Amyotrophic Lateral Sclerosis (ALS) prediction model derived from plasma and CSF biomarkers

PLoS One. 2021 Feb 19;16(2):e0247025. doi: 10.1371/journal.pone.0247025. eCollection 2021.

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a degenerative disorder of motor neurons which leads to complete loss of movement in patients. The only FDA approved drug Riluzole provides only symptomatic relief to patients. Early Diagnosis of the disease warrants the importance of diagnostic and prognostic models for predicting disease and disease progression respectively. In the present study we represent the predictive statistical model for ALS using plasma and CSF biomarkers. Forward stepwise (Binary likelihood) Logistic regression model is developed for prediction of ALS. The model has been shown to have excellent validity (94%) with good sensitivity (98%) and specificity (93%). The area under the ROC curve is 99.3%. Along with age and BMI, VEGF (Vascular Endothelial Growth Factor), VEGFR2 (Vascular Endothelial Growth Factor Receptor 2) and TDP43 (TAR DNA Binding Protein 43) in CSF and VEGFR2 and OPTN (Optineurin) in plasma are good predictors of ALS.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amyotrophic Lateral Sclerosis / blood
  • Amyotrophic Lateral Sclerosis / cerebrospinal fluid
  • Amyotrophic Lateral Sclerosis / diagnosis*
  • Biomarkers / blood
  • Biomarkers / cerebrospinal fluid
  • Disease Progression
  • Early Diagnosis
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical*
  • Prognosis
  • ROC Curve

Substances

  • Biomarkers

Grants and funding

The funding for the study was provided to AA with grant number (sanction letter no. No.5/4-5/122Neuro/2013-NCD-I by Indian Council of Medical Research (ICMR). https://main.icmr.nic.in/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.