using regression analysis.
However, regardless of mechanism, it is apparent from this study that GSE has an association with metabolic, neuroendocrine, and immunological status and, in the case of depression, clinical manifestations of disease.
History of previous and current medical diagnoses (ie, allergies, asthma, arthritis, depression, diabetes, irritable bowel syndrome, obesity), as well as self-reported generalized health status, were collected. Controlling factors or covariates (ie, age, gender, race, BMI, smoking status, education level, self-reported health status, and housing density) were incorporated into the regression models.
Green space environment (GSE) was inversely associated with composite allostatic load, as well as multiple individual biomarkers. This occurred across various GSE distribution models. Most notably:
- Ten biomarkers (epinephrine, norepinephrine, dopamine, α-amylase, CRP, fibrinogen, HDL, IL-8, VCAM-1, DHEA) had a statistically significant adjusted odds ratio (aOR) in at least 1 of the 7 GSE models, showing there was an association between GSE and biomarker status.
- Four biomarkers (epinephrine, norepinephrine, dopamine, and α-amylase) had statistically significant inverse associations with GSE in all 7 models, showing that these proxy measures of psychoneurological stress were benefited by the presence of vegetation, no matter which method of GSE analysis was used.
- Effect of GSE on allostatic load was most strongly measured in Model 5 (slow exponential decay of GSE effect with increasing radius from home), which showed that for every 25% increase in amount of green space within 500 m (ie, interquartile range [IQR]), there was a 34.4% reduction in odds of an unhealthy composite biomarker score.
- In Model 5, 8 of the biomarkers demonstrated statistically significant inverse associations with GSE factors, with all 8 having an aOR below 50% for every GSE IQR. Other biomarkers (HDL, SAA, ICAM-1, CRP) also had reduced aOR below or near 50%, but these were not statistically significant (Table).
- In assessing risk for previous or current diagnosis of disease, depression was inversely associated with GSE for each of the 7 GSE models, with Model 3 (fast exponential decay of GSE effect with increasing radius from home, λ = 0.01) showing the strongest association (aOR: 0.44; 95% confidence interval [CI]: 0.27-0.72). Depression showed a statistically significant inverse aOR per GSE IQR at or near 50% for all 50 m concentric circles from the home, up to 200-250 m. No other diagnoses demonstrated an association with any of the GSE models, though self-reported health status did have a statistically significant association (aOR: 0.55; 95% CI: 0.33-0.92) with GSE IQR in the proximal 0-50m GSE radius.
Table. Adjusted Odds Ratios of Biomarkers in Model 5
|Adjusted odds ratio (aOR)
|95% Confidence interval (CI)
Abbreviations: IL, interleukin; VCAM-1, vascular cell adhesion molecule 1; DHEA, dehydroepiandrosterone; HDL, high-density lipoprotein; SAA, serum amyloid A; ICAM-1, intercellular adhesion molecule 1.
This study is the first of its kind to directly match individual biomarkers of allostatic load with personalized geocoded GSE data based on an individual’s residence. Many studies of health effects of green space or “nature” have investigated subjective or psychological outcomes,2,3 or they have investigated physiological effects from short-term or acute exposure to natural settings. To the author’s knowledge, 1 pilot study (N=25) has looked at associations of salivary cortisol to residential green space density.4 But the Egorov et al study reviewed here is the first to perform an extensive, objective physiological assessment of urban residents in relation to the green space surrounding their homes.
Allostatic load, formally defined, is “the cost of chronic exposure to fluctuating or heightened neural or neuroendocrine response resulting from repeated or chronic environmental challenge that an individual reacts to as being particularly stressful.”5 Stress is a convenient but vague term that has no direct medical applications. In this study allostatic load is a useful measure for understanding the detrimental effects of “stress.” Proxy measures of stress, such as salivary cortisol, provide snapshots of one aspect of health status but are limited in scope. The composite score of allostatic load approximates the total physiological impact of inflammation, neurotransmitter imbalance, and hormonal dysregulation that result in chronic disease and the deterioration of health.6
Landscape architect Roger Ulrich developed the most well-studied and directly applicable mechanism of action for allostatic load and GSE. Ulrich’s theory, referred to as the “psycho-evolutionary stress theory” or “psycho-environmental stress model” (Ulrich himself never named the theory he developed),7 is derived from the “biophilia hypothesis” of Harvard biologist and author EO Wilson. The biophilia hypothesis postulates that human beings have “an inherent affinity for life and other living things” due to millions of years of evolutionary adaptation.8 Our sensoriperceptual baseline, and by extension our autonomic nervous system responses, evolved in the contextual sights, sounds, and smells of natural environments. These environmental stimuli have a “hard-wired” effect on our psychological and physiological responses and assist in homeostatic regulation of stress, thereby decreasing the negative health effects of accumulated allostatic load. Reviews of many studies,9,10 and recently a meta-analysis,11 have validated these theories.
Reduction of psychophysiological stress is just one mechanism by which residential GSE may influence allostatic load and the prevalence of disease. Review of the literature demonstrates 4 substantiated mechanisms:12
- Stress modulation (see above)
- Physical activity: physical activity is known to increase with increasing access to green spaces,13 and the study of “green exercise” has shown the physical and psychological enhancement effects of being physically active in nature vs indoors.14,15
- Social support: green spaces such as parks and community gardens facilitate social interactions and community development that reduce loneliness, provide resources for assistance, and promote social capital, all of which directly impact health status.16,17 Green spaces also foster sense of place and mental/emotional attachment and development of self/group/place identity, which influence psychophysiological health.18
- Air quality: the presence of vegetation affects air quality by filtering particulate matter,19 which is a known contributor to inflammation, respiratory and cardiovascular disease, and allostatic load.20 The famous Shinrin-yoku (“forest-air bathing”) studies of Japan and Korea found that certain trees, mostly coniferous tree species such as cedar, pine, and cypress, produce immune-enhancing phytoncides, which modulate cytokine and white blood cell activity and could contribute to a reduction in allostatic load.21
Some studies have attempted to investigate the relative contribution of these mechanisms toward overall health status.22 Green space is a complex, environmental exposure; it may be best to apply green space as a conglomerate with interactive, synergistic effects, rather than attempt to parse out individual mechanisms.
The results of the different GSE models show that vegetation closest to the home, within the first 50 m radius, is important for establishing the primary effect on allostatic biomarker levels. However, GSE up to 500 m was also important at modulating biomarker response, which suggests that total surrounding environment, and not just what is outside the front door, has a role in determining stress biomarker and health status.
This was a well-designed study that demonstrated strong associations between the exposure and outcome variables. The sample size (N=206) was small for an epidemiological study, but as noted this was the first study of its kind and statistical significance was achieved for 8 of the 18 biomarkers in all GSE models. It is likely that a larger sample size would have increased the statistical significance of other biomarkers.
Use of covariates such as education, age, and housing density is critical for studies such as this, to rule out or adjust for confounding factors. One large limitation was the inclusion of income as a measure of socioeconomic status (SES), which is a direct determinant of allostatic load.23 Income has also been shown to directly mediate the effects of green space on health outcomes.24 Level of education, as used in this study, is a commonly used proxy measure of SES, but is not as specific as income.
As an epidemiological study these results cannot show causality. However, this is a limitation of all epidemiological studies and is not unique to the current study. The thorough design and robust findings of this study suggest that follow-up experimental study (ie, pre-post data collection, with high and low GSE exposure and control groups) is warranted. Such studies are currently underway.25
The presence of green space is beneficial (some would say essential) for the creation and maintenance of human health states. This study suggests that the health benefits of green space are achieved through the modulation of allostatic load. More research is required to establish causality and the exact mechanisms of action responsible for the benefits of chronic GSE in residential settings. However, regardless of mechanism, it is apparent from this study that GSE has an association with metabolic, neuroendocrine, and immunological status and, in the case of depression, clinical manifestations of disease. These findings have implications for healthcare practitioners, public health officials, urban developers, and any person(s) wanting to understand how their environmental surroundings, particularly the presence or absence of natural surroundings around their home, influence their health.