Nuclear Magnetic Resonance Spectroscopic Analysis of Urinary Metabolome in Sarcoidosis (RMN-SARCURINES)

NCT ID: NCT05181930

Last Updated: 2023-05-08

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 42 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-01-30
• Study Completion Date: 2021-06-01

Brief Summary

"Sarcoidosis is a systemic granulomatous disease of unknown cause(s).Determining disease activity is a major element in the treatment decision.

1H- Nuclear magnetic resonance (NMR) spectroscopy allows the identification of biomarkers in different pathologies and in particular in a pilot study of saliva of patients with sarcoidosis. Applications to sarcoidosis are still rare, having concerned only serum and saliva. In this context we hypothesize the existence of a difference in the metabolic products found in the urine of sarcoidosis patients with different degrees of activity and/or disease severity.

We designed an analysis of urinary metabolomics in sarcoidosis patient using NMR spectroscopy with multivariate statistical analysis, followed by metabolite identification and pathway analysis. "

Detailed Description

"Sarcoidosis is a systemic granulomatous disease of unknown cause(s) with an almost constant lung involvement. Clinical expression varies greatly in both evolution and severity. The activity of the disease is currently based on serum markers (elevation of angiotensin-converting enzyme) with poor sensitivity/specificity or on imaging techniques (18F-FDG PET-CT) which are more invasive and more expensive. Determining disease activity is a major element in the treatment decision.

The spectra of small molecules resulting from metabolism (metabolome/metabolomics) evaluated by spectrometry in different biological fluids (blood, saliva, urine) during different physiological or pathological conditions have been identified as promising biomarkers. 1H- Nuclear magnetic resonance (NMR) spectrometry allows the identification of biomarkers in different pathologies and in particular in a pilot study of saliva of patients with sarcoidosis. Applications to sarcoidosis are still rare having concerned only serum and saliva. The application of this method to the urinary metabolome has shown its effectiveness in other situations, for example in celiac disease.

In this context we hypothesize the existence of a difference in the metabolic products found in the urine of sarcoidosis patients with different degrees of activity and/or disease severity.

We designed an analysis of urinary metabolomics in sarcoidosis patient using NMR spectroscopy with multivariate statistical analysis, followed by metabolite identification and pathway analysis. "

Conditions

Sarcoidosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Patients with a diagnosis of sarcoidosis according to ATS / ERS / WASOG criteria.

Exclusion Criteria

• Treatment (corticosteroids, plaquenil, immunosuppressants) within the last 6 months
• Patients with diabetes.
• Patient with long-term or recent drug treatment (e.g. antibiotics)
• Patients with a urinary tract infection or urinary lithiasis at the time of collection.
• Renal failure

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Assistance Publique - Hôpitaux de Paris

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Jean-François BERNAUDIN, MD PhD

Role: PRINCIPAL_INVESTIGATOR

APHP

Locations

Hopital Avicenne,AP-HP

Bobigny, , France

Countries

France

References

Duchemann B, Triba MN, Guez D, Rzeznik M, Savarin P, Nunes H, Valeyre D, Bernaudin JF, Le Moyec L. Nuclear magnetic resonance spectroscopic analysis of salivary metabolome in sarcoidosis. Sarcoidosis Vasc Diffuse Lung Dis. 2016 Mar 29;33(1):10-6.

Reference Type: BACKGROUND

PMID: 27055831 (View on PubMed)

Other Identifiers

2017-A01555-48

Identifier Type: -

Identifier Source: org_study_id