General Information
Conflict of Interest
Request Clinical Trials
Associated Trial(s): What type of data are you looking for?: Individual Participant-Level Data, which includes Full CSR and all supporting documentationRequest Clinical Trials
Data Request Status
Status: OngoingResearch Proposal
Project Title: Investigation of Biomarkers in NPC
Scientific Abstract:
Background: This Critical Path for Lysosomal Diseases (CPLD) initiative is aimed at tackling the challenges posed by Niemann-Pick disease type C (NPC). Recent clinical trials that have shown promise, yet still have not led to approved therapies, underscore an urgent need for effective biomarkers that can guide drug development to advance through the regulatory approval processes for NPC.
Objective: The overarching question(s) in this project which represents a key component of the CPLD Consortia is whether and to what extent biomarkers values vary across the life course among individuals with NPC.
Study Design: Data are contributed from different organizations and companies around the world. C-Path has extensive experience in building integrated databases for many diseases, including existing rare disease databases.
Participants; All individuals with NPC.
Primary Outcome Measure; Outcomes for biomarker investigations in Niemann-Pick disease (NPD) and other lysosomal storage disorders focus on the utility of biomarkers as indicators of disease progression. In addition, genotype-phenotype relationships: NfL reduction: Improvement in neuronal health and reduced neurodegeneration, Oxysterol levels.
Secondary outcomes: Cognitive and motor function: Improvement or stabilization in neurodegenerative progression. CSF biomarker correlation, seizure burden, neuropsychiatric manifestations, sleep disturbance.
Statistical tests, including ANOVA, Kruskal-Wallis, Pearson/Spearman correlations, and multivariate regression models, clinical outcomes, adjusting for confounders.
Brief Project Background and Statement of Project Significance: The data from the trial will be used to develop an ontological approach for identifying biomarkers for NPC as well as genotype phenotype relationships. Specifically biomarkers that can be linked to neuronal damage in lysosomal storage diseases like oxysterols may correlate with neurodegeneration severity. Alongside these, other biomarkers also indicate lysosomal and neuronal stress, offering a broader picture of neurodegeneration in NPC patients, aiding in the assessment of disease progression and treatment efficacy. Other outcomes include seizure and psychiatric manifestations that may provide insight and underlying mechanisms of CNS involvement.
Specific Aims of the Project:
Objective 1: Characterize Genotypic Heterogeneity and Its Impact on Disease Phenotype
Aims: 1) Stratify patients based on gene variants; 2) Assess genotype-phenotype relationships by analyzing clinical features such as neurological involvement, somatic disease burden, and rate of progression; 3) Determine the prevalence of novel or rare mutations and their potential functional consequences.
Objective 2: Evaluate Longitudinal Clinical Outcomes in Relation to Genotype
Aims: 1) Analyze cognitive, motor, seizure burden, Neuropsychiatric manifestations and Sleep disturbance over time in patients with different genotypic variants;2) Validation of these markers with clinical endpoints to support their use in assessing therapeutic efficacy; 3) Investigate correlations between specific genetic mutations and treatment responses
- Identify potential disease-modifying factors that influence progression within genotypic subgroups.
Objective 3: Identify and Validate Biomarkers for Disease Progression and Treatment Response
Aims:1) Assess oxysterols and other biomarkers; 2) Determine the predictive value of these biomarkers for monitoring disease progression and therapeutic efficacy; 3)Explore biomarker var
Study Design: Individual trial analysis
What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations Participant-level data meta-analysis Meta-analysis using only data from the YODA Project Research on comparison group Research on clinical prediction or risk prediction
Software Used: RStudio
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
AC-056C501 (YODA)
All individuals with a diagnosis of NPC. No exclusion criteria
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Outcomes for biomarker investigations in Niemann-Pick disease (NPD) and other lysosomal storage disorders focus on the utility of biomarkers as indicators of disease progression. In addition, genotype-phenotype relationships, in the context of disease severity will also be explored. Specific outcomes include:
NfL reduction: Improvement in neuronal health and reduced neurodegeneration.
Oxysterol levels: Changes reflecting shifts in lipid metabolism and storage.
Cognitive and motor function: Improvement or stabilization in neurodegenerative progression.
CSF biomarker correlation: Validation of these markers with clinical endpoints to support their use in assessing therapeutic efficacy.
Seizure burden
Neuropsychiatric manifestations
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
biomarkers- particularly fluidic biomarkers linked to CNS involvement
seizure burden
neuropsychiatric manifestations
genotype-phenotype relationship in context of disease severity as evidenced by above variables.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: age of onset, age of diagnosis, clinical outcomes assessments (NPCSS), sex, number of seizure, reported neuropsychiatric manifestation, medications, genotype, variant, disease severity as measured by NPCSS
Statistical Analysis Plan:
Descriptive statistics will be used to summarize patient demographics, genotype distributions, clinical phenotypes, and biomarker levels. Continuous variables (e.g., biomarker concentrations, enzyme activity, clinical severity scores) will be presented as mean +/- standard deviation (or median and interquartile range for non-normally distributed data), while categorical variables (e.g., mutation types, treatment responses) will be expressed as frequencies and percentages. Normality of continuous variables will be assessed using the Shapiro-Wilk test, and appropriate transformations will be applied if needed. To evaluate genotype-phenotype correlations, one-way ANOVA (or Kruskal-Wallis test for non-parametric data) will be used to compare clinical severity scores and biomarker levels across different mutation categories. Post-hoc pairwise comparisons will be conducted using Tukey's test (or Dunn's test for non-parametric data). Pearson or Spearman correlation coefficients will be calculated to assess relationships between IDS mutation types and biomarker concentrations. A multivariate linear regression model will be constructed to determine the independent effect of genotype on clinical severity, adjusting for potential confounders such as age, baseline enzyme activity, and treatment status. Logistic regression will be used to assess the association between mutation type and binary clinical outcomes (e.g., presence/absence of specific disease manifestations). Biomarker performance in predicting disease severity will be evaluated using receiver operating characteristic (ROC) curves, with area under the curve (AUC) values used to assess predictive accuracy. Missing data will be handled using multiple imputation if necessary. Sensitivity analyses will be performed to assess the robustness of findings across different patient subgroups (e.g., early vs. late-onset MPS II). A significance threshold of p < 0.05 will be used for all statistical tests, with Bonferroni correction applied for multiple comparisons where applicable.
Missingness will be dealt with via multiple imputations or exclusion. Multiple imputation will account for the uncertainty of missing data by creating several plausible datasets and combining results across them, preserving the variability and structure of the data. Excluding subjects with missing data (complete case analysis) is simpler but may reduce statistical power and introduce bias if data are not missing completely at random. To statistically test whether there is a difference between groups a mixed-effects model, w Sensitivity analyses will assess robustness, and missing data will be handled using multiple imputation or appropriate statistical techniques. All analyses will be performed using R or SAS. Mediation analysis may also be considered to explore whether biomarkers, linking lysosomal accumulation of cholesterol with neurodegeneration and subsequent cognitive impairment. The SAP will include sensitivity analyses to examine the robustness of these relationships across different patient subgroups (e.g., treatment-naïve versus treated patients) and to assess any non-linear trends. All statistical tests will be two-tailed, with a significance level set at p< 0.05. Missingness will be dealt with via multiple imputations or exclusion. Multiple imputation will account for the uncertainty of missing data by creating several plausible datasets and combining results across them, preserving the variability and structure of the data. Excluding subjects with missing data (complete case analysis) is simpler but may reduce statistical power and introduce bias if data are not missing completely at random. To statistically test whether there is a difference between groups a mixed-effects model, which handles missing data without requiring imputation will be used. Additionally, after imputation, traditional tests like t-tests or ANOVA can be applied, depending on the data structure and hypothesis being tested.
Narrative Summary:
This research request seeks access to completed clinical trial datasets to systematically investigate genotypic heterogeneity, genotype-phenotype relationships, and biomarker discovery in Neimann-Pick Type C (NPC) Specifically, we aim to: (1) stratify patients based on genetic mutations, to assess genetic impact on disease severity and treatment response; (2) analyze longitudinal clinical outcomes (e.g., cognitive function, seizure, swallowing, ambulation) in relation to genotype; and (3) evaluate biochemical biomarkers to determine their predictive value in monitoring disease progression and response to investigational treatments.
Project Timeline:
Months 1--3: Data Integration and Preliminary Exploration Collect and harmonize datasets from clinical trials, patient registries, and biorepositories. Ensure data standardization and quality control (e.g., resolving missing values, aligning variable definitions).Conduct exploratory data analysis (EDA), including summary statistics, distribution checks, and missing data assessment. Identify potential outliers and assess dataset completeness. Define subgroup classifications for genotype-phenotype and disease severity analyses.
Months 4--6: Statistical Modeling and Hypothesis Testing
Week 13--16: Perform univariate and bivariate analyses, including comparisons across genotypic groups and disease severity categories.
Week 17--20: Develop and validate regression and mixed-effects models to examine biomarker-disease relationships over time. Implement adjustments for confounding factors.
Week 21--24: Apply machine learning techniques (if applicable) to identify biomarker clusters or predictive patterns. Conduct sensitivity analyses and finalize statistical models.
Months 7--9: Interpretation and Validation
Week 25--28: Subgroup analysis
Week 29--32: Interpret results in the context of disease mechanisms and clinical relevance. Prepare tables and figures for reporting.
Months 10--12: Reporting
Dissemination Plan: The dissemination plan will include targeted scientific publications, conference presentations, and stakeholder engagement to maximize the impact of findings. Manuscripts will be submitted to high-impact journals in genetics, neurology, and rare disease research, ensuring broad academic reach. Key results will be presented at international conferences such as WORLDSymposium, SSIEM, and relevant lysosomal disease meetings. Collaboration with patient advocacy groups and clinical networks will facilitate knowledge translation to clinicians, researchers, and families. A data-sharing strategy will be implemented through consortium platforms, ensuring accessibility for future research. Additionally, findings will be summarized in white papers, newsletters, and webinars to engage regulatory agencies, industry partners, and the broader scientific community.
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