An Australian-developed tool combining genotype information with clinical and environmental information may improve the accuracy of predicting new relapses and worsening disability in relapsing-onset MS.
Data from 2,858 observations over 10 years in 253 patients in the Ausimmune Longitudinal (AusLong) study was used to compare the predictive accuracy of a Clinical–Env Prognostic Index (CEPI), Genetic Prognostic Index (GPI) and a Clinical–Env–Genotypic Prognostic Index (CEGPI).
The study, published in Brain Communications, found clinical and environmental inputs were major contributors to disease progression.
For example, baseline BMI was borderline predictive of relapses, but had much stronger effects on worsening events.
“Regardless of previous EDSS and CDMS status, each 1 kg/m2 increase in BMI was associated with an 81% increased risk of worsening each year. Moreover, we noted that this effect was persistent up to 10 years post-FDE (0.8110=11% risk), thus rendering it a good clinical marker for long-term prognostication.”
Other predictors of worsening included relapse counts, recent immunisation status, Hospital Anxiety Depression Scale, seasonal changes in hours of sunlight exposure, and income levels.
Vitamin D supplementation and shorter inter-attack intervals reduced the likelihood of worsening each year.
“The CEPIs provided the best discrimination between good and worse prognoses in the first 5 years of clinical symptoms, meanwhile the GPI had a greater effect after 5 years of symptomatic disease.”
“Interestingly, the genetic variants found to be significant in this study also interacted with latitude to increase the risk for worsening symptoms at higher latitudes.”
The study authors said combining the effects of clinical–env predictors, and the genetic variations into a prognostic index improved the overall prediction accuracy in both the short and long term.
“The CEGPIs are capable of discriminating individuals having a poor prognosis (high-risk) from those having a good prognosis (low-risk),” it said.
The researchers, including senior investigator Professor Bruce Taylor from the Menzies Institute for Medical Research (Tas), said the CEGPI might be of most use as a research tool for risk stratification and selection criteria in clinical trials.
“If validated, it may be used as a tool for prognostication at an individual level to identify individuals who need greater surveillance and earlier use of more intensive therapy, and likewise in risk averse individuals. It may also provide some support for lesser interventions in those with a low-risk score,” they wrote.