Screening of colorectal cancer by a fecal gas compound analysis

Screening of colorectal cancer by a fecal gas compound analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Screening of colorectal cancer by a fecal gas compound analysis Atsushi Ishibe, Jun Watanabe, Hideki Yamakoshi, Shigeru Yamagishi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5951396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND Colorectal cancer (CRC) remains a significant cause of mortality worldwide. Although FOBT has demonstrated efficacy in reducing colorectal cancer-related deaths, it has limitations. We explored an innovative and user-friendly screening method using gas sensors installed in toilets. METHODS We developed gas sensors that can be installed in toilets. We conducted a demonstration experiment using these sensors to monitor individuals in their bathrooms. The experiment involved 101 colorectal cancer (CRC) patients, 50 colorectal polyp patients, and 90 healthy individuals who were registered and monitored for a week at home. Gas data collected during defecation were analyzed to assess the feasibility of colorectal cancer screening. RESULTS Age and sulfur-containing gas, CH 4 , and CO 2 content were significantly higher in the CRC group than in the non-CRC group in a univariate analysis. The area under the receiver operating characteristic curve of the discriminant formula for diagnosing colorectal cancer was 0.859. CONCLUSION A sensor analysis of defecation gas constitutes a promising, novel, and non-invasive approach for CRC screening. colorectal cancer gas volatile organic compounds gas sensor Figures Figure 1 Figure 2 Figure 3 1 Introduction Colorectal cancer (CRC) is the second-most common cancer diagnosed in women and third-most common cancer in men ( 1 ). High-sensitivity guaiac fecal occult blood test (gFOBT), fecal immunochemical test (FIT), and colonoscopy are recommended for screening by the U.S. Preventive Services Task Force (USPSTF). However, the FOBT detects blood in the stool that is not always associated with CRC, with the positive predictive value of the FOBT for CRC being 10.6%-12.0% ( 2 , 3 ), and colonoscopy is an invasive method with screening capacity limitations. A simpler method than existing screening modalities is thus required. Odor gas components have recently attracted attention as diagnostic screening tools for cancer, and their usefulness has been reported in skin and breast cancers ( 4 ) ( 5 ). Several studies have demonstrated the reliability of volatile organic compounds (VOCs) for detecting CRC in different materials, including urine ( 6 ) ( 7 ) ( 8 ), exhaled breath ( 9 ) ( 10 ), blood ( 11 ), and feces ( 12 ) ( 13 ). However, the sample collection was inconvenient in these studies. Therefore, it is necessary to develop a convenient method. We previously reported a pilot study in which the sulfur-containing gas component from defecation was significantly greater in CRC patients than in healthy individuals ( 14 ). We believe that defecation gas measurements may be a useful new tool for CRC screening. Therefore, we conducted a prospective cohort study to verify whether or not defecation gas is useful for CRC screening. In this study, the gas analysis was performed using a gas sensor. Gas sensors can be installed in toilets at home and data can be collected daily. We examined the gas components in bowel movements from patients with CRC and non-CRC patients using a gas sensor, a novel method, and evaluated the diagnostic value of this method for CRC detection. 2 Methods 2.1 Patients We conducted a multicenter prospective observational study at four institutions from December 2018 to February 2020. The enrolled patients had three different statuses: CRC, colorectal polyp, and normal colon. Inclusion criteria were age ≥ 20 years old, histologically proven adenocarcinoma, and any clinical stage in the CRC group. In the colorectal polyp group, the patients had adenomatous polyps. All patients underwent colonoscopy. Exclusion criteria were inflammatory bowel disease, infectious enteritis, bowel obstruction, and other conditions deemed ineligible by the physician. This study was approved by the Yokohama City University Ethics Committee and conducted in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects (IRB number B191200063, UMIN 000035342). Informed consent was obtained from all patients before the study procedures were performed. 2.2 Gas sensor device and analyses The gas sensor apparatus was placed in the bathroom of each subject’s home for one week (Fig. 1 ). The gas sensor detects sulfur-containing gases, such as hydrogen sulfide (H 2 S) and methylmercaptan, in addition to methane (CH4), hydrogen (H 2 ), and carbon dioxide (CO 2 ). Gas data were collected for each bowel movement and analyzed based on the semiconductor resistance values obtained. 2.3 Statistical analyses When samples were recorded more than once, the mean and standard deviation (SD) were calculated and used for subsequent analyses. Using a multivariable logistic regression model, we developed a formula to discriminate between patients with CRC and healthy volunteers using gas data from study participants. A discriminant formula was provided as the probability of CRC occurrence using a logistic regression model. All analyses were performed using R version 4.1.0 (R, Foundation for Statistical Computing, Vienna, Austria). 3 Results 3.1 Patient characteristics Two hundred and sixty patients were enrolled in this study. A consort diagram of the patient registration is shown in Fig. 2 . Owing to a malfunction of the semiconductor device, data from five CRC patients and 14 non-CRC cases could not be obtained and were therefore excluded from the analysis. All patients in the non-CRC group underwent total colonoscopy and were divided into the colorectal adenoma group and healthy control group. A total of 101 patients with CRC, 50 with colorectal adenoma, and 90 healthy controls were analyzed. The median age of patients with CRC was 72 years old; they were older than subjects in the other two groups (Table 1 ). The characteristics of colorectal cancer patients are summarized in Table 2 . Of the cancer patients, 57.4% were male, and the location of the cancer was the rectum in 42.6%. Table 1 Characteristics of each group Group Characteristics Number Age (years) (Median, IQR) Gender Male/Female Colorectal cancer Colorectal adenocarcinoma 101 72 (67, 77) 58/43 Colorectal adenoma Colorectal adenoma 50 55 (50, 64) 31/19 Healthy control No colorectal disease 90 52 (46, 59) 48/42 IQR: interquartile range Table 2 Characteristics of colorectal cancer patients (N = 101) variable (%) Age (years)* 72 (67, 77) Gender Male/Female 58(57.4%)/43(42.6%) Location of tumor Cecum 6 (5.9%) Ascending 14(13.9%) Transvers 12(11.9%) Descending 2(2%) Sigmoid 24(23.7%) Rectum 43(42.6%) Tumor size (mm)* 40 (25, 75) CEA (mg/dl) 3.4 (2.2, 7.0) TNM stage Stage 0 3(3%) Stage I 25(24.7%) Stage II 32(31.7%) Stage III 32(31.7%) Stage IV 6(5.9%) N.A. 3(3%) *Median (IQR: interquartile range) 3.2 Gas measurements obtained from the sampling apparatus Data obtained from semiconductor sensors and the ratio of each sensor were compared between patients with and without CRC. In a univariate analysis, levels of sulfur-containing gas, CH 4 , and CO 2 were significantly higher in the CRC group than in the non-CRC group. In addition, ratios of sulfur-containing gas/H 2 , CH 4 /H 2 were significantly different between the two groups (Table 3 ). In a logistic regression analysis including age and sex, there were no significant differences in gas sensor data between the two groups (Table 4 ). In the non-CRC group, no marked difference in gas distribution was noted between healthy individuals and polyp patients (Table 5 ). Table 3 Univariate analyses of the gas sensor data in the CRC and non-CRC groups variable CRC Non-CRC p-value Age (years) 72 (67, 77) 54 (47, 61) < 0.001 Gender (Male/Female) 58/43 79/61 0.707 H 2 53.873 (28.242, 86.552) 43.491 (28.98, 71.424) 0.237 Sulfur-containing gas 21.859 (11.357, 45.507) 15.663 (8.093, 29.978) 0.0039 CH 4 41.146 (21.713, 62.38) 27.924 (17.913, 47.103) 0.003 CO 2 161.006 (95.351, 279.992) 128.858 (75.268, 206.143) 0.0069 Sulfur-containing gas/H 2 2.255 (1.21, 4.335) 3.56 (1.609, 7.076) 0.0024 Sulfur-containing gas/CH 4 1.529 (0.866, 3.799) 2.206 (0.992, 4.591) 0.084 CH 4 /H 2 1.217 (0.777, 2.093) 1.663 (1.022, 2.559) 0.006 CO 2 / H 2 0.275 (0.175, 0.504) 0.366 (0.205, 0.557) 0.093 CO 2 /H 2 0.14 (0.073, 0.258) 0.132 (0.071, 0.223) 0.362 CO 2 /Sulfur-containing gas 0.229 (0.146, 0.355) 0.221 (0.136, 0.33) 0.648 Median (IQR: interquartile range ) Table 4 Logistic regression analyses for colorectal cancer patients variable Odd ratio 95% CI p-value Age 1.1365 1.0984–1.176 < 0.001 Gender (male) 0.7671 0.3845–1.5302 0.451 H 2 0.8715 0.309–2.4576 0.794 Sulfur-containing gas 1.1504 0.4352–3.0409 0.777 CO 2 1.1348 0.5305–2.4273 0.744 Sulfur-containing gas/H 2 0.8223 0.5893–1.1476 0.25 CH 2 /H 2 1.2175 0.6568–2.2569 0.531 CO 2 /H 2 0.4255 0.0111–162581 0.645 CO 2 /Sulfur-containing gas 0.7812 0.0013-484.5464 0.939 Table 5 Univariate analysis analyses of gas sensors in colorectal adenoma patients and healthy controls variable Colorectal adenoma Healthy controls p-value Age 55 (49, 64) 52 (46, 59) 0.218 Gender (Male/Female) 31/19 49/41 0.357 H 2 47.584 (33.336, 65.439) 42.723 (28.779, 73.09) 0.498 Sulfur-containing gas 15.252 (8.283, 27.481) 17.892 (8.191, 36.954) 0.228 CH 4 31.304 (21.199, 45.941) 26.681 (17.397, 50.41) 0.387 CO 2 142.236 (77.003, 199.332) 120.593 (78.49, 221.662) 0.361 Sulfur-containing gas / H 2 4.214 (1.871, 9.941) 3.29 (1.474, 6.843) 0.315 Sulfur-containing gas / CH 4 2.599 (1.105, 6.413) 1.99 (0.966, 4.184) 0.287 CH 4 / H 2 1.506 (1.017, 2.711) 1.697 (1.035, 2.5) 0.655 CO 2 / H 2 0.357(0.193, 0.44) 0.375 (0.218, 0.581) 0.326 CO 2 / Sulfur-containing gas 0.139 (0.056, 0.265) 0.132(0.08, 0.21) 0.820 CO 2 / CH 4 0.196 (0.137, 0.304) 0.234(0.147, 0.365) 0.277 Median (IQR: interquartile range ) 3.3 Discriminant formula We applied a logistic regression model to the gas sensor data and proposed the following discriminative formula: 1/(1 + exp(-( -8.26-0.14×A + 0.14×B + 0.13×C-0.2×D + 0.2×E-0.85×F-0.25×G + 0.13×H-0.27×I ))) where A: H 2 , B: Sulfur-containing gas, C: CO 2 , D: Sulfur-containing gas/H 2 , E: CH 4 /H 2 , F: CO 2 /H 2 , G: CO 2 /Sulfur-containing gas, H: age, and I: gender (M). The ROC curve of the discriminant formula is shown in Fig. 3 , and the area under the ROC curve (AUC) was 0.859. The ROC curve of the discriminant formula in stages 0/I/II showed similar findings, and the AUC was 0.839 (Supplemental figure). 4 Discussion We demonstrated the utility of gas sensors attached to the toilet bowl for discriminating defecating gases in patients with CRC. This is a new method for CRC screening. Our findings support the possibility of screening for CRC using a gas sensor that measures gas components during defecation. In the previous study, we collected gas during defecation and analyzed it using gas chromatography. In contrast to our previous study, this investigation employed a semiconductor gas sensor to measure bowel gas. The prior method, using 25-liter sampling bags, was cumbersome, time-consuming, and impractical for large-scale studies. The compact gas sensor allows for repeated measurements at home, enabling more precise data collection. Several studies have reported that volatile organic compounds are biomarkers for CRC screening. ( 15 ) ( 16 ) ( 17 ) Kelly et al. examined 447 patients who underwent a breath test using an electronic nose, and final models for detecting CRC and advanced adenomas yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively. ( 16 ) Bond et al. showed that the abundance of several volatile organic compounds differed significantly between samples from CRC patients and controls using gas chromatography mass spectrometry of feces. ( 15 ) Smiełowska et al. investigated volatile biomarkers in both breath and feces using gas chromatography coupled with mass spectrometry and found compounds that were positively or negatively associated with the presence of CRC, including acetone, heptanoic acid, and 2,6,10-trimethyldodecane in breath samples and n-hexane, acetone, dimethyl trisulfide, and skatole in fecal samples. ( 17 ) Several studies have examined the relationship between VOCs and CRC, but all of them have used gas chromatography, and no studies have used gas sensors. Yamagishi et al. found that sulfur-containing gas could be produced by reacting sulfur-containing amino acids with glucose or lactic acid and showed that the concentrations of sulfur-containing compounds in the samples of flatus from patients with colon cancer and in the samples of exhaled air from patients with lung cancer were significantly higher than in those from healthy individuals. ( 18 ) Gut bacteria play an important role in human health, and diet influences the composition of these bacteria. Sulfur is metabolized in the intestine to produce a gas called hydrogen sulfide (H2S). Recently, sulfur-metabolizing bacteria that reduce dietary sulfur to hydrogen sulfide have been found to be associated with CRC. However, few studies have investigated the association between diet and sulfur-metabolizing bacteria in CRC development. ( 19 ) Wong et al. showed that the sulfur microbial diet was characterized by high intakes of low-calorie beverages, French fries, red meats, and processed meats and low intakes of fruits, yellow vegetables, whole grains, legumes, leafy vegetables, and cruciferous vegetables, and greater adherence to the sulfur microbial diet was associated with an increased risk of CRC, with a hazard ratio (HR) of 1.27 (95% CI, 1.12–1.44) comparing the highest versus the lowest quintile of the diet score after adjustment for other risk factors. ( 19 ) In our study, the concentration of sulfur-containing gas was significantly higher in the CRC groups. We previously demonstrated that methylmercaptan, a sulfur-containing gas, is increased in CRC patients compared to healthy individuals. ( 14 ) Nakano et al. reported that Fusobacterium nucleatum is one of the most potent producers of methylmercaptan from L-methionine by L-methionine-a-deamino-c-mercaptomethane-lyase. ( 20 ) F. nucleatum is frequently found in the tissues and saliva of colon cancer patients. ( 21 ) It is primarily found in the oral cavity and is known to cause periodontal disease. Recent studies have reported that this may influence the development of CRC. ( 22 ) However, further research is needed to determine the intestinal bacteria involved in the production of sulfur-containing gases. Several limitations associated with the present study warrant mention. First, there were significant differences in age between the CRC and non-CRC groups. This may influence the intestinal microbiome. Second, the relationship between diet and defecating gas is unknown, and its effect on defecating gas is not well understood. Third, the relationship between CRC and the production of S-containing gases remains unclear. Whether or not CRC causes the generation of sulfur-containing gases or if patients with high levels of sulfur-containing gases are more prone to developing CRC than others is unclear. Finally, we utilized cross-validation in this single-arm prospective cohort study. Given the inherent risk of overfitting associated with cross-validation, it is necessary to validate the newly developed model on an independent cohort. 5 Conclusions Our findings suggested that differences in gas components during defecation were observed between CRC patients and non-CRC patients. It might be possible to perform convenient CRC screenings routinely in daily life by installing gas sensors in bathrooms. The usefulness of the discriminant function should be verified in future studies using different groups. Declarations Author contributions Study concept and design: A. Ishibe, and I. Endo. Acquisition of data: J. Watanabe, S. Yamagishi, H. Yamakoshi, K. Goto, Y. Suwa, K. Nakagawa, and M Ozawa; analysis and interpretation of data: A. Ishibe, Y. Saigusa; writing of the manuscript: A. Ishibe, J. Watanabe, C. Kunisaki, and I. Endo. All authors have approved the final article. Funding infomation This work was supported by JSPS KAKENHI Grant Number JP18H03550. Conflict of interest statement Authors declare no Conflict of Interests for this article. Ethics statement This study was approved by the Yokohama City University Ethics Committee and conducted in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects (IRB number B191200063). Informed Consent: Informed consent was obtained from all participants included in the study. Registry and the Registration No. of the study/trial: UMIN 000035342. Animal Studies: N/A. Data availability No datasets were generated or analyzed during the current study. Competing interests: The authors declare no competing interests. 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Methyl mercaptan production by periodontal bacteria. International dental journal. 2002;52 Suppl 3:217-20. Komiya Y, Shimomura Y, Higurashi T, Sugi Y, Arimoto J, Umezawa S, et al. Patients with colorectal cancer have identical strains of Fusobacterium nucleatum in their colorectal cancer and oral cavity. Gut. 2019;68(7):1335-7. Wang N, Fang JY. Fusobacterium nucleatum, a key pathogenic factor and microbial biomarker for colorectal cancer. Trends Microbiol. 2023;31(2):159-72. Additional Declarations No competing interests reported. Supplementary Files Supplementalfigure.ppt Supplemental figure. The ROC curve of the discriminant formula for stage 0/I/II CRC Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mayumi","middleName":"","lastName":"Ozawa","suffix":""},{"id":411340009,"identity":"ef582ba9-aa6a-4cc4-bf53-f5417d1a4d58","order_by":8,"name":"Yusuke Saigusa","email":"","orcid":"","institution":"Yokohama City University Graduate School Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Saigusa","suffix":""},{"id":411340010,"identity":"7383cf2a-56e7-4f85-818a-029e7b1d296f","order_by":9,"name":"Chikara Kunisaki","email":"","orcid":"","institution":"Yokohama City University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Chikara","middleName":"","lastName":"Kunisaki","suffix":""},{"id":411340011,"identity":"ee1dcf22-2a5f-47bf-b3d3-33057237f034","order_by":10,"name":"Itaru Endo","email":"","orcid":"","institution":"Yokohama City University Graduate School Medicine","correspondingAuthor":false,"prefix":"","firstName":"Itaru","middleName":"","lastName":"Endo","suffix":""}],"badges":[],"createdAt":"2025-02-03 13:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5951396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5951396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75707054,"identity":"15bbb3aa-74c1-4365-8698-f35588db1e12","added_by":"auto","created_at":"2025-02-07 10:28:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":515193,"visible":true,"origin":"","legend":"\u003cp\u003eGas sensor system\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5951396/v1/0cb54433a8803aadb082fb7b.png"},{"id":75707052,"identity":"618348cc-c082-4a66-b27b-3427401aef27","added_by":"auto","created_at":"2025-02-07 10:28:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58212,"visible":true,"origin":"","legend":"\u003cp\u003eTrial profile\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5951396/v1/4d363a8abbf439860594f75d.png"},{"id":75707332,"identity":"ac047adf-bf75-4632-a349-ad05c27bdb96","added_by":"auto","created_at":"2025-02-07 10:36:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51636,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of the discriminant formula for CRC\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5951396/v1/b14fe8e6101a0ecb79ae69ee.png"},{"id":107819084,"identity":"2a5750bc-4e56-48d6-96f9-8725d2e4cb73","added_by":"auto","created_at":"2026-04-26 09:10:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5951396/v1/f684277b-f755-49c1-9ae4-a0aee899f1e7.pdf"},{"id":75707057,"identity":"c35e4f9a-596e-4da5-825f-222314520768","added_by":"auto","created_at":"2025-02-07 10:28:10","extension":"ppt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114688,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental figure. The ROC curve of the discriminant formula for stage 0/I/II CRC\u003c/p\u003e","description":"","filename":"Supplementalfigure.ppt","url":"https://assets-eu.researchsquare.com/files/rs-5951396/v1/39ba91555dd56e310feefaef.ppt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening of colorectal cancer by a fecal gas compound analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the second-most common cancer diagnosed in women and third-most common cancer in men (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). High-sensitivity guaiac fecal occult blood test (gFOBT), fecal immunochemical test (FIT), and colonoscopy are recommended for screening by the U.S. Preventive Services Task Force (USPSTF). However, the FOBT detects blood in the stool that is not always associated with CRC, with the positive predictive value of the FOBT for CRC being 10.6%-12.0% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and colonoscopy is an invasive method with screening capacity limitations. A simpler method than existing screening modalities is thus required.\u003c/p\u003e \u003cp\u003eOdor gas components have recently attracted attention as diagnostic screening tools for cancer, and their usefulness has been reported in skin and breast cancers (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Several studies have demonstrated the reliability of volatile organic compounds (VOCs) for detecting CRC in different materials, including urine (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), exhaled breath (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), blood (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and feces (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, the sample collection was inconvenient in these studies. Therefore, it is necessary to develop a convenient method. We previously reported a pilot study in which the sulfur-containing gas component from defecation was significantly greater in CRC patients than in healthy individuals (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We believe that defecation gas measurements may be a useful new tool for CRC screening.\u003c/p\u003e \u003cp\u003eTherefore, we conducted a prospective cohort study to verify whether or not defecation gas is useful for CRC screening. In this study, the gas analysis was performed using a gas sensor. Gas sensors can be installed in toilets at home and data can be collected daily. We examined the gas components in bowel movements from patients with CRC and non-CRC patients using a gas sensor, a novel method, and evaluated the diagnostic value of this method for CRC detection.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eWe conducted a multicenter prospective observational study at four institutions from December 2018 to February 2020. The enrolled patients had three different statuses: CRC, colorectal polyp, and normal colon. Inclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;20 years old, histologically proven adenocarcinoma, and any clinical stage in the CRC group. In the colorectal polyp group, the patients had adenomatous polyps. All patients underwent colonoscopy. Exclusion criteria were inflammatory bowel disease, infectious enteritis, bowel obstruction, and other conditions deemed ineligible by the physician.\u003c/p\u003e \u003cp\u003e This study was approved by the Yokohama City University Ethics Committee and conducted in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects (IRB number B191200063, UMIN 000035342). Informed consent was obtained from all patients before the study procedures were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Gas sensor device and analyses\u003c/h2\u003e \u003cp\u003eThe gas sensor apparatus was placed in the bathroom of each subject\u0026rsquo;s home for one week (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The gas sensor detects sulfur-containing gases, such as hydrogen sulfide (H\u003csub\u003e2\u003c/sub\u003eS) and methylmercaptan, in addition to methane (CH4), hydrogen (H\u003csub\u003e2\u003c/sub\u003e), and carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e). Gas data were collected for each bowel movement and analyzed based on the semiconductor resistance values obtained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eWhen samples were recorded more than once, the mean and standard deviation (SD) were calculated and used for subsequent analyses. Using a multivariable logistic regression model, we developed a formula to discriminate between patients with CRC and healthy volunteers using gas data from study participants. A discriminant formula was provided as the probability of CRC occurrence using a logistic regression model. All analyses were performed using R version 4.1.0 (R, Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e \u003cp\u003eTwo hundred and sixty patients were enrolled in this study. A consort diagram of the patient registration is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Owing to a malfunction of the semiconductor device, data from five CRC patients and 14 non-CRC cases could not be obtained and were therefore excluded from the analysis. All patients in the non-CRC group underwent total colonoscopy and were divided into the colorectal adenoma group and healthy control group. A total of 101 patients with CRC, 50 with colorectal adenoma, and 90 healthy controls were analyzed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe median age of patients with CRC was 72 years old; they were older than subjects in the other two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The characteristics of colorectal cancer patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Of the cancer patients, 57.4% were male, and the location of the cancer was the rectum in 42.6%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCharacteristics of each group\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003cp\u003e(Median, IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003e Male/Female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (67, 77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58/43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal adenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal adenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (50, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31/19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo colorectal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (46, 59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48/42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIQR: interquartile range\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCharacteristics of colorectal cancer patients (N\u0026thinsp;=\u0026thinsp;101)\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (67, 77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale/Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(57.4%)/43(42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCecum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscending\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransvers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(11.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescending\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSigmoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (25, 75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (2.2, 7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(24.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(31.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(31.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e*Median (IQR: interquartile range)\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Gas measurements obtained from the sampling apparatus\u003c/h2\u003e \u003cp\u003eData obtained from semiconductor sensors and the ratio of each sensor were compared between patients with and without CRC. In a univariate analysis, levels of sulfur-containing gas, CH\u003csub\u003e4\u003c/sub\u003e, and CO\u003csub\u003e2\u003c/sub\u003e were significantly higher in the CRC group than in the non-CRC group. In addition, ratios of sulfur-containing gas/H\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e were significantly different between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In a logistic regression analysis including age and sex, there were no significant differences in gas sensor data between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the non-CRC group, no marked difference in gas distribution was noted between healthy individuals and polyp patients (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eUnivariate analyses of the gas sensor data in the CRC and non-CRC groups\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-CRC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (67, 77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (47, 61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79/61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.873 (28.242, 86.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.491 (28.98, 71.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.859 (11.357, 45.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.663 (8.093, 29.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.146 (21.713, 62.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.924 (17.913, 47.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.006 (95.351, 279.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.858 (75.268, 206.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.255 (1.21, 4.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.56 (1.609, 7.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas/CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.529 (0.866, 3.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.206 (0.992, 4.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.217 (0.777, 2.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.663 (1.022, 2.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003cb\u003e/\u003c/b\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.275 (0.175, 0.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.366 (0.205, 0.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14 (0.073, 0.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132 (0.071, 0.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e/Sulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229 (0.146, 0.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221 (0.136, 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMedian (IQR: interquartile range )\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eLogistic regression analyses for colorectal cancer patients\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdd ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0984\u0026ndash;1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3845\u0026ndash;1.5302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.309\u0026ndash;2.4576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4352\u0026ndash;3.0409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5305\u0026ndash;2.4273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5893\u0026ndash;1.1476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6568\u0026ndash;2.2569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0111\u0026ndash;162581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e/Sulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0013-484.5464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eUnivariate analysis analyses of gas sensors in colorectal adenoma patients and healthy controls\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal adenoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (49, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (46, 59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.584 (33.336, 65.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.723 (28.779, 73.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.252 (8.283, 27.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.892 (8.191, 36.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.304 (21.199, 45.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.681 (17.397, 50.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142.236 (77.003, 199.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.593 (78.49, 221.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas / H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.214 (1.871, 9.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29 (1.474, 6.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur-containing gas / CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.599 (1.105, 6.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99 (0.966, 4.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e / H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.506 (1.017, 2.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.697 (1.035, 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e / H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.357(0.193, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375 (0.218, 0.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e / Sulfur-containing gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139 (0.056, 0.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132(0.08, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e / CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196 (0.137, 0.304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234(0.147, 0.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMedian (IQR: interquartile range )\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Discriminant formula\u003c/h2\u003e \u003cp\u003eWe applied a logistic regression model to the gas sensor data and proposed the following discriminative formula: 1/(1\u0026thinsp;+\u0026thinsp;exp(-( -8.26-0.14\u0026times;A\u0026thinsp;+\u0026thinsp;0.14\u0026times;B\u0026thinsp;+\u0026thinsp;0.13\u0026times;C-0.2\u0026times;D\u0026thinsp;+\u0026thinsp;0.2\u0026times;E-0.85\u0026times;F-0.25\u0026times;G\u0026thinsp;+\u0026thinsp;0.13\u0026times;H-0.27\u0026times;I )))\u003c/p\u003e \u003cp\u003ewhere A: H\u003csub\u003e2\u003c/sub\u003e, B: Sulfur-containing gas, C: CO\u003csub\u003e2\u003c/sub\u003e, D: Sulfur-containing gas/H\u003csub\u003e2\u003c/sub\u003e, E: CH\u003csub\u003e4\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e, F: CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003e, G: CO\u003csub\u003e2\u003c/sub\u003e/Sulfur-containing gas, H: age, and I: gender (M).\u003c/p\u003e \u003cp\u003eThe ROC curve of the discriminant formula is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the area under the ROC curve (AUC) was 0.859. The ROC curve of the discriminant formula in stages 0/I/II showed similar findings, and the AUC was 0.839 (Supplemental figure).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWe demonstrated the utility of gas sensors attached to the toilet bowl for discriminating defecating gases in patients with CRC. This is a new method for CRC screening. Our findings support the possibility of screening for CRC using a gas sensor that measures gas components during defecation.\u003c/p\u003e \u003cp\u003eIn the previous study, we collected gas during defecation and analyzed it using gas chromatography. In contrast to our previous study, this investigation employed a semiconductor gas sensor to measure bowel gas. The prior method, using 25-liter sampling bags, was cumbersome, time-consuming, and impractical for large-scale studies. The compact gas sensor allows for repeated measurements at home, enabling more precise data collection.\u003c/p\u003e \u003cp\u003eSeveral studies have reported that volatile organic compounds are biomarkers for CRC screening. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Kelly et al. examined 447 patients who underwent a breath test using an electronic nose, and final models for detecting CRC and advanced adenomas yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Bond et al. showed that the abundance of several volatile organic compounds differed significantly between samples from CRC patients and controls using gas chromatography mass spectrometry of feces. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Smiełowska et al. investigated volatile biomarkers in both breath and feces using gas chromatography coupled with mass spectrometry and found compounds that were positively or negatively associated with the presence of CRC, including acetone, heptanoic acid, and 2,6,10-trimethyldodecane in breath samples and n-hexane, acetone, dimethyl trisulfide, and skatole in fecal samples. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Several studies have examined the relationship between VOCs and CRC, but all of them have used gas chromatography, and no studies have used gas sensors.\u003c/p\u003e \u003cp\u003eYamagishi et al. found that sulfur-containing gas could be produced by reacting sulfur-containing amino acids with glucose or lactic acid and showed that the concentrations of sulfur-containing compounds in the samples of flatus from patients with colon cancer and in the samples of exhaled air from patients with lung cancer were significantly higher than in those from healthy individuals. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eGut bacteria play an important role in human health, and diet influences the composition of these bacteria. Sulfur is metabolized in the intestine to produce a gas called hydrogen sulfide (H2S). Recently, sulfur-metabolizing bacteria that reduce dietary sulfur to hydrogen sulfide have been found to be associated with CRC. However, few studies have investigated the association between diet and sulfur-metabolizing bacteria in CRC development. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Wong et al. showed that the sulfur microbial diet was characterized by high intakes of low-calorie beverages, French fries, red meats, and processed meats and low intakes of fruits, yellow vegetables, whole grains, legumes, leafy vegetables, and cruciferous vegetables, and greater adherence to the sulfur microbial diet was associated with an increased risk of CRC, with a hazard ratio (HR) of 1.27 (95% CI, 1.12\u0026ndash;1.44) comparing the highest versus the lowest quintile of the diet score after adjustment for other risk factors. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) In our study, the concentration of sulfur-containing gas was significantly higher in the CRC groups.\u003c/p\u003e \u003cp\u003eWe previously demonstrated that methylmercaptan, a sulfur-containing gas, is increased in CRC patients compared to healthy individuals. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Nakano et al. reported that \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e is one of the most potent producers of methylmercaptan from L-methionine by L-methionine-a-deamino-c-mercaptomethane-lyase. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) \u003cem\u003eF. nucleatum\u003c/em\u003e is frequently found in the tissues and saliva of colon cancer patients. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) It is primarily found in the oral cavity and is known to cause periodontal disease. Recent studies have reported that this may influence the development of CRC. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) However, further research is needed to determine the intestinal bacteria involved in the production of sulfur-containing gases.\u003c/p\u003e \u003cp\u003eSeveral limitations associated with the present study warrant mention. First, there were significant differences in age between the CRC and non-CRC groups. This may influence the intestinal microbiome. Second, the relationship between diet and defecating gas is unknown, and its effect on defecating gas is not well understood. Third, the relationship between CRC and the production of S-containing gases remains unclear. Whether or not CRC causes the generation of sulfur-containing gases or if patients with high levels of sulfur-containing gases are more prone to developing CRC than others is unclear. Finally, we utilized cross-validation in this single-arm prospective cohort study. Given the inherent risk of overfitting associated with cross-validation, it is necessary to validate the newly developed model on an independent cohort.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eOur findings suggested that differences in gas components during defecation were observed between CRC patients and non-CRC patients. It might be possible to perform convenient CRC screenings routinely in daily life by installing gas sensors in bathrooms. The usefulness of the discriminant function should be verified in future studies using different groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: A. Ishibe, and I. Endo. Acquisition of data: J. Watanabe, S. Yamagishi, H. Yamakoshi, K. Goto, Y. Suwa, K. Nakagawa, and M Ozawa; analysis and interpretation of data: A. Ishibe, Y. Saigusa; writing of the manuscript: A. Ishibe, J. Watanabe, C. Kunisaki, and I. Endo. All authors have approved the final article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding infomation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number JP18H03550.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no Conflict of Interests for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Yokohama City University Ethics Committee and conducted in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects (IRB number B191200063).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed Consent: Informed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003eRegistry and the Registration No. of the study/trial: UMIN 000035342.\u003c/p\u003e\n\u003cp\u003eAnimal Studies: N/A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp; No datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modifed the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u0026rsquo;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet (London, England). 2019;394(10207):1467-80.\u003c/li\u003e\n\u003cli\u003eGroup. UCCSP. Results of the first round of a demonstration pilot of screening for colorectal cancer in the United Kingdom. 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Development and application of a new electronic nose instrument for the detection of colorectal cancer. Biosensors \u0026amp; bioelectronics. 2015;67:733-8.\u003c/li\u003e\n\u003cli\u003eArasaradnam RP, McFarlane MJ, Ryan-Fisher C, Westenbrink E, Hodges P, Thomas MG, et al. Detection of colorectal cancer (CRC) by urinary volatile organic compound analysis. PloS one. 2014;9(9):e108750.\u003c/li\u003e\n\u003cli\u003eAltomare DF, Di Lena M, Porcelli F, Trizio L, Travaglio E, Tutino M, et al. Exhaled volatile organic compounds identify patients with colorectal cancer. The British journal of surgery. 2013;100(1):144-50.\u003c/li\u003e\n\u003cli\u003eAmal H, Leja M, Funka K, Lasina I, Skapars R, Sivins A, et al. Breath testing as potential colorectal cancer screening tool. International journal of cancer Journal international du cancer. 2016;138(1):229-36.\u003c/li\u003e\n\u003cli\u003eWang C, Li P, Lian A, Sun B, Wang X, Guo L, et al. 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Generation of gaseous sulfur-containing compounds in tumour tissue and suppression of gas diffusion as an antitumour treatment. Gut. 2012;61(4):554-61.\u003c/li\u003e\n\u003cli\u003eWang Y, Nguyen LH, Mehta RS, Song M, Huttenhower C, Chan AT. Association Between the Sulfur Microbial Diet and Risk of Colorectal Cancer. JAMA Netw Open. 2021;4(11):e2134308.\u003c/li\u003e\n\u003cli\u003eNakano Y, Yoshimura M, Koga T. Methyl mercaptan production by periodontal bacteria. International dental journal. 2002;52 Suppl 3:217-20.\u003c/li\u003e\n\u003cli\u003eKomiya Y, Shimomura Y, Higurashi T, Sugi Y, Arimoto J, Umezawa S, et al. Patients with colorectal cancer have identical strains of \u0026lt;em\u0026gt;Fusobacterium nucleatum\u0026lt;/em\u0026gt; in their colorectal cancer and oral cavity. Gut. 2019;68(7):1335-7.\u003c/li\u003e\n\u003cli\u003eWang N, Fang JY. Fusobacterium nucleatum, a key pathogenic factor and microbial biomarker for colorectal cancer. Trends Microbiol. 2023;31(2):159-72.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, gas, volatile organic compounds, gas sensor","lastPublishedDoi":"10.21203/rs.3.rs-5951396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5951396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) remains a significant cause of mortality worldwide. Although FOBT has demonstrated efficacy in reducing colorectal cancer-related deaths, it has limitations. We explored an innovative and user-friendly screening method using gas sensors installed in toilets.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eWe developed gas sensors that can be installed in toilets. We conducted a demonstration experiment using these sensors to monitor individuals in their bathrooms. The experiment involved 101 colorectal cancer (CRC) patients, 50 colorectal polyp patients, and 90 healthy individuals who were registered and monitored for a week at home. Gas data collected during defecation were analyzed to assess the feasibility of colorectal cancer screening.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eAge and sulfur-containing gas, CH\u003csub\u003e4\u003c/sub\u003e, and CO\u003csub\u003e2\u003c/sub\u003e content were significantly higher in the CRC group than in the non-CRC group in a univariate analysis. The area under the receiver operating characteristic curve of the discriminant formula for diagnosing colorectal cancer was 0.859.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eA sensor analysis of defecation gas constitutes a promising, novel, and non-invasive approach for CRC screening.\u003c/p\u003e","manuscriptTitle":"Screening of colorectal cancer by a fecal gas compound analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:28:05","doi":"10.21203/rs.3.rs-5951396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22fb6470-f107-4e80-8de1-1f203f58f988","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T09:09:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 10:28:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5951396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5951396","identity":"rs-5951396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}