Identifying risk factors for COPD and adult-onset asthma: an umbrella review

Abstract

Background

COPD and adult-onset asthma (AOA) are the most common noncommunicable respiratory diseases. To improve early identification and prevention, an overview of risk factors is needed. We therefore aimed to systematically summarise the nongenetic (exposome) risk factors for AOA and COPD. Additionally, we aimed to compare the risk factors for COPD and AOA.

Methods

In this umbrella review, we searched PubMed for articles from inception until 1 February 2023 and screened the references of relevant articles. We included systematic reviews and meta-analyses of observational epidemiological studies in humans that assessed a minimum of one lifestyle or environmental risk factor for AOA or COPD.

Results

In total, 75 reviews were included, of which 45 focused on risk factors for COPD, 28 on AOA and two examined both. For asthma, 43 different risk factors were identified while 45 were identified for COPD. For AOA, smoking, a high body mass index (BMI), wood dust exposure and residential chemical exposures, such as formaldehyde exposure or exposure to volatile organic compounds, were amongst the risk factors found. For COPD, smoking, ambient air pollution including nitrogen dioxide, a low BMI, indoor biomass burning, childhood asthma, occupational dust exposure and diet were amongst the risk factors found.

Conclusions

Many different factors for COPD and asthma have been found, highlighting the differences and similarities. The results of this systematic review can be used to target and identify people at high risk for COPD or AOA.

Short abstract

This umbrella review provided an overview of nongenetic risk factors for both COPD and adult-onset asthma. Commonly found factors included smoking, BMI, air pollution, early life exposures and occupational exposures. https://bit.ly/3kyeWZL

Background

COPD is one of the most common chronic respiratory disorders, with a prevalence ranging from 3.6 to 10.1% [1, 2] Currently, COPD is the third leading cause of death worldwide [3], with 2–6% of global disability-adjusted life years attributed to COPD in 2015 [4]. Additionally, COPD patients are at an increased risk of developing extrapulmonary manifestations or comorbidities that highly affect disease burden, such as cardiovascular diseases and lung cancer [5, 6, 7]. Besides COPD, another 300 million children and adults globally are estimated to be suffering from asthma [8]. Even though adult-onset asthma (AOA) is less studied than childhood asthma, previous research shows that incidence has been rising over the past two decades [9]. For example, de Marco et al. [10] showed asthma prevalence in Italy to have increased by 38% between 1991 and 2010, irrespective of age. Distinguishing between COPD and asthma can be difficult, especially in older adults, as the predicted forced expiratory volume in 1 s falls to 50% in both asthma and COPD patients over 60 years old [11]. Some patients with asthma develop COPD later in life and some patients with COPD show clinical features commonly observed in asthma [12]. For patients who show characteristics of both COPD and asthma, the label asthma–COPD overlap syndrome (ACOS) is being considered [13]. It has been widely reported that patients suffering from ACOS have worse disease outcomes, such as more exacerbations and a more rapid decline in lung function, than patients suffering from only asthma or COPD [13, 14, 15]. As there is considerable overlap between the two conditions, this could mean overlapping risk factors as well.

As both COPD and AOA have a high disease burden, and there is no curative treatment for COPD [16], early identification and prevention are key. To optimise prevention strategies, a better understanding of especially nongenetic risk factors for developing either condition is needed. Additionally, risk factors for childhood asthma are well established, but it is unclear how those risk factors translate to AOA. Furthermore, previous research has shown similarities in nongenetic risk factors for developing either COPD or AOA, such as smoking and high exposure to air pollution [17]. However, to our knowledge, there is currently no overview comparing risk factors for COPD and asthma. The aim of this systematic umbrella review is to provide an overview and comparison of all nongenetic risk factors for COPD and AOA, identifying similarities as well as differences.

Methods

Search strategy

We conducted an umbrella review, a systematic collection and assessment of multiple systematic reviews and meta-analyses [18]. The review was conducted according to the study protocol, registered in the International Prospective Register of Systematic Reviews (PROSPERO-CRD42021284614) and according to PRISMA guidelines. The full PRISMA checklist can be found in the supplemental material.

PubMed was systematically searched from inception to 1 February 2023 to identify systematic reviews and meta-analyses of studies examining one or multiple nongenetic risk factors for either COPD or AOA. The search strategy consisted of terms on COPD, asthma, epidemiology, aetiology, risk factors and study design (full search strategy in the supplemental material). In addition, reference lists of included papers were screened for additional relevant reviews that might have been missed during the initial search. A first screening was independently done by two researchers (J.C.S.H. and L.D.B.) based on title and abstract. Subsequently, the full text of all relevant articles was assessed for eligibility by one researcher (J.C.S.H.), with an additional researcher (M.E.B.C.) assessing a random 10% of the articles. Initial discrepancies were first discussed amongst the reviewers with a third reviewer (R.J.H.C.G.B.) being available for final decision making in case of disagreement. However, this did not occur.

Inclusion and exclusion criteria

Systematic reviews with or without meta-analyses of observational epidemiological studies, including systematic reviews of Mendelian randomisation studies, in humans that assessed nongenetic risk factors for incidence asthma or COPD were included. Regarding AOA, reviews were included regardless of specifying age of onset. Only studies available in English were included. Studies examining risk factors for either mortality, exacerbations or management of either disease were excluded. Additionally, studies examining the impact of another disease on development of either COPD or AOA were considered noneligible. When multiple analyses or reviews included the same original studies, the most recent one was included.

Data extraction

Data extraction was performed by one reviewer (J.C.S.H.), with a second reviewer (M.E.B.C.) extracting data from a random 10% for quality control purposes. From each eligible article, the authors, year of publication, examined nongenetic risk factor(s), examined outcome, number of epidemiological studies included, the total number of participants per review and the main conclusions were recorded. Additionally, for AOA it was recorded whether age of onset was specified. Study quality and risk of bias of the included reviews was checked by applying the Joanna Briggs Institute (JBI) checklist for systematic reviews and research synthesis [19]. Scores were given for every included aspect, resulting in a score ranging from 1 (poorly conducted) to 11 (well conducted). For studies without a meta-analysis, the maximum score is 10.

Results

Literature review

Our literature search identified 2040 publications. Of those, 1867 were excluded following title and abstract review (figure 1), mainly because they focused on genetic risk factors or on exacerbations or management of disease. Additionally, one review was identified through snowballing. Of those, 99 were excluded after reviewing the full texts, resulting in 75 eligible articles included in this umbrella review. Of these, 45 focused on risk factors for COPD, 28 on asthma and two reviews included both COPD and asthma. Out of the 75 included reviews, 45 included a meta-analysis and narrative review, 12 conducted only a meta-analysis but no narrative review and 18 only included a narrative review.

FIGURE 1.

Results of the searches, screening and data extraction.

Regarding AOA, 47 of the included studies mentioned COPD, addressing 45 different risk factors. A selection of characteristics of the included articles are described in tables 1 and 2 for AOA and COPD, respectively. Full characteristics are shown in tables S1 and S2. A summary of the found risk factors and their corresponding effect sizes is shown in tables 3 and 4, respectively. For both AOA and COPD, the JBI scores ranged between 5 and 11. For AOA, age of onset was not clear for all papers, with only seven specifying AOA. An additional four compared exposed versus nonexposed individuals within occupational settings, implying adult-onset. All were included, but this uncertainty was noted. When a meta-analysis was conducted, the meta-results are given as opposed to single-study results.

TABLE 1.

Characteristics of 30 included systematic reviews for adult-onset asthma, ranked by Joanna Briggs Institute (JBI) score

Authors	Risk factor(s)	Main findings	Adult-onset asthma #	JBI score
Romero Starke et al. [ 20 ]	Occupational exposure to cleaning and disinfection agents	Nurses exposed to cleaning products to have an increased risk of asthma.	Yes	11
Yu et al. [ 21 ]	Formaldehyde exposure	A significantly increased risk of asthma in adults with high concentrations of formaldehyde exposure was found.	Unclear	11
Wang et al. [ 22 ]	Exposure to greenness	The results are contradictory, with all three included studies showing different associations.	Unclear	10
Chen et al. [ 23 ]	Zinc and selenium levels	The meta-analysis provides evidence that lower circulating Zn and Se levels are associated with an increased risk of asthma.	Unclear	10
Etminan et al. [ 24 ]	Acetaminophen use	The results are consistent with an increase in the risk of asthma in adults exposed to acetaminophen.	Unclear	10
Zhang et al. [ 25 ]	Organic dust exposure	The meta-analysis found organic dust exposure to be a risk factor for asthma.	No	9
Shen et al. [ 26 ]	Early life vitamin D deficiency	No statistically significant association between early life vitamin D deficiency and asthma development later in life was found.	Unclear	9
Nurmatov et al. [ 27 ]	Volatile organic compounds	The results of the effect on volatile organic compounds on the development of asthma are inconsistent.	Unclear	9
Sharpe et al. [ 28 ]	Exposure to indoor fungi	Exposure to certain species of fungi might increase the risk of developing asthma.	Unclear	9
Mu et al. [ 29 ]	Birth weight	The results suggest that low birth weight (<2500 g) is associated with increased risk of asthma both in children in adults, but high birth weight (>4000 g) was not associated with increased risk of asthma.	Unclear	9
Macan et al. [ 30 ]	Persulphates	Persulphates were associated with asthma in hairdressers, in particular bleaching products.	Yes	8
Sio et al. [ 31 ]	Wide range	The study showed housing related factors such as mould, male sex, smoke exposure and BMI-related factors to be associated with asthma.	Mixed	8
Rodriguez et al. [ 32 ]	Urban–rural differences	The findings provide evidence that urban residence and urbanisation are important determinants of asthma.	Unclear	8
Cong et al. [ 33 ]	Temperature changes	There was no increased risk for asthma development in adults when temperature dropped.	No	8
Wiggans et al. [ 34 ]	Wood dust	Work in this sector was associated with a significantly increased risk of respiratory symptoms and asthma.	Unclear	8
Uphoff et al. [ 35 ]	Socioeconomic position	Low socioeconomic position was found to be associated with asthma risk.	Unclear	8
Tan et al. [ 36 ]	Risk factors associated with age of onset	Adults with late-onset current asthma are more likely to be female (58–75%), smokers (56%).	Yes	8
Lieberoth et al. [ 37 ]	Age at menarche	Early menarche (<12 years) appears to be associated with increased risk of asthma.	Unclear	8
Kakutani et al. [ 38 ]	Arachidonic acid intake	The results suggest that arachidonic acid exposure is not consistently associated with asthma risk.	Mixed	8
Takkouche et al. [ 39 ]	Exposure to furry pets	In adults, the evidence regarding either an increased or decreased risk of asthma when exposed to furry pets is inconclusive.	Unclear	8
Beuther et al. [ 40 ]	BMI	Higher BMI is associated with developing asthma.	Yes	8
Jaakkola et al. [ 41 ]	Pre-term delivery	Premature babies seem to have an increased risk of developing asthma later in life, but the results in adults are inconclusive.	Unclear	8
Mikkelsen et al. [ 42 ]	Wide range	A higher BMI and early puberty increase asthma risk. Lifetime smoking, alcohol consumption, late puberty and linoleic acid seem to be protective factors. Vitamin B12, iron and folate intake are not significant.	Yes	7
Canova et al. [ 43 ]	Domestic paints	The variable quality of the exposure assignment makes it difficult to draw firm conclusions on whether there is an association.	Unclear	7
Baur et al. [ 44 ]	Workplace irritants	There is evidence that long-term exposure to workplace irritants could increase asthma development.	Unclear	7
Flaherman et al. [ 45 ]	High childhood BMI	High body weight in childhood seems to increase the risk of asthma later in life. However, in adults, results are not conclusive.	Yes	7
Doust et al. [ 46 ]	Pesticide exposure	The results were suggestive of potentially adverse associations between pesticide exposure and an increased likelihood of asthma.	Mixed	7
Jaakkola et al. [ 47 ]	Exposure to phthalates	Heated PVC fumes possibly contribute to development of asthma in adults.	Unclear	6
Folletti et al. [ 48 ]	Cleaning work/products	Increased risk of asthma has been shown when exposed to cleaning products.	Unclear	6
Vincent et al. [ 49 ]	Cleaning products	The evidence linking exposure to cleaning agents as a risk factor for causing new onset asthma is limited.	No	6

^#Adult-onset asthma indicates whether the study specifies if the target group had adult-onset asthma or whether it is asthma in adults. BMI: body mass index; PVC: polyvinyl chloride.

TABLE 2.

Characteristics of 47 included systematic reviews for COPD, ranked by Joanna Briggs Institute (JBI) score

Authors	Risk factor(s)	Main findings	JBI score
Duan et al. [ 50 ]	Early life exposures	Childhood respiratory disease, maltreatment, maternal smoking and low birth weight increase the risk of COPD development.	11
Parvizian et al. [ 51 ]	Unhealthy dietary patterns	Unhealthy dietary patterns seemed to be associated with a higher risk of COPD; however, the meta-analysis was not statistically significant. Healthy dietary patterns were associated with a reduced risk.	11
Asamoah-Boaheng et al. [ 52 ]	History of asthma	Individuals with a previous history of asthma were found to have an increasing likelihood of developing COPD in later life.	11
Su et al. [ 53 ]	Inflammatory markers	The findings suggested that COPD was associated with elevated serum CRP, leukocytes, IL-6, IL-8 and fibrinogen, without any significant relationship with TNF-α.	11
Awokola et al. [ 54 ]	Age, biomass exposure, smoking	An increased prevalence of COPD was associated with increasing age, smoking and biomass smoke exposure.	10
Kamal et al. [ 55 ]	Smoking	The results suggest a positive association between current smokers and the prevalence of COPD compared with former and nonsmokers.	10
Sadhra et al. [ 56 ]	Occupational dust exposure	Overall occupational exposure to airborne pollutants as assessed by JEMs reported an increased risk of COPD.	10
Yang et al. [ 57 ]	Wide range investigating risk factors	12 risk factors were found to be associated with the occurrence of COPD on the Chinese mainland: male sex, smoking, low education level, low BMI, family history of respiratory disease, allergy history, respiratory infection during childhood, recurrent respiratory infection, occupational dust exposure, biomass burning, poor housing ventilation and living around polluted areas.	10
Peng et al. [ 58 ]	Occupational dust exposure	Being exposed to occupational dust was found to increase COPD risk.	10
Xiong et al. [ 59 ]	High altitude	The meta-analysis found a higher prevalence of COPD at high altitudes.	10
Park et al. [ 60 ]	PM2.5, PM10, NO2	Both higher PM2.5 and NO2 levels were shown to increase risk of COPD. No association was found for PM10.	10
Njoku et al. [ 61 ]	Wide range	Smoking, previous tuberculosis, use of biomass fuels, older age, wheeze and asthma were associated with increased COPD risk.	9
Gershon et al. [ 62 ]	Low SES	The study found consistent inverse associations between SES and COPD prevalence.	9
Zheng et al. [ 63 ]	Dietary patterns	An increase in the risk of COPD development was shown for the highest compared with the lowest categories of “unhealthy/Western-style” dietary patterns.	9
Ryu et al. [ 64 ]	Exposure to VGDFs	The study suggests that exposure to VGDFs is associated with a higher risk of COPD development.	9
Li et al. [ 65 ]	Parental COPD	The prevalence of COPD in adult offspring of people with COPD is greater than population-based estimates, showing that parental COPD is associated with COPD development in offspring.	9
Borup et al. [ 66 ]	Construction dust	The review suggests that COPD occurs more often among construction workers than among workers who are not exposed to construction dust.	9
Guillien et al. [ 67 ]	Agricultural work	The meta-analysis reported that cattle farming, swine farming and poultry farming are strongly associated with airflow limitation and chronic bronchitis. However, the results were inconsistent	9
Ma et al. [ 68 ]	Childhood wheezing	The results suggest an association between childhood wheezing and an increased risk of COPD.	9
Kamal et al. [ 69 ]	Indoor biomass burning	The meta-data analysis has shown that household air pollutants may be a factor associated with increased risk of COPD in women.	9
Sutradhar et al. [ 70 ]	All risk factors	Tobacco consumption, exposure to biomass fuel, old age and history of asthma were identified as major risk factors for COPD development.	9
Vinnikov et al. [ 71 ]	Occupational exposure to VGDFs	The study reported occupational exposure to VGDFs increased the risk of COPD.	9
Chen et al. [ 72 ]	Wide range	PM exposure, smoking history, passive smoking history, sex, exposure to biomass burning, childhood respiratory infections and family history were found to be risk factors for COPD in the Chinese population	8
van Iersel et al. [ 73 ]	Nutrition	A high dietary intake of red and/or processed meats, refined grains, sweets, desserts and French fries are associated with an increased risk of COPD.	8
Zhang et al. [ 74 ]	BMI	The study reported underweight might increase the risk of COPD development, where being overweight might reduce the risk.	8
Gan et al. [ 75 ]	Age in female adult smokers	As female smokers age, they appear to be at higher risk of developing COPD when compared to male smokers.	8
Finney et al. [ 76 ]	Exploration of risk factors	The current study identified biomass burning and smoking as risk factors for COPD development.	8
Zhu et al. [ 77 ]	Tobacco exposure, biomass fuel/solid fuel usage, gender, age, low BMI, family history, history of respiratory disease, occupational dust exposure, low education level	The study found all aforementioned risk factors to be important risk factors for COPD development.	8
Salari-Moghaddam et al. [ 78 ]	Processed red meat intake	The meta-analysis found a positive association between processed red meat intake and risk of COPD development.	8
Bellou et al. [ 79 ]	Environmental risk factors	The review showed that active and passive smoking, exposure to biomass fuels, history of tuberculosis and history of rheumatoid arthritis were associated with COPD.	7
Ali [ 80 ]	Childhood asthma	Children with asthma were found to be at risk of developing COPD later in life.	7
Pathak et al. [ 81 ]	Indoor air pollution from biomass cooking fuel	Exposure to indoor air pollution due to solid biomass fuels was associated with an increased risk of COPD.	7
Zhang et al. [ 82 ]	Exposure to nitrogen dioxide	The study found consistent evidence of the positive association between NO2 exposure and COPD risk.	7
Kurmi et al. [ 83 ]	Indoor air pollution from solid fuel	Exposure to solid fuel smoke was found to be consistently associated with COPD development.	7
Doust et al. [ 46 ]	Pesticide exposure	There was weak evidence for an association between pesticide exposure and increased likelihood of COPD.	7
Brüske et al. [ 84 ]	Bio persistent granular dust	Exposure to impersistent granular dust was associated with COPD development.	7
Baur et al. [ 44 ]	Workplace irritants	There is evidence that long-term exposure to workplace irritants could increase COPD development.	7
Adeloye et al. [ 85 ]	Wide range	Male sex, age, smoking status, second-hand tobacco smoke, biomass exposure, occupational exposure to dust or smoke, BMI, previous respiratory illness, SES, education level and area of living were associated with COPD.	6
Budhathoki et al. [ 86 ]	Smoking and traditional firewood cooking	Smoking and traditional firewood cooking were identified as major risk factors for COPD	6
Lee et al. [ 87 ]	Cigarette smoking	An increased risk of COPD development with current smoking and a lesser increase with ex-smoking was found.	6
Fontana et al. [ 88 ]	Agricultural work	The review found farming work to be associated with a greater risk of developing COPD.	6
Wang et al. [ 89 ]	Smoking	Risk of COPD for ever-smokers is higher than the risk of COPD development in never-smokers.	6
Forey et al. [ 90 ]	Smoking	The risk of male smokers seems to be higher than female smokers, as well as the risk of cigarette smoking as compared to smoking other products.	6
Chaudhary et al. [ 91 ]	Elevated serum homocysteine	Elevated serum homocysteine was found to contribute to risk of COPD development.	6
Lee et al. [ 92 ]	ETS	The evidence suggests that exposure to ETS is a risk factor for COPD development.	6
Cunalata-Paredes et al. [ 93 ]	Wide range	The study found associations with second-hand smoking, male sex, older age, biomass exposure, asthma, respiratory problems in childhood and tuberculosis and COPD development.	5
Pando-Sandoval et al. [ 94 ]	Wide range in never-smokers	Increased COPD risk was associated with exposure to biomass, occupational exposure and passive smoking to having a history of asthma, tuberculosis or respiratory infections during childhood.	5

BMI: body mass index; CRP: c-reactive protein; ETS: environmental tobacco smoke; IL: interleukin; PM: particulate matter; JEM: job exposure matrix; SES: socioeconomic status; TNF-α: tumour necrosis factor-α; VGDFs: vapours, gases, dusts or fumes.

TABLE 3.

Overview of the identified risk factors for adult-onset asthma, and their corresponding effect sizes

Category	Risk factor	Number of reviews	Effect measure	Effect size (95% CI), reference
Air pollution and chemical exposures	Indoor formaldehyde exposure	1	OR	1.81 (1.18–2.78) [21]
	VOC exposure	1	NA	NA [27]
	Air pollution (NO2, PM10, O3)	1	OR	Range: 1.03–1.22 [31]
	Pesticide exposure	1	OR	Range: 0.41 (0.15­–1.11)–3.67 (1.19–11.30) [46]
	Phthalate exposure	1	OR	1.55 (1.18–2.05) [47]
	Domestic paint exposure	1	OR	1.60 (1.00–2.50) [43]
	Arachidonic acid intake	1	NA	NA [38]
	Domestic cleaning products	1	NA	NA [49]
Early life exposures	Low birth weight	1	OR	1.25 (1.12–1.40) [29]
	Early life vitamin D deficiency	1	OR	0.57 (0.35–0.93) [26]
	High birth weight	1	OR	1.26 (0.88–1.82) [45]
	High weight during childhood	1	OR	1.32 (0.82–2.40) [45]
	Pre-term delivery	1	OR	Range: 1.86 (0.23–12.00)–1.14 (0.92–1.40) [41]
Nutrition and weight status	BMI between 25 and 29.9 kg·m−2	1	OR	1.38 (1.17–1.62) [40]
	BMI ≥30 kg·m−2	2	OR	1.92 (1.43–2.59) [40]; 2.02 (1.63–2.50) [31]
	BMI per kg·m−2 increase	1	Risk ratio	1.05 (1.03–1.07) [42]
	Vitamin B12	1	Risk ratio	0.99 (0.95–1.04) [42]
	Iron	1	Risk ratio	0.92 (0.67–1.25) [42]
	Folate	1	Risk ratio	0.80 (0.43–1.21) [42]
	Linoleic acid	1	Risk ratio	0.89 (0.85–0.93) [42]
	Alcohol intake	1	Risk ratio	0.95 (0.91–0.99) [42]
	Zinc plasma levels	1	SMD	−0.26 (−0.40– −0.13) [23]
	Selenium plasma levels	1	SMD	−0.06 (−0.13–0.02) [23]
Occupational exposures	Cleaning products	2	Risk ratio/OR	Risk ratio: 1.67 (1.11–2.50) [20]; OR range: 1.50–3.00 [48]
	Workplace irritants	1	NA	NA [44]
	Wood dust exposure	1	Risk ratio	1.53 (1.25–1.87) [34]
	Organic dust (wood, textile, etc.)	1	OR	Paper/wood: 1.62 (1.38–1.90); flour/grain: 1.48 (1.11–1.97); textile: 1.50 (1.08–2.09) [25]
	Persulphates (e.g. hair bleach)	1	NA	NA [30]
Physiological, personal and socioeconomic characteristics	Younger age at menarche	1	OR	1.37 (1.15–1.64) [37]
	Age at puberty: early versus average	1	Risk ratio	1.10 (1.04–1.15) [42]
	Age at puberty: late versus average	1	Risk ratio	0.93 (0.90–0.96) [42]
	Smoking	3	Percentage/risk ratio/OR	56% [36]; 0.97 (0.96–0.99) (risk ratio) [42]; 1.66 (1.44–1.90) (OR) [31]
	Second-hand smoke	1	OR	1.44 (1.30–1.60) [31]
	Housing-related factors, e.g. mould	1	OR	Range: 1.43–1.73 [31]
	Acetaminophen use	1	OR	1.63 (1.46–1.77) [24]
	Living in urban areas	1	OR	1.89 (1.47–2.41) [32]
	Low SES	1	OR	1.38 (1.37–1.39) [35]
	Female sex	1	Percentage	58–75% [36]
	Male sex	1	OR	1.30 (1.23–1.38) [31]
	Indoor fungi exposure	1	OR	1.25 (0.99–1.58) [28]
	Temperature drops	1	OR	1.00 (0.93–1.08) [33]
	Indoor furry pets	1	OR	1.58 (0.99–2.54) [39]
	Exposure to greenness	1	NA	NA [22]

95% confidence intervals are given when available. BMI: body mass index; NA: not assigned, indicates studies without a meta-analysis and without effect sizes; PM: particulate matter; SES: socioeconomic status; SMD: standardised mean difference; VOC: volatile organic compound.

TABLE 4.

Overview of the identified risk factors for COPD and their corresponding effect sizes

Category	Risk factor	Number of reviews	Effect measure	Effect size (95% CI)
Air pollution and chemical exposures	NO2 exposure	2	HR/risk ratio	HR: 1.07 (1.00–1.16) [60]; risk ratio: 1.02 [82]
	Ambient PM2.5 exposure	2	HR/OR	HR: 1.18 (1.13–1.23) [60]; OR: 1.73 (1.16–2.58) [72]
	Indoor biomass burning	15	OR	Range from 1.52 (1.39–1.67) [61] to 3.16 (1.82–5.49) [69]
	Pesticide exposure	1	OR	Range from 1.05 (0.74–1.51) to 4.10 (2.20–6.30) [46]
	Living in polluted areas	1	OR	1.63 (1.20–2.21) [57]
	VGDF exposure	1	OR	1.43 (1.19–1.73) [64]
Early life exposures	Low birth weight	1	OR	1.58 (1.08–2.32) [50]
	Childhood wheeze	1	Risk ratio	5.31 (1.03–27.27) [68]
	Early life respiratory problems	6	OR	Range from 2.23 (1.63–3.07) [50] to 3.44 (1.33–8.90) [72]
	Childhood asthma	4	OR	Range from 3.00 (2.25–4.0) [80] to 7.87 (5.40–11.45) [52]
	Child maltreatment	1	OR	1.30 (1.18–1.42) [50]
	Childhood environmental tobacco smoke exposure	2	OR/risk ratio	OR: 1.30 (0.83–1.61) [50]; risk ratio: 0.88 (0.72–1.07) [92]
	Maternal smoking	1	OR	1.42 (1.17–1.72) [50]
	Pre-term birth	1	OR	1.17 (0.087–1.58) [50]
Nutrition and weight status	BMI between 25.0 and 29.9 kg·m−2	3	OR	Range from 0.80 (0.73–0.87) [74] to 0.96 (0.76–1.22) [72]
	BMI ≥30 kg·m−2	1	OR	0.86 (0.73–1.02) [74]
	Low BMI	5	OR	Range from 3.83 (2.22–6.60) [57] to 1.96 (1.78–2.17) [74]
	Consumption of red meat	2	HR	1.08 (1.03–1.13) [78]
	Western style/unhealthy diet	3	OR	2.12 (1.64–2.74) [63], 1.22 (0.84–1.76) [51]
	Vitamin D deficiency	1	OR	1.77 (1.18–2.64) [79]
	Drinking history	1	OR	0.82 (0.54–1.23) [72]
	Healthy diet	1	OR	0.88 (0.82–0.94) [51]
Occupational exposures	Dust exposure (wood, mineral)	11	OR	Range from 0.97 (0.68–1.39) [79] to 1.79 (1.15–2.79) [57]
	Workplace irritants	1	NA	NA [44]
	Agricultural work	2	OR	1.77 (1.50–2.08) [67]
Physiological, personal, and socioeconomic characteristics	Smoking	12	OR	Range from 2.89 (2.63–3.17) [90] to 5.50 (4.20–7.20) [70]
	Second-hand smoke	6	Risk ratio/OR	Range from 1.20 (1.08–1.34) [92] to 1.56 (1.40–1.74) [79]
	Chewing tobacco	1	OR	12.90 (3.40–49.40) [70]
	Parental COPD	4	OR	1.57 (1.29–1.93) [65], 2.07 (1.47–2.92) [57]
	Recurrent respiratory infections	1	OR	15.02 (4.54–49.68) [57]
	History of rheumatoid arthritis	1	OR	1.99 (1.61–2.45) [79]
	History of psoriasis	1	OR	1.45 (1.21–1.73) [79]
	History of tuberculosis	4	OR	2.80 (1.90–4.00) [85], 5.98 (4.18–8.56) [61]
	Waterpipe smoking	1	OR	3.18 (1.25–8.09) [79]
	Poor housing ventilation	1	OR	3.99 (1.24–12.82) [57]
	Low SES	5	OR	Range from 0.80 (0.50–1.30) [62] to 1.61 (1.21–2.15) [57]
	Age	4	OR	Range from 1.50 (1.30–1.50) [85] to 4.70 (3.50–6.40) [70]
	Male sex	3	OR	Range from 1.47 (1.10–1.96) [57] to 2.10 (1.90–2.30) [85]
	Serum homocysteine	1	MD	3.05 [91]
	Inflammatory markers in serum	1	SMD	Range from 0.87 (0.44–1.31) [53] to 2.34 (0.69–4.00) [53]
	Traffic intensity	1	OR	1.26 (0.95–1.70) [79], 1.30 (0.92–1.82) [79]
	Living at high altitudes	1	OR	1.18 (0.85–1.62) [59]
	Living in urban areas	1	OR	1.20 (1.00–1.50) [85]
	Living in rural areas	1	OR	1.40 (1.30–1.60) [85]

95% confidence intervals are given when available. BMI: body mass index; HR: hazard ratio; PM: particulate matter; (S)MD: (standardised) mean difference; NA: not assigned, indicates studies without a meta-analysis and without effect sizes; SES: socioeconomic status; VGDF: vapours, gases, dusts and fumes.

Asthma

Air pollution and chemical exposures

Eight reviews identified either chemical exposures or air pollution as risk factors for asthma [21, 27, 31, 38, 43, 46, 47, 49]. Sio and Chew [31] identified exposure to several types of air pollution, including nitrogen dioxide (NO₂), particulate matter and ozone as risk factors for AOA, with included pooled odds ratios (ORs) ranging between 1.03 and 1.22. Yu et al. [21] reported that high indoor residential formaldehyde exposure (>22.5 µg·m⁻³) was associated with an increased risk of asthma as compared to low exposure (≤22.5 µg·m⁻³), with a meta OR (mOR) of 1.81 (95% CI 1.18–2.78). Moreover, Nurmatov et al. [27] reported that domestic exposure to volatile organic compounds (VOCs), i.e. aromatic VOCs, chlorinated hydrocarbons, propylene glycol and glycol ethers, alkanes, alcohols, aldehydes, ketones, and terpenes, increased asthma risk. Doust et al. [46] reported both occupational and nonoccupational pesticide exposure to increase asthma risk. However, there was variation in pooled estimates for pesticide exposure, ranging from 0.41 (95% CI 0.15–1.11) to 3.67 (95% CI 1.19–11.30) for exposed versus nonexposed, with inconsistent reporting on the type of pesticide examined. Jaakkola and Knight [47] found a high exposure to phthalates, such as heated polyvinyl chloride fumes, increased asthma risk, with an mOR of 1.55 (95% CI 1.18–2.05). Canova et al. [43] found that domestic paint use was associated with asthma, with one study providing an OR of 1.60 (95% CI 1.00–2.50). The final two reviews did not find either consistent or significant results. Vincent et al. [49] did not find significant associations between exposure to cleaning products and an increased risk of asthma. The review by Kakutani et al. [38] did not show arachidonic acid intake to be consistently associated with asthma.

Early life exposures

Four reviews identified early life factors as risk factors for asthma later in life [26, 29, 41, 45]. First, a review by Flaherman and Rutherford [45] identified that both a high weight during middle childhood (body mass index (BMI) ≥85th percentile for age and gender) (mOR 1.32, 95% CI 0.82–2.40) and high birth weight (≥3.8 kg) (mOR 1.26, 95% CI 0.88–1.82) could increase the risk of asthma. Jaakkola et al. [41] identified pre-term delivery as a possible risk factor, with ORs of included studies ranging between 1.86 (95% CI 0.23–12.00) and 1.14 (95% CI 0.92–1.40). However, these results were not statistically significant. Mu et al. [29] reported low birth weight to be a risk factor, with a birth weight of <2500 g to be associated with a higher risk of asthma (mOR 1.25, 95% CI 1.12–1.40) as compared to babies with a birth weight between 2500 and 4000 g. Lastly, Shen et al. [26] studied early life vitamin D deficiency and risk of asthma later in life, but found no statistically significant association (mOR 0.57, 95% CI 0.35–0.93).

Nutrition and weight status

Four reviews focused on nutritional or weight-related risk factors [23, 31, 40, 42]. Beuther and Sutherland [40] found that compared to a healthy BMI, overweight and obese participants had mORs of 1.38 (95% CI 1.17–1.62) and 1.92 (95% CI 1.43–2.59) for developing asthma, respectively. This is in accordance with Sio and Chew [31], who found an mOR of 2.02 (95% CI 1.63–2.50) for participants with a BMI of ≥30 kg·m⁻². Additionally, Mikkelsen et al. [42] found a meta risk ratio (mRR) of 1.05 (95% CI 1.03–1.07) per kg·m⁻² increase in BMI. Chen et al. [23] identified lower zinc and selenium plasma levels as possible risk factors for asthma, with a standardised mean difference (SMD) between asthma patients and healthy controls of −0.26 (95% CI −0.40–−0.13) for zinc and −0.06 (95% CI −0.13–0.02) for selenium. However, they did not provide which unit they standardised to.

Occupational exposures

Six reviews examined occupational risk factors for asthma [20, 25, 30, 34, 44, 48]. Romero Starke et al. [20] focused on healthcare workers exposed to cleaning products with an mRR of 1.67 (95% CI 1.11–2.50) among nurses exposed to different types of cleaning products compared to unexposed nurses. Folletti et al. [48] also reported cleaning workers to be at an increased risk, with ORs of the included studies ranging between 1.50 and 3.00. Baur et al. [44] reported exposure to workplace irritants, such as chloride, led to an increased asthma risk. Wiggans et al. [34] reported wood dust exposure in wood workers to increase asthma risk, with only one study providing a risk ratio of 1.53 (95% CI 1.25–1.87) as compared to the general population. Furthermore, Zhang et al. [25] identified exposure to organic dust when compared to healthy individuals to increase asthma risk in different occupations. They found associations with paper/wood dust (mOR 1.62, 95% CI 1.38–1.90), flour/grain (mOR 1.48, 95% CI 1.11–1.97) and textile dust (mOR 1.50, 95% CI 1.08–2.09). Lastly, Macan et al. [30] found that exposure to persulphates, such as hair bleach, was associated with asthma in hairdressers.

Physiological, personal and socioeconomic characteristics

Of the included reviews, 11 studied either physiological, personal, or socioeconomic risk factors [22, 24, 28, 31–33, 35–37, 39, 42]. Wang et al. [22] examined exposure to greenness as a risk factor for asthma, but found contradicting results, with one of their included studies finding an increased asthma risk, one finding exposure to greenness to be a protective factor and one not showing an association. Etminan et al. [24] found acetaminophen use was associated with an increased risk of asthma when compared to nonexposed individuals (mOR 1.63, 95% CI 1.46–1.77). Four studies focused on aspects of the home environment [28, 31, 32, 39]. The first, by Rodriguez et al. [32], compared participants living in urban environments to participants in rural environments and found people living in urban areas to have an increased asthma risk (mOR 1.89, 95% CI 1.47–2.41). The second, by Sharpe et al. [28], reported that exposure to indoor fungi from the Penicillium, Aspergillus and Cladosporium species, could increase asthma risk when compared to individuals with lower exposure. However, an OR was only given for one of the included studies (1.25, 95% CI 0.99–1.58). The third, by Takkouche et al. [39], examined having furry pets indoors as a risk factor for asthma and found an mOR for exposure to any pet of 1.58 (95% CI 0.99–2.54). The fourth, by Sio and Chew [31], found household factors such as mould to be associated with asthma, with ORs of the included studies ranging between 1.43 and 1.73. Cong et al. [33] investigated the effect of ambient temperature drops on asthma risk, per 1°C decrease, but found no significant results (mOR 1.00, 95% CI 0.93–1.08). Furthermore, Uphoff et al. [35] found a lower socioeconomic status (SES) to be associated with increased asthma risk (mOR 1.38, 95% CI 1.37–1.39). Tan et al. [36] reported that participants with late-onset asthma were more likely to be female (58–75%) and smokers (56%), while Sio and Chew [31] found males to be at an increased risk (mOR 1.30, 95% CI 1.23–1.38). Both Mikkelsen et al. [42] and Sio and Chew [31] examined smoking as a risk factor, with the former finding it a protective factor (risk ratio 0.97, 95% CI 0.96–0.99) and the latter finding an association with an increased asthma risk (mOR 1.66, 95% CI 1.44–1.90). However, Mikkelsen et al. [42] only included two papers on smoking, with the other included paper finding smoking to be associated with an increased asthma risk. Additionally, Sio and Chew [31] found an association with second-hand smoke exposure (mOR 1.44, 95% CI 1.30–1.60) [31]. Mikkelsen et al. [42] found early as compared to average age at puberty to be a risk factor (mRR 1.37, 95% CI 1.15–1.64), where late versus average age at puberty was found to be a protective factor (mRR 0.93, 95% CI 0.90–0.96) [42]. Lastly, Lieberoth et al. [37] found a younger age at menarche to be associated with asthma risk (mOR 1.37, 95% CI 1.15–1.64).

COPD

Air pollution and chemical exposures

We identified 19 reviews, studying six different risk factors [46, 54, 57, 60, 61, 64, 69, 70, 72, 76, 77, 79, 81–83, 85, 86, 93, 94]. Park et al. [60] found an association between exposure to ambient fine particulate matter (PM_2.5) and COPD (meta hazard ratio (mHR) 1.18, 95% CI 1.13–1.23) per 10 μg·m^-3 increase, which was also found by Chen et al. [72] (mOR 1.73, 95% CI 1.16–2.58). Additionally, they found significant associations between NO₂ and COPD, which was also found by Zhang et al. [82], with a 10 μg·m^-3 increase in NO₂ being associated with an mHR of 1.07 (95% CI 1.00–1.16) [60] and an mRR of 1.02, respectively. Indoor air pollution caused by indoor biomass burning was found to be a risk factor for COPD in several reviews [54, 57, 61, 69, 70, 72, 76, 77, 79, 81, 83, 85, 86, 93, 94]. Found mORs ranged between 1.52 (95% CI 1.39–1.67), found by Njoku et al. [61] and 3.16 (95% CI 1.82–5.49), found by Kamal et al. [69], when compared to unexposed controls. Doust et al. [46] identified both occupational and nonoccupational pesticide exposure as a possible risk factor, with ORs of the included studies ranging from 1.05 (95% CI 0.74–1.51) to 4.10 (95% CI 2.20–6.30) for exposed versus nonexposed individuals, with inconsistent reporting on the type of pesticide examined. Yang et al. [57] reported living in polluted areas, defined as areas with high levels of outdoor air pollution, as a risk factor for COPD, with an mOR of 1.63 (95% CI 1.20–2.21). Lastly, Ryu et al. [64] reported being exposed to vapours, gases, dusts and fumes to increase COPD risk (mOR 1.43, 95% CI 1.19–1.73).

Early life exposures

12 reviews identified risk factors in early life [50, 52, 57, 68, 70, 72, 80, 85, 92, 93, 94]. Six of these focused on respiratory problems in early life and COPD risk [50, 57, 72, 85, 93, 94], with found mORs ranging between 2.23 (95% CI 1.63–3.07) [50] and 3.44 (95% CI 1.33–8.90) [72]. Additionally, Duan et al. [50] identified childhood asthma (mOR 3.45, 95% CI 2.37–5.02), maternal smoking during both pregnancy and childhood (mOR 1.42, 95% CI 1.17–1.72), child maltreatment, e.g. physical abuse (mOR 1.30, 95% CI 1.18–1.42), and low birth weight (mOR 1.58, 95% CI 1.08–2.32) as risk factors for COPD. They did not find statistical significant associations for childhood environmental tobacco smoke exposure (mOR 1.30, 95% CI 0.83–1.61), which is in accordance with results found by Lee et al. [92] (mRR 0.88, 95% CI 0.72–1.07), or premature birth (mOR 1.17, 95% CI 0.87–1.58) [50]. Childhood asthma was found to be a risk factor by Sutradhar et al. [70], Ali [80] and Asamoah-Boaheng et al. [52], with, respectively, an OR of 6.90 (95% CI 4.90–9.50), an mOR of 3.00 (95% CI 2.25–4.00) and an mOR of 7.87 (95% CI 5.40–11.45). Lastly, Ma et al. [68] found childhood wheeze to increase the risk of COPD later in life (mRR 5.31, 95% CI 1.03–27.27).

Nutrition and weight status

10 reviews identified nutritional factors or weight-related aspects as risk factors for COPD [51, 57, 63, 72–74, 76, 78, 79, 85], of which five studies identified having a low BMI as a risk factor for COPD [57, 72, 74, 76, 85]. The found mORs ranged from 1.96 (95% CI 1.78–2.17), found by Zhang et al. [74], to 3.83 (95% CI 2.22–6.60), found by Yang et al. [57]. Additionally, Zhang et al. [74] found an association between COPD and overweight (mOR 0.80, 95% CI 0.73–0.87) and obese (mOR 0.86, 95% CI 0.73–1.02) participants, but found it to be a protective factor, which was also found by Adeloye et al. [85] (mOR 0.90, 95% CI 0.80–0.90). Chen et al. [72] found an mOR of 0.96 (95% CI 0.76–1.22) for participants with a BMI >28 kg·m⁻².

Both van Iersel et al. [73] and Zheng et al. [63] identified a Western-style diet, characterised by a high intake of refined grains, red and processed meat, saturated fats, sweets, and desserts, as a risk factor for COPD, with the latter providing an mOR of 2.12 (95% CI 1.64–2.74) [63]. Parvizian et al. [51] studied unhealthy dietary patterns, i.e. Western diets and diets high in carbohydrates, but the results were not statistically significant (mOR 1.22, 95% CI 0.84–1.76). Additionally, they found a healthy dietary pattern, i.e. high in fruit and vegetables, to reduce COPD risk (mOR 0.88, 95% CI 0.82–0.94)) [51]. Bellou et al. [79] found vitamin D deficiency to be a risk factor for COPD (mOR 1.77, 95% CI 1.18–2.64). Chen et al. [72] identified drinking history as a risk factor and found an mOR of 0.82 (95% CI 0.54–1.23). Drinking history was not further defined. Lastly, both van Iersel et al. [73] and Salari-Moghaddam et al. [78] identified high consumption of red and processed meat as a risk factor for COPD, with the latter finding an mHR of 1.08 (95% CI 1.03–1.13) for every 50 g increase per week in intake.

Occupational exposures

11 reviews identified occupational exposures associated with an increased risk of COPD [44, 56–58, 66, 67, 71, 79, 84, 88, 93]. Eight of those studies found that workers exposed to dust as compared to nonexposed had an increased risk of COPD [56–58, 66, 71, 79, 84, 93], with found mORs ranging from 0.97 (95% CI 0.68–1.39) by Bellou et al. [79] to 1.79 (95% CI 1.15–2.79), found by Yang et al. [57]. Both Guillien et al. [67] and Fontana et al. [88] found an association between COPD and agricultural work, with only the former providing an mOR of 1.77 (95% CI 1.50–2.08) [67]. Lastly, Baur et al. [44] found an association between workplace irritants, such as chloride or welding fumes, and COPD.

Physiological, personal and socioeconomic characteristics

Physiological, personal and socioeconomic risk factors for COPD were examined by 20 reviews [53–55, 57, 59, 61, 62, 65, 70, 72, 75, 76, 79, 85–87, 89–91, 93]. 12 reviews identified active smoking as the main risk factor for COPD [54, 55, 57, 61, 70, 72, 79, 85–87, 89, 90] and six identified second-hand smoking to be a risk factor [72, 79, 85, 92–94]. Regarding active smoking, found mORs ranged from 2.89 (95% CI 2.63–3.17), found by Forey et al. [90], to 5.50 (95% CI 4.20–7.20), found by Sutradhar et al. [70]. Regarding second-hand smoke exposure, found risk estimates range from an mRR of 1.20 (95% CI 1.08–1.34) found by Lee et al. [92], to an mOR of 1.56 (95% CI 1.40–1.74) [79]. Sutradhar et al. [70] found women who chewed tobacco to be at an increased risk for COPD (OR 12.90, 95% CI 3.40–49.40) [70]. Bellou et al. [79] identified waterpipe smoking as a risk factor (mOR 3.18, 95% CI 1.25–8.09).

Five studies identified having a low SES as a risk factor [57, 62, 76, 85, 93], with an mOR of 1.61 (95% CI 1.21–2.15) found by Yang et al. [57], using an education level of ≤9 years as a proxy for low SES, and ORs ranging from 0.80 (95% CI 0.50–1.30) to 3.70 (95% CI 1.90–7.00) in included studies in the review by Gershon et al. [62], using measures such as education, occupation and income as proxies for SES.

Additionally, four reviews identified older age as a risk factor [54, 70, 75, 85]. The first, by Sutradhar et al. [70], found that compared to 40–49-year-old participants, participants aged 50–59 and 60–69 years have an OR of 2.20 (95% CI 1.60–3.00) and 4.70 (95% CI 3.50–6.40), respectively. The second, by Gan et al. [75] found that with age, female smokers have a greater incline in lung function. The third, by Adeloye et al. [85] found that under the age of 50 years, COPD risk increased with an mOR of 1.50 (1.30–1.50) per 10-year increase, as well as an increased risk in participants aged 50–59 (mOR 2.10, 95% CI 1.80–2.60) and older than 60 (mOR 4.2, 95% CI 3.10–5.60).

Two reviews identified biomarkers as risk factors [53, 91]. Chaudhary et al. [91] found an association between elevated serum homocysteine and COPD risk (mean difference 3.05) as compared to healthy controls, with elevated serum homocysteine being associated with cigarette smoking. Su et al. [53] found several inflammatory markers to be associated with COPD risk. Elevated C-reactive protein compared to regular levels had an SMD of 1.21 (95% CI 0.92–1.50), leukocytes SMD 1.07 (95% CI 0.25–1.88), interleukin (IL)-6 SMD 0.90 (95% CI 0.48–1.31), IL-8 SMD 2.34 (95% CI 0.69–4.00) and fibrinogen SMD 0.87 (95% CI 0.44–1.31). They did not find a significant relationship between COPD and tumour necrosis factor-α levels.

Xiong et al. [59] identified living at high altitudes (>1500 m) as a possible risk factor for COPD, but it was noted it was not an independent risk factor (mOR 1.18, 95% CI 0.85–1.62). Bellou et al. [79] identified traffic intensity as a possible risk factor and found an mOR of 1.30 (95% CI 0.92–1.82) per 5000 vehicles per day increase on the nearest road and an mOR of 1.26 (95% CI 0.95–1.70) for traffic load on major roads within 100 m per 500 000 vehicles per day increase. However, results were not significant. Adeloye et al. [85] identified living in both rural (mOR 1.40, 95% CI 1.30–1.60) and urban (mOR 1.20, 95% CI 1.00–1.50) areas as a risk factor; however, only the results for living in rural areas were significant.

Yang et al. [57] reported males to be at an increased risk for COPD (mOR 1.47, 95% CI 1.10–1.96), which is in accordance with results from Adeloye et al. [85] (mOR 2.10, 95% CI 1.90–2.30) and Chen et al. [72] (mOR 1.70, 95% CI 1.31–2.22)). They also reported poor housing ventilation, e.g. poor kitchen ventilation (mOR 3.99, 95% CI 1.24–12.82), to increase COPD risk [57]. Yang et al. [57] also found recurrent respiratory infections to increase the risk of COPD (mOR 15.02, 95% CI 4.54–49.68). Bellou et al. [79] identified a history of rheumatoid arthritis (mOR 1.99, 95% CI 1.61–2.45) or psoriasis (mOR 1.45, 95% CI 1.21–1.73) as risk factors for COPD. Four studies identified previous tuberculosis as a risk factor for COPD [61, 85, 93, 94], with mORs of 2.80 (95% CI 1.90–4.00) [85] and 5.98 (95% CI 4.18–8.56) [61].

Lastly, four reviews identified an increased risk of COPD when one of the parents has the condition as well [57, 65, 72, 85], with found mORs ranging from 1.57 (95% CI 1.29–1.93) [65] to 2.07 (95% CI 1.47–2.92) [57], respectively.

Discussion

To the best of our knowledge, this is the first umbrella review that provides an overview and comparison of nongenetic risk factors for COPD and asthma. We identified 43 risk factors for AOA and 45 for COPD. Risk factors were grouped into different categories.

AOA

Two reviews reported wood dust workers to be at an increased risk for asthma [25, 34], which was also one of the workplace irritants included by Baur et al. [44]. This indicates the importance of occupational factors in asthma development, which is supported by the asthma-specific job exposure matrix developed by Kennedy et al. [95]. Additionally, the lowest JBI score was six out of 10, indicating no major issues. Second, a strong association was found between a higher BMI and increased asthma risk [31, 40, 42]. The association was found for both overweight and obese participants, compared to participants with a healthy BMI. Female sex was also associated with late-onset asthma [36] in a review specifically examining AOA. They stated the underlying mechanism was unclear, but that it could be related to lifetime changes in female sex hormones and gender-specific differences in allergic sensitivities [36]. However, male sex was also identified as a risk factor in one review [31]. Lastly, several chemical exposures were associated with asthma, such as formaldehyde [21], VOCs [27], pesticides [46] and phthalates [47]. Three reviews [31, 36, 42] focused on smoking as a risk factor, of which two specified age of onset [36, 42]. Two showed an association with AOA [31, 36]. The third, not showing an association with an increased risk, only included two papers on smoking in total with contradicting results. One found a protective effect and one showed an increased risk [42]. We did not differentiate between allergic and nonallergic asthma as this was not clearly stated in each review, but since we are focusing on adult asthma, most are likely nonallergic cases.

COPD

12 reviews [54, 55, 57, 61, 70, 72, 79, 85–87, 89, 90] stated active smoking, as compared to nonsmokers, to be the main risk factor for COPD. 15 reviews [54, 57, 61, 69, 70, 72, 76, 77, 79, 81, 83, 85, 86, 93, 94] identified indoor biomass burning as a risk factor for COPD. Even though most of those studies were performed in lower income countries, it highlights the importance of particulate matter exposure, and the need for proper ventilation and the use of less damaging fuels indoors (e.g. natural gas, electric instead of solid fuels). Four reviews [57, 60, 72, 82] found ambient PM_2.5 and NO₂ concentrations to increase COPD risk. According to the World Health Organization (WHO), 99% of the global population lives in places where air pollution levels exceed the WHO requirements [96]. This highlights the need for concrete policies to reduce emissions. Several studies identified early life exposures [50, 52, 57, 68, 70, 72, 80, 85, 92, 93, 94], such as childhood asthma and respiratory problems, as risk factors for COPD. This shows the importance of prioritising children's respiratory health, as the consequences might linger into adulthood.

Three reviews reported a western, unhealthy diet to increase COPD risk [51, 63, 73] and five studies identified low BMI as a risk factor [57, 74, 76]. A proposed biological pathway has been oxidative stress, which is associated with COPD development, and certain foods could have a protective role [97]. This shows how efforts of prevention could take advantage of this protective effect by including lifestyle measures, beyond smoking cessation, and include nutritional counselling. Eight reviews [56–58, 66, 71, 79, 84, 93] identified occupational dust as a risk factor, indicating efforts should be made to reduce dust exposure in the workplace and/or provide workers with protective equipment to mitigate the risk. Lastly, four reviews identified parental COPD as a risk factor independent of having specific genetic variations [57, 65, 72, 85], possibly making it easier to identify people at risk early on.

Differences and similarities

A second aim of this review was to identify both differences and similarities between risk factors for COPD and asthma. One of the main differences is BMI, with a high BMI being a risk factor for asthma, as opposed to a low BMI being a risk factor for COPD, and a high BMI being a protective factor. Second, both male and female sex were found to be risk factors for AOA, while males had a higher risk of COPD. However, Yang et al. [57] mention that currently COPD prevalence in males and females is close to equal, due to changes in smoking behaviour. Furthermore, even though most found risk factors differed, they were not contradicting.

We also found several similarities. First, for both diseases a low birth weight and pre-term delivery were implied as risk factors, even though pre-term delivery was not statistically significant for either. Second, occupational dust exposure was found in multiple reviews for both conditions, indicating an area for prevention. Third, active and passive smoking are important risk factors for both COPD and AOA. Even though the number of included reviews specifically focusing on smoking is limited, most studies adjusted for smoking. Fourth, ambient air pollution, in the form of NO₂ and particulate matter, was found to be a risk factor for both COPD and AOA. Finally, both pesticide exposure and exposure to workplace irritants such as chloride, were identified as risk factors for both AOA and COPD.

Strengths and limitations

There were several strengths. First, we conducted a comprehensive search on two highly prevalent diseases and provided an overview of nongenetic risk factors. This could help further develop prevention strategies and aid in the correct classification of both. Second, multiple authors did a partial screening and extraction of data, to limit errors in inclusion and data extraction. Last, because of the design, we were able to include most nongenetic risk factors for both asthma and COPD, which could have been unfeasible otherwise.

The review also has several limitations, which need to be considered. First, only seven out of the 30 studies that examined risk factors for asthma specified AOA, as opposed to asthma in adults. Four more only imply adult onset, as they investigate asthma development since starting work in certain occupational groups, but it is not explicitly specified that this is the case. As we were specifically interested in AOA and not asthma developed early in life, this might have impacted our results. This could be because risk factors for childhood asthma, lingering on into adulthood, are not the same as risk factors for developing asthma at a later age. Additionally, only a few studies confirmed asthma diagnosis by means of specific symptoms in combination with airway reversibility, the other studies did not specify the diagnosis which may result in the inclusion of patients with airway symptoms due to other diseases. Second, asthma studies including only adults were scarce. For most studies, the main focus was on asthma in children, leading to small sample sizes when excluding the population younger than 18 years of age. This lack of studies makes it difficult to draw firm conclusions. Third, for both asthma and COPD, not every review included a meta-analysis and/or provided a quantitative outcome measure or pooled estimates. This made comparing the results more difficult and could have led to some findings being over- or under-represented. However, as we did a risk of bias assessment, with JBI scores ranging between five and 11, this risk might be limited, as none indicated a high risk of bias. Fourth, we included every review regardless of quality and did not distinguish between the different JBI scores when interpreting results. Last, since we focused on incidence, we might have missed risk factors that become more apparent when taking mortality and exacerbations into account, such as ozone.

Implications

As our review provides an overview of risk factors of asthma and COPD, this provides possible targets for early identification and prevention. Since the costs of chronic diseases, both for healthcare and for individual health are high, this might result in significant financial savings and reduce the loss of quality of life [98]. Further, the Global Burden of Disease Study currently calculates risk based on six risk factors for COPD (smoking, second-hand smoke, household air pollution, ambient particulate matter, ozone and occupational particulates) and two for asthma (smoking and occupational asthmagens, e.g. occupational wood dust and cleaning materials) [4]. As this review identified many more risk factors, the burden of disease might currently be underestimated. Additionally, our review has highlighted the differences in risk factors for COPD and AOA, meaning interventions need to be targeted to the specific population at risk.

Our review has also highlighted areas in which more research is needed. Few reviews focus on asthma in adults and even fewer focus on AOA. As research has shown AOA is less atopic and has a worse prognosis than childhood asthma [9], more research into this specific population is needed.

Conclusions

In this umbrella review, we provided an overview of nongenetic risk factors for COPD and AOA. Importantly, both diseases share several known lung-damaging and nongenetic factors such as smoking and occupational exposures. There are also some notable differences, such as BMI and air pollution. More research specifically on AOA should be performed to better understand these risk factors. Overall, the results of this review could help in differentiating between these diseases when diagnosing, as well as more targeted and earlier identification of cases, by identifying people at risk.

Supplementary material

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Supplementary material ERR-0009-2023.SUPPLEMENT ^{(348.9KB, pdf)}