Novel biomarkers to assess mold exposure among children with Asthma

Household mold is a major problem in communities which face natural disasters such as hurricanes or flooding, and in homes with other sources of significant water intrusion; a biomarker for exposure to indoor mold could support public health investigations. We analyzed serum from 132 children with asthma living in government-subsidized housing for six microbial volatile organic compounds (2-ethyl-1-hexanol, 2-heptanone, 2-hexanone, 3-methylfuran, 3-octanone, and geosmin) using GC-MS. Fewer than 10% of the samples for three compounds (2-ethyl-1-hexanol, 2-heptanone, and 2-hexanone were quantified below the limit of detection. Associations between mold/water damage variables and mVOCs were assessed via regression analyses, adjusting for urinary cotinine and self-reported home characteristics. Children with household mold (assessed by occupant report of visual mold, mold odor, or water damage) had 32% higher serum concentrations of 2-hexanone than those living in homes without reported mold or water damage. We investigated indoor tobacco use via urinary cotinine analysis of a “first morning void spot sample” (FMV) and found that children with higher urinary cotinine had significantly higher serum 2-ethyl-1-hexanol. We found that children in homes where residents reported tobacco smoking indoors had significantly higher serum 2-ethyl-1-hexanol compared with those without reported household smoke exposure. Tobacco smoke, indoor painting, gas stoves, and carpets were not confounders in the relationship between mVOCs and mold/water damage variables. 2-hexanone, along with an index variable which included all detectable mVOCs in our panel, are promising biomarkers of recent mold exposure that could be used in concert with other detection methods. Key Message: Our study has indicated that 2-hexanone could be used as a serum biomarker for recent exposure to indoor mold. It has significant associations with well-established proxies of indoor mold growth such as mold odor and water damage. When mass home inspections are impractical such as after a hurricane, the use of 2-hexanone or the mold index of mVOCs as a biomarker would be instrumental in assessing current disaster-related mold exposure. In addition, since mold can be hidden in walls and under carpets, a mold biomarker could still be of import if hurricane clean-up has occurred and the occupants still have possible mold-related health symptoms. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Title: Novel biomarkers to assess mold exposure among children with Asthma Authors: Lalith K. Silva 1 PhD, Cody A. Newman 1 BS, Idris J. Wazeerud-Din 1 PhD, Mitchell M. Smith 1 MS, Can Zhang 1 PhD, Wanzhe Zhu 1 PhD, Deepak Bhandari 1 PhD, Peter J. Ashley 2 PhD, Gary Adamkiewicz 3 PhD, Tiina Reponen 4 PhD, Derek Werthmann 5 PhD, Felicia A. Rabito 5 PhD, Benjamin C. Blount 1 PhD, and Ginger L. Chew 1 ScD Affiliations: 1 Division of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA 2 US Department of Housing and Urban Development, Washington, DC 3 Associate Professor of Environmental Health and Exposure Disparities, Department of Environmental Health, Harvard T.H. Chan School of Public Health 4 Department of Environmental and Public Health Sciences, University of Cincinnati 5 Department of Epidemiology, Tulane University Celia Scott Weatherhead School of Public Health and Tropical Medicine Conflicts of Interest: The authors report no conflicts of interest.

Microbial Volatile Organic Compounds, Mold, Water Damage, Tobacco Smoke, 2-Hexanone, 2-Ethyl-1-Hexanol, 2-Heptanone

Household mold is a major problem in communities which face natural disasters such as hurricanes or flooding, and in homes with other sources of significant water intrusion; a biomarker for exposure to indoor mold could support public health investigations. We analyzed serum from 132 children with asthma living in government-subsidized housing for six microbial volatile organic compounds (2-ethyl-1-hexanol, 2-heptanone, 2-hexanone, 3-methylfuran, 3-octanone, and geosmin) using GC-MS. Fewer than 10% of the samples for three compounds (2-ethyl-1-hexanol, 2-heptanone, and 2-hexanone were quantified below the limit of detection. Associations between mold/water damage variables and mVOCs were assessed via regression analyses, adjusting for urinary cotinine and self-reported home characteristics. Children with household mold (assessed by occupant report of visual mold, mold odor, or water damage) had 32% higher serum concentrations of 2-hexanone than those living in homes without reported mold or water damage. We investigated indoor tobacco use via urinary cotinine analysis of a “first morning void spot sample” (FMV) and found that children with higher urinary cotinine had significantly higher serum 2-ethyl-1-hexanol. We found that children in homes where residents reported tobacco smoking indoors had significantly higher serum 2-ethyl-1-hexanol compared with those without reported household smoke exposure. Tobacco smoke, indoor painting, gas stoves, and carpets were not confounders in the relationship between mVOCs and mold/water damage variables. 2-hexanone, along with an index variable which included all detectable mVOCs in our panel, are promising biomarkers of recent mold exposure that could be used in concert with other detection methods. Key Message: Our study has indicated that 2-hexanone could be used as a serum biomarker for recent exposure to indoor mold. It has significant associations with well-established proxies of indoor mold growth such as mold odor and water damage. When mass home inspections are impractical such as after a hurricane, the use of 2-hexanone or the mold index of mVOCs as a biomarker would be instrumental in assessing current disaster-related mold exposure. In addition, since mold can be hidden in walls and under carpets, a mold biomarker could still be of import if hurricane clean-up has occurred and the occupants still have possible mold-related health symptoms.

The presence of bacteria and fungi in indoor environments is ubiquitous. 1 Research into the effects of microbes and their volatile byproducts becomes even more important as humans spend more of their time indoors. Fungi and bacteria are known to produce primary and secondary metabolites called microbial volatile organic compounds (mVOCs) which are released into the air where they can cause potential exposure to humans. mVOCs are biological contaminants described as having a musty or earthy scent and are a cause of moldy odors indoors. This group of volatile molecules can include aldehydes, alcohols, ketones, terpenes, and other classes of chemicals. 2 For example, 2-heptanone is formed in fungi by the beta-oxidation of a fatty acid, followed by decarboxylation of the resulting metabolite. 3 Increased fungal growth often results when a building has moisture damage. An extreme example of increased mold growth is as a result of floods. Houses renovated after flood damage have been shown to have higher concentrations of airborne particulate matter, microorganisms, and VOCs. 4 Even buildings without flood damage can have conditions where fungi can grow. 5 A nationally representative survey of U.S. housing that was completed in 2019 published self-reported prevalence estimates of 13.4% and 21.0% for musty odors and recent moisture or leak problems, respectively, and 2.76% for technician-observed mold. 6 An estimated 21% (95% Confidence Interval (CI), 12-29%) of asthma cases in the United States are attributable to dampness and mold. 7 Jaakola et al. found that moldy odor was a strongly significant indicator of mold exposure as it relates to asthma compared to both dampness and presence of visual mold. Their study also indicated that exposure to mold increases the risk of the development of childhood asthma. 8 Positive associations have also been found between sick building syndrome (symptoms can include headaches and mucosal and airway irritation) and certain mVOCs such as 2-hexanone, 1-octen-3-ol, and 3-methylfuran. 9 Many studies have shown that building materials can be effective substrates for bacterial and fungal growth when conditions such as moisture are optimal. Penicillium verrucosum was shown to emit 1-octen-3-ol, 3-octanone, 2-hexanone, and 2-heptanone when cultured on aspen wood and wallpaper. 10 The fungus Aspergillus versicolor growing on household dust was found to release 2-ethyl-1-hexanol, 1-octen-3-ol, 3-octanone, 2-heptanone, and 2-hexanone. 11 Several species of Aspergillus were cultivated on malt extract agar and gypsum board and released detectable amounts of 2-heptanone, 1-octen-3-ol, 2-hexanone, and 3-octanone. 12 Most of these compounds cannot be used as true biomarkers as most are not unique to bacteria or fungi. We collected 132 serum samples from children with asthma living in multi-family government-subsidized housing in Boston, New Orleans, and Cincinnati (U.S.A.) in order to investigate potential relationships between home characteristics and mVOC exposure. Each sample was analyzed using our recently published quantitation method 13 for concentrations of six mVOCs (3-methylfuran, 2-hexanone, 2-heptanone, 3-octanone, 2-ethyl-1-hexanol, and geosmin). These analytes were selected for analysis as they are known to be released from microorganisms which grow on household materials when wet, such as carpet, particle board, and gypsum board. 10-12, 14, 15 Exposure to these compounds in homes with reported mold or water damage would be likely. While literature on half-lives in human matrices for these compounds is limited, 2-hexanone is known to have a half-life of 78 minutes and a clearance time of 6 hours in guinea pig serum. The same study found that a compound similar to 2-ethyl-1-hexanol is 2-hexanol, which has a half-life in the same matrix of 72 minutes and a clearance time of 6 hours. 16 A similar compound to 2-pentanone is 4-methyl-2-pentanone, which has two phase elimination kinetics with half-lives of 12 and 70 minutes in human blood. 17 These compounds can thus be thought to have half-lives within the range of minutes to hours. To our knowledge there have been no studies investigating these compounds as biomarkers for exposure to household mold. We subsequently tested the hypothesis that these microbial VOCs will be in higher concentrations in serum of children living in homes with recent reported water damage or mold.

Quantification of mVOCs in Human Serum: Study samples were prepared by aliquoting 250µL of human serum into a solid phase microextraction (SPME) vial followed by a 20µL spike of an isotopically labeled Internal Standard (ISTD). An 80-µm Carboxen-PDMS coated SPME fiber was inserted into the headspace of the vial and the sample was extracted. Analytes were desorbed by inserting the SPME fiber into a 250°C gas chromatograph (GC) inlet. The instruments used for this process were a 7890A Agilent Gas Chromatograph coupled to an Agilent 7010 triple quadrupole mass spectrometer. A mounted PAL-DHR Autosampler from Leap Technologies Inc. (Cary, NC) was used for automated sampling. mVOCs were quantified using calibration curves made for each analyte using relative responses of native peak area and internal standard peak area. Calibrators were prepared weekly and re-aliquoted to be analyzed with each analytical sequence. Two sets of quality control materials (QCs) were prepared and analyzed with each analytical run. Multiple water blanks spiked with ISTD were analyzed throughout each sequence to ensure that no contamination was observed. Further details on this quantitative method can be found in the manuscript published by the Volatile Organic Compounds Laboratory at the U.S. Centers for Disease Control and Prevention (CDC). 13 Institutional Review Board (IRB) Approval: The study was reviewed and approved by CDC’s Institutional Review Board (IRB) and the IRBs of Tulane University and Harvard T.H. Chan School of Public Health. Questionnaires were completed by the mothers/caregivers of the children and the children provided assent for all study procedures. We had parental permission for the collection of all study information. Study Design: Sera were sampled from 132 children ages 7-12 years with current asthma living in government-subsidized multi-family housing in Cincinnati (N=24), New Orleans (N=63), and Boston (N=45) (U.S.A.) as previously described. 18 Mothers or caregivers of the children completed questionnaire data. Household exposure to mold was assessed via questionnaire. Three questions were asked to assess probable mold exposure in the home: 1. During the past 6 months, have you seen mold in your home? (yes/no) 2. During the past 6 months, have you smelled any mold, mildew, or musty odor in your home? (yes/no) 3. During the last 6 months, has there been water damage to your home? (Ceilings, floors or walls, or dampness from leaks, broken pipes, heavy rain, or floods etc.) (yes/no) Data were modeled separately for each of the three mold assessment questions. A fourth model was run with exposure defined as a positive response to any of the three mold exposure questions, “any mold/water damage.” Additional variables that were investigated for potential relationships to serum mVOCs were stove type, recent indoor painting, carpeting (adding or replacing), and smoking tobacco products by residents in the home. Paint, carpeting, and tobacco were investigated because they are known sources of exposure for many of these compounds. 19-21 Gas stoves were selected as they have been previously found to be associated with other VOCs. 22 These were collected via survey questionnaire at the time of the blood collection using the following questions: 1. What type of stove do you have? (gas or electric) 2. Have you painted any rooms in your home in the past 6 months? (yes/no) 3. Currently, do you or others in your household smoke cigarettes, cigarillos, cigars, pipes, or other tobacco products? (yes/no) 4. Have you changed any carpeting (including rugs) in your home in the last 6 months? (Added carpet/rug?) (yes/no) It should be noted that question c did not include possible exposures from visitors to the home that smoked infrequently. Tobacco smoke exposure was also assessed via urinary cotinine concentrations, which were determined using a Liquid Chromatography tandem Mass Spectrometry (LC/MS/MS) method developed by the Tobacco and Volatiles Branch of the Centers for Disease Control and Prevention. 23 Urine was collected at two timepoints for the “baseline” home visit. On day 1, the field technician explained the urine collection to the mother/caregiver and child and gave a urine collection cup to the child to collect a urine convenience spot sample (so the technician knew that the urine sample was indeed collected by that specific child) . Then the technician gave an empty collection cup to the mother/caregiver to collect an FMV at any time on days 2 to 5. The mother/caregiver was instructed to place that sample in a freezer until it could be picked up by study personnel. On day 5, the field technician returned to the home to pick up the FMV from the home’s -20°C freezer, Urine samples were transported on icepacks back to a -80°C freezer at the laboratory. In several cases, the child was not ready to void on day 1, so the mother/caregiver kept both urine collection cups and was instructed to collect at least one FMV (the other could be a convenience spot sample). For some cases, it was not clear if the second urine collection or the first urine collection was the FMV sample. Therefore, an algorithm was established to label any void as the FMV if the collection time was before 11 am. If both samples were taken prior to 11 am, the urinary cotinine of both samples was averaged together. Cotinine levels were natural log transformed. Statistical Analysis: Analytes detected in fewer than 60% of samples were excluded from the main analysis because of unstable estimates. Measurements below the LOD were imputed as LOD divided by the square root of 2. To approximate a normal distribution, mVOC concentrations were natural log transformed. We calculated Pearson correlation coefficients to investigate the strength of the relationship between mVOC analytes (2-hexanone, 2-heptanone, and 2-ethyl-1-hexanol). Geometric means (and 95% confidence interval of the geometric mean) and medians (and 25th and 75th percentiles) stratified by participant-reported observed visual mold, mold odor, and water damage were calculated. Among the possible exposure factors for mVOCs analyzed were the presence of household mold/water damage, recent painting of rooms in home, type of stove, carpeting, and household exposure to tobacco smoke. We used a two-sample t-test for each exposure factor, or a nonparametric test (the Wilcoxon Rank Sum test) in cases when normality assumptions did not hold even after log transformation. To further explore the relationship between mold/water damage and serum mVOC concentrations, we used linear regression (unadjusted and adjusted for potential confounders including interaction terms). We also developed an index of the mVOCs; the number of analyte peaks above LOD for each mVOC were summed across all six analytes (the minimum number of peaks above LOD was 2 and the maximum was 5). Because of the small number in each category, the mVOC index was reduced to a binary variable: if index sum = 2 or 3, then mVOC index = low; if index sum = 4 or 5, then mVOC index = high. Mold/water damage variables were tested for associations with the mVOC index via logistic regression. SAS software (SAS 9.4, SAS Institute Inc., Cary, NC) was used to carry out the statistical analysis.

Figure 1 shows the possible exposure variables for participants of this study stratified by city of residence. Each of the three mold/water damage exposure categories (visible mold, mold odor, and water damage) were investigated for association with increased mVOC concentrations. The analysis was also stratified by stove type, recent painting, carpeting and tobacco smoke (via urinary cotinine and self-report). Table 1 displays descriptive statistics for the three most prevalent mVOCs stratified by each of the mold/water damage categories. Concentrations of 2-ethyl-1-hexanol were the highest among the target analytes. 2-heptanone and 2-hexanone were detectable in the majority of children’s sera. For mVOCs with a substantial number of samples that had detectable concentrations (i.e., those with fewer than 40% below the limit of detection), associations between each mold/water damage exposure category and each of the mVOCs were tested using linear regression analysis (Table 2). As the visible mold category was not significantly associated with any of the mVOCs, results for this category were presented in Table SI 1. However, mold odor and water damage were positively associated with serum 2-hexanone (Table 2). Any reported mold/water damage was positively associated with 2-hexanone using simple linear regression analysis (Table 2). In multiple linear regressions, interaction terms among visible mold, mold odor, and water damage were found to be nonsignificant for all three mVOCs (results not shown). Presented in Table 3, children in homes with a gas stove had significantly higher serum 2-ethyl-1-hexanol compared with those in homes with an electric stove. The levels of 2-ethyl-1-hexanol were also higher in serum from homes with reported tobacco smoke (33 vs 26 ng/mL). 2-hexanone and 2-heptanone were not associated with stove type or tobacco smoke. However, both 2-hexanone and 2-heptanone were higher for children in homes that did not have recent indoor painting. Since 2-hexanone was also significantly related to the any mold/water damage variable, we performed an additional Wilcoxon Rank Sum Test to determine the effects of the recent indoor painting for each of the mold/water damage categories. Participants who had lived in recently painted homes were removed from the data set for this model (Table SI 2). The visible mold exposure category became significantly associated with 2-hexanone in this new model. After removing the participants with any mold/water damage, recent indoor painting had no significant association with 2-hexanone or 2-heptanone (Table 3). We also investigated the relationship between mVOC concentrations and exposure to tobacco smoke. Children who were more exposed to tobacco smoke were associated with significantly higher serum 2-ethyl-1-hexanol measured as both urinary cotinine of FMV samples (Table SI 3) and self-reported exposure (Table 3). Exposure to both tobacco smoke and gas stoves were found to have a significant association with serum 2-ethyl-1-hexanol. We included an interaction term of these two variables in the regression model, but it was not statistically significant. Carpeting was also not associated with 2-ethyl-1-hexanol, 2-heptanone, or 2-hexanone. The median levels of the mVOCs did not vary with addition or removal of carpets or rugs (Table SI 4). Finally, the more analyte peaks above the LOD for any of the six mVOCs, the higher the association with mold/water damage variables (Table 4). The odds of the index being high (4-5 analytes detected above LOD) were 288% higher when mold was seen, 684% higher when mold was smelled, and 359% higher when water damage was reported.

We found evidence that one of the mVOCs, 2-hexanone, could be a biomarker for mold/water damage in homes. Concentrations of 2-hexanone were higher in children whose homes were positive for the variable, “any mold/water damage exposure”. Also, those with mold odor in the home had higher 2-hexanone compared with participants without report of mold odor. These results coincide with reports of 2-hexanone being among the most concentrated compounds detected in the headspace of wet carpet samples which were collected from buildings with moisture problems. 24 We found that the serum concentrations of other mVOCs were not significantly affected by any mold/water damage exposure category. In previous studies, other mVOCs have been found to be associated with mold damage in buildings and respiratory symptoms in occupants. 25 The fact that the other individual mVOCs that we studied were not independently associated with mold/water damage is not in conflict with these studies. Rather they show the wide variation in mold species and the environmental conditions that give rise to mVOC production might not always lead to a single mVOC. In fact, we found that using a mold index with all of the mVOCs was highly associated with mold/water damage in homes. The fact that the strongest association with the mold index was for mold odor is of note considering that this has been found to have the strongest association with mold-related symptoms in epidemiological studies. 26 As more biomarkers for mVOCs become available, it might be possible to predict a “fingerprint biomarker” for some mold profiles as has been done in previous research. 27 This fingerprint biomarker could be tested for associations with symptoms as has been done for a single mVOC, 3-methylfuran. 28 We investigated potential relationships between recent indoor painting and the mold/water damage exposure categories and found that increased 2-hexanone was still significantly associated with all three mold exposure categories, including visible mold, when participants who had painted their homes recently were removed from the data set. Unlike the findings of Norbäck et al., 19 paint contamination with microbial growth did not seem to be a factor in our study. However, due to the small number of remaining participants in recently painted homes (N=12), the results of this analysis will need further investigation. Similarly, the small sample size of homes that had added carpeting in the past 6 months (N=8) could have precluded our ability to assess potential associations between carpeting and 2-ethyl-1-hexanol, 21 as well as potentially other mVOCs. It has been reported in the literature that tobacco smoke contains 2-ethyl-1-hexanol, 2-heptanone, and 2-hexanone. 20 We analyzed participant’s urine samples to investigate any potential associations between serum mVOCs and exposure to tobacco smoke (determined via urinary cotinine) to investigate this exposure source. Using the first collected urine sample for each participant (Urine 1), we determined that children exposed to tobacco smoke had significantly higher serum 2-ethyl-1-hexanol. Using the second collected urine sample (Urine 2), results were marginally significant. We found a significant association when taking the average cotinine of the two urine samples. Using the FMV urine sample (collected before 11am), we found a strong significant association between serum cotinine and 2-ethyl-1-hexanol. We also compared this result to the questionnaire data in a sensitivity analysis. Children who were reported as living in homes where any household residents smoked tobacco were found to have significantly higher serum 2-ethyl-1-hexanol when analyzed using linear regression. None of the other analytes were shown to have a significant association with urinary cotinine or self-reported tobacco smoke exposure. Household mold is not the only possible pathway for exposure to these mVOCs. It is thought that exposure to certain VOCs could be related to the use of gas stoves. 22 We found that children with gas stoves in their home had significantly higher serum 2-ethyl-1-hexanol. While 2-hexanone did not have a statistically significant association with gas stoves, there was a noticeable trend. Although gas stoves and tobacco smoke exposure were associated with 2-ethyl-1-hexanol, their associations were only additive. The interaction of these two variables was not statistically significant and was dropped from the final model. Of note, our small sample size could have precluded our ability to detect an interaction effect if one existed. A full summary of mVOC exposure variables and their significance for target analytes is shown in Table SI 3. Based on these data, 2-ethyl-1-hexanol exposure is a result of a variety of sources and would not be a suitable biomarker on its own. 2-hexanone is the only mVOC examined that may be useful as a mold exposure biomarker according to this study; a mVOC biomarker index is another strong indicator of mold exposure. A limitation of this study is that we were unable to perform multiple linear regression analysis due to the small number of samples, aside from the any mold/water damage exposure variable analysis. Also, the age of these serum samples (some samples had been stored in freezers for several years) could contribute to lower prevalence for some more volatile mVOCs on the panel. We also did not have resources to measure the mVOCs in the air of the sample homes. Ideally, air vs. biomarker levels would provide complementary measurements in order to attribute home exposure to the biomarkers themselves. Finally, the visual presence of mold in a home may not be the most effective method of determining the presence of mold growth. Mold is known to release certain mVOCs as part of their metabolism, and these mVOCs can be noticeable as a moldy odor regardless of visible mold on surfaces. Mold growth can occur in hidden spaces including wall cavities and under carpet for example. Although qualitative mold has been associated with respiratory health (e.g., asthma and allergic rhinitis), quantitative measurements of mold have shown inconsistent associations. 26, 29 The measurement of mVOC biomarkers could serve as another tool in efforts to assess mold exposure in buildings. To our knowledge, this is the first use of mVOC biomarkers rather than measurement of mVOC air concentrations in a housing study that characterized mold exposure. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Conclusion Our study has indicated that 2-hexanone could be used as a serum biomarker for recent exposure to indoor mold. It has significant associations with well-established proxies of indoor mold growth such as mold odor and water damage. When mass home inspections are impractical such as after a hurricane, the use of 2-hexanone or the mold index of mVOCs as a biomarker would be instrumental in assessing current disaster-related mold exposure. Also, if hurricane clean-up has occurred and the occupants still have health symptoms that might be mold-related, a mold biomarker could be helpful since mold can be hidden in walls or under carpets. Then, further remediation efforts could be explored. Possible future studies could attempt to determine if the biomarker is associated with human health, as well as if interventions in mold-exposed homes could decrease serum 2-hexanone and the mold index of mVOC biomarkers. Disclaimers: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Department of Health and Human Services, or the U.S. Centers for Disease Control and Prevention (Division of Laboratory Sciences). Use of trade names and commercial sources is for identification only and does not constitute endorsement by the U.S. Department of Health and Human Services, or the U.S. Centers for Disease Control and Prevention (Division of Laboratory Sciences). Figure 1: Variables of participant exposure by city of residence (Total N=132) Table 1: Descriptive statistics for the three most prevalent mVOCs a stratified by reported visible mold, mold odor, and water damage b | Percent below limit of detection (%) | Geometric Mean [95% CI] (ng/mL) | Median [25th%, 75th%] (ng/mL) | Percent below limit of detection (%) | Geometric Mean [95% CI] (ng/mL) | Median [25th%, 75th%] (ng/mL) | || | S2EH | 0 | 29.5 [26.0, 33.5] | 27.1 [22.6, 41.3] | 1 | 27.34 [25.5, 29.3] | 27.9 [23.3, 33.4] | | | S2HP | 0 | 0.764 [0.619, 0.941] | 0.699 [0.556, 1.13] | 0 | 0.811 [0.736,0.894] | 0.749 [0.567, 1.03] | | | S2HX | 7 | 0.692 [0.503, 0.952] | 0.610 [0.389, 1.22] | 0 | 0.582 [0.514, 0.659] | 0.504 [0.379, 0.730] | | | Water Damage (N=20) | No Water Damage (N=111) | |||||| | S2EH | 0 | 29.8 [25.0, 35.5] | 27.5 [23.0, 42.3] | 1 | 27.6 [25.8, 29.4] | 27.5 [22.6, 33.5] | | | S2HP | 0 | 0.906 [0.710, 1.16] | 0.898 [0.672, 1.31] | 0 | 0.789 [0.717, 0.868] | 0.729 [0.556, 1.03] | | | S2HX | 0 | 0.919 [0.662, 1.28] | 0.909 [0.591, 1.35] | 2 | 0.570 [0.503, 0.647] | 0.487 [0.373, 0.730] | | | Mold Odor (N=16) | No Mold Odor (N=112) | |||||| | S2EH | 0 | 30.4 [24.9, 37.3] | 33.2 [21.6, 42.7] | 1 | 27.4 [25.7, 29.2] | 27.3 [23.3, 33.3] | | | S2HP | 0 | 0.901 [0.695, 1.17] | 0.728 [0.611, 1.35] | 0 | 0.786 [0.715, 0.864] | 0.740 [0.554, 0.971] | | | S2HX | 0 | 0.977 [0.661, 1.44] | 0.903 [0.565, 1.92] | 2 | 0.564 [0.499, 0.636] | 0.486 [0.375, 0.731] | a : S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone b : Due to a few participants missing necessary questionnaire data, each exposure category displayed is missing between 1 to 4 participants. Table 2: Unadjusted linear regression models for mVOCs a and reported mold/water damage | Mold Odor (N=128) | ||||| | S2EH | 128 | 0.2511 | 1.11 | [0.927, 1.34] | | | S2HP | 128 | 0.3091 | 1.15 | [0.879, 1.50] | | | S2HX | 128 | 0.0022* | 1.73 | [1.22, 2.46] | | | Water Damage (N=131) | ||||| | S2EH | 131 | 0.3572 | 1.08 | [0.915, 1.28] | | | S2HP | 131 | 0.2662 | 1.15 | [0.899, 1.46] | | | S2HX | 131 | 0.0042* | 1.61 | [1.17, 2.22] | | | Any Visible Mold, Mold Odor, or Water Damage (N=130) | ||||| | S2EH | 130 | 0.2965 | 1.07 | [0.941, 1.22] | | | S2HP | 130 | 0.8887 | 1.01 | [0.839, 1.22] | | | S2HX | 130 | 0.0296* | 1.32 | [1.03, 1.70] | a : S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone b : Parameter estimates were exponentiated for better interpretation. *Statistically Significant (p<0.05) Table 3: Wilcoxon Rank Sum Tests for possible mVOC a exposure sources: Recent Interior Painting, Stove Type, and Indoor Tobacco Smoke | Analyte | Median (ng/mL) | p-value | Median (ng/mL) | p-value | Median (ng/mL) | p-value | Median (ng/mL) | p-value | |||| | Yes (N=19) | No (N=109) | Yes (N=12) | No (N=75) | Gas (N=62) | Electric (N=70) | Yes (N=43) | No (N=88) | ||||| | S2EH | 29.5 | 27.1 | 0.3656 | 32.3 | 27.1 | 0.3688 | 29.6 | 26.6 | 0.0473 | 32.8 | 26.1 | 0.0005* | | S2HP | 0.673 | 0.763 | 0.0164* | 0.699 | 0.763 | 0.1447 | 0.753 | 0.730 | 0.4940 | 0.667 | 0.770 | 0.7818 | | S2HX | 0.379 | 0.569 | 0.0068* | 0.384 | 0.504 | 0.2207 | 0.628 | 0.482 | 0.0713 | 0.560 | 0.504 | 0.1692 | a : S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone b : All instances of positive mold/water damage were removed from data set for this model. *Statistically Significant (p<0.05) Table 4. Logistic regression models for mVOC index associations with mold/water damage jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf | Visible mold | 3.88 | 0.0020* | | Mold odor | 7.84 | 0.0008* | | Water damage | 4.59 | 0.0030* | a Parameter estimates were exponentiated for better interpretation. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf *Statistically Significant (p<0.05) Supplemental Information Table SI 1: Unadjusted linear regression models for mVOCs a and reported visible mold | S2EH | 130 | 0.2943 | 1.08 | [0.935, 1.25] | | | S2HP | 130 | 0.5705 | 0.941 | [0.762, 1.16] | | | S2HX | 130 | 0.2330 | 1.19 | [0.894, 1.58] | a : S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone b : Parameter estimates were exponentiated for better interpretation. Table SI 2: Wilcoxon Rank Sum Test results for 2-hexanone on participants living in homes with no recent interior painting | Median (ng/mL) | p-value | ||| | Yes | No | ||| | Visible mold | 0.922 (N=20) | 0.516 (N=87) | 0.0211* | | | Water damage | 0.984 (N=19) | 0.516 (N=89) | 0.0049* | | | Mold odor | 1.01 (N=15) | 0.515 (N=90) | 0.0088* | *Statistically Significant (p<0.05) Table SI 3: Summary of p-Values and Medians of mVOC a exposure variables for target analytes | p value b | Median (Yes) | Median (No) | p value b | Median (Yes) | Median (No) | p value b | Median (Yes) | Median (No) | | | 1. Visible mold in the previous 6 months | 27.1 | 27.9 | 0.699 | 0.749 | 0.610 | 0.504 | ||| | 2. Mold odor the previous 6 months | 27.5 | 27.5 | 0.898 | 0.729 | ** | 0.909 | 0.487 | || | 3. Water damage the previous 6 months | 33.2 | 27.3 | 0.728 | 0.74 | ** | 0.903 | 0.486 | || | 4. Interior painting | 29.5 | 27.1 | * | 0.673 | 0.763 | ** | 0.379 | 0.569 | | | 5. Interior painting and no mold | 32.3 | 27.1 | 0.699 | 0.763 | 0.384 | 0.504 | ||| | 6. Visible mold and no recent interior painting | 26.1 | 27.4 | 0.823 | 0.763 | * | 0.922 | 0.516 | || | 7. Mold odor and no recent interior painting | 32.2 | 27.1 | 0.757 | 0.769 | ** | 1.01 | 0.515 | || | 8. Water damage and no recent interior painting | 27.1 | 27.1 | 0.906 | 0.754 | ** | 0.984 | 0.516 | || | 9. Stove type c | * | 29.6 | 26.6 | 0.753 | 0.730 | 0.628 | 0.482 | || | 10. Tobacco use in the home (Questionnaire) | *** | 32.8 | 26.1 | 0.667 | 0.770 | 0.560 | 0.504 | || | 11. Tobacco use in the home (Established FMV) | ** | – | – | – | – | – | – | || | 12. Tobacco use in the home (Average Urinary Cotinine) | * | – | – | – | – | – | – | jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf a : S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone b : 1-3, 11-12: Unadjusted Linear Regression Model. 4-10: Wilcoxon Rank Sum Test *p value<0.05, **p value<0.01, ***p value<0.001, Blank p values are nonsignificant. c : Yes refers to Gas-powered and No refers to Electric. –: Not applicable, as these variables were analyzed as continuous. Table SI 4: Wilcoxon Rank Sum Test for possible mVOC exposure from adding or replacing carpets | Median (ng/mL) | p-value | ||| | Yes (N=8) | No (N=124) | ||| | S2EH | 35.4 | 27.3 | 0.4230 | | | S2HP | 0.651 | 0.756 | 0.2963 | | | S2HX | 0.512 | 0.518 | 0.9468 | S2EH: 2-ethyl-1-hexanol, S2HP: 2-heptanone, S2HX: 2-hexanone Acknowledgments The authors of this paper thank Chris Reese for his assistance with quality assurance of laboratory methodology and data. jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf References (1) Kelley, S. T.; Gilbert, J. A. Studying the microbiology of the indoor environment. Genome Biol 2013, 14 (2), 202. DOI: 10.1186/gb-2013-14-2-202 From NLM Medline. (2) Kozicki, M.; Wiejak, A.; Piasecki, M.; Abram, A. Identification of MVOCs Produced by Coniophora puteana and Poria placenta Growing on WPC Boards by Using Subtraction Mass Spectra. Int J Environ Res Public Health 2019, 16 (14). DOI: 10.3390/ijerph16142499 From NLM Medline. (3) De Jesus Miranda, V. R. The role of fungal metabolic by -products in indoor air chemistry: Analytical considerations for the evaluation of poor indoor environments. Ph.D., Georgia Institute of Technology, United States – Georgia, 2001. (4) M.P. Fabian, S. L. M., T. Reponen, M.T. Hernandez. Ambient bioaerosol indices for indoor air quality assessments of flood reclamation. Journal of Aerosol Science 2005, 36 (5-6), 763-783. (5) Feng C. Tsai, J. M. M., Yun-Yi Hung,. Biodiversity and concentrations of airborne fungi in large US office buildings from the BASE study, Atmospheric Environment. Atmospheric Environment 2007, 41 (25), 5181-5191. (6) Garrison, V. E. H.; Bachand, J.; Ashley, P. J. Musty Smells, Mold, and Moisture in the U.S. Housing Stock Results from Two National Surveys. Cityscape 2021, 23 (1), 223-236. (acccessed 2024/12/16/).JSTOR. (7) Mudarri, D.; Fisk, W. J. Public health and economic impact of dampness and mold. Indoor Air 2007, 17 (3), 226-235. DOI: 10.1111/j.1600-0668.2007.00474.x From NLM Medline. (8) Jaakkola, J. J.; Hwang, B. F.; Jaakkola, N. Home dampness and molds, parental atopy, and asthma in childhood: a six-year population-based cohort study. Environ Health Perspect 2005, 113 (3), 357-361. DOI: 10.1289/ehp.7242 From NLM Medline. (9) Sahlberg, B.; Gunnbjornsdottir, M.; Soon, A.; Jogi, R.; Gislason, T.; Wieslander, G.; Janson, C.; Norback, D. Airborne molds and bacteria, microbial volatile organic compounds (MVOC), plasticizers and formaldehyde in dwellings in three North European cities in relation to sick building syndrome (SBS). Sci Total Environ 2013, 444, 433-440. DOI: 10.1016/j.scitotenv.2012.10.114 From NLM Medline. (10) Pasanen, A.-L.; Lappalainen, S.; Pasanen, P. Volatile organic metabolites associated with some toxic fungi and their mycotoxins. Analyst 1996, 121 (12), 1949-1953, 10.1039/AN9962101949. DOI: 10.1039/AN9962101949. (11) Perrti Pasanen, A. K., Pentti Kalliokoski, Anna-Liisa Pasanen. Growth and volatile metabolite production of Aspergillus versicolor in house dust. Environmental International 1997, 23 (4), 425-432. (12) Gao, P.; Korley, F.; Martin, J.; Chen, B. T. Determination of unique microbial volatile organic compounds produced by five Aspergillus species commonly found in problem buildings. AIHA J (Fairfax, Va) 2002, 63 (2), 135-140. DOI: 10.1080/15428110208984696 From NLM Medline. (13) Wazeerud-Din, I. J.; Silva, L. K.; Smith, M. M.; Newman, C. A.; Blount, B. C.; De Jesus, V. R. Quantification of seven microbial volatile organic compounds in human serum by solid-phase microextraction gas chromatography-tandem mass spectrometry. Chemosphere 2021, 266, 128970. DOI: 10.1016/j.chemosphere.2020.128970 From NLM Medline. (14) Polizzi, V.; Adams, A.; De Saeger, S.; Van Peteghem, C.; Moretti, A.; De Kimpe, N. Influence of various growth parameters on fungal growth and volatile metabolite production by indoor molds. Sci Total Environ 2012, 414, 277-286. DOI: 10.1016/j.scitotenv.2011.10.035 From NLM Medline. (15) Claeson, A. S.; Levin, J. O.; Blomquist, G.; Sunesson, A. L. Volatile metabolites from microorganisms grown on humid building materials and synthetic media. J Environ Monit 2002, 4 (5), 667-672. DOI: 10.1039/b202571j From NLM Medline. (16) DiVincenzo, G. D.; Kaplan, C. J.; Dedinas, J. Characterization of the metabolites of methyl n-butyl ketone, methyl iso-butyl ketone, and methyl ethyl ketone in guinea pig serum and their clearance. Toxicol Appl Pharmacol 1976, 36 (3), 511-522. DOI: 10.1016/0041-008x(76)90230-1 From NLM Medline. (17) Göen, T. M. J. E., E; Hoppe H.W.; Bader, M.; Bäcker, S; Hartwig, A. Alcohols, ketones and ethers – Determination of alcohols, ketones and ethers in urine by headspace GC-MS. The MAK Collection for Occupational Health and Safety 2020 2020, 5 . DOI: 10.34865/bi6756e5_2or. (18) Rabito, F. A.; Werthmann, D. W.; Straubing, R.; Adamkiewicz, G.; Reponen, T.; Ashley, P. J.; Chew, G. L. A multi-city study of indoor air quality in green vs non-green low-income housing. Environ Res 2024, 240 (Pt 2), 117576. DOI: 10.1016/j.envres.2023.117576 From NLM Medline. (19) Norbäck, D.; Wieslander, G.; Ström, G.; Edling, C. Exposure to Volatile Organic Compounds of Microbial Origin (MVOC) during Indoor Application of Water-based Paints. Indoor Air 1995, 5 (3), 166-170. (20) Rodgman, A., & Perfetti, T.A. The Chemical Components of Tobacco and Tobacco Smoke ; CRC Press, 2013. (21) Haines, S. R.; Adams, R. I.; Boor, B. E.; Bruton, T. A.; Downey, J.; Ferro, A. R.; Gall, E.; Green, B. J.; Hegarty, B.; Horner, E.; et al. Ten questions concerning the implications of carpet on indoor chemistry and microbiology. Build Environ 2019, 170, 1-16. DOI: 10.1016/j.buildenv.2019.106589 From NLM PubMed-not-MEDLINE. (22) Michanowicz, D. R.; Dayalu, A.; Nordgaard, C. L.; Buonocore, J. J.; Fairchild, M. W.; Ackley, R.; Schiff, J. E.; Liu, A.; Phillips, N. G.; Schulman, A.; et al. Home is Where the Pipeline Ends: Characterization of Volatile Organic Compounds Present in Natural Gas at the Point of the Residential End User. Environ Sci Technol 2022, 56 (14), 10258-10268. DOI: 10.1021/acs.est.1c08298 From NLM Medline. (23) Sosnoff, C. Cotinine and Hydroxycotinine Laboratory Procedure Manual. Prevention, C. f. D. C. a., Ed.; 2018; pp 1-62. (24) Claeson, A. S.; Sandstrom, M.; Sunesson, A. L. Volatile organic compounds (VOCs) emitted from materials collected from buildings affected by microorganisms. J Environ Monit 2007, 9 (3), 240-245. DOI: 10.1039/b614766f From NLM Medline. (25) Korpi, A.; Jarnberg, J.; Pasanen, A. L. Microbial volatile organic compounds. Crit Rev Toxicol 2009, 39 (2), 139-193. DOI: 10.1080/10408440802291497 From NLM Medline. (26) Mendell, M. J.; Kumagai, K. Observation-based metrics for residential dampness and mold with dose-response relationships to health: A review. Indoor Air 2017, 27 (3), 506-517. DOI: 10.1111/ina.12342 From NLM Medline. (27) Moularat, S.; Robine, E.; Ramalho, O.; Oturan, M. A. Detection of fungal development in a closed environment through the identification of specific VOC: demonstration of a specific VOC fingerprint for fungal development. Sci Total Environ 2008, 407 (1), 139-146. DOI: 10.1016/j.scitotenv.2008.08.023 From NLM Medline. (28) Walinder, R.; Ernstgard, L.; Johanson, G.; Norback, D.; Venge, P.; Wieslander, G. Acute effects of a fungal volatile compound. Environ Health Perspect 2005, 113 (12), 1775-1778. DOI: 10.1289/ehp.8193 From NLM Medline. (29) Mendell, M. J.; Adams, R. I. The challenge for microbial measurements in buildings. Indoor Air 2019, 29 (4), 523-526. DOI: 10.1111/ina.12550 From NLM Medline. Information & Authors Information Version history Peer review timeline Published Pediatric Allergy and Immunology Version of Record20 Aug 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Authors Metrics & Citations Metrics Article Usage 397views 207downloads Citations Download citation Lalith K. Silva, Cody A. Newman, Idris Wazeerud-Din, et al. Novel biomarkers to assess mold exposure among children with Asthma. Authorea. 29 May 2025. DOI: https://doi.org/10.22541/au.174850431.15596263/v1 DOI: https://doi.org/10.22541/au.174850431.15596263/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.