Metabolically Healthy Obesity, Metabolic Syndrome, Obesity & Overweight
Conditions
Keywords
Breath Analysis, Volatile Organic Compounds, Proton-Transfer-Reaction Mass Spectrometry, Artificial Intelligence, Machine Learning, Metabolic syndrome, Obesity, Metabolically healthy obesity
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTBreath Sampling and Analysis by PTR-MS
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).
Sponsors
I.M. Sechenov First Moscow State Medical University
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* For Group 1 (Metabolic syndrome): Age \>18 years, signed informed consent, diagnosis of Metabolic Syndrome (IDF 2006 criteria: waist circumference \>94 cm (men)/ \>80 cm (women) + ≥2 of: BP ≥130/85 mmHg or antihypertensive therapy; TG ≥1.7 mmol/L; HDL-C \<1.03 mmol/L (men) / \<1.29 mmol/L (women); Fasting glucose ≥5.6 mmol/L). * For Group 2 (Metabolically healthy obesity): Age \>18 years, signed informed consent, waist circumference ≥94 cm (men) / ≥80 cm (women), absence of other Metabolic Syndrome criteria (hypertension, dyslipidemia, impaired fasting glucose). * For Group 3 (Control): Age \>18 years, signed informed consent, normal BMI, absence of signs of Metabolic Syndrome. Non-inclusion criteria for all groups: * Inability to provide informed consent; * History of myocardial infarction or stroke; * Chronic kidney disease stage 3B, 4, 5 (eGFR \<30 ml/min/1.73m2); * Acute or subacute cardiovascular disease; * Familial hypercholesterolemia; * Bronchopulmonary diseases; * Acute or chronic infectious diseases; * Type 1 or Type 2 diabetes mellitus; * Systemic connective tissue diseases; * Current or past history of oncological diseases; * Severe liver dysfunction, decompensated liver cirrhosis (Child-Pugh class C); * Pregnancy or lactation; * Severe mental illness (severe dementia, schizophrenia); * Comorbid conditions with life expectancy less than 1 year.
Exclusion criteria
* Patient refusal to continue participation in the study; * Identification of any non-inclusion criteria after enrollment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Specificity of the combined PTR-MS and machine learning model. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). | 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. |
| Sensitivity of the combined PTR-MS and machine learning model. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). | 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. |
| Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the combined PTR-MS and machine learning model. | Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year). | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Identification of specific Volatile Organic Compound (VOC) patterns. | Through study completion, after mass spectrometric data processing and database search are completed (anticipated within 1 year). | 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. |
Countries
Russia
Contacts
Primary ContactPhilipp Kopylov
Backup ContactAida Gadzhiakhmedova
Outcome results
None listed