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Published on: September 15, 2018
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia
Jasmine Gratton1, Marta Futema1,2, Steve E Humphries1
1Institute of Cardiovascular Science, University College London, London, United Kingdom.
Insights
Familial hypercholesterolemia (FH) screening can be improved with a new algorithm identifying at-risk individuals. This approach aids early detection and intervention for coronary heart disease prevention.
Area of Science:
- Cardiovascular Genetics
- Biomarker Discovery
- Public Health Screening
Background:
- Monogenic familial hypercholesterolemia (FH) significantly increases the risk of premature coronary heart disease and mortality.
- Despite its prevalence (1:250), FH is often diagnosed late, delaying crucial interventions.
- Current population screening strategies for FH are lacking, leading to missed opportunities for early management.
Purpose of the Study:
- To develop a predictive algorithm for identifying individuals with suspected monogenic FH.
- To facilitate subsequent confirmatory genetic testing and cascade screening for FH.
Main Methods:
- A logistic regression model was employed using UK Biobank data (139,779 participants).
- Predictors included medical/family history, anthropometrics, biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS).
- Model derivation and validation were conducted on independent training and testing datasets.
Main Results:
- A 14-variable FH algorithm achieved an area under the curve (AUC) of 0.77.
- Key predictors included triglyceride, LDL-C, apolipoprotein A1, statin use, and LDL-C PGS.
- A 9-variable model excluding PGS showed comparable performance (AUC 0.76), outperforming LDL-C-based screening.
Conclusions:
- Enhanced FH detection is achievable by incorporating additional predictive variables.
- This strategy can optimize population screening by reducing the overall sequencing burden.
- Improved identification enables timely interventions, mitigating cardiovascular risks associated with FH.
Background:
People with monogenic familial hypercholesterolemia (FH) are at an increased risk of premature coronary heart disease and death. With a prevalence of 1:250, FH is relatively common; but currently there is no population screening strategy in place and most carriers are identified late in life, delaying timely and cost-effective interventions.
Objectives:
The purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade screening.
Methods:
A least absolute shrinkage and selection operator logistic regression model was used to identify predictors that accurately identified people with FH in 139,779 unrelated participants of the UK Biobank. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS). Model derivation and evaluation were performed in independent training and testing data.
Results:
A total of 488 FH variant carriers were identified using whole-exome sequencing of the low-density lipoprotein receptor, apolipoprotein B, apolipoprotein E, proprotein convertase subtilisin/kexin type 9 genes. A 14-variable algorithm for FH was derived, with an area under the curve of 0.77 (95% CI: 0.71-0.83), where the top 5 most important variables included triglyceride, LDL-C, apolipoprotein A1 concentrations, self-reported statin use, and LDL-C PGS. Excluding the PGS as a candidate feature resulted in a 9-variable model with a comparable area under the curve: 0.76 (95% CI: 0.71-0.82). Both multivariable models (w/wo the PGS) outperformed screening-prioritization based on LDL-C adjusted for statin use.
Conclusions:
Detecting individuals with FH can be improved by considering additional predictors. This would reduce the sequencing burden in a 2-stage population screening strategy for FH.
