Clinical trial

Breath Mass Spectrometry in Metabolic Syndrome and Metabolically Healthy Obesity

Recruiting now · Not applicable · 1 countries · Registry ID NCT07330297

Recruiting nowNot applicableObservational

What this study is about

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.

A promising-looking record is not the same as confirmed eligibility. The study team must review the full criteria and current recruitment status.

Basic eligibility

Age18 Years to Not listed
SexAll
Healthy volunteersAccepted
ConditionMetabolic Syndrome, Obesity & Overweight, Metabolically Healthy Obesity

Full registry criteria

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.

Treatments and study arms

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).

Primary outcomes

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.

Study locations

1 locations were listed when this page was built. The first 40 are shown.

University Clinical Hospital №1, Sechenov University🌐 Moscow, Russia
Philipp KopylovContact