Metabolic Drift Analysis Method for Early Disease Detection
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Solution Overview
Problem
Current biological analysis methods rely on reference values to diagnose pathologies, but they fail to detect abnormalities within these ranges, particularly metabolic drifts that may indicate asymptomatic diseases.
Innovation Solution
A method to analyze metabolic drift by measuring biological parameters at different times, determining if they fluctuate around a mean reference value, and identifying potential drift or stability, allowing for early disease diagnosis and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference value ranges are used to diagnose pathologies, then diagnosis is simplified and standardized, but abnormalities within normal ranges (metabolic drifts) cannot be detected
Solution Approach 1:
The method performs preliminary actions by calculating the mean and standard deviation of biological parameters over time before making a diagnostic determination. This preliminary statistical analysis establishes a baseline pattern that enables detection of drifts within normal reference ranges, resolving the contradiction between detection precision and method complexity.
Solution Approach 2:
The invention transitions from static reference value comparison to dynamic temporal analysis. By analyzing how biological parameters evolve over time and comparing this dynamic behavior against statistical norms (mean and standard deviation), the method detects metabolic drifts that static ranges would miss, thereby improving detection precision without excessive complexity.
2Reliability
If only current biological parameter values are analyzed, then analysis is quick and simple, but early disease detection is missed
Solution Approach 1:
The method collects and stores biological parameter values at multiple time points as preliminary data before final analysis. This preliminary data accumulation enables early disease detection by identifying drift patterns before symptoms appear, while the structured approach minimizes additional analysis time through efficient statistical calculations.
Solution Approach 2:
The system uses feedback from historical parameter measurements to improve current diagnostic reliability. By continuously comparing current values against the established mean and standard deviation from previous measurements, the method detects deviations indicating early disease states, making the detection process both reliable and time-efficient through iterative refinement.
3Measurement precision
If reference values are strictly applied, then diagnostic consistency is maintained, but asymptomatic conditions are overlooked
Solution Approach 1:
The invention changes the diagnostic parameter from a single reference value threshold to a statistical distribution model (mean and standard deviation). This parameter transformation enables detection of asymptomatic conditions by identifying values that, while within traditional reference ranges, represent significant deviations from an individual's baseline, thereby improving sensitivity without sacrificing diagnostic simplicity.
Solution Approach 2:
The method introduces dynamic temporal dimension to the diagnostic process. By analyzing the trajectory of biological parameters over time rather than isolated snapshots, the system maintains diagnostic consistency through standardized statistical methods while simultaneously improving sensitivity to detect gradual drifts characteristic of asymptomatic conditions.
Data Source
AI summary
The present invention relates to a method for analyzing the metabolic drift of at least one quantitative biological parameter in a subject and thereby make it possible to provide a method for evaluating the drift state of a subject and/or helping diagnose an illness or evaluate a subject's risk of suffering from an illness.The present invention also provides a method for defining an average reference value and a reference standard deviation of a biological parameter which are appropriate for analyzing the metabolic drift of said biological parameter and a method for optimizing a cohort of subjects in order to study a biological parameter.