Human subjects:
All experiments were conducted according to the amended Declaration of Helsinki guidelines and signed informed consent from 30 subjects (aged between 20 – 80 years) were obtained (Approval number: A2021-0012 – issued by the Institutional Ethics Committee of University Medicine Rostock, Germany) prior to inclusion. Subjects were not suffering from any acute diseases/health condition and were not undertaking any special diet and/or medication. Among the subjects above the age of 60, three had mild COPD (one male and two female) and two had chronic bronchitis (female) in the past.
Determination of sample size:
We applied the analysis of variance (ANOVA) test for calculation of sample size. For a minimum detectable difference in mean substance intensities of 450 cps, a standard deviation of 300 was estimated. To attain an alpha value of 0.005 and a test power of 0.99 experimental groups, while considering a population of 100,000, the sample size resulted at 26 (with minimal group size of at least 13 each). In this study, we have included 30 subjects for analysis in order to detect even less than 5% differences in exhaled VOCs up to low parts per trillion by volume (pptV) levels.
Table 1: Anthropometric information of subjects.
|
Demographic Data
|
Lifestyle Habits / Life Events
|
|
Participant groups
|
Gender
|
Number
|
Age range (Years)
|
BMI range (Kg/sq.m)
|
Smoker
|
Alcoholic
|
Special diet
|
Acute, Chronic Disease / Medication
|
Contraception
|
Pregnancy/ Expecting
|
|
Adults
|
M
|
8
|
20 to 59
|
(18 - 25)
|
Yes
(n = 01)
|
No
|
No
|
No
|
N/A
|
N/A
|
|
|
F
|
9
|
20 to 59
|
(19 - 29)
|
Yes
(n = 03)
|
No
|
No
|
No
|
No
|
No
|
|
|
Elderly
|
M
|
7
|
60 to 80
|
(21 - 27)
|
Yes
(n = 02)
|
No
|
No
|
Mild COPD (n =1) Chronic Bronchitis
(n = 2)
|
N/A
|
N/A
|
|
|
F
|
6
|
60 to 80
|
(23 - 29)
|
Yes
(n = 02)
|
No
|
No
|
Mild COPD (n =2)
|
N/A
|
N/A
|
|
|
Number of subjects in each group, age range and body mass index (BMI) range are listed along with life style attributes e.g. cigarette smoking or alcohol drinking or any special dietary habits and clinically important parameters e.g. any health condition or medication etc.
Assignment of groups:
We have divided the study population in two groups; namely: adults (<60 years of age) and elderly (>60 years of age). Anthropometric data were confirmed by participants during inclusion and are presented in Table 1.
Experimental setup:
Three devices were synchronized for real-time measurements of several parameters simultaneously (Fig. 1). Continuous monitoring of breath VOCs, exhaled abundances of O2, CO2 and humidity via PTR-ToF-MS, non-invasive measurements of haemodynamic parameters via volume clamp method, SpO2 monitoring via pulseoximetry. Main-stream capnography (for pET-CO2) was performed immediately before and after the mask use. Data acquisition was initiated in parallel.
Breath sampling protocol:
Volunteers rested by sitting on a chair for at least 10 min before actual sampling. Each participant were instructed to maintained the sitting posture[20] and then wore a face mask to breathe orally. They spontaneously inhaled and exhaled only via mouth[21].
The transfer-line of PTR-ToF-MS was connected (via PEEK finger-tight fittings) to a PEEK extension line (i.e. 30 cm long, with outer diameter of 1 mm and inner diameter of 0.75 mm) in order to directly sample breath-resolved VOCs from the mask dead space (Fig. 1). The PTR transfer line was fixed (via metal clamps) at the back of subject’s head (at a level below the left/right earlobe). The PEEK line was placed along the subject’s right/left cheek (following the maxillary line) and was inserted within the mask dead space till the front of subject’s lips. The tip of this sampling line was cased within a conical PEEK ferrule in order to avoid any unwanted contact with mask surface or with subject’s lips. These extension lines were sterilized for reuse.
In each volunteer, measurements with two different masks (viz. FFP2 and surgical) were conducted on two consecutive days and at the same time. Adults were measured for 30 min and elderly subjects were measured for 15 min. The measurements in elderly subjects were stopped once they attained a SpO2 level <94%.
PTR-ToF-MS measurements of breath VOCs:
Breath VOCs were measured continuously via a PTR-ToF-MS 8000 (Ionicon Analytik GmbH, Innsbruck, Austria) and with pre-optimized experimental conditions[15, 22], i.e. continuous side-stream mode of sampling via a 6m long heated (at 75°C) silico-steel transfer-line connected to a sterile mouthpiece. A continuous sampling flow of 20 ml/min was applied and the time resolution of the PTR-ToF-MS measurements was 200 ms. Thus, data points were generated after every 200 ms and on each data point hundreds of compounds were measured at their trace abundances (in both expiratory- and room air). The ion source current was set to 4 mA and the H2O flow was set to 6 ml/min. Drift tube temperature was set to 75°C, voltage was 610 V and the pressure was 2.3 mbar. The resulting E/N ratio was 139 Td. After every minute a new data file was recorded automatically and the mass scale was recalibrated after each run (60 s). We used the following masses for mass calibration: 21.0226 (H3O+-Isotope), 29.9980 (NO+) and 59.049 (C3H6O).
VOC data processing:
VOCs were measured in counts per seconds (cps) and corresponding intensities were normalised onto primary ion (H3O+) counts. Raw data was processed via PTR-MS viewer software (version 3.4). As PTR-MS continuously records both exhaled breath and inhaled room-air, the ‘breath tracker’ algorithm (based on Matlab version 7.12.0.635, R2011a) was applied to identify expiratory and inspiratory phases[15]. Here, acetone was used as the tracker mass as it is an endogenous substance, which has significantly higher signal intensity in expiration than in inhalation. As the high mass resolution of PTR-ToF-MS (4000–5000 Δm/m) can assign volatiles upon their measured mass and corresponding sum formula with high precision[21], compound names are used while discussing results. VOCs were quantified via multi-component mixture of standard reference substances. Quantification process under adapted sample humidity (as in exhaled breath) using a liquid calibration unit (LCU, Ionicon Analytik GmbH, Innsbruck, Austria) is our pre-established state-of-the-art[23].
Selection of VOCs for analysis:
Here we considered compounds with expiratory abundances significantly above the inspiratory/room-air abundance. Out of those markers 32 substances were selected. These VOCs are well-known breath markers in clinical breathomics and reflect different origins, physicochemical characters and dependencies on physiology, metabolism, pathology, therapy and lifestyle/habits[18, 19, 21, 24, 25]. None of these VOCs were contributed from the applied masks as we examined the mask emissions for direct comparisons.
Continuous haemodynamic monitoring:
Non-invasive measurements of haemodynamic parameters (e.g. cardiac output, stroke volume, pulse rate and mean arterial pressure etc.) were performed via our pre-optimised setup by using volume clamp method (ClearSight system-EV1000, Edwards Lifesciences, California, USA)[14, 20].
Mainstream capnography:
Main stream capnography was performed just before and after each mask use via a small portable capnograph (EMMATM PN 3639, Ref: 605102, Masimoâ Sweden AB, Danderyd, Sweden) attached to a sterile breathing mouthpiece. pET-CO2 values were recorded in mmHg unit. Absolute values are considered from the 3rd breath onward as first two to three breaths are used to calibrate the CO2 and RR sensor.
Statistical analysis:
Analytical mean values (of measured parameters) from each participant were calculated over each minute of breath-resolved measurement. Data from every 5th minute were included for statistical analysis. In case of any non-parametric distribution of data, median values were considered for statistical analysis.
In order to reduce the evident intra-individual variations in measured variables, each participant was used as his/her own control. Thus, variables from each subject were normalised onto the corresponding initial values (of the first minute). Normalisation was performed separately for each mask types (FFP2 and surgical) and in each age groups (adults and elderly).
As every group mean/median value are contributed by each volunteer (of that group), the relative standard deviations (RSDs) in VOC abundances from each group were also calculated for each substance. The RSDs were calculated (in %) by rating sample standard deviations (SDs) over corresponding sample means.
Statistically significant differences within groups were assessed via repeated measurement ANOVA on ranks (Friedman repeated measures analysis of variance on ranks, Shapiro-Wilk test for normal distribution and post hoc Student–Newman–Keuls method for pairwise multiple comparisons between all groups; p-value ≤ 0.005) in SigmaPlot software (version 14).
For all measured variables, from all pairwise comparisons, the differences are presented by referring to the corresponding values at the 1st minute of each mask and within each age group.
In order to compare the effects of both mask types on both age groups, relative changes (in %) over time (with respect to initial values) were calculated for selected variables. Here, we have selected the principal physio-metabolic denominators and candidate VOCs that are potentially originating from several in vivo metabolic processes. Relative changes were calculated at 15th and 30th min in adults and at 15th min in elderly cohort. The changes in pET-CO2 values were calculated between immediately before and after mask use. In case of inter-group comparisons, one-way ANOVA was applied due to unequal group size. All groups were compared to each other.
In order to understand the correlations between exhaled VOCs and physiological parameters within each mask type, dimension reduction factor analysis (Factor extraction via principal components method, factor scores via regression method and 1-tailed significance at p-value ≤ 0.005) were performed in SPSS.