Sickle Cell DisordersGenoMed4ALLNCT06019208
GenoMed4ALL: Improving SCD Classification and Prognosis by AI
An observational study of Sickle Cell Disorders, run by Hospital Universitari Vall d'Hebron Research Institute at 5 sites in 4 countries.
Trial summary
GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by… Read more about this study
- Who is it for?
- Ages 1 year and older
- Conditions the study is for
- Sickle Cell Disorders
- How many people
- 1,000 participants
- Where
- 5 sitesin 4 countries
- Study dates
- 1 Jan 2021to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients affected by SCD
Who may be able to join
- Patients older than 1 year, diagnosed with SCD, all genotypes.
Who cannot join
- Patients treated with stem cell transplant or gene therapy.
- Patients younger than 1 year old.
Trial locations and contacts
5 sites in 4 countries.
5 sites in 4 countries
Spain1 site
Hospital Universitari Vall d'Hebron Research Institute
Barcelona, 08035
About this study
Official title: Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases
GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by the identification of biomarkers and the development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic, clinical data and other "-omics" health through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models and standardised interoperable sharing of cross-border data, without needing to directly share any sensitive clinical patients' data. The SCD Use case will gather multi-modal clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The Netherlands.
In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, GA101157011), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform.
Read the detailed description
SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited, caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme phenotypic variability that is incompletely understood. Several genetic and environmental factors are supposed to have an impact on disease phenotype, clinical manifestations, progression of organ damage and quality of life throughout the lifespan.
Although significant progress has been made over the past few decades in the highly complex pathophysiology of SCD, the possibility of personalised medicine is still in its infancy. There is a lack of markers of disease severity, prognosis, and response to treatment. In particular, the heterogeneity of clinical expression of the disease along with long-term chronic complications due to the increased lifespan of patients should be addressed by innovative and personalised treatments. Furthermore, assessing the role of the novel treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio are required. The scarcity and fragmentation of SCD data prevent researchers from reaching the critical numbers needed for basic and clinical research. Research and data-driven solutions are therefore essential to improve the care of SCD patients and their quality of life.
The availability of numerous treatment options as well as the high disease heterogeneity highlight the need to address patients' severity profiles and offer the best care for each affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great importance for the in-depth characterization and prediction of the diverse complications of SCD. The primary endpoints of interest include:
- Improving SCD classification
- Develop a probability score to predict various patterns recognized by Artificial Intelligence (AI) based analyzing brain magnetic resonance imaging (Radiomics)
- To develop predictive risk scores for the occurrence of most prevalent and severe clinical outcomes
- To develop predictive risk scores over time for the appearance of most prevalent and severe clinical outcomes.
RADeep will be used for standardization of existing clinical and laboratory data. A CRF was developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work performed by RADeep and includes the "set of common data elements for rare disease registration", which was released in December 2017 as result of a dedicated working group facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability with other similar initiatives in Europe and will also enable the collected data to be reused for future research studies.
Genome-wide Association Studies (GWAS) extends the concept of association studies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a cost-effective way to explore genetic variants across the whole genome. But despite considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS searched for genetic linkage and association with HbF levels, an established ameliorating factor of disease severity. Addintionally, the utilization of data science and artificial intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data combined with the use of the most recent imputation panel for imputation offers an opportunity for the development of novel AI techniques and for novel discoveries in SCD.
Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25% of children by the age of 6 and 40% by the age of 18 with consequences on cognition, schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI on MRIs, secondly, to correlate imaging data with other types of OMICS data in order to predict risk of occurrence and recurrence.
The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal, regardless of genotype. Several studies reported some associations between the degree of impairment of RBC deformability measured at steady state in SCD patients and the presence of chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently developed technique of oxygen gradient ektacytometry allows for a more comprehensive functional characterization and rheological behavior of SCD RBCs over a range of oxygen tensions to test whether the rheological changes could reflect clinical severity/complications.Data on rheological characteristics of RBC on all patients in steady state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca) ektacytometer (RR Mechatronics).
By combining a large amount standardized multimodal (clinical, multi-omics, and imaging) datasets, the investigators hypothesize that AI will allow to understand better SCD biology and classification, enhance prognostic/predictive capacity of currently available tools and apply treatments in a more targeted way, thus facilitating the implementation of personalized medicine program across EU.
- Conditions
- Sickle Cell Disorders
- Keywords
- Artificial Intelligence, Hematological diseases, GWAS, Personalized medicine, Prognosis, Disease classification, Sickle cell disease, Radiomics, Metabolomics, Clinical unmet needs
- Medical subject headings
- Anemia, Sickle Cell, Hematologic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| GENOMED4ALL - SCD patients | Non transplanted SCD patients aged over 1 year. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Improving SCD classification
To improve classification of SCD by integrating clinical and hematological information with genomic features. To address this issue, different methods of statistical learning (Dirichlet processes (DP), Bayesian networks (BN)) and machine learning (deep learning physics informed neural network, constrained regression and deep models) will be compared in order to define specific genotype-phenotype correlations and to develop a new disease classification.
Measured: through study completion, an average of 2 years
Improve diagnosis of cerebrovascular complications.
Develop an artificial intelligence algorithm for early diagnosis of silent infarcts by analyzing brain magnetic resonance imaging (Radiomics).
Measured: through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2023
- Study start1 Jan 2021
- Primary completion30 Sep 2023
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Sickle Cell DisordersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital Universitari Vall d'Hebron Research Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Federico Alvarez, Principal investigator, Universidad Politecnica de MadridGastone Castellani, Principal investigator, University of BolognaRaffaella Colombatti, Principal investigator, University of PadovaEduard van Beers, Principal investigator, UMC UtrechtMarianne de Montalembert, Principal investigator, APHP NeckerPablo Bartolucci, Principal investigator, APHP Henri MondorTiziana Sanavia, Principal investigator, University of TorinoPetros Kountouris, Principal investigator, Cyprus Institute of Neurology and Genetics
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06019208
- Sponsor's protocol ID
- 928338
- Acronym
- GenoMed4ALL
- Status last verified
- Jun 2023
- First submitted
- 15 Jun 2023
- Last updated
- 12 Apr 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Aguilar Martinez P, Angastiniotis M, Eleftheriou A, Gulbis B, Manu Pereira Mdel M, Petrova-Benedict R, Corrons JL. Haemoglobinopathies in Europe: health & migration policy perspectives. Orphanet J Rare Dis. 2014 Jul 1;9:97. doi: 10.1186/1750-1172-9-97.
PubMed 24980780INGRAM VM. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim Biophys Acta. 1959 Dec;36:402-11. doi: 10.1016/0006-3002(59)90183-0. No abstract available.
PubMed 13852872Bunn HF. Pathogenesis and treatment of sickle cell disease. N Engl J Med. 1997 Sep 11;337(11):762-9. doi: 10.1056/NEJM199709113371107. No abstract available.
PubMed 9287233Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP. Sickle cell disease. Nat Rev Dis Primers. 2018 Mar 15;4:18010. doi: 10.1038/nrdp.2018.10.
PubMed 29542687Steinberg MH, Sebastiani P. Genetic modifiers of sickle cell disease. Am J Hematol. 2012 Aug;87(8):795-803. doi: 10.1002/ajh.23232. Epub 2012 May 28.
PubMed 22641398Alapan Y, Fraiwan A, Kucukal E, Hasan MN, Ung R, Kim M, Odame I, Little JA, Gurkan UA. Emerging point-of-care technologies for sickle cell disease screening and monitoring. Expert Rev Med Devices. 2016 Dec;13(12):1073-1093. doi: 10.1080/17434440.2016.1254038. Epub 2016 Nov 22.
PubMed 27785945Bao EL, Lareau CA, Brugnara C, Fulcher IR, Barau C, Moutereau S, Habibi A, Badaoui B, Berkenou J, Bartolucci P, Galacteros F, Platt OS, Mahaney M, Sankaran VG. Heritability of fetal hemoglobin, white cell count, and other clinical traits from a sickle cell disease family cohort. Am J Hematol. 2019 May;94(5):522-527. doi: 10.1002/ajh.25421. Epub 2019 Feb 6.
PubMed 30680775Thein SL, Menzel S, Peng X, Best S, Jiang J, Close J, Silver N, Gerovasilli A, Ping C, Yamaguchi M, Wahlberg K, Ulug P, Spector TD, Garner C, Matsuda F, Farrall M, Lathrop M. Intergenic variants of HBS1L-MYB are responsible for a major quantitative trait locus on chromosome 6q23 influencing fetal hemoglobin levels in adults. Proc Natl Acad Sci U S A. 2007 Jul 3;104(27):11346-51. doi: 10.1073/pnas.0611393104. Epub 2007 Jun 25.
PubMed 17592125Thein SL. Genetic modifiers of the beta-haemoglobinopathies. Br J Haematol. 2008 May;141(3):357-66. doi: 10.1111/j.1365-2141.2008.07084.x.
PubMed 18410570Rab MAE, van Oirschot BA, Bos J, Merkx TH, van Wesel ACW, Abdulmalik O, Safo MK, Versluijs BA, Houwing ME, Cnossen MH, Riedl J, Schutgens REG, Pasterkamp G, Bartels M, van Beers EJ, van Wijk R. Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. Am J Hematol. 2019 May;94(5):575-584. doi: 10.1002/ajh.25443. Epub 2019 Mar 8.
PubMed 30784099Collado A, Boaro MP, van der Veen S, Idrizovic A, Biemond BJ, Beneitez Pastor D, Ortuno A, Cela E, Ruiz-Llobet A, Bartolucci P, de Montalembert M, Castellani G, Biondi R, Manara R, Sanavia T, Fariselli P, Kountouris P, Kleanthous M, Alvarez F, Zazo S, Colombatti R, van Beers EJ, Manu-Pereira MDM. Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet. Hemasphere. 2023 Feb 22;7(3):e844. doi: 10.1097/HS9.0000000000000844. eCollection 2023 Mar. No abstract available.
PubMed 36844183
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases
- ClinicalTrials.gov ID
- NCT06019208
- Acronym / sponsor ID
- GenoMed4ALL / 928338
- Conditions
- Sickle Cell Disorders
- Keywords
- Artificial Intelligence, Hematological diseases, GWAS, Personalized medicine, Prognosis, Disease classification, Sickle cell disease, Radiomics
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Hospital Universitari Vall d'Hebron Research InstituteUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 30 Sep 2023 (actual)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 31 Aug 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by the identification of biomarkers and the development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic, clinical data and other "-omics" health through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models and standardised interoperable sharing of cross-border data, without needing to directly share any sensitive clinical patients' data. The SCD Use case will gather multi-modal clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The Netherlands.
In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, GA101157011), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform.
Detailed description
SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited, caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme phenotypic variability that is incompletely understood. Several genetic and environmental factors are supposed to have an impact on disease phenotype, clinical manifestations, progression of organ damage and quality of life throughout the lifespan.
Although significant progress has been made over the past few decades in the highly complex pathophysiology of SCD, the possibility of personalised medicine is still in its infancy. There is a lack of markers of disease severity, prognosis, and response to treatment. In particular, the heterogeneity of clinical expression of the disease along with long-term chronic complications due to the increased lifespan of patients should be addressed by innovative and personalised treatments. Furthermore, assessing the role of the novel treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio are required. The scarcity and fragmentation of SCD data prevent researchers from reaching the critical numbers needed for basic and clinical research. Research and data-driven solutions are therefore essential to improve the care of SCD patients and their quality of life.
The availability of numerous treatment options as well as the high disease heterogeneity highlight the need to address patients' severity profiles and offer the best care for each affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great importance for the in-depth characterization and prediction of the diverse complications of SCD. The primary endpoints of interest include:
- Improving SCD classification
- Develop a probability score to predict various patterns recognized by Artificial Intelligence (AI) based analyzing brain magnetic resonance imaging (Radiomics)
- To develop predictive risk scores for the occurrence of most prevalent and severe clinical outcomes
- To develop predictive risk scores over time for the appearance of most prevalent and severe clinical outcomes.
RADeep will be used for standardization of existing clinical and laboratory data. A CRF was developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work performed by RADeep and includes the "set of common data elements for rare disease registration", which was released in December 2017 as result of a dedicated working group facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability with other similar initiatives in Europe and will also enable the collected data to be reused for future research studies.
Genome-wide Association Studies (GWAS) extends the concept of association studies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a cost-effective way to explore genetic variants across the whole genome. But despite considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS searched for genetic linkage and association with HbF levels, an established ameliorating factor of disease severity. Addintionally, the utilization of data science and artificial intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data combined with the use of the most recent imputation panel for imputation offers an opportunity for the development of novel AI techniques and for novel discoveries in SCD.
Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25% of children by the age of 6 and 40% by the age of 18 with consequences on cognition, schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI on MRIs, secondly, to correlate imaging data with other types of OMICS data in order to predict risk of occurrence and recurrence.
The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal, regardless of genotype. Several studies reported some associations between the degree of impairment of RBC deformability measured at steady state in SCD patients and the presence of chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently developed technique of oxygen gradient ektacytometry allows for a more comprehensive functional characterization and rheological behavior of SCD RBCs over a range of oxygen tensions to test whether the rheological changes could reflect clinical severity/complications.Data on rheological characteristics of RBC on all patients in steady state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca) ektacytometer (RR Mechatronics).
By combining a large amount standardized multimodal (clinical, multi-omics, and imaging) datasets, the investigators hypothesize that AI will allow to understand better SCD biology and classification, enhance prognostic/predictive capacity of currently available tools and apply treatments in a more targeted way, thus facilitating the implementation of personalized medicine program across EU.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| GENOMED4ALL - SCD patients | – | –Non transplanted SCD patients aged over 1 year. |
Who may qualify
- Ages
- 1 year and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients affected by SCD
Key inclusion criteria (1 listed)
- Patients older than 1 year, diagnosed with SCD, all genotypes.
Key exclusion criteria (2 listed)
- Patients treated with stem cell transplant or gene therapy.
- Patients younger than 1 year old.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Improving SCD classification
Time frame: through study completion, an average of 2 years
To improve classification of SCD by integrating clinical and hematological information with genomic features. To address this issue, different methods of statistical learning (Dirichlet processes (DP), Bayesian networks (BN)) and machine learning (deep learning physics informed neural network, constrained regression and deep models) will be compared in order to define specific genotype-phenotype correlations and to develop a new disease classification.
Improve diagnosis of cerebrovascular complications.
Time frame: through study completion, an average of 2 years
Develop an artificial intelligence algorithm for early diagnosis of silent infarcts by analyzing brain magnetic resonance imaging (Radiomics).
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 2024.
- Background: PubMed 24980780Aguilar Martinez P, Angastiniotis M, Eleftheriou A, Gulbis B, Manu Pereira Mdel M, Petrova-Benedict R, Corrons JL. Haemoglobinopathies in Europe: health & migration policy perspectives. Orphanet J Rare Dis. 2014 Jul 1;9:97. doi: 10.1186/1750-1172-9-97.
- Background: PubMed 13852872INGRAM VM. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim Biophys Acta. 1959 Dec;36:402-11. doi: 10.1016/0006-3002(59)90183-0. No abstract available.
- Background: PubMed 9287233Bunn HF. Pathogenesis and treatment of sickle cell disease. N Engl J Med. 1997 Sep 11;337(11):762-9. doi: 10.1056/NEJM199709113371107. No abstract available.
- Background: PubMed 29542687Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP. Sickle cell disease. Nat Rev Dis Primers. 2018 Mar 15;4:18010. doi: 10.1038/nrdp.2018.10.
- Background: PubMed 22641398Steinberg MH, Sebastiani P. Genetic modifiers of sickle cell disease. Am J Hematol. 2012 Aug;87(8):795-803. doi: 10.1002/ajh.23232. Epub 2012 May 28.
- Background: PubMed 27785945Alapan Y, Fraiwan A, Kucukal E, Hasan MN, Ung R, Kim M, Odame I, Little JA, Gurkan UA. Emerging point-of-care technologies for sickle cell disease screening and monitoring. Expert Rev Med Devices. 2016 Dec;13(12):1073-1093. doi: 10.1080/17434440.2016.1254038. Epub 2016 Nov 22.
- Background: PubMed 30680775Bao EL, Lareau CA, Brugnara C, Fulcher IR, Barau C, Moutereau S, Habibi A, Badaoui B, Berkenou J, Bartolucci P, Galacteros F, Platt OS, Mahaney M, Sankaran VG. Heritability of fetal hemoglobin, white cell count, and other clinical traits from a sickle cell disease family cohort. Am J Hematol. 2019 May;94(5):522-527. doi: 10.1002/ajh.25421. Epub 2019 Feb 6.
- Background: PubMed 17592125Thein SL, Menzel S, Peng X, Best S, Jiang J, Close J, Silver N, Gerovasilli A, Ping C, Yamaguchi M, Wahlberg K, Ulug P, Spector TD, Garner C, Matsuda F, Farrall M, Lathrop M. Intergenic variants of HBS1L-MYB are responsible for a major quantitative trait locus on chromosome 6q23 influencing fetal hemoglobin levels in adults. Proc Natl Acad Sci U S A. 2007 Jul 3;104(27):11346-51. doi: 10.1073/pnas.0611393104. Epub 2007 Jun 25.
- Background: PubMed 18410570Thein SL. Genetic modifiers of the beta-haemoglobinopathies. Br J Haematol. 2008 May;141(3):357-66. doi: 10.1111/j.1365-2141.2008.07084.x.
- Background: PubMed 30784099Rab MAE, van Oirschot BA, Bos J, Merkx TH, van Wesel ACW, Abdulmalik O, Safo MK, Versluijs BA, Houwing ME, Cnossen MH, Riedl J, Schutgens REG, Pasterkamp G, Bartels M, van Beers EJ, van Wijk R. Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. Am J Hematol. 2019 May;94(5):575-584. doi: 10.1002/ajh.25443. Epub 2019 Mar 8.
- Background: PubMed 36844183Collado A, Boaro MP, van der Veen S, Idrizovic A, Biemond BJ, Beneitez Pastor D, Ortuno A, Cela E, Ruiz-Llobet A, Bartolucci P, de Montalembert M, Castellani G, Biondi R, Manara R, Sanavia T, Fariselli P, Kountouris P, Kleanthous M, Alvarez F, Zazo S, Colombatti R, van Beers EJ, Manu-Pereira MDM. Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet. Hemasphere. 2023 Feb 22;7(3):e844. doi: 10.1097/HS9.0000000000000844. eCollection 2023 Mar. No abstract available.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Federico Alvarez principal investigator
Universidad Politecnica de Madrid
Gastone Castellani principal investigator
University of Bologna
Raffaella Colombatti principal investigator
University of Padova
5 sites in 4 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sickle Cell Disorders, run by Hospital Universitari Vall d'Hebron Research Institute at 5 sites in 4 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in France, Italy, Netherlands, Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Improving SCD classification (time frame: through study completion, an average of 2 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06019208, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Apr 2024.