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Identification of risk features for complication in Gaucher's disease patients: a machine learning analysis of the Spanish registry of Gaucher disease.

ABSTRACT: BACKGROUND:Since enzyme replacement therapy for Gaucher disease (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry has worked since 1993 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from more than 400 patients. The aims of this study were to discover correlations between patients' characteristics at diagnosis and to identify risk features for the development of late complications; for this a machine learning approach involving correlation networks and decision trees analyses was applied. RESULTS:A total of 358 patients, 340 type 1 Gaucher disease and 18 type 3 cases were selected. 18% were splenectomyzed and 39% had advanced bone disease. 81% of cases carried heterozygous genotype. 47% of them were diagnosed before the year 2000. Mean age at diagnosis and therapy were 28 and 31.5?years old (y.o.) respectively. 4% developed monoclonal gammopathy undetermined significance or Parkinson Disease, 6% cancer, and 10% died before this study. Previous splenectomy correlates with the development of skeletal complications and severe bone disease (p?=?0.005); serum levels of IgA, delayed age at start therapy (>?9.5 y.o. since diagnosis) also correlates with severe bone disease at diagnosis and with the incidence of bone crisis during therapy. High IgG (>?1750?mg/dL) levels and age over 60 y.o. at diagnosis were found to be related with the development of cancer. When modelling the decision tree, patients with a delayed diagnosis and therapy were the most severe and with higher risk of complications. CONCLUSIONS:Our work confirms previous observations, highlights the importance of early diagnosis and therapy and identifies new risk features such as high IgA and IgG levels for long-term complications.

SUBMITTER: Andrade-Campos MM

PROVIDER: S-EPMC7507684 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identification of risk features for complication in Gaucher's disease patients: a machine learning analysis of the Spanish registry of Gaucher disease.

Andrade-Campos Marcio M MM de Frutos Laura López LL Cebolla Jorge J JJ Serrano-Gonzalo Irene I Medrano-Engay Blanca B Roca-Espiau Mercedes M Gomez-Barrera Beatriz B Pérez-Heredia Jorge J Iniguez David D Giraldo Pilar P

Orphanet journal of rare diseases 20200922 1

<h4>Background</h4>Since enzyme replacement therapy for Gaucher disease (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry has worked since 1993 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from ...[more]

PMID: 32962737

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Project description:BackgroundThe enzymatic replacement therapy (ERT) availability for Gaucher disease (GD) has changed the landscape of the disease, several countries have screening programs. These actions have promoted the early diagnosis and avoided many complications in pediatric patients. In Spain ERT has been available since 1993 and 386 patients have been included in the Spanish Registry of Gaucher Disease (SpRGD). The aim of this study is to analyze the impact of ERT on the characteristics at time of diagnosis and initial complications in pediatric Gaucher disease patients.AimTo analyze the impact of ERT on the characteristics at time of diagnosis and initial complications in pediatric Gaucher disease patients.MethodsA review of data in SpRGD from patients' diagnosed before 18 years old was performed. The cohort was split according the year of diagnosis (≤1994, cohort A; ≥1995, cohort B).ResultsA total of 98 pediatric patients were included, GD1: 80, GD3: 18; mean age: 7.2 (0.17-16.5) years, 58 (59.2%) males and 40 (40.8%) females. Forty-five were diagnosed ≤ 1994 and 53 ≥ 1995. Genotype: N370S/N370S: 2 (2.0%), N370S/L444P: 27 (27.5%), N370S/other: 47 (48%), L444P/L444P: 7 (7.1%), L444P/D409H: 2 (2.0%), L444P/other: 3 (6.2%), other/other: 10 (10.2%). The mean age at diagnosis was earlier in patients diagnosed after 1995 (p < 0.001) and different between the subtypes, GD1: 8.2 (0.2-16.5) years and GD3: 2.8 (0.17-10.2) years (p < 0.001). There were more severe patients in the group diagnosed before 1994 (p = 0.045) carrying L444P (2), D409H (2), G377S (1), G195W (1) or the recombinant mutation. The patients' diagnosed ≤1994 showed worse cytopenias, higher chance of bone vascular complications at diagnosis and previous spleen removal. The patients started ERT at a median time after diagnosis of 5.2 years [cohort A] and 1.6 years [cohort B] (p < 0.001).ConclusionsThe early diagnosis of Gaucher disease in the era of ERT availability has permitted to reduce the incidence of severe and irreversible initial complication in pediatric patients, and this has permitted better development of these patients. This is the largest pediatric cohort from a national registry.

Project description:BackgroundGaucher's disease is associated with a high variety of structural and functional abnormalities in the eye, which do not always affect visual acuity. The purpose of this study was to analyse ocular features in Spanish patients with Gaucher's disease type I, and to investigate their possible correlation with phenotypic and burden parameters of this entity.MethodsThis cross-sectional observational study compared parameters belonging to 18 eyes from 9 Spanish patients with Gaucher's disease Type I with 80 eyes from 40 healthy controls. Complete ophthalmological examination included choroidal and retinal thickness maps with swept source optical coherence tomography. Systemic analysis included genotype, plasmatic biomarkers, [ferritin, chemokine ligand 18 (CCL18) and chitotriosidase (ChT)] and severity scoring systems results ["Gaucher Disease Severity Score Index Type I" (GauSSI-I) and "Gaucher disease severity scoring system" (GD-DS3)].ResultsNine subjects (18 eyes) were cases (female: 55.5%, mean age 45 years; male: 44.5%, mean age 36 years) and 40 subjects (80 eyes) were controls (female: 49%, mean age 50 years; male: 51%, mean age 55 years). There were no statistically significant differences when comparing ocular parameters (visual acuity; axial length, refractive errors, corneal parameters, lens, retinal and choroidal thickness) between case and control subjects (p>0.05). A statistically significant moderate correlation was observed between lower retinal thickness and choroidal quadrants thickness and greater disease severity scores. A lower central retinal thickness also correlates with higher biological plasmatic levels, and has a statistically significant association with the most affected patient with genotype N370S/Del 55pb. Conversely, higher pachymetry involves a more severe plasmatic concentration of biomarkers.ConclusionsOur results suggest that pachymetry, and retinal and choroidal thickness, are associated with burden biomarkers and disease severity index scores in Spanish patients with Gaucher's disease Type I.

Project description:ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Project description:BackgroundCuproptosis, a new cell death mode, is majorly modulated by mitochondrial metabolism and protein lipoylation. Nonetheless, cuproptosis-related genes (CRGs) have not yet been thoroughly studied for their clinical significance and relationship with the immune microenvironment in inflammatory bowel disease (IBD).MethodsWe screened CRGs that had a significant correlation with immune status, which was determined utilizing single-sample GSEA (ssGSEA) and Gene Expression Omnibus datasets (GSE75214). Furthermore, utilizing the R package "CensusClusterPlus", these CRGs' expression was used to obtain different patient clusters. Subsequently, gene-set enrichment analysis (GSEA), gene set variation analysis (GSVA), and CIBERSORT assessed the variations in the enrichment of gene function and the abundance of immune cell infiltration and immune functions across these clusters. Additionally, weighted gene co-expression network analysis (WGCNA) and analysis of differentially expressed genes (DEGs) were executed, and for the purpose of identifying hub genes between these clusters, the construction of protein-protein interaction (PPI) network was done. Lastly, we used the GSE36807 and GSE10616 datasets as external validation cohorts to validate the immune profiles linked to the expression of CRG. ScRNA-seq profiling was then carried out using the publicly available dataset to examine the CRGs expression in various cell clusters and under various conditions.ResultsThree CRGs, PDHA1, DLD, and FDX1, had a significant association with different immune profiles in IBD. Patients were subsequently classified into two clusters: low expression levels of DLD and PDHA1, and high expression levels of FDX1 were observed in Cluster 1 compared to Cluster 2. According to GSEA, Cluster 2 had a close association with the RNA processes and protein synthesis whereas Cluster 1 was substantially linked to environmental stress response and metabolism regulations. Furthermore, Cluster 2 had more immune cell types, which were characterized by abundant memory B cells, CD4+ T memory activated cells, and follicular helper T cells, and higher levels of immune-related molecules (CD44, CD276,CTLA4 and ICOS) than Cluster 1. During the analysis, the PPI network was divided into three significant MCODEs using the Molecular Complex Detection (MCODE) algorithm. The three MCODEs containing four genes respectively were linked to mitochondrial metabolism, cell development, ion and amino acid transport. Finally, external validation cohorts validated these findings, and scRNA-seq profiling demonstrated diverse intestinal cellular compositions with a wide variation in CRGs expression in the gut of IBD patients.ConclusionsCuproptosis has been implicated in IBD, with PDHA1, DLD, and FDX1 having the potential as immune biomarkers and therapeutic targets. These results offer a better understanding of the development of precise, dependable, and cutting-edge diagnosis and treatment of IBD.

Project description:Investigate the incidence of Parkinsonism among patients with Gaucher disease type 1 (GD1) and describe demographics, genotypes, and Gaucher disease (GD)-related characteristics for affected and non-affected patients.Cohort study with age- and gender-matched nested case-control analysis. Calculation of event incidence, standardized morbidity ratio, and event-free survival (Kaplan-Meier).The International Collaborative Gaucher Group (ICGG) Gaucher Registry data as of June 2010. Study cohort: GD1 patients with any report of Parkinsonism. Pre-matching control group: All GD1 patients with no report of Parkinsonism.The matched study cohort comprised of 68 patients with reports of Parkinsonism and 649 patients without Parkinsonism. Demographic and clinical characteristics suggest a milder GD phenotype in patients with Parkinsonism compared to the control group. The most prevalent GD1 genotype was N370S/N370S (39% for controls; 46% for patients with Parkinsonism). Patients with Parkinsonism were diagnosed with GD1 at a mean age of 37 years compared to 31 years in control patients. The standardized morbidity ratio for the development of Parkinsonism among all GD1 patients indicated an approximately 6 to 17 fold increase over that of 2 reference populations. The mean age of reported Parkinsonism onset was 57 years compared to 60 years in the general population (Lees, Hardy, and Revesz, 2009 [1]). The probability that a patient with GD1 will develop Parkinsonism before age 70 years is 5 to 7% and 9 to 12% before age 80 years.The incidence of Parkinsonism among GD1 patients is significantly increased compared to two reference populations. GD1 patients with Parkinsonism have a later median age at GD diagnosis, later age at the start of treatment, and later age at death than patients with GD1 alone. The Gaucher-related clinical profile of GD1 patients with Parkinsonism is similar to or milder than the GD1 alone group. Therefore, severity of the common GD1 clinical manifestations does not appear to be predictive for the onset of Parkinsonism.

Dataset Information

Identification of risk features for complication in Gaucher's disease patients: a machine learning analysis of the Spanish registry of Gaucher disease.

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Identification of risk features for complication in Gaucher's disease patients: a machine learning analysis of the Spanish registry of Gaucher disease.

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