Introduction
Health loss from smoking | Biomarkers | Sources, comment |
---|---|---|
Chronic obstructive pulmonary disease (COPD) | Volatile organic compounds (VOCs) e.g., acrolein, crotonaldehyde | The WHO [19], considers these agents to be hazardous with acrolein considered to be: “an intense irritant, is toxic to lung cilia and has been proposed as a lung carcinogen”. Similarly, “crotonaldehyde is a potent irritant and a weak hepatocarcinogen and forms DNA adducts in the human lung.” |
All cancers | Tobacco-specific N´-nitrosamines (TSNAs) Polycyclic aromatic hydrocarbons (PAHs) | The WHO [19], notes that two TSNAs, “NNK and NNN, are probably responsible for cancers of the lung, pancreas, oral cavity and oesophagus in tobacco users”. “Both have been classified as human carcinogens by working groups at [International Agency for Research on Cancer] IARC.” (See Table 3 regarding NNK and NNN and the full terms). The WHO [19], notes that: “many PAHs are potent carcinogens or toxicants in laboratory animals (57), and many are present in cigarette smoke, including the prototypic PAH benzo [a] pyrene, classified as a human carcinogen” by a working group convened by the IARC. |
Cardiovascular disease | Carbon monoxide (CO) Acrolein | The WHO [19], states that: “CO is a well established cardiovascular toxicant, which competes with oxygen for binding to haemoglobin. In smokers, it is considered to reduce oxygen delivery, cause endothelial dysfunction and promote the progression of atherosclerosis and other cardiovascular diseases”. A US Surgeon General’s Report also states that: “the mechanisms by which CO may contribute to acute cardiovascular events are well characterized” [20]. |
Methods
Linking key biomarkers with categories of health loss
Literature searches to identify relevant biomarker studies of the differences in biomarkers between ENDS users and smokers
Health loss by disease categories
Health condition / condition group | Proportion of HALYs gained* from preventing uptake and promoting quitting of smoking (undiscounted) |
---|---|
Chronic obstructive pulmonary disease (COPD) | 48.9% |
Cancers (12 types**) | 28.3% |
CVD (coronary heart disease and stroke) | 22.4% |
Lower respiratory tract infection | 0.4% |
Total | 100% |
Integration of the relative biomarker results with the health impact results
Study | Level in exclusive ENDS users [A] | Level in exclusive smokers [B] | % of [A] relative to [B] | ENDS users (N) | Smokers (N) | Additional details* (with further details in the Supplementary Information) |
---|---|---|---|---|---|---|
Non-cancer chronic respiratory disease (VOCs) | ||||||
Jay et al. 2020 [32] | 0.2 | 1.87 | 10.7% | 60 | 15 | 3-HPMA; within group experiment**; mean level in mg over 24 h (urine). |
Hatsukami et al. 2020 [36] | 0.34 | 1.00 | 34.0% | 58 | 63 | CEMA (biomarker for acrylonitrile) showing ratio relative to exclusive smoking; RCT; within group relative change** |
Hatsukami et al. 2020 [36] | 0.53 | 1.00 | 53.0% | 59 | 63 | 3-HPMA showing ratio relative to exclusive smoking; RCT; within group relative change** |
Hatsukami et al. 2020 [36] | 0.53 | 1.00 | 53.0% | 58 | 63 | HMPMA showing ratio relative to exclusive smoking; RCT; within group relative change** |
Oliveri et al. 2020 [33] | 655.1 | 1232.4 | 53.2% | 59 | 54 | 3-HPMA; cartridge-based product. Least squares mean level in μg/g creatinine (urine). |
Weighted mean# | 40.5% | |||||
All cancers (TSNAs and PAHs) | ||||||
Oliveri et al. 2020 [33] | 28.6 | 230.1 | 12.4% | 59 | 57 | Total NNAL; ng/g creatinine (urine), least squares mean level; cartridge based product |
Boykan et al. 2019 [35] | 10 | 56 | 17.9% | 51 | 9 | Total NNAL (the proportion above threshold of 14.5 pg/mL, %); aged 12 to 21 years old; convenience sample of outpatients. |
Jay et al. 2020 [32] | 6.1 | 15.8 | 38.6% | 60 | 15 | NNN; mean ng over 24 h (urine); within group experiment**; the authors noted some anomalous results for NNN that concerned them. |
Hatsukami et al. 2020 [36] | 0.47 | 1.00 | 47.0% | 56 | 76 | Total NNAL showing ratio relative to exclusive smoking; RCT; within group relative change**. There was little difference between the relative levels at 4 weeks (0.44) and 8 weeks (0.47). |
Hatsukami et al. 2020 [36] | 0.79 | 1.00 | 79.0% | 56 | 62 | PheT (phenanthrene tetraol) a PAH showing ratio relative to exclusive smoking; RCT; within group relative change** |
Weighted mean# | 41.8% | |||||
Cardiovascular disease (CO and acrolein) | ||||||
Jay et al. 2020 [32] | 0.2 | 1.87 | 10.7% | 60 | 15 | 3-HPMA; within group experiment**; mean level in mg over 24 h (urine). |
Jay et al. 2020 [32] | 1.9 | 7.0 | 27.1% | 60 | 15 | COHb in blood (percent saturation); within group experiment** |
Nga et al. 2020 [34] | 6.40 | 16.47 | 38.9% | 15 | 15 | eCO as end tidal CO at 45 min; quasi-experimental with no randomisation (participants allowed to select products) |
Hatsukami et al. 2020 [36] | 0.43 | 1.00 | 43.0% | 58 | 76 | eCO showing ratio relative to exclusive smoking; RCT; within group relative change** |
Hatsukami et al. 2020 [36] | 0.53 | 1.00 | 53.0% | 59 | 63 | 3-HPMA showing ratio relative to exclusive smoking; RCT; within group relative change** |
Oliveri et al. 2020 [33] | 655.1 | 1232.4 | 53.2% | 59 | 54 | 3-HPMA; cartridge based product. Least squares mean level in μg/g creatinine (urine). |
Oliveri et al. 2020 [33] | 2.2 | 4.1 | 53.7% | 61 | 62 | COHb in blood, least squares mean level; cartridge based product |
Weighted mean# | 42.9% |
Results
Identified biomarker studies
Biomarker results by disease categories
Integrated analysis of biomarkers and health loss
Disease grouping | % HALY loss (Table 2) [A] | Relative harm of ENDS use vs smoking (Table 3 plus adjusted for acrolein from other sources) [B] | Relative harm in terms of HALY loss (i.e., [A] x [B]) |
---|---|---|---|
Chronic obstructive pulmonary disease (COPD) | 48.9% | 27.6%* | 13.5% |
Cancers (12 types) | 28.3% | 41.8% | 11.8% |
Cardiovascular disease | 22.4% | 34.7%* | 7.8% |
Lower respiratory tract infection | 0.4% | 27.6%* (as per COPD**) | 0.1% |
Total | 100% | 33.2% |
Discussion
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Another reason why we may have over-estimated the relative harm of ENDS is that some “exclusive” ENDS users may have been “ex-smokers”, some of whom may have still been smoking. This would result in an underestimate of the true difference in exposure between the groups. This may have been less likely in the experimental studies as each included a measure expected to reduce the likelihood of this bias operating. These measures were use of incentives for compliance [36], confinement to maximise restriction to the allocated product type [32], and screening for evidence of continued smoking [34, 35]. Nevertheless, although there was variation in the findings between studies, the two cross-sectional studies [33, 35], did not report a systematically higher level of biomarkers than the experimental studies, as might be expected if contamination by unreported continued smoking among exclusive ENDS users were greater in these studies.
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The disease categories we analysed only covered four main groupings of tobacco-related disease, but omitted less major ones. For example, there is evidence that smoking causes diabetes and increases the risk of tuberculosis, various eye diseases and immune system disorders such as rheumatoid arthritis [18]. Furthermore, some toxicants in ENDS products (e.g., acrolein) have also been associated with increasing the risk of diabetes [19].
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Within the disease categories we did not differentially weight particular toxicants by their likely importance in disease causation e.g., TSNAs vs PAHs in the “all cancers” grouping. While some work on relative prioritisation has been done (e.g., in tobacco-industry funded research [66]), this work does not appear to be comprehensive enough to produce reliable rankings. Furthermore, we did not consider non-linear dose response relationships. For example, lower levels of smoking intensity and second-hand smoke exposure have disproportionately higher relative risks for CVD than would be expected if the dose-response relationship was linear [18]. These non-linear relationships could mean that we have partly under-estimated the relative harm from toxicants that ENDS users are exposed to and that are associated with cardiovascular disease.
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The biomarker studies represent points in time in the long-term trajectory of ENDS use by individuals and within populations, and include diverse brands and product types (of both ENDS products and comparative tobacco brands). Trajectories of ENDS use and smoking may diverge further in the future. For example, smoked tobacco products have changed little over many decades and we suspect that many smokers will continue smoking long term at approximately the same intensity. However, we are less certain for ENDS use. ENDS users may be more or less likely to continue ENDS use long term compared to smokers. There may also be future changes to ENDS technology and usage patterns that affect exposure levels among ENDS users (e.g., based on changes in relative nicotine levels, or potential delineation of smokefree and vapefree areas, or if public tolerance of ENDS increases relative to smoking, or if ENDS products evolve further).
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More specifically, two of the biomarker studies involved short-term use of ENDS (i.e., for only five days [32], or just a matter of hours [34]). Usage patterns among short-term users may have differed from those exhibited by more experienced ENDS users and this could have impacted on their biomarker measurements.
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Some of the included biomarker studies had limitations and potential biases in their assessment of specific biomarkers among ENDS users. For example, while our analysis adjusted for other sources of acrolein (e.g. dietary sources), we did not have the data to adjust other biomarkers by exposure to secondhand smoke (or secondhand exposure to aerosol from ENDS). Nevertheless, such exposures are likely to be relatively minor given evidence that NNAL levels in non-smokers are typically 1–5% those of smokers (due to exposure to second-hand smoke) [22]. Also, although one study included results for a PAH [36], which has other sources (e.g., cooking emissions, vehicle emissions, and industrial air pollution [67]), this study had the advantages of being a randomised trial, thus such exposures should have been non-differential. But this study was still suboptimal for our purposes in terms of not also measuring PAH in a control group (non-ENDS using and non-smoking), but it did show that PAH levels declined significantly in those switching to exclusive vaping.