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

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 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.

Effect of a Digitally-Enabled, Preventive Health Program on Blood Pressure in an Adult, Dutch General Population Cohort: An Observational Pilot Study

“…Patients were subsequently stratified as normotensive, or as having stage 1 (DBP between 80 and 90, or SBP between 130 and 140) or stage 2 hypertension (DBP ≥ 90 or SBP ≥ 140), according to the ACC/AHA guidelines [ 22 ]. Polygenic risk scores (PRS) were calculated using an additive model, as described in more detail in a previous publication [ 23 ]. In short, individuals were binned into deciles based on their PRS and the average disease incidence was calculated for each decile.…”

Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis

“…Use of genetic information. In total, we found 10 studies investigating the efficacy of using detailed genetic information like genetic risk scores to improve risk prediction for incident hypertension [31,34,[36][37][38]44,55,58,63,66,73]. In almost all cases, the resulting models' AUC/C-stat.…”

“…The inclusion of genetic information along with clinical information was seen in multiple studies yet displayed little comparative improvement to models without it [31,34,[36][37][38]44,55,58,66]. A single exception was found where ML models improved with the introduction of genetic information, but not the traditional model [63].…”