Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events

Abstract

Importance: Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain.

Objective: To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations.

Design, setting, and participants: The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals.

Exposures: Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling.

Main outcomes and measures: Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death.

Results: In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population.

Conclusions and relevance: A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.

Figure 1.. Overview of Study of Derivation of Protein Risk Score, Polygenic Risk Scores, and the Comparative Analysis

The diagram gives an overview of the populations used and how the study is divided into derivation and testing data sets. Some boxes do not show total individuals because subcategories are not mutually exclusive. ^aPopulation sources: UK Biobank, FinnGen, Copenhagen Hospital Biobank (part of the Genetics of Cardiovascular Disease Study), Danish Blood Donor Study, and CARDIoGRAMplusC4D. ^bPopulation sources: UK Biobank, Finngen, and Megastroke. ^cComposite end point of first myocardial infarction, first stroke, or coronary heart disease death. ^dIndicates the composite end point of myocardial infarction, stroke, or cardiovascular disease death. ^eThe counts for the proteomics set include those with both proteomics and genotyping (ie, the sets are not mutually exclusive). ^fTest data for this analysis were separate from derivation of polygenic and protein risk scores.

Figure 2.. Reclassification in the Primary Event Population

The figure shows reclassification results when protein risk score and polygenic risk scores are added on top of clinical risk factors (age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index [calculated as weight in kilograms divided by height in meters squared], and smoking status at the time of plasma collection) in the primary event population test set (4018 participants; 465 events; 284 events within 10 years). The results are shown for predicted 10-year risk using a 7.5% risk threshold to separate between low and intermediate risk. Three groups are considered: (i) those with an atherosclerotic cardiovascular disease (ASCVD) event within 10 years; (ii) those who die of causes other than ASCVD within 10 years; and (iii) those who survive 10 years without an ASCVD event. Panel A shows a reclassification table for the addition of protein risk score on top of the clinical risk factors. The total categorical net reclassification improvement is 0.040 (95% CI, −0.009 to 0.094) and 0.044 (95% CI, −0.013 to 0.096) when excluding those who die from non-ASCVD causes. Net reclassification improvement for ASCVD events is −0.007 (95% CI, −0.056 to 0.046), 0.047 (95% CI, 0.035 to 0.059) for ASCVD nonevents (groups [ii] and [iii]), and 0.052 (95% CI, 0.039 to 0.064) for those who survive 10 years without ASCVD event (group [iii]). Panel B shows a reclassification table when both protein risk score and polygenic risk scores are added on top of the clinical risk factors. The total net reclassification improvement is 0.053 (95% CI, −0.006 to 0.110) and 0.056 (95% CI, −0.001 to 0.114) when excluding those who die from non-ASCVD causes. Net reclassification improvement for ASCVD events is 0.004 (95% CI, −0.054 to 0.058), 0.049 (95% CI, 0.037 to 0.061) for ASCVD nonevents (groups [ii] and [iii]), and 0.053 (95% CI, 0.039 to 0.066) for those who survive 10 years without ASCVD event (group [iii]).