Predicting Menopause Onset via Polygenic Risk Scores
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Solution Overview
Problem
Current methods for predicting menopause onset are limited in accuracy and reliability, particularly in correlating genetic risk with age, making it difficult to plan reproductive life effectively, as existing predictors like AMH provide short-term value and GWAS studies only evaluate statistical association without predictive capacity.
Innovation Solution
A computer-implemented method using machine learning and artificial intelligence to process a woman's genetic data, identifying personalized subsets of SNPs and calculating polygenic risk scores to predict menopause onset based on phenotypes such as age groups or expected ages, providing a more reliable and precise prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AMH (antimüllerian hormone) is used as a predictor, then predictive value is provided for a few years, but it is of little use in supporting long-term reproductive life planning
Solution Approach 1:
The patent transitions from using AMH hormone levels as the predictive parameter to using genetic markers (SNPs) and polygenic risk scores. This parameter change enables long-term predictive capability while maintaining reliability, as genetic factors are stable and can predict menopause onset many years in advance, thereby resolving the contradiction between predictive reliability and time range
2Loss of information
If GWAS (Genome Wide Association Study) is used to evaluate statistical association between SNPs and menopause age, then genetic components are identified, but predictive capacity is not analyzed making it not directly applicable in clinical practice
Solution Approach 1:
The patent implements a feedback mechanism by using known menopause age data from cohort studies to train and validate polygenic risk score models. This feedback loop transforms static GWAS association data into dynamic predictive tools with validated clinical utility, resolving the contradiction between preserving genetic association information and achieving reliable predictive capacity
Solution Approach 2:
The patent performs preliminary action by pre-calculating polygenic risk scores based on GWAS data before clinical application. This preliminary processing of genetic data into actionable risk scores makes the information directly applicable in clinical practice while maintaining the integrity of the underlying genetic associations
3Reliability
If there is no reliable measure to directly associate genetic risk with early menopause, then genetic risk cannot be correlated with other risk factors, but developing such measures increases method complexity
Solution Approach 1:
The patent merges multiple data sources including genetic data (SNPs), clinical data (AMH levels), lifestyle factors (smoking, BMI), and medical history into a unified prognostic model. This integration creates a comprehensive risk assessment tool that reliably associates genetic risk with early menopause while incorporating other relevant risk factors, resolving the contradiction between achieving reliable genetic risk association and maintaining method simplicity
Data Source
AI summary
A method is for predictive prognosis of a woman's menopause onset. The method includes accessing the Single Nucleotide Polymorphisms (SNPs) of the woman; processing the woman's genetic data, to provide a predictive prognosis of menopause onset in relation to the phenotype. The phenotype includes an age group/limit with respect to the predictive prognosis, or indication of a woman's likely age for menopause onset. The processing includes identifying a predetermined set and subset of SNPs associated with the phenotype. Each of the SNPs the set includes an identifier of SNPs, and is associated with a respective pre-calculated first relevance parameter. A first value of polygenic risk score is calculated based on the first personalized subset of SNPs and respective first pre-calculated relevance parameters. The predictive prognosis of menopause onset relative to each phenotype is determined based on the polygenic risk score.