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This study aims to develop a non-invasive diagnostic method for metabolic syndrome (MetS) and metabolically healthy obesity (MHO) through analysis of exhaled air. Using proton-transfer-reaction mass spectrometry combined with machine learning algorithms, we will characterize volatile organic compound profiles in 300 participants across three groups: MetS patients, MHO patients, and healthy controls. The primary goal is to create and validate a classification model capable of accurately differentiating these metabolic states based on breath analysis.
This study focuses on characterizing the volatilome - the complete set of volatile organic compounds in exhaled air - as a novel biomarker source for metabolic health assessment.
The study represents the first comprehensive attempt to compare volatilome signatures between metabolically healthy and unhealthy obesity phenotypes. Successful validation of this approach could establish breath analysis as a new diagnostic paradigm in metabolic medicine, enabling rapid, non-invasive screening and personalized treatment strategies for patients with obesity-related conditions.
Methodological innovations include real-time breath analysis capabilities and development of specialized machine learning algorithms for pattern recognition in complex mass spectrometry data. The findings are expected to contribute significantly to understanding metabolic pathway alterations in different obesity phenotypes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Metabolic Syndrome Group | 100 patients diagnosed with Metabolic Syndrome according to International Diabetes Federation (IDF) 2006 criteria. |
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| Metabolically Healthy Obesity Group | 100 patients with waist circumference ≥94 cm (men)/≥80 cm (women), but without other components of Metabolic Syndrome (hypertension, dyslipidemia, impaired fasting glucose). |
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| Control Group | 100 individuals with normal BMI and no signs of Metabolic Syndrome. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Breath Sampling and Analysis by PTR-MS | Diagnostic Test | A single sample of exhaled breath will be collected from each participant during quiet breathing. The sample will be analyzed in real-time using Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (Compact PTR-TOF-MS 1000, Ionicon, Austria) to identify and quantify the spectrum of volatile organic compounds (VOCs). |
| Measure | Description | Time Frame |
|---|---|---|
| Specificity of the combined PTR-MS and machine learning model. | Specificity (true negative rate) of the diagnostic model, based on the analysis of exhaled breath VOCs by PTR-MS and subsequent machine learning classification, for distinguishing between participants with Metabolic Syndrome, Metabolically Healthy Obesity, and healthy controls. The value will be reported with a 95% confidence interval. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). |
| Sensitivity of the combined PTR-MS and machine learning model. | Sensitivity (true positive rate) of the diagnostic model, based on the analysis of exhaled breath Volatile Organic Compounds (VOCs) by Proton Transfer Reaction Mass Spectrometry (PTR-MS) and subsequent machine learning classification, for distinguishing between participants with Metabolic Syndrome, Metabolically Healthy Obesity, and healthy controls. The value will be reported with a 95% confidence interval. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). |
| Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the combined PTR-MS and machine learning model. | The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) as a composite measure of the diagnostic performance of the model based on PTR-MS breath analysis and machine learning. The AUC will be calculated for pairwise comparisons between the three study groups (Metabolic Syndrome vs. Metabolically Healthy Obesity; Metabolic Syndrome vs. Control; Metabolically Healthy Obesity vs. Control) and reported with a 95% confidence interval. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). |
| Measure | Description | Time Frame |
|---|---|---|
| Identification of specific Volatile Organic Compound (VOC) patterns. | Qualitative and quantitative assessment of specific Volatile Organic Compounds (VOCs) and VOC profiles significantly associated with Metabolic Syndrome and Metabolically Healthy Obesity compared to the control group. The identification of molecules will be performed using the Human Metabolome Database (HMDB) with an accuracy of ± 100 ppm. |
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Inclusion Criteria:
Non-inclusion criteria for all groups:
Exclusion Criteria:
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The study population consists of adults (≥18 years) from three groups: 1) Patients with Metabolic Syndrome (IDF, 2005 criteria), 2) Patients with metabolically healthy obesity (BMI≥30, no metabolic abnormalities), and 3) Healthy controls (BMI<25, no metabolic disorders).
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Philipp Kopylov | Contact | +7 (903) 687-72-64 | kopylov_f_yu@staff.sechenov.ru | |
| Aida Gadzhiakhmedova | Contact | +7 (989) 899-53-01 | ai.kidman@mail.ru |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Clinical Hospital №1, Sechenov University | Recruiting | Moscow | Russia |
IPD will not be available for public sharing. The local Ethics Committee and national data protection laws do not permit public deposition of individual patient data. The informed consent does not include such provisions.
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| ID | Term |
|---|---|
| D024821 | Metabolic Syndrome |
| D009765 | Obesity |
| D050177 | Overweight |
| D000067329 | Obesity, Metabolically Benign |
| ID | Term |
|---|---|
| D007333 | Insulin Resistance |
| D006946 | Hyperinsulinism |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
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| Through study completion, after mass spectrometric data processing and database search are completed (anticipated within 1 year). |
| D009750 |
| Nutritional and Metabolic Diseases |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |