Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool: A cross-sectional proof-of-concept study.

Clostridioides difficile infection (CDI) is an important infectious cause of antibiotic-associated diarrhea, with significant morbidity and mortality. Current diagnostic algorithms are based on identifying toxin by enzyme immunoassay (EIA) and toxin gene by real-time polymerase chain reaction (PCR) in patients with diarrhea. EIA's sensitivity is poor, and PCR, although highly sensitive and specific, cannot differentiate infection from colonization. An ideal test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection, and assess prognosis and recurrence. The study of volatile organic compounds (VOCs) has the potential to be an ideal diagnostic test. The presence of VOCs accounts for the characteristic odor of stool in CDI but their presence in breath and plasma has not been studied yet. A cross-sectional proof-of-concept study analyzing VOCs using selected ion flow tube mass spectrometry (SIFT-MS) was done on breath, stool, and plasma of patients with clinical features and positive PCR for CDI (cases) and compared with patients with clinical features but a negative PCR (control). Our results showed that VOC patterns in breath, stool, and plasma, had good accuracy [area under the receiver operating characteristic curve (ROC) 93%, 86%, and 91%, respectively] for identifying patients with CDI.

Introduction

Clostridioides (formerly known as Clostridium) difficile infection (CDI) is an important infectious cause of antibiotic-associated diarrhea [1]. The annual burden of infections in the United States was close to 16,000 in 2018, with in-hospital mortality of 8.4% in patients older than 65 [2]. CDI is predominantly a healthcare-associated infection with prior antibiotic exposure considered a significant risk factor [1]. The clinical spectrum ranges from asymptomatic colonization, in 3%-26% of adult inpatients, to severe, life-threatening, and fulminant colitis. The current diagnostic strategy that involves a multi-step test using toxin and PCR in the stool has many challenges [3]. Although highly sensitive and specific, PCR tests alone cannot differentiate infection from colonization, a common scenario in clinical practice. EIA test for stool toxin lacks sensitivity, with values as low as 45% being reported [4]. Turn-around time from ordering a test to result may take up to 24 hours as stool samples cannot be produced ’on-demand,’ and samples need to be transported to the lab before being analyzed [5]. Rapid, sensitive, specific, point-of-care testing methods are required for early diagnosis of CDI.

Volatile organic compounds (VOCs) are aromatic hydrocarbon end product metabolites of physiological and pathophysiological processes [6]. VOCs are transported through the blood from different organs to the lungs and subsequently exhaled. Patients with CDI have been noted to have a characteristic odor (’horse barn odor’) from the assemblage of VOCs present [5, 7, 8]. Previous studies using gas chromatography-mass spectrometry (GC-MS) characterized this ’volatile molecular signature’ in the stool of patients with CDI [7]. Rees et al. identified 77 molecules using headspace VOC analysis from C.difficile cultures. Another recent study of stored stool samples from 53 cases and 53 controls, using thermal desorption-gas chromatography-time-of-flight gas chromatography, identified seven compounds (propan-1-ol, 3-methylbutanal, ethyl propionate, hexanoic acid, 4-methyl phenol, dodecane, and indole) indicative of CDI with a ROC >0.7 [5]. Selected ion flow tube mass spectroscopy (SIFT-MS) enables the faster measurement of lower concentrations (parts per billion or even trillion) of VOCs in clinical samples [9]. SIFT-MS technology has shown high discriminatory capacity in other syndromes like inflammatory bowel disease, nonalcoholic fatty liver disease, and pulmonary artery hypertension, but has not been used to study CDI [10-12]. The purpose of this study was to determine if VOCs in stool, blood, and breath, of patients with CDI, as measured by SIFT-MS, differ from those in age and gender-matched controls without CDI.

Materials and methods

This cross-sectional study enrolled patients > 18 years old with diarrhea who had stools tested for Clostridioides difficile by PCR [done using BD GeneOhm™ (BD Diagnostics, Franklin Lakes, NJ)]. Written informed consent was obtained from all patients before enrollment. Patients with >3 episodes of diarrhea in the preceding 24 hours and an illness suggestive of C.difficile infection (abdominal pain, fever, elevated WBC count) with a stool specimen positive for C. difficile by PCR were considered to have CDI. The single best age and gender-matched patient, with liquid stools but negative C. difficile PCR on the same day, was selected as a control for each patient included in this study. Consecutive cases and controls identified during working days (Monday through Friday) were included. Those without a clinical illness compatible with CDI, those who refused or were unable to give informed consent (e.g., due to intubation, encephalopathy, delirium, or pharmacologic sedation), those requiring supplemental oxygen, and those with CDI in the previous four weeks were excluded. The study was approved by the Cleveland Clinic Institutional Review Board, IRB # 18–030.

Stool samples sent to the microbiology laboratory for C.difficile PCR testing, and plasma samples drawn within 24 hours of stool collection were identified, and residual stool specimen and 100 μL of residual plasma specimen were obtained. Breath samples were collected at the patient’s bedside within 24 hours of collection of the stool specimen. Initially, tidal volume exhalation was done to clear residual air from the anatomic dead space, followed by a deep breath through a disposable micro-filtered mouthpiece, which prevented exposure to viral and bacterial pathogens in ambient air and eliminated exogenous VOCs, followed by tidal volume exhalation back through the mouthpiece. Exhaled breath was collected in a Mylar balloon bag. All samples (breath, stool, and plasma) were incubated at 37°C for 30 minutes before analysis to desorb the VOC’s from the surface of the container. For stool and plasma samples, 20 mL of headspace gas was removed from the vials using a glass syringe. For breath samples, the Mylar bag was connected to the mass spectrometry device directly.

The gas from samples was analyzed using a VOICE200 SIFT-MS instrument (Syft Technologies Ltd, Christchurch, New Zealand). VOCs were measured in real-time after they underwent chemical ionization using H3O+, NO+ and O2+ precursor ions [13]. Product ion masses of VOC analytes were detected and counted by a downstream mass spectrometer (MS). A complete mass spectrum was obtained for mass-to-charge ratio values between 14–200 to identify significant peaks of product ion masses representing VOCs relating to CDI. The count rate of product ions was directly proportional to the concentration of the VOC.

The laboratory personnel analyzing the samples, and the microbiology technologists aliquoting the stool and plasma samples, were blinded to the stool C. difficile test results. The distribution of clinical and the VOC analyte variables were compared for CDI and control patients. Prediction models were developed for each sample type, based on K-nearest neighbors (KNN) regression, to classify each observation into CDI or not CDI, using VOC analytes only, and separately using clinical and VOC analyte variables. Model performances were validated using 5-fold cross-validation. Receiver operating characteristics (ROC) curves were generated for the prediction for each sample type. Analyses were done using R version 4.0.5.

Results

Of 67 patients with positive stool C.difficile PCR screened for inclusion in the study, 36 were excluded for various reasons (Fig 1). Each of the remaining 31 patients had a matched control.

Fig 1

Study flow chart.

Flow chart showing the number of patients screened, patients included, and their comparison to age- and gender-matched controls.

Baseline characteristics

The CDI and non-CDI groups were comparable for the examined clinical variables, except that a higher proportion of the cases had heart failure (Table 1).

Table 1

Baseline demographic characteristics.

Characteristic	Cases (n = 31)	Controls (n = 31)	P value
Age (years)	56.9 ± 15.1	52.8 ± 15.3	0.29
Race			0.71
Caucasian	26 (84%)	27 (87%)
African American	5 (16%)	3 (10%)
Others	0 (0%)	1 (3%)
Male	15 (48%)	15 (48%)	0.99
Body Mass Index (kg/m2)	28.8 ± 9.6	29.0 ± 7.2	0.94
Comorbidities
Diabetes Mellitus	9 (29%)	7 (23%)	0.56
Coronary Artery Disease	6 (19%)	2 (6%)	0.26
Heart Failure	6 (19%)	0 (0%)	0.02
Chronic Obstructive Pulmonary Disease	2 (6%)	1 (3%)	0.99
Chronic Kidney Disease	4 (13%)	5 (16%)	0.99
Chronic Liver Disease	1 (3%)	3 (10%)	0.61
Inflammatory Bowel Disease	2 (6%)	7 (23%)	0.15
Malignancy	12 (39%)	5 (16%)	0.05
History of Transplant	5 (16%)	9 (29%)	0.22
Solid Organ Transplant	4	5
Hematopoietic Stem Cell Transplant	1	3
Concurrent Infection(s)	11 (35%)	9 (29%)	0.59
Smoking			0.99
Current Smoker	5 (16%)	5 (16%)
Ex-Smoker	9 (29%)	9 (29%)
Non-Smoker	17 (55%)	17 (55%)
Alcoholism	10 (32%)	10 (32%)	0.99
Clostridioides difficile Severity*
Non–severe	18 (58%)	NA	---
Severe	8 (26%)	NA	---
Fulminant	5 (16%)	NA	s---
Prior history of Clostridioides difficile (> 4 weeks ago)	4 (13%)	1 (3%)	0.35
Peak WBC Count (x 109/ L)	11.5 (5.2, 20.6)	10.1 (5.3, 12.9)	0.35
Peak Serum Creatinine (mg/dL)	1.1 (0.8, 4.0)	0.9 (0.8, 1.4)	0.10
Nadir Serum Albumin (g/dL)	2.9 (2.4, 3.6)	2.8 (2.3, 3.3)	0.49

Descriptive statistics reported as either mean ± standard deviation, median (Q1, Q3) or count (%)

Means are compared with t-tests, medians compared with Wilcoxon rank-sum tests and proportions compared with chi-square or Fisher’s exact test as appropriate. Abbreviations: NA, not applicable. Non-severe disease is defined as leukocytosis with a white blood cell count of ≤15 000 cells/mL and a serum creatinine level <1.5 mg/dL, severe disease—leukocytosis with a white blood cell count of ≥15 000 cells/mL or a serum creatinine level >1.5 mg/dL and fulminant disease–presence of hypotension or shock, ileus, megacolon.

Volatile organic compounds

Product ion concentrations in patients with and without CDI are compared in S1–S3 Tables, for breath, stool, and plasma samples, respectively. Those product ions that are statistically significant (p < 0.05 on univariable analysis) with good branching ratios (50% or greater branching ratio), that are identified by multiple reagent ions and that had a possible identification (putative ID) are depicted in Table 2. An example of mass spectra using H3O+ precursor from a patient with CDI is provided in S1 Fig.

Table 2

Product ions that were significantly different in breath, stool and plasma.

Product ions in breath	Putative ID	Product ions in stool	Putative ID	Product ions in plasma	Putative ID
H3O+65+	ethanol	H3O+185+	1,2,4-trichlorobenzene, 1,1,2,2-tetrachloroethane	O2+180+	1,2,4-trichlorobenzene
H3O+70+	putresccine	H3O+187+	1,2,4-trichlorobenzene, 1,1,2,2-tetrachloroethane	O2+182+	1,2,4-trichlorobenzene
NO+45+	ethanol			O2+184+	1,2,4-trichlorobenzene
NO+63+	ethanol
NO+87+	putrescine
NO+174+	dimethyl fumarate
O2+29+	formaldehyde
O2+45+	ethanol
O2+63+	ethanol (water cluster)
O2+65+	ethanol
O2+113+	dimethyl fumarate

(P- value < 0.05) with their possible identification. (Putative IDs are for those product ions having 50% or greater branching ratio and the compounds that are significantly identified by multiple reagent ions with good branching ratios.)

Distribution of VOC concentrations

There were differences in concentrations of various product ions in CDI cases and controls. A heatmap with cluster dendrograms showing the distribution of concentrations of the various product ions in breath samples across the study participants is shown in Fig 2. This shows some clustering according to whether or not patients had CDI.

Fig 2

Relative abundance of breath VOCs in CDI.

This heatmap with cluster dendrogram shows the distribution of concentration of various product ions in patients with (red labels) and without CDI (blue labels). Scaled relative concentrations of product ions are depicted in the heatmap by colors on a spectral scale. The linkage method used for hierarchical clustering was the nearest neighbor method.

Diagnosis of CDI using VOC analysis

For predicting the presence or absence of CDI, the optimal KNN classifier model was achieved with k = 7, 9, and 9, for breath, stool, and plasma samples, respectively. The areas under the curve (AUCs) for predicting the presence of CDI from these models were 93%, 86%, and 91%, for breath, stool, and plasma samples, respectively (Fig 3). Accuracy was better if clinical variables were considered in addition to product ion concentrations in the model (AUC of 95%, 85% and 94% for breath, stool and plasma samples respectively) (S2 Fig). Receiver operating characteristic (ROC) curves for identification of CDI using breath, stool, and plasma samples, are shown in Fig 3.

Fig 3

Receiver operating characteristic (ROC) curves with area under the curve for breath, stool and plasma samples respectively.

Model accuracy was not appreciably better if only positives with C. difficile PCR cycle threshold (CT) < 30 cycles were included (S3 Fig).

Discussion

Prior studies using SIFT-MS technology have demonstrated the utility of breath analysis in a wide range of non-infectious conditions, including inflammatory bowel disease, non- alcoholic fatty liver disease, and fibrosis associated with chronic liver disease [10]. This proof-of-concept study showed that VOC patterns in breath, stool, and plasma, are different in patients with and without CDI, and that analysis of these differences has good accuracy in making a diagnosis of CDI. One molecule in breath that was significantly identified by multiple reagent ions that deserves special mention is putrescine. Putrescine belongs to the family of polyamines that is involved in arginine and ornithine metabolism [14]. They are present inside the cells of all mammalian species, where they are involved in various cellular functions including protein synthesis and cell growth [15]. Arginine increases the growth of Clostridium difficile in vitro, and seems to have a role in toxin production in some strains [16]. Larger studies are required to validate this finding and it is also unclear why these molecules are not observed in stool or plasma.

The small sample size is an important limitation of our study. Larger studies to validate our findings are needed. It is possible that molecules outside the range of those analyzed by SIFT-MS may provide further discrimination. But this proof-of-concept study shows that there is promise in analyzing breath to diagnose CDI. Another limitation is that breath collection using Mylar bags and transportation to the laboratory may have resulted in the loss of VOCs. More robust breath collection devices may mitigate this limitation. Lastly, the small number of patients with chronic conditions made it difficult to interpret the confounding effect of comorbid conditions.

In conclusion, this proof-of-concept study shows that VOC patterns in breath, stool, or plasma, using SIFT-MS had good accuracy for identifying patients with CDI. If validated in future studies, easier to collect and readily available breath samples can be used for rapid, bedside diagnosis of CDI.

An example of mass spectra using H3O+ precursor from a patient with CDI.

(TIF)

Click here for additional data file.

Model accuracy for breath, stool and plasma, if clinical variables are considered in addition to VOC analyte variables.

(TIF)

Click here for additional data file.

Model accuracy for breath, stool, and plasma specimens, if only positives with C. difficile PCR cycle threshold (CT) < 30 cycles are included.

(TIF)

Click here for additional data file.

Comparison of product ion concentrations in breath of patients with and without CDI.

(CSV)

Click here for additional data file.

Comparison of product ion concentrations in stool of patients with and without CDI.

(CSV)

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Comparison of concentrations in plasma of patients with and without CDI.

(CSV)

Click here for additional data file.

4 Nov 2020

PONE-D-20-23689

Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool – a cross-sectional proof-of-concept study.

PLOS ONE

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Reviewer #1: Notable point on the article is the use of paired controls to reduce if not eliminate any bias due to other factors unrelated to the disease. Some typographical errors found but not in a way that can influence the article.

Line 125 Capitalize C on Celsius

Fig 1 “Stautus” to “status”

However, a major issue that is detrimental to the study is the use of breath metabolites as metrics to differentiate or classify stool and plasma samples. It is not surprising that there is low accuracy in these samples, and therefore invalid comparison. The authors have even stated that there is a characteristic odor of infected stool, which also suggests to the readers that there may be challenges in using SIFT MS for detection of these characteristic compounds. A helpful suggestion is to reclassify the stool and plasma samples using their intrinsic metabolites.

Reviewer #2: The authors provide a very brief manuscript describing the use of SIFT-MS to analyze molecules released from different samples from patients and controls for the detection of C. difficile. The paper has some interesting data, but needs to be expanded on a number of different points to enable clearer understanding of the need for the research, reproducibility of the methods for data analysis, more thorough evaluation of the results, and confirmation that the results support the conclusions that are presented in the manuscript. Major critiques include:

1. Please provide the individual data for the patients including clinical status and values for each analyte observed in SIFT-MS.

2. Example mass spectra for each sample type would be helpful to the reader.

3. Representation of the data using principal component analysis, hierarchical clustering with a heat map, or some similar approach would enable the authors’ claims of separation of groups (C. diff and control) to be evaluated more effectively. At the current time, the claims for separation of the groups can’t be evaluated by the way the data are presented.

The draft is well written, but minor editing for spelling and grammar is needed.

Additional specific requests for revisions are included below.

Abstract:

Please add detail about why C. difficile is important and what methods are currently available for its detection to describe the need for the research presented here.

Introduction:

In the previous GC-MS work on C. difficile, which compounds could be used to differentiate infected people from controls? Are they detected with SIFT-MS?

Table 1: Do the authors expect that the confounding variables from other conditions (e.g. cancer) will impact the differences between the groups? Data may need to be presented in the figures with color codes to indicate patients these other chronic diseases.

Why was the K nearest neighbors strategy selected? Please explain the rationale.

Do any of the molecules detected in SIFT-MS have potential as individual biomarkers? This point is addressed in part by the comparisons and p values in the supplemental tables, but it should also be presented in the results section of the manuscript.

Supplemental Figures and Tables need captions on the page with the data or image.

Supplemental Tables need headings to explain the data, which are presented as average and standard deviation but not labeled.

Reviewer #3: Here the authors present a clinical study which shows that breath samples could be used to diagnose if someone has a CDI. In addition to breath, they also analyzed the headspace of both plasma and stool. Both of the other diagnostic mediums had poor predictive power which could have been cause by the loss in VOCs during the sample handling process. I find the scientific work interesting and generally well done. With that said, this paper would benefit if the authors would expand the introduction and addressed concerns below.

Introduction: This section is very short. It almost reads like an abstract. The authors should do a better job introducing breath analysis, its prior use for CDI, and the various analytical techniques. The section could end on the rationale used for the selection of SIFT-MS. This section should be at least two paragraphs longer.

Materials and Methods:

Why were weekend samples excluded? Please do a better job explaining the rationale.

Why were samples incubated before analysis? Is this common practice?

Table 1: Does not significantly contribute to the paper. I would recommend moving this to the supplemental section or shortening it for the main text.

Supplemental Tables 1-3. Given that these numbers are at the heart of the study, I would move supplemental table 1 into the main text. For all supplemental tables, what are the units? What is pos and neg? CDI pos, CDI neg? Please label more clearly.

Supplemental Figure 1 and 2 need figure legends.

Results:

I understand that the entire 22 VOC panel was used for the ROC curves. However, was there a subset of VOCs in the panel that was MORE predictive than the full panel for each type of sample? If so what?

Other general comments:

Why was blood plasma used? Why not whole blood? The sample processing of whole blood to plasma would significantly change and or remove important VOCs.

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Reviewer #2: No

Reviewer #3: No

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2 Apr 2021

Thank you for reviewing our manuscript and providing valuable suggestions. We hereby respond to reviewer and editors comments in a question and response format.

1. 'Approved by Cleveland Clinic institutional review board, IRB# 18-030. Written informed consent obtained from all participants. Please also include this information in the ethics statement in the Methods section

Response from authors: Thank you for your suggestion. We included these key sentences in the methods section.

2. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. "Findings were presented at ID week 2018 as a poster and abstract was published in Open Forum of Infectious Diseases." Please clarify whether this publication was peer-reviewed and formally published. If this work was previously peer reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript. Response from authors: Findings were presented as a poster at ID week 2018, the abstract of it was published in a supplement of OFID as a conference abstract. While conference abstracts are reviewed by the scientific committee, they are not considered peer-reviewed.

Reviewer # 1 comments:

1. Some typographical errors found but not in a way that can influence the article. Line 125 Capitalize C on Celsius and Fig 1 “Stautus” to “status”

Response from authors: Thank you; both these typographical errors are corrected.

2. However, a major issue that is detrimental to the study is the use of breath metabolites as metrics to differentiate or classify stool and plasma samples. It is not surprising that there is low accuracy in these samples, and therefore invalid comparison. The authors have even stated that there is a characteristic odor of infected stool, which also suggests to the readers that there may be challenges in using SIFT MS for detection of these characteristic compounds. A helpful suggestion is to reclassify the stool and plasma samples using their intrinsic metabolites.

Response from authors: Thank you for your suggestion. The characteristic odor of stool in patients with Clostridioides difficile infection (CDI) is well known, and it is thought to be secondary to metabolites in the stool. Studies have shown that dogs can scent stool odor and correctly classify CDI. Multiple invitro studies based on stool cultures also confirm the presence of VOCs in the stool. We want to clarify this study has not focused on classifying stool and plasma samples based on breath metabolites. On the other hand, we have studied cross-sectionally collected stool, plasma, and breath samples (all collected within a 24-hour time frame) of patients with CDI. We think the lack of signal in stool and plasma is due to using left-over samples that was not stored appropriately resulting in loss of VOCs.

Reviewer #2:

1. Please provide the individual data for the patients including clinical status and values for each analyte observed in SIFT-MS.

Response from authors: Thank you for your suggestion. Clinical data is provided in Table 1. This has already been deposited in a public repository at https://osf.io/5hv49/. The repository contains the analysis dataset and the analysis to reproduce the results

2. Example mass spectra for each sample type would be helpful to the reader. Response from authors: This is a very good suggestion. A sample mass spectra for each type of sample – namely breath, plasma and stool for a patient is added as a supplementary figure.

3. Representation of the data using principal component analysis, hierarchical clustering with a heat map, or some similar approach would enable the authors’ claims of separation of groups (C. diff and control) to be evaluated more effectively. At the current time, the claims for separation of the groups can’t be evaluated by the way the data are presented.

Response from authors: Thank you for this suggestion. We have provided a cluster dendrogram that shows clustering of patients with CDI(in red). (Figure 3)

4. Abstract :Please add detail about why C. difficile is important and what methods are currently available for its detection to describe the need for the research presented here.

Response from authors: The abstract section will be elaborated to provide more background about current status of C.difficile diagnostic strategies. Thank you for this suggestion.

5. Introduction: In the previous GC-MS work on C. difficile, which compounds could be used to differentiate infected people from controls? Are they detected with SIFT-MS?

Response from authors : A recent study of stored stool samples from 53 cases and 53 controls, using thermal desorption-gas chromatography-time-of-flight gas chromatography, identified seven compounds (propan-1-ol, 3-methylbutanal, ethyl propionate, hexanoic acid, 4-methyl phenol, dodecane, and indole) indicative of CDI with a ROC >0.7. This information is provided in the introduction section. None of these molecules were part of the panel that was used in SIFT-MS. SIFT-MS is equipped to measure 22 common analytes that are present in breath in patients with disease states.

6. Table 1: Do the authors expect that the confounding variables from other conditions (e.g. cancer) will impact the differences between the groups? Data may need to be presented in the figures with color codes to indicate patients these other chronic diseases.

Response from authors: Thank you for this query and suggestion ; Yes, we expect other disease states to affect VOC profile including cancer and metabolic conditions like diabetes mellitus. It is certainly possible that some of these comorbid conditions may be confounding factors. However, this was a pilot study and there were too few patients with each of these chronic conditions to evaluate if these chronic conditions were confounding factors. Future larger studies with larger sample size that is controlled for all confounding factors are needed. This point is added to the limitation section.

7. Why was the K nearest neighbors strategy selected? Please explain the rationale.

Response from authors: A KNN classifier was selected because it is a simple and intuitive classification tool. This being a pilot study with a small sample size we did not evaluate and compare different classification methods .

8. Do any of the molecules detected in SIFT-MS have potential as individual biomarkers? This point is addressed in part by the comparisons and p values in the supplemental tables, but it should also be presented in the results section of the manuscript.

Response from authors: Thank you for your suggestion. We have included Table 2 describing the concentration of VOCs in patients with and without CDI. None of the molecules on their own achieved statistical significance. Based on our findings, it does not appear that any of these compounds could serve as individual biomarkers that could effectively separate CDI from no CDI. but the pattern of VOC concentrations rather than individual concentrations was more meaningful. This is added to the discussion section of the manuscript.

9. Supplemental Figures and Tables need captions on the page with the data or image.

10. Supplemental Tables need headings to explain the data, which are presented as average and standard deviation but not labeled.

Response from authors: Supplementary Tables 1 and 2 are labelled with headings and data explained. Thank you for this suggestion.

Reviewer 3:

This paper would benefit if the authors would expand the introduction and addressed concerns below.

1.Introduction: This section is very short. It almost reads like an abstract. The authors should do a bett er job introducing breath analysis, its prior use for CDI, and the various analytical techniques. The section could end on the rationale used for the selection of SIFT-MS. This section should be at least two paragraphs longer.

Response from authors: Thank you for this suggestion. We have expanded the introduction section that now includes a problem statement why CDI is important, current diagnostic modalities and its limitations, VOCs in CDI and various methods that are utilized. We wanted to study usefulness of SIFT-MS as it was available in our center and no prior CDI studies have been done using this technology (mentioned in the introduction)

2.Materials and Methods:

a) Why were weekend samples excluded? Please do a better job explaining the rationale.

Response from authors: Research technicians worked only from Monday through Friday and hence weekend samples were not studied as it would not have been possible to have weekend samples analyzed promptly.

b) Why were samples incubated before analysis? Is this common practice?

Response from authors: Thanks for this question. Samples were incubated to desorb the VOC's from the surface of the bag for analysis. Yes, this is a common practice in our VOC lab. This information is added to the methods section.

c) Table 1: Does not significantly contribute to the paper. I would recommend moving this to the supplemental section or shortening it for the main text. Response from authors: Thank you for this suggestion. Table 1 represents the baseline demographic data of cases and controls enrolled in this study. The authors feel that this is important to the study highlighting that these characteristics were not statistically different in 2 groups studied. But we will shorten it by removing lab features of cases and controls. Foot note is added to Table 1.

3.Supplemental Tables 1-3. Given that these numbers are at the heart of the study, I would move supplemental table 1 into the main text. For all supplemental tables, what are the units? What is pos and neg? CDI pos, CDI neg? Please label more clearly.

Response from authors: Thank you for this suggestion. We have moved supplemental table to the main text as table 2.

3. Supplemental Figure 1 and 2 need figure legends.

Response from authors: Thank you. We provided figure legends for supplementary figure 1 and 2

4. Results:

I understand that the entire 22 VOC panel was used for the ROC curves. However, was there a subset of VOCs in the panel that was MORE predictive than the full panel for each type of sample? If so what?

Response from authors: Thank you. No, we did not find a subset of VOCs that was more predictive than the full panel for each type of sample

Other general comments:

5.Why was blood plasma used? Why not whole blood? The sample processing of whole blood to plasma would significantly change and or remove important VOCs.

Response from authors: Thank you for this suggestion. Blood plasma was used as it was the most easily available left-over sample after daily testing. We agree that plasma extraction from the whole blood, might have affected the amount and number of VOCs and will include this as a limitation of the study.

Submitted filename: Response to reviewers 2.22 Final.docx

Click here for additional data file.

28 Apr 2021

PONE-D-20-23689R1

Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool – a cross-sectional proof-of-concept study.

PLOS ONE

Dear Dr. John,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Thank you for responding to the initial review.  There was still a major concern from one reviewer regarding the VOC analysis and in particular the biological aspects of measuring VOCs across the samples types.  It is critical that you carefully address this comment in you revision.

==============================

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Is there a reference on how the monitored VOCs were selected and their presence are common in breath, stool and blood? This is to give justification that the same VOCs monitored in breath can be monitored in stool and blood. Physically, the classes of samples differ in composition significantly, thus it is not obvious to the reviewer how the blood and stool samples are expected to contain the same VOCs found in breath.

The data presented does not support the conclusions claimed by the paper. It is egregious that the authors would attempt to monitor common breath metabolites (as stated in page 5 line 102) in stool and blood, and apply it as of same significance in presence and amount in contrast to breath samples. The previous question was not answered satisfactorily.

Reviewer #2: The authors have addressed most of the criticisms from the previous review, but a few minor changes need to be made.

The manuscript needs to be spellchecked to remove errors in the updated version.

For Figure 2, what sample set is used for the heat map? It would be helpful to center and normalize the data per analyte, so that you use more of the color spectrum. Now, everything looks yellow except for a couple of samples at the top, which are high outliers. Note that red/green designations will not be available to the color blind; red/blue is recommended or use of another indicator (bold font or -CDI appended after the sample name for positive cases would work well.

Reviewer #3: Thank you. All concerns have been addressed. If find this paper interesting and timely.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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16 Jul 2021

On behalf of the authors of the manuscript, ‘Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool – a cross sectional proof-of-concept study’, we want to thank you and the reviewers for reviewing our manuscript and giving valuable suggestions. We have updated the manuscript to reflect the minor corrections suggested by the two reviewers.

Below are our responses to the specific points raised by the reviewers.

Comment 1: Is there a reference on how the monitored VOCs were selected and their presence are common in breath, stool and blood? This is to give justification that the same VOCs monitored in breath can be monitored in stool and blood. Physically, the classes of samples differ in composition significantly, thus it is not obvious to the reviewer how the blood and stool samples are expected to contain the same VOCs found in breath Thank you for allowing us to present the revised manuscript. Author Response: The authors want to thank the reviewer for this great suggestion. We agree that we don’t have a solid justification on why we looked for the same 22 VOCs in three biologically different samples. This made us focus on the ‘full scan data’ set that includes all molecules or product ions that comes in the range of 14-200 mass to charge ratio values that are present in the sample compared to the ‘sim scan data’ which looks at only 22 VOCs. This analysis improved the ROC curves of all the 3 clinical samples we studied. Figures are updated in the re-submission

Comment 2: The manuscript needs to be spellchecked to remove errors in the updated version Author Response: Thank you for this suggestion. The manuscript has been spell checked.

Comment 3: For Figure 2, what sample set is used for the heat map? It would be helpful to center and normalize the data per analyte, so that you use more of the color spectrum. Now, everything looks yellow except for a couple of samples at the top, which are high outliers. Note that red/green designations will not be available to the color blind; red/blue is recommended or use of another indicator (bold font or -CDI appended after the sample name for positive cases would work well.

Author Response: Thank you for this suggestion. We used breath product ion concentration to build the heatmap. As per the suggestion from the reviewer, we changed the color spectrum to red and blue color.

Thank you for allowing us to review this manuscript. Hope you find the revisions acceptable.

4 Aug 2021

Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool – a cross-sectional proof-of-concept study.

PONE-D-20-23689R2

Dear Dr. John,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Timothy J Garrett, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I appreciate your clear and scientific valuable additions made after the second revision. The key aspect was providing clarity on VOCs in plasma, stool and breath and I think you have clearly done that. At this point, you have addressed all major concerns regarding the publication of this work.

Reviewers' comments:

9 Aug 2021

PONE-D-20-23689R2

Diagnosis of Clostridioides difficile infection by analysis of volatile organic compounds in breath, plasma, and stool – a cross-sectional proof-of-concept study.

Dear Dr. John:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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PLOS ONE