Metabolic Vulnerability Index for Premature Mortality Prediction
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Solution Overview
Problem
Traditional cardiovascular disease (CVD) risk calculators fail to accurately predict premature death, as they rely on composite outcomes that do not differentiate between fatal and non-fatal events, and do not account for the complex pathophysiology of various disease states, leading to inadequate assessment of mortality risk in patients.
Innovation Solution
A method and system using NMR analysis to evaluate a person's relative risk of premature all-cause mortality by calculating a Metabolic Vulnerability Index (MVX) score based on measurements such as small HDL particles, GlycA, branched-chain amino acids, ketone bodies, and optionally citrate and serum protein, through defined mathematical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CVD risk calculators are used to predict CVD outcomes, then composite CVD event prediction is achieved, but the ability to predict premature death accurately is compromised
Solution Approach 1:
The patent segments the CVD outcome into two distinct components: fatal CVD events and non-fatal CVD events. By separating these components, the risk calculator can independently predict and assess the risk of premature death from CVD, rather than relying on a composite outcome that obscures the fatal component. This segmentation allows for more accurate prediction of premature death while maintaining model clarity.
Solution Approach 2:
The patent extracts the fatal CVD event component from the composite CVD outcome. By taking out the fatal event data separately, the model can focus specifically on predicting premature death without the diluting effect of non-fatal events. This extraction enables the development of a risk calculator that accurately predicts mortality risk while avoiding the complexity of trying to predict all CVD outcomes uniformly.
2Measurement precision
If composite CVD outcomes are used as study endpoints, then clinical trial analysis is simplified, but the ability to detect heterogeneous drug effects on fatal and non-fatal components is lost
Solution Approach 1:
The patent applies segmentation to clinical trial outcomes by dividing the composite CVD endpoint into fatal and non-fatal components. This allows researchers to analyze drug effects on mortality separately from non-fatal events, enabling detection of heterogeneous drug effects that would be masked in a composite outcome analysis. The segmented approach maintains analytical precision while remaining compatible with standard clinical trial methodologies.
Solution Approach 2:
The patent introduces an intermediary analysis framework that allows simultaneous examination of both composite and component outcomes. This intermediary approach enables researchers to maintain the simplicity of composite outcome analysis while also extracting and analyzing the fatal and non-fatal components separately, thus detecting heterogeneous drug effects without sacrificing productivity.
3Reliability
If traditional CVD risk factors are used, then ease of data collection is maintained, but the ability to predict mortality risk in patients with chronic diseases is compromised
Solution Approach 1:
The patent changes the parameters used in risk assessment by incorporating disease-specific markers and chronic disease status as independent variables. This parameter modification enables accurate mortality risk prediction in patients with chronic diseases while maintaining the simplicity of the overall assessment framework. The updated parameters allow the model to account for reverse epidemiology effects in chronic disease populations without complicating the implementation process.
Solution Approach 2:
The patent introduces dynamic adjustment capabilities that allow the risk calculator to adapt to different patient populations, particularly those with chronic diseases. The model can dynamically modify risk factor weights and thresholds based on disease status, enabling accurate mortality prediction across diverse patient groups while maintaining ease of implementation through automated adjustment mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The MVX score effectively stratifies mortality risk, providing a more accurate prediction of premature death risk independent of traditional CVD risk factors, allowing for better assessment and management of cardiovascular mortality.
Implementation Method 1
measuring at least one NMR signal for a defined GlycA fitting region of NMR spectra associated with GlycA of a blood plasma or serum specimen
Data Source
AI summary
Disclosed are methods and systems to determine a subject's metabolic vulnerability index (MVX) score using at least one defined mathematical model of risk. The methods comprise evaluating various biomarkers to distinguish various health risks. In one embodiment, the method comprises evaluating biomarkers to determine a relative risk of premature all-cause mortality. The model may include NMR-derived measurements of GlycA, S-HDLP, branched chain amino acids (BCAAs), ketone bodies, total serum protein, and citrate in at least one biosample of the subject.