Blood Spot and Urine Metabolomic Screening Applied to Rare Diseases
Blood Spot and Urine Metabolomic Screening Applied to Rare Diseases
The primary goal of this study is to establish a biobank of dried blood spots and urines from a large control cohort and collect several cohorts as large as possible of patients affected or suspected of being affected by rare diseases (mainly hereditary metabolic diseases) or by autism spectrum disorders.
A metabolomic database using a high-resolution mass spectrometer (i.e. the "Device") will be generated and specific biomarkers for the diseases will be confirmed or uncovered. The ultimate goal is to facilitate and improve the diagnosis and screening of the patients affected by these disorders, but also to improve the knowledge about the biochemical mechanisms involved over the course of the selected pathologies.
High-resolution mass spectrometry allows the measurement of thousands of metabolites in a single analysis. The current biochemical tests used for the diagnosis of hereditary metabolic diseases are only using a combination of maximum a few dozens of biomarkers in one analysis.
Objectives Unravel new biomarkers for diagnosis (+/- explore the altered pathways…) Uncover and/or validate newborn screening biomarkers through retrospective analysis of preserved newborn DBS from confirmed patients (useful for first or second tier biochemical NBS testing!) Validation of LC-MS qTOF for metabolomics screening as first line diagnostic test (thousands of metabolites) using diagnostic algorithms (modified z-scores) & continuous optimization by adding new cases and new controls in the database Generation of a biobank of urines and DBS from rare diseases (IEMs) & from a large reference population useful for other research applications
DESIGN OF THE CLINICAL INVESTIGATION
Interventional multicenter study using high-resolution mass spectrometry applied to dried blood spots and urine samples, generating metabolomic data.
The study will start by the establishment of a biobank according to group description.
The samples (dried blood spots (DBS) and urine samples for each subject) will be collected in respective co-investigator centers, before being shipped to the central biobank of Cliniques universitaires Saint Luc (CUSL). DBS are allowed to be kept at room temperature (dry and protected from light) if shipped to CUSL within 2 weeks upon sampling.
The sampling procedure, occurring at each co-investigating center will include in the following order:
The identity and participation of the subjects in the experimentation will remain strictly confidential in accordance with the European General Data Protection Regulation of April 27, 2016 (in application since May 25, 2018), the Belgian law of July 30, 2018 on the protection of privacy with regard to the processing of personal data, as well as the law of August 22, 2002 on the rights of patients. Personal data will be coded. Subjects will not be identified by name or in any other recognisable way in any of the records, results or publications related to the experimentation.
The investigators of the study will identify the potential subjects, that seem to meet the eligibility criteria.
The medical doctor or delegate will explain the study. Then, if the subject and/or the parents agree, the informed consent will be signed before any sampling.
Multiple sampling at different time points will be allowed for a single informed consent form (ICF).
The co-investigator centers will temporarily keep pseudonymized samples and will send periodically the accumulated samples (frozen) to the CUSL laboratory (for biobanking). The samples will be stored at -80°C in CUSL biobank before and after analysis. The time of reception and transfer at -80°C at CUSL will be Recorded in REDCAP.
Later, generation of datasets representative of the metabolome in DBS and urine samples by liquid chromatography coupled to high resolution mass spectrometry (LC-MS-q-TOF) analysis will be performed.
The metabolomics analysis by LC-MS-q-TOF will use a SYNAPT-XS instrument from Waters. Several columns will be used in negative and positive electrospray acquisition modes, allowing the generation of complementary metabolomic datasets containing several thousands of features ranged by exact mass (m/z) (including spectra after energy collision) and retention time measurements. Quality controls and pooled samples will be analyzed at each batch of run during the metabolomics study.
After data analysis using progenesis QI, Unifi and R-package for metabolomics analysis, the results will be discussed and shared with the co-investigators and the expected results will hopefully be published in a peer-reviewed journal. If necessary (i.e. to confirm genetically an unusual metabolomic profile or a secondary finding) and if the consent is obtained from the patient, complementary biochemical testing on initial DBS and/or urine samples; and genetic testing from the DBS will be performed, upon discussion and agreement with the co-investigators of the study. If required (i.e. if a new diagnosis is suspected biochemically and/or genetically), subjects will be contacted after agreement of the co-investigators of the study.
The samples needed for this study will be divided into 3 main groups, each subdivided according to age categories; the first group is the main healthy group (Group 1) allowing to generate a reference metabolomic database. The two other groups correspond to groups of patients with pathologies whose diagnosis is either confirmed (Group 2) or suspected (Group 3).
Total expected: 3366 subjects
The minimum number of samples necessary to generate a reference interval using a non-parametric method is 120 samples according to CLSI28-A3C guidelines. Therefore, the investigators set a target of minimum 120 samples collected per category of age. Samples will be equitably distributed by sex, season and participating centers (thus, geographical origin). For the newborn categories, since the easier availability of large number of control samples using anonymized residual samples from newborn screening centers, the target number of control samples will be larger. On the other hand, urines samples won't be collected for subjects for newborn categories.
Group 2 includes individuals with a known pathology whose diagnosis is confirmed by a genetic test (genetic variant(s) considered as pathogenic or likely pathogenic according to American College of Human Genetics classification) or a biochemical reference test associated to a typical clinical feature. For autism spectrum disorders, recruited patients should be included according to the Diagnostic and Statistical Manual (DSM) V classification. Subgroups are defined according to the pathology affecting each individual (classification according to the affected gene, mode of inheritance and type of mutation); the primarily target pathologies considered are genetic metabolic diseases, but other rare diseases and autism spectrum disorders are also targeted.
Group 3 includes individuals for whom an rare disease is suspected based on the clinical data but not confirmed (including patients for whom the genetic and biochemical tests are still in progress or inconclusive). If the status of an individual changes before the end of the study after the confirmation of a diagnosis communicated anonymously by the recruiting co-investigator (due to results obtained independently outside the study) or due to the results of the study (specific biomarkers and/or scoring and/or genetic confirmation using second tier testing) the subject will then be transferred to its corresponding group 2. These patients will be recruited in all the participating centers.
Inclusion Criteria:
Exclusion Criteria:
Etdclin.Busard@saintluc.uclouvain.be02/7646836
Brussels, B-1020, Belgium