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Article

Asthma Is Associated with Overweight, Obesity and Residential Grey Space in an Italian General Population Sample

by
Ilaria Stanisci
1,2,*,
Anna Antonietta Angino
1,
Sara Maio
1,
Giuseppe Sarno
1,
Patrizia Silvi
1,
Sofia Tagliaferro
1,3,
Giovanni Viegi
1,4 and
Sandra Baldacci
1
1
Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy
2
Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy
3
Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
4
Institute of Translational Pharmacology, National Research Council, 90146 Palermo, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(24), 11300; https://doi.org/10.3390/su172411300
Submission received: 20 October 2025 / Revised: 3 December 2025 / Accepted: 12 December 2025 / Published: 17 December 2025
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
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Versions Notes

Abstract

Background: Overweight and obesity frequently occur as comorbid conditions in people with asthma, particularly among those with poor disease control or more severe clinical profiles. However, the extent to which exposure to grey spaces may influence the link between overweight/obesity and asthma remains insufficiently explored. Aim: To assess the association between overweight/obesity and asthma in an Italian general population sample and the influence of residential grey space on such relationship. Methods: A total of 2841 individuals (54.7% women; age range 8–97 years) residing in Pisa, Italy, were surveyed in 1991–1993 using a standardised questionnaire on health conditions and relevant risk factors. The proportion of grey space within a 1000 m buffer around each participant’s home was quantified using the CORINE Land Cover database. Multinomial logistic regression models were applied to assess the association between asthma status (1. asthma symptoms without doctor diagnosis, 2. diagnosis ± symptoms, 3. no diagnosis/symptoms − reference category) and overweight/obesity, adjusting for sex, age, educational level, smoking, physical activity and grey space exposure. Analyses were further stratified according to high vs. low grey space exposure (above vs. below 63%, corresponding to the second tertile). Mediation and interaction analyses were also performed. Results: The prevalence of asthma diagnosis ± symptoms, overweight and obesity was 18.7%, 35.8% and 12.8%, respectively. In the full sample, asthma symptoms without medical diagnosis were positively associated with overweight (Odds Ratio—OR 1.43; 95% Confidence Interval—CI 1.08–1.88), obesity (OR 1.99; 95% CI 1.38–2.88) and residential grey space (OR 1.06; 95% CI 1.01–1.13). Stratified models showed that, among participants with high exposure to grey areas, asthma symptoms were linked to both overweight (OR 2.03; 95% CI 1.29–3.19) and obesity (OR 2.57; 95% CI 1.36–4.86). In individuals with low grey space exposure, an association was observed only with obesity (OR 1.80; 95% CI 1.15–2.82). Mediation analysis did not reveal any weight-related effect modification. Measures of additive interaction indicated that 32% of asthma symptoms were attributable to the interaction between excess body weight and high grey space exposure. Conclusions: This study showed that overweight/obesity and grey space exposure are factors associated with asthma symptoms. These findings advocate for an early identification of overweight/obese-asthma symptom phenotype since it may help prevent the onset or worsening of asthma, particularly in urban environments. These insights highlight the need for integrated public health and urban planning strategies to promote more sustainable, health-supportive environments.

1. Introduction

Asthma is a common condition caused by chronic inflammation of the lower respiratory tract. This disease is characterised by symptoms such as wheezing, shortness of breath, chest tightness and cough [1]. Asthma is a global public health issue, with an estimated 260 million people worldwide suffering from this disease in 2024, and it is responsible for over 450,000 deaths each year worldwide [2]. In the last decades, the prevalence of asthma has doubled in Westernised countries and may have reached a plateau in recent years [3,4,5]. The causes of this alarming change are unclear, but cannot be attributed only to changes in diagnosis and patient awareness over time [5]. Since the East–West Germany comparison [6], many studies have shown an association between the rise in allergic diseases and modern Westernised lifestyle, which is characterised by increased urbanization, air pollution, time spent indoors, unhealthy dietary patterns and antibiotic usage [4,7]. Lifestyle-related factors, such as high body mass index (BMI), smoking, salty-snack eating and time spent on television watching, have been associated with increased asthma prevalence in the literature from Western countries [8,9,10].
As reported by the Global Burden of Diseases study (GBD) in 2021, obesity is a risk factor for asthma [11,12]. It is also considered a disease modifier for asthma in both adults and children, and it is associated with increased asthma severity [13]. A meta-analysis of prospective epidemiologic studies has quantified the relationship between categories of Body Mass Index (BMI) and incidence of asthma in adults and has concluded that the more overweight an individual is, the greater the likelihood of having or developing asthma [14]. Compared with normal weight people, asthma in obese individuals is associated with poorer asthma-related quality of life, greater health care utilization and a worst control partly due to reduced response to standard drug therapy [13,15,16]. Moreover, different studies have reported that weight loss leads to significant improvements in asthma control and lung function [13,17].
According to the World Health Organization (WHO), 90% of individuals worldwide, especially those in urban areas, inhale air contaminated with levels of pollutants above the WHO Guidelines, and it was estimated that approximately 7 million people die each year due to particles in polluted air [18,19]. More recently, the State of Global Air report has estimated 8.1 million deaths annually due to air pollution, which has become the 2nd leading cause of avoidable deaths [20]. Many studies have so far demonstrated a clear association between short-term exposure to outdoor air pollutants and different asthma outcomes, including asthma control [21], lung function [22], consumption of asthma medications [23], asthma exacerbations [24], hospitalizations [25] and deaths [26]. Particulate matter (PM) has the greatest impact on human health and is commonly used as a measure of air quality [27,28]. Evidence suggests that air pollution has a negative impact on asthma outcomes in both adult and paediatric populations, and 13% of the global incidence of asthma in children could be attributable to traffic-related air pollution, a complex urban mixture rich in PM [29].
Recently, data from the Coordination of the Information on the Environment (CORINE) Land Cover (CLC) [30,31] were used in epidemiological studies [32]. CLC is a European-wide standardised land cover map, managed by the European Environmental Agency (EEA), combining geospatial environmental information from national databases and satellite images [33] to provide data about specific land cover types such as urban/grey spaces, green spaces and proximity to agricultural areas or water bodies (blue space). In particular, the grey spaces, including several urban places such as industries, urban fabric and transport infrastructures, can be used as a proxy of urban environment exposure, where various pollutants abound.
Most previous research has separately analysed the role of extra weight and grey space in asthma risk assessment. Recent evidence suggests that PM may trigger inflammation in adipose tissue, oxidative stress injury and metabolic dysfunction, affecting weight status [34]. At the same time, obesity can worsen the adverse effects of air pollution on the human body, enhancing susceptibility to the PM-related harmful effects, and also enhancing the association of PM exposure with chronic obstructive pulmonary disease, hypertension and cardiovascular disease [34]. Thus, the interaction between weight and grey space, as a proxy of air pollution exposure, is biologically plausible. Nevertheless, no studies have exhaustively explored the role of this interaction on asthma.
This study aims to assess the association between overweight/obesity and asthma in an Italian general population sample and the influence of residential grey space on such a relationship.

2. Methods

2.1. Study Population and Design

A multistage stratified random sample of the general population living in Pisa, Italy, was investigated in three subsequent cross-sectional surveys [35]: first survey (PI1) (1985–1988); second survey (PI2) (1991–1993); third survey (PI3) (2009–2011). In this paper, data from the second survey (PI2), consisting of 2841 subjects, have been used. Data were collected through the use of a standardised interviewer-administered questionnaire developed by the National Research Council on the basis of the National Heart Blood and Lung Institute questionnaire [36]. At the time of the PI2 study, Italian law did not require Ethical Committee approval. The protocol was approved by an Internal Review Board. The study was carried out in accordance with the fundamental principles of the Helsinki Declaration.

2.2. Health Outcome

Subjects answered questions about lifetime asthma symptoms and asthma doctor diagnosis. The following asthma symptoms were considered: wheezes or whistles and attacks of shortness of breath with wheeze and whistle.
In order to detect possible effects at an early disease stage, the following mutually exclusive outcome variables were computed: 1. asthma symptoms without doctor diagnosis (“symptoms without diagnosis”), 2. asthma diagnosis with or without symptoms (“diagnosis ± symptoms”), 3. no asthma diagnosis nor asthma symptoms (“no diagnosis/symptoms” − reference category).

2.3. Exposure

BMI categories and grey space exposure were considered exposure variables.
BMI categories were computed as follow: 1. underweight and normal weight (BMI < 25 kg/m2), 2. overweight (25 ≤ BMI < 30 kg/m2), 3. obese (BMI ≥ 30 kg/m2).
The percentage of grey spaces within a 1000 m buffer surrounding the geocoded home address was computed through the CLC. Grey space was analysed in two ways: (a) as a continuous variable, considering 10% increments; (b) as a categorical variable, stratifying the subjects in highly exposed to grey spaces (those with a surrounding residential grey space ≥ 63% (2nd tertile)) and lowly exposed to grey spaces (<63%)). Moreover, a combined variable considering both the weight and grey exposure was computed as follows: 1. underweight-normal weight and low grey exposure (reference category); 2. underweight-normal weight and high grey exposure; 3. overweight-obese and low grey exposure; 4. overweight-obese and high grey exposure.

2.4. Confounding Factors

Different potential confounders collected through questionnaire were considered: sex; age (1. ≤18 years, 2. >18 and ≤59 years, 3. >59 years); educational level (1. literate/elementary/middle high school (≤8 years of education), 2. college/high school (9–13 years of education), 3. graduate (>13 years of education)); smoking habits (1. non-smoker (subjects who had never smoked any kind of tobacco), 2. smoker (subjects who were smoking), 3. ex-smoker (subjects who had smoked in the past but were not smoking at the time of the survey)); active lifestyle in terms of daily time spent outdoors (1. ≥2 h, 2. <2 h) [37].

2.5. Statistical Analysis

The characteristics of the study sample have been summarised with the use of frequency distribution (%). Bivariate analyses were carried out using a chi-square test to compare categorical variables and analysis of variance to compare continuous variables.
A multinomial logistic regression model was carried out to estimate the independent effects of overweight/obesity and grey space (10% increase) on asthma outcome, adjusting for the potential confounders described above.
In order to consider the possible effect modification by grey space exposure on the relationship between overweight/obesity and asthma outcomes (that is if there is a heterogeneous effect according to grey space exposure), the analyses were also carried out by stratifying the subjects into highly exposed and lowly exposed to grey spaces. The analyses were adjusted for the potential confounders. A Causal Mediation Analysis (CMA) was also carried out to determine whether overweight or obesity mediated, either partially or entirely, the association between grey space exposure and asthma. To assess potential effect modification by weight status, we tested an interaction term between grey space and weight group in the multivariable logistic regression model. The interaction was evaluated by including the product term (grey × weight) and examining its statistical significance while adjusting for all covariates. Finally, a multinomial logistic regression model was used to assess the interaction between grey and overweight/obesity using the combined variable and adjusting for the potential confounders. To assess the additive interaction between exposure to grey (high vs. low) and weight status (overweight/obese vs. normal/underweight), the two variables were combined into a four-level joint exposure variable (00 = low grey/underweight-normal weight; 10 = high grey/underweight-normal weight; 01 = low grey/overweight-obese; 11 = high grey/ overweight-obese). A binary logistic regression model was fitted to estimate the odds ratios for each exposure category, adjusting for relevant covariates. Based on these estimates, we calculated the following standard additive interaction measures: the Relative Excess Risk due to Interaction (RERI), the Attributable Proportion due to interaction (AP), and the Synergy Index (S). Confidence intervals of 95% were computed using the Delta method. More details on calculating additive measures are reported in the Supplementary Materials.
The odds ratios (OR) with 95% confidence interval (CI) were calculated. The statistical significance level was set at a p-value of 0.05.
Statistical analyses were carried out using the Statistical Package for the Social Sciences (SPSS version 26.0, IBM Corp., Armonk, NY, USA) or R statistical software (R version 2025.09.2 Core Team, 2024).

3. Results

This study was conducted on a sample of 2841 subjects who filled in the questionnaire, as presented in Table 1.
The sample consisted of 54.7% females, aged 8–97 years (mean age 48.0 ± 20.6 years), mainly with a low education level (75.6% with ≤8 years of education). The percentage of overweight people was 35.8%, while the percentage of obese people was 12.8%. Smokers made up 23.1% of the sample. Most of the subjects spent ≥2 h outdoors daily (56.4%) and had a low grey space exposure (67.6%); 18.7% had an asthma diagnosis or symptoms (Table 1).
In Table 2, the distribution of risk factors in relation to asthma diagnosis/symptoms is reported.
The table shows that all the variables considered in the analyses present a statistically significant difference between the three categories of the outcome. A higher percentage of subjects ≤ 18 yrs emerged in those without asthma symptoms and diagnosis (37.2%), while a higher percentage of >59 yrs subjects emerged in those with an asthma diagnosis (38.8%); males were prevalent in those with symptoms without diagnosis (66.2%). With regard to education levels, low education level (≤8 yrs) was significantly more frequent in those with an asthma diagnosis (81.1%). Smokers and ex-smokers were more frequent in those with symptoms without diagnosis (44.3% and 33.8%, respectively). The percentage of subjects who spent less than two hours a day outdoors was higher in subjects with asthmatic symptoms without diagnosis (49.5%). Although the mean level of grey space did not significantly differ across the asthma symptoms/diagnosis categories (p-value = 0.19), high grey exposure was borderline associated with having symptoms without diagnosis (37.8%). Finally, considering BMI categories, both overweight and obese subjects had a higher percentage in those with asthmatic symptoms without diagnosis (44.3% and 17.5%, respectively).
Table 3 shows the results of the multinomial logistic regression model on the total sample (n = 2834).
Apart from males, smokers, ex-smokers and <2 h a day outdoor, being overweight (OR 1.43, 95% CI 1.08–1.88), being obese (OR 1.99, 95% CI 1.38–2.88) and an increase of 10% in grey space exposure (OR 1.06, 95% CI 1.01–1.13) were associated with asthma symptoms without diagnosis. No association was found with asthma diagnosis ± symptoms.
Figure 1 shows the results of the multinomial logistic regression models after stratification by high exposure to grey spaces (n = 919) and low exposure (n = 1915).
Being overweight (OR 2.03, 95% CI 1.29–3.19) and being obese (OR 2.57, 95% CI 1.36–4.86) were significantly associated with asthma symptoms in subjects with high grey exposure. Obesity (OR 1.80, 95% CI 1.15–2.82) was also found to be associated with asthma symptoms in subjects with a low grey exposure. More detailed results are contained in Tables S1 and S2 of the Supplementary Materials.
In addition to the main models, we performed a causal mediation analysis to evaluate whether the association between grey space (exposure) and asthma (outcome) was mediated by weight status, while adjusting for age, sex, level of education, smoking habits and hours spent outdoors.
We fitted a logistic regression model for the mediator (weight status) and a logistic regression model for the outcome, including both grey space and weight status, and then applied the mediate function (R package mediation) with 4000 simulations and robust standard errors. The mediation analysis showed that the indirect effect of grey space on asthma through weight status was essentially absent and not statistically significant, indicating that weight does not act as a mediator in this relationship. Conversely, the direct effect of grey space on asthma remained statistically significant, and the total effect closely reflected this direct component.
We also evaluated whether the effect of grey space on asthma differed by weight status by including an interaction term between grey space and weight group in the logistic regression model, adjusting for age, sex, education, smoking and hours outdoors. The interaction term was not statistically significant, and we did not observe any clear pattern suggesting effect modification by weight status.
Taken together, the mediation and interaction analyses suggest that weight status neither mediates nor modifies the association between grey space and asthma in this cohort.
Table 4 shows the results of the multinomial regression model in which the combined variable of grey and BMI was used.
Exposure to both overweight/obesity and high grey space (≥63%) was associated with a 2.5-fold higher odds of having asthma symptoms without diagnosis (OR 2.53, 95% CI 1.36–4.71) compared to subjects without symptoms/diagnosis; subjects exposed only to overweight/obesity showed borderline lower odds (OR 1.37, 95% CI 0.99–1.88). These results suggest a synergistic interaction between BMI and grey space exposure.
To estimate the interaction effect of weight and grey, the calculation of the additive measures was carried out, and the results are presented in Table 5.
The results show a positive interaction on an additive scale (relative excess risk due to interaction—RERI: 0.84), an attributable proportion (AP) of 0.32, namely 32% of the effect (asthma symptoms) in subjects exposed to both overweight/obesity and high grey spaces is attributable to interaction and a synergistic interaction (i.e., the joint effect is 1.5 times larger than what would be expected if exposures were simply additive) (synergy index—S: 1.47). All the three measures indicate the presence of a positive interaction, although only AP is statistically significant, indicating that the interaction contributes significantly to symptoms among those exposed to both factors. Results regarding the diagnosis ± symptoms category are shown in the Supplementary Materials.

4. Discussion

This study investigated the association between body weight and asthma, as well as the role of the urban environment, particularly exposure to grey spaces in this relationship. In particular, our findings show that overweight or obesity is associated with increased odds of having asthma symptoms, and that this relationship is further enhanced by living in densely built and likely polluted urban areas. The association between overweight/obesity and asthma varies according to strata of grey space exposure, and the additive interaction analysis showed a significant attributable proportion, suggesting a possible positive departure from additivity.
The results showed that being overweight or obese increases the odds of asthma symptoms, in line with other evidence showing that a high BMI is associated with asthma and impaired lung function [38,39,40]. Moreover, asthma and obesity frequently co-occur, and individuals with obesity often experience a more severe form of asthma. This has led to the recognition of an “obesity-related asthma” phenotype, which may require tailored therapeutic approaches [41]. In fact, asthma patients with obesity usually have more frequent difficult-to-treat asthma, are less responsive to steroids, have poorer symptom control, larger use of medications, poorer quality of life and larger disease severity [42,43,44,45,46,47]. People with severe asthma begin to be less active, which leads to weight gain and is also linked to a larger use of drugs [48]. Indeed, several studies have highlighted how obesity worsens the inflammatory condition of patients, for example, by increasing the quantity of eosinophils and neutrophils in the blood and the release of pro-inflammatory mediators [49,50]. This pathogenic role of obesity and inflammatory conditions has been seen to play an important role in several diseases, including COPD and asthma [48,51]. High BMI has also a direct impact on the airways through increased airway hyperresponsiveness in asthmatic populations [52], and the effect of obesity on human airway smooth muscle is exaggerated in adult females [53]. The reduction of weight, leading to an improvement in systemic inflammation, is one of the intervention strategies that are currently used to contain the pathological complications related to obesity [54,55,56].
Moreover, our findings showed that an increase of 10% in grey space exposure is associated with asthma symptoms without diagnosis. Other studies have shown that air pollution is related to the prevalence of asthmatic symptoms, increased odds of asthma prevalence, hospital admissions for asthma and decreased lung function [57,58,59,60]. In particular, a more recent study suggests that elevated levels of vehicle exhaust outside the home increase the risk of onset and incident asthma among adults [61]. Some studies have also demonstrated epidemiological links between air pollution and increased respiratory tract infections in patients of all ages, which are considered the cause of asthma exacerbations [62,63]. Indeed, several inflammatory mechanisms link pollution to asthma, with airway epithelial cells forming the respiratory mucosa that acts as both a physical and immune barrier through tight junctions, mucus production, and innate immune responses [64]. Some epidemiological studies have also reported links between air pollution and increased respiratory tract infections, which are considered the cause of asthma exacerbations [65].
In our study, a modification effect emerged. Contrary to the absence of a mediation effect by weight, the results from the additive interaction analysis were still noteworthy. Although RERI and S did not reach statistical significance, the Attributable Proportion due to interaction (AP) indicated a meaningful departure from additivity, suggesting that a non-negligible share of the joint effect between grey space exposure and weight may be attributable to their interaction. The possible link between pollution and weight has been studied mainly in the paediatric population, highlighting that among children and adolescents in urban communities, the relationship between household pollution (fine particulate matter, nitric oxide and nicotine) and asthma symptoms was more pronounced among overweight and obese children [66,67]. The authors of these studies suggest that overweight may increase susceptibility to the pulmonary effects of indoor PM2.5 and NO2, and that the combination of overweight and high exposure to indoor pollutants in urban children with asthma may explain part of the disproportionate asthma morbidity observed in their population [66]. A very recent review suggests another type of relationship between pollution and obesity and, in particular, highlights that pollution promotes stress, leading to obesity [68].
A limitation of our study is that the data were collected in the 90’s, so there could be some differences compared to the conditions of the general population today. There is certainly no risk of overestimating the number of asthma cases given the growth in the disease prevalence of asthma in recent decades, which has reached a plateau only in recent years although not in all areas of the world [69]. Even considering the environment, in recent years an increase in urbanization has been observed which has led to an increase in the prevalence of allergic diseases and in the development and exacerbation of asthma [7]. Furthermore, as highlighted in previous studies [70,71] and other major European investigations [72,73], traffic-related air pollutants tend to exhibit strong spatial stability, with within-city spatial patterns remaining consistent over time, even when overall mean concentrations change. Based on this evidence, it is reasonable to assume that the relative differences (spatial contrasts) in average levels of grey space exposure across residential locations have also remained quite stable. In other words, the effect associated with a 10% increase in grey exposure in the past is likely still informative for present-day populations. The use of a questionnaire for data collection on one hand can be considered a limitation because it can be affected by a reporting bias; however, for large analytical epidemiology studies, it remains the primary means of data collection [74]. In addition, asthma status in our study was solely based on self-reported diagnosis and/or symptoms without objective clinical validation (e.g., spirometry or bronchodilator testing). This might introduce recall or diagnostic bias and should be taken into account when interpreting prevalence estimates. Future studies should include lung function measurements in order to avoid misclassification errors. The strength of our study is instead the large size of the population sample considered and in the use of a standardised questionnaire.
In summary, it has been increasingly recognised in recent decades that asthma is a highly heterogeneous disorder with diverse underlying mechanisms and pathways that result in variable responses to standard treatment in different subgroups or clinical phenotypes [75]. This highlights the need to identify the various asthmatic phenotypes and all the environmental and host factors that can influence such disease in order to create personalised intervention strategies for patients affected by different forms of the disease.

5. Conclusions

Our findings show that overweight and obesity are associated with increased odds of having asthma symptoms, and that these relationships are influenced by residential grey exposure: living in densely built and likely polluted urban areas increases the odds of asthma symptoms. Early identification of at-risk phenotypes may help prevent disease occurrence and progression, particularly in urban areas. By identifying environmental and individual factors that interact to increase asthma risk, this study provides evidence that can inform sustainable public health policies. Reducing exposure to grey space and improving preventive strategies for high-risk groups may help reduce long-term clinical burden and contribute to the sustainability of healthcare systems. Future longitudinal studies are needed to clarify causal pathways and guide effective prevention strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su172411300/s1, Table S1. Risk factors for asthma symptoms in subjects with high grey space exposure (≥63%): OR and 95% CI; Table S2. Risk factors for asthma symptoms/diagnosis in subjects with low grey space exposure (<63%): OR and 95% CI; Table S3. Results of the calculation of additive measures for the variable Diagnosis ± symptoms.

Author Contributions

Conceptualization, S.B. and I.S.; methodology, S.B. and I.S.; validation, G.V.; formal analysis, I.S. and A.A.A.; investigation, S.B. and G.V.; data curation, A.A.A., G.S. and S.M.; writing—original draft preparation, I.S.; writing—review and editing, I.S., S.M., G.S., P.S., S.T., G.V. and S.B.; supervision, S.B.; project administration, P.S.; funding acquisition, G.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the CNR-ENEL (Italian Electric Power Authority) ‘Interaction of Energy Systems with Human Health and Environment’ Project (1989) and by the Italian National Research Council Targeted Project ‘Prevention and Control Disease Factors-SP 2’ (Contract No. 91.00171.PF41; 1991).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki. Ethical review and approval were waived for this study because at the time of the survey, Italian regulations did not require approval from an Ethical Committee. An Internal Review Board within the National Research Council (CNR) Preventive Medicine Targeted Project endorsed the protocol in 1985.

Informed Consent Statement

Participants voluntarily and verbally agreed to take part in the study after they have been fully informed about all relevant aspects, including the purpose, procedures, potential risks, and benefits by the physicians/research personnel. Participants’ data were completely anonymised, ensuring that individuals could not be identified.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 17 11300 g001
Figure 1. Effect of BMI on asthma symptoms/diagnosis in subjects with high/low grey space exposure: OR and 95% CI.
Figure 1. Effect of BMI on asthma symptoms/diagnosis in subjects with high/low grey space exposure: OR and 95% CI.
Sustainability 17 11300 g001
Table 1. Descriptive characteristics of the study sample (frequency distributions, means + SD) (n = 2841).
Table 1. Descriptive characteristics of the study sample (frequency distributions, means + SD) (n = 2841).
Age n (%):
≤18266 (9.4)
>18 and ≤591637 (57.6)
>59938 (33.0)
Age (mean ± SD)
Age range (yrs)		48.0 ± 20.6
8–97
Sex n (%):
Female
Male		1553 (54.7)
1288 (45.3)
Level of education n (%):
≤8 years2147 (75.6)
9–13 years589 (20.7)
>13 years105 (3.7)
BMI n (%):
Normal weight and underweight1460 (51.4)
Overweight1017 (35.8)
Obese364 (12.8)
BMI kg/m2 (mean ± SD)25.2 ± 4.4
Smoking habits n (%):
Non-smokers1397 (49.2)
Ex-smokers786 (27.7)
Smokers658 (23.1)
Hours outdoors n (%):
<2 h a day1235 (43.6)
≥2 h a day1599 (56.4)
Grey space exposure n (%):
Low (<63%)1921 (67.6)
High (≥63%)920 (32.4)
Asthma n (%):
Symptoms without diagnosis325 (11.4)
Diagnosis ± symptoms206 (7.3)
No diagnosis/symptoms2310 (81.3)
BMI: body mass index; SD: standard deviation.
Table 2. Prevalence (%) or mean value of risk factors in relation to asthma symptoms/diagnosis.
Table 2. Prevalence (%) or mean value of risk factors in relation to asthma symptoms/diagnosis.
VariablesSymptoms Without Diagnosis n = 325 Diagnosis ± Symptoms n = 206 No Symptoms/Diagnosis n = 2310 p-Value
Age
≤18121 (37.2%)28 (13.6%)737 (31.9%)<0.001
>18 and ≤59193 (59.4%)98 (47.6%)1346 (58.3%)
>5911 (3.4%)80 (38.8%)227 (9.8%)
Sex
Male215 (66.2%)94 (45.6%)979 (42.4%)<0.001
Female110 (33.8%)112 (54.4%)1331 (57.6%)
Level of education
≤8 years263 (80.9%)167 (81.1%)1717 (74.3%)0.017
>8 and ≤13 years54 (16.6%)36 (17.5%)499 (21.6%)
>13 years8 (2.5%)3 (1.5%)94 (4.1%)
BMI
Underweight-Normal weight124 (38.2%)108 (52.4%)1228 (53.2%)<0.001
Overweight144 (44.3%)67 (32.5%)806 (34.9%)
Obese57 (17.5%)31 (15.0%)276 (11.9%)
Smoking habits
Smokers144 (44.3%)40 (19.4%)474 (20.5%)<0.001
Ex-smokers110 (33.8%)64 (31.1%)612 (26.5%)
Non-smokers71 (21.8%)102 (49.5%)1224 (53.0%)
Hours outdoors
<2 h a day161 (49.5%)100 (48.5%)974 (42.3%)0.016
≥2 h a day164 (50.5%)106 (51.5%)1329 (57.7%)
Grey space exposure
Low (<63%)202 (62.2%)139 (67.5%)1580 (68.4%)0.079
High (≥63%)123 (37.8%)67 (32.5%)730 (31.6%)
% Grey space
Mean ± SD56.5 ± 22.455.1 ± 22.354.2 ± 22.10.195
Statistically significant values are reported in bold. Borderline values are reported in italics. BMI: body mass index; SD: standard deviation.
Table 3. Risk factors for asthma symptoms/diagnosis in the total sample (n = 2834): OR and 95% CI.
Table 3. Risk factors for asthma symptoms/diagnosis in the total sample (n = 2834): OR and 95% CI.
VariablesSymptoms Without DiagnosisDiagnosis ± Symptoms
Risk factorsOR (95% CI)OR (95% CI)
BMI:
Underweight-Normal weight11
Overweight1.43 (1.08–1.88)0.91 (0.65–1.28)
Obese1.99 (1.38–2.88)1.21 (0.78–1.89)
% Grey space exposure1.06 (1.01–1.13)1.03 (0.97–1.10)
Age:
≤1811
>18 and ≤591.58 (0.81–3.11)0.54 (0.32–0.90)
>591.89 (0.96–3.71)0.75 (0.45–1.25)
Sex:
Female11
Male2.09 (1.59–2.75)1.10 (0.80–1.51)
Level of education:
>13 years11
>8 and ≤13 years1.33 (0.60–2.95)2.34 (0.70–7.77)
≤8 years1.99 (0.93–4.27)2.65 (0.82–8.59)
Smoking habits:
Non-smokers11
Smokers4.34 (3.11–6.06)1.18 (0.78–1.78)
Ex-smokers2.13 (1.50–3.01)1.40 (0.97–2.03)
Hours outdoors:
≥2 h a day11
<2 h a day1.42 (1.10–1.83)1.37 (1.02–1.85)
Statistically significant values are reported in bold. Reference category is 1. Dependent variables: asthma symptoms/diagnosis; no diagnosis/symptoms (reference category). Independent variables: BMI; % grey space exposure. The analysis was adjusted for age, sex, level of education, smoking habits and hours outdoors.
Table 4. Risk factors for asthma symptoms/diagnosis considering the combined variable BMI–grey space exposure: OR and 95% CI.
Table 4. Risk factors for asthma symptoms/diagnosis considering the combined variable BMI–grey space exposure: OR and 95% CI.
VariablesSymptoms Without DiagnosisDiagnosis ± Symptoms
Risk factorsOR (95% CI)OR (95% CI)
BMI–grey space exposure:
Underweight-normal weight + low grey exposure11
Underweight-normal weight + high grey exposure1.33 (0.69–2.55)1.06 (0.52–2.16)
Overweight-obese + low grey exposure1.37 (0.99–1.88)1.08 (0.75–1.56)
Overweight-obese + high grey exposure2.53 (1.36–4.71)0.86 (0.41–1.83)
Age:
≤1811
>18 and ≤591.61 (0.82–3.17)0.54 (0.32–0.89)
>591.93 (0.98–3.79)0.76 (0.46–1.26)
Sex:
Female11
Male2.05 (1.56–2.69)1.07 (0.78–1.46)
Level of education:
>13 years11
>8 and ≤13 years1.34 (0.61–2.98)2.38 (0.72–7.89)
≤8 years2.05 (0.96–4.39)2.72 (0.84–8.82)
Smoking habits:
Non-smokers11
Smokers4.28 (3.07–5.97)1.18 (0.78–1.79)
Ex-smokers2.09 (1.48–2.96)1.41 (0.97–2.04)
Hours outdoors:
≥2 h a day11
<2 h a day1.44 (1.12–1.85)1.37 (1.02–1.85)
% Grey space exposure0.96 (0.86–1.09)1.04 (0.91–1.20)
Statistically significant values are reported in bold. Borderline values are reported in italics. Reference category is 1. Dependent variables: asthma symptoms/diagnosis; no symptoms/diagnosis (reference category). Independent variables: BMI–grey space exposure. The analysis was adjusted for age, sex, level of education, smoking habits, hours outdoors and % grey space exposure.
Table 5. Results of the calculation of additive measures.
Table 5. Results of the calculation of additive measures.
VariablesRERI (95% CI)AP (95% CI)S (95% CI)
Symptoms without diagnosis0.84 (−0.24–1.91)0.32 (0.03–0.61)1.47 (0.84–2.11)
Statistically significant values are reported in bold. RERI: Relative Excess Risk due to Interaction, AP: Attributable Proportion due to interaction, S: Synergy Index.
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MDPI and ACS Style

Stanisci, I.; Angino, A.A.; Maio, S.; Sarno, G.; Silvi, P.; Tagliaferro, S.; Viegi, G.; Baldacci, S. Asthma Is Associated with Overweight, Obesity and Residential Grey Space in an Italian General Population Sample. Sustainability 2025, 17, 11300. https://doi.org/10.3390/su172411300

AMA Style

Stanisci I, Angino AA, Maio S, Sarno G, Silvi P, Tagliaferro S, Viegi G, Baldacci S. Asthma Is Associated with Overweight, Obesity and Residential Grey Space in an Italian General Population Sample. Sustainability. 2025; 17(24):11300. https://doi.org/10.3390/su172411300

Chicago/Turabian Style

Stanisci, Ilaria, Anna Antonietta Angino, Sara Maio, Giuseppe Sarno, Patrizia Silvi, Sofia Tagliaferro, Giovanni Viegi, and Sandra Baldacci. 2025. "Asthma Is Associated with Overweight, Obesity and Residential Grey Space in an Italian General Population Sample" Sustainability 17, no. 24: 11300. https://doi.org/10.3390/su172411300

APA Style

Stanisci, I., Angino, A. A., Maio, S., Sarno, G., Silvi, P., Tagliaferro, S., Viegi, G., & Baldacci, S. (2025). Asthma Is Associated with Overweight, Obesity and Residential Grey Space in an Italian General Population Sample. Sustainability, 17(24), 11300. https://doi.org/10.3390/su172411300

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