Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care : Cohort Study of Returning UK Biobank Participants
(2021) In Journal of Personalized Medicine 11(12).- Abstract
Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and... (More)
Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.
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- author
- Castela Forte, José ; Folkertsma, Pytrik ; Gannamani, Rahul ; Kumaraswamy, Sridhar ; Mount, Sarah ; de Koning, Tom J LU ; van Dam, Sipko and Wolffenbuttel, Bruce H R
- organization
- publishing date
- 2021-12-07
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Personalized Medicine
- volume
- 11
- issue
- 12
- article number
- 1322
- publisher
- MDPI AG
- external identifiers
-
- scopus:85121593981
- pmid:34945794
- ISSN
- 2075-4426
- DOI
- 10.3390/jpm11121322
- language
- English
- LU publication?
- yes
- id
- 0f746758-e7f3-4a27-949b-7564be025626
- date added to LUP
- 2022-01-03 14:05:10
- date last changed
- 2024-11-11 01:10:49
@article{0f746758-e7f3-4a27-949b-7564be025626,
abstract = {{<p>Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.</p>}},
author = {{Castela Forte, José and Folkertsma, Pytrik and Gannamani, Rahul and Kumaraswamy, Sridhar and Mount, Sarah and de Koning, Tom J and van Dam, Sipko and Wolffenbuttel, Bruce H R}},
issn = {{2075-4426}},
language = {{eng}},
month = {{12}},
number = {{12}},
publisher = {{MDPI AG}},
series = {{Journal of Personalized Medicine}},
title = {{Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care : Cohort Study of Returning UK Biobank Participants}},
url = {{http://dx.doi.org/10.3390/jpm11121322}},
doi = {{10.3390/jpm11121322}},
volume = {{11}},
year = {{2021}},
}