Melatonin Metabolite Modeling for Accurate Circadian Phase Estimation
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Solution Overview
Problem
Current methods for measuring circadian rhythm factors, particularly melatonin metabolites like 6-sulfatoxymelatonin (aMT6s), are inadequate in accurately estimating phase markers due to sensitivity to collection intervals and intra-day variations, leading to unreliable onset and offset determinations.
Innovation Solution
A method involving obtaining circadian rhythm factor metabolite measurements at multiple collection times, generating Z-scores, and constructing a normal distribution to estimate phase markers using a modified normal distribution function, which accounts for total nightly excretion and Z-score proportions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to measure circadian rhythm factors, then the measurement process is simple, but the accuracy of phase marker estimation is poor due to sensitivity to collection intervals and intra-day variations
Solution Approach 1:
The patent transforms the measurement approach by changing from direct phase marker measurement to metabolite measurement, and from single-point measurement to multi-timepoint measurement. It introduces Z-score standardization and normal distribution modeling to convert raw metabolite data into standardized phase marker estimates, thereby improving measurement precision while managing complexity through systematic data transformation.
Solution Approach 2:
The patent replaces complex direct measurement systems with a computational approach. Instead of using complex instruments to directly measure phase markers, it uses simpler metabolite measurements combined with mathematical modeling (Z-scores, normal distribution functions) to derive phase marker estimates, substituting mechanical complexity with computational simplicity.
2Measurement precision
If multiple collection times are used to improve measurement accuracy, then the precision of phase marker estimation improves, but the complexity of data processing increases
Solution Approach 1:
The patent implements feedback through iterative data processing: raw metabolite measurements at multiple times are converted to Z-scores, which are then used to generate normal distribution parameters, which in turn produce phase marker estimates. This feedback loop continuously refines the estimates using the same data, improving precision while keeping the processing algorithm systematic and manageable.
Solution Approach 2:
The patent segments the data processing into distinct modular steps: (1) obtaining metabolite measurements at multiple times, (2) calculating Z-scores for each timepoint, (3) generating normal distribution parameters from the Z-scores, and (4) computing phase marker values. This segmentation reduces the perceived complexity by breaking down the overall process into manageable, sequential operations.
3Reliability
If traditional single-point measurement methods are used, then the ease of operation is high, but the reliability of onset and offset determinations is low
Solution Approach 1:
The patent performs preliminary actions by measuring metabolite levels at multiple predetermined timepoints before final phase marker determination. It pre-calculates Z-scores and establishes normal distribution parameters in advance, creating a foundation that enables reliable onset and offset determinations without requiring complex real-time analysis during the actual measurement phase.
Data Source
AI summary
The technology relates in part to methods for measuring circadian rhythm factors. In some aspects, the technology relates to measuring circadian rhythm factor metabolites. In some aspects, the technology relates to measuring melatonin metabolites. In some aspects, the technology relates to measuring melatonin metabolites and estimating circadian rhythm phase markers according to a distribution function.