Related Experiment Video
Updated: Jul 16, 2025
Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice
Published on: October 13, 2023
Multi-Criterial Model for Weighting Biological Risk Factors in Multiple Sclerosis: Clinical and Health Insurance
Roberto De Masi1,2, Stefania Orlando2, Chiara Leo3
1Complex Operative Unit of Neurology, "F. Ferrari" Hospital, Casarano, 73042 Lecce, Italy.
Abstract:
The etiology of Multiple Sclerosis (MS) remains undetermined. Its pathogenic risk factors are thought to play a negligible role individually in the development of the disease, instead assuming a pathogenic role when they interact with each other. Unfortunately, the statistical weighting of this pathogenic role in predicting MS risk is currently elusive, preventing clinical and health insurance applications. Here, we aim to develop a population-based multi-criterial model for weighting biological risk factors in MS; also, to calculate the individual MS risk value useful for health insurance application. Accordingly, among 596 MS patients retrospectively assessed at the time of diagnosis, the value of vitamin D < 10 nm/L, BMI (Body Mass Index) < 15 Kg/m2 and >30 Kg/m2, female sex, degree of family kinship, and the range of age at onset of 20-45 years were considered as biological risk factors for MS. As a result, in a 30-year-old representative patient having a BMI of 15 and second degree of family kinship for MS, the major developmental contributor for disease is the low vitamin D serum level of 10 nm/L, resulting in an MS risk of 0.110 and 0.106 for female and male, respectively. Furthermore, the Choquet integral applied to uncertain variables, such as biological risk factors, evidenced the family kinship as the main contributor, especially if coincident with the others, to the MS risk. This model allows, for the first time, for the risk stratification of getting sick and the application of the health insurance in people at risk for MS.
Insights
This study developed a model to assess Multiple Sclerosis (MS) risk by weighting interacting biological factors. Low vitamin D levels significantly contribute to MS risk, enabling personalized risk stratification and health insurance applications.
Area of Science:
- Neurology
- Epidemiology
- Biostatistics
Background:
- The exact causes of Multiple Sclerosis (MS) are unknown, with individual risk factors playing a minor role until interacting.
- Quantifying the combined effect of these factors for MS risk prediction is challenging, limiting clinical and insurance use.
Purpose of the Study:
- To create a population-based model for weighting biological risk factors in MS.
- To calculate individual MS risk for health insurance purposes.
Main Methods:
- Retrospective analysis of 596 MS patients at diagnosis.
- Identified risk factors: low vitamin D (<10 nm/L), extreme BMI (<15 or >30 Kg/m²), female sex, family kinship, and age at onset (20-45 years).
- Applied the Choquet integral to model the interplay of these uncertain biological risk factors.
Main Results:
- In a representative 30-year-old patient with BMI 15 and second-degree kinship, low vitamin D (<10 nm/L) was the primary MS risk contributor.
- Calculated MS risks were 0.110 for females and 0.106 for males.
- Family kinship emerged as a major contributor, especially when combined with other factors.
Conclusions:
- A novel multi-criterial model enables risk stratification for Multiple Sclerosis.
- This model facilitates health insurance applications by providing individual MS risk values.
- The study highlights the significant role of interacting biological factors, particularly vitamin D and family history, in MS etiology.
More Related Videos
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:18Factors Affecting Illness
For instance, risk factors are connected to illness,...
01:15Methods of Documentation VI: Case Management Model
For example, a patient with a chronic...
01:21Dimensions of Health and Illness
01:25Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:29Bias in Epidemiological Studies