Metabolic Profiling of Neuromuscular Diseases (MetabNMD) – subproject SMA

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Other
• If ‘other’, further details on the scope of the study:
  Biomarker discovery

Non-interventional study

• Non-interventional study design:
  Other
• Non-interventional study design, other:
  Intensive monitoring schemes

Study drug and medical condition

• Medical condition to be studied:
  Spinal muscular atrophy

Population studied

• Age groups:
  • Term newborn infants (0 – 27 days)
  • Infants and toddlers (28 days – 23 months)
  • Children (2 to < 12 years)
  • Adolescents (12 to < 18 years)
  • Adults (18 to < 46 years)
  • Adults (46 to < 65 years)
  • Adults (65 to < 75 years)
• Estimated number of subjects:
  300

Study design details

• Main study objective:
  To identify specific disease-defining metabolic profiles for SMA in urine, blood and CSF before and under therapy with Nusinersen (MetabNMD)
• Outcomes:
  1) to establish a prediction model of disease severity for SMA based on metabolic profiles providing “metabotype-phenotype” correlations and to inform about timepoints of therapy initiation2) to establish a biochemical tool for therapeutic monitoring of SMA under antisense-oligonucleotide therapy, gene therapy and further in the future approved therapies
• Data analysis plan:
  Variables: diagnosis, age, gender, weight, motoric function measures (HFSME, RULM, CHOP-INTENT, 6-MWT).Descriptive statistics: variables will be summarized according to their measurement scale. For continuous variables, mean, median, min, max, quantiles and sd will be computed, for discreet variables frequency counts and percent will be provided.Analysis of basic NMR profiles with TopSpin software (provided by Bruker BioSpin GmbH) according to the publication of Dumas, M.E. and Davidovic, L. Parametric tests and models (e.g. t-Test, ANOVA and regression models) will be used to analyze continuous outcome variables with respect to various predictor variables. When parametric models are inappropriate (e.g. due to large skewness of outcome variables), a non-parametric pendant will be chosen, e.g. Mann-Whitney Test or aligned rank transform (ART) ANOVA. Count data will be analyzed with log-linear models. Multivariate statistical methods: Principal Component Analysis