AHA PREVENT Equations and Lipoprotein(a) for Cardiovascular Disease Risk: Insights From MESA and the UK Biobank

Harpreet S Bhatia; Matthew Ambrosio; Alexander C Razavi; Pamela L Alebna; Calvin Yeang; Jared A Spitz; Jaideep Patel; Michael Y Tsai; Laurence Sperling; Michael D Shapiro; Sotirios Tsimikas; Anurag Mehta

doi:10.1001/jamacardio.2025.1603

. 2025 Jun 4;10(8):810–818. doi: 10.1001/jamacardio.2025.1603

AHA PREVENT Equations and Lipoprotein(a) for Cardiovascular Disease Risk

Insights From MESA and the UK Biobank

Harpreet S Bhatia ^1,^✉, Matthew Ambrosio ², Alexander C Razavi ³, Pamela L Alebna ⁴, Calvin Yeang ¹, Jared A Spitz ⁵, Jaideep Patel ⁶, Michael Y Tsai ⁷, Laurence Sperling ³, Michael D Shapiro ⁸, Sotirios Tsimikas ¹, Anurag Mehta ⁴

¹Division of Cardiovascular Medicine, University of California, San Diego, La Jolla

²Department of Biostatistics, Virginia Commonwealth University, Richmond

³Emory Clinical Cardiovascular Research Institute, Emory University School of Medicine, Atlanta, Georgia

⁴Pauley Heart Center, Virginia Commonwealth University, Richmond

⁵Inova Schar Heart and Vascular, Fairfax, Virginia

⁶Ciccarone Center for the Prevention of Atherosclerotic Cardiovascular Disease, Baltimore, Maryland

⁷Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis

⁸Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Accepted for Publication: March 26, 2025.

Published Online: June 4, 2025. doi:10.1001/jamacardio.2025.1603

^✉

Corresponding Author: Harpreet S. Bhatia, MD, MAS, Division of Cardiovascular Medicine, University of California, San Diego, 9500 Gilman Dr, MC 7411, La Jolla, CA 92093 (hsbhatia@health.ucsd.edu).

Author Contributions: Dr Bhatia had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bhatia, Spitz, Mehta.

Acquisition, analysis, or interpretation of data: Bhatia, Ambrosio, Razavi, Alebna, Yeang, Patel, Tsai, Sperling, Shapiro, Tsimikas, Mehta.

Drafting of the manuscript: Bhatia, Spitz.

Critical review of the manuscript for important intellectual content: Ambrosio, Razavi, Alebna, Yeang, Spitz, Patel, Tsai, Sperling, Shapiro, Tsimikas, Mehta.

Statistical analysis: Bhatia, Ambrosio, Alebna, Spitz.

Obtained funding: Bhatia.

Administrative, technical, or material support: Tsai.

Supervision: Patel, Sperling, Tsimikas, Mehta.

Conflict of Interest Disclosures: Dr Bhatia reported receiving consulting fees from Abbott, Arrowhead, Kaneka, and Novartis outside the submitted work. Dr Shapiro reported receiving grants from Amgen, 89Bio, Esperion, Novartis, Ionis, Lilly, Merck, and New Amsterdam and personal fees from Amgen, Arrowhead, Novartis, Ionis, Merck, New Amsterdam, Novo Nordisk, and Tourmaline outside the submitted work. Dr Tsimikas reported being a coinventor and receiving royalties patents owned by the University of California, San Diego; being a founder of with equity interest in Oxitope and Kleanthi Diagnostics; receiving consulting fees from Novartis; and having a dual appointment at University of California, San Diego, and Ionis Pharmaceutical, which is an arrangement that has been reviewed and by the University of California, San Diego, in accordance with its conflict-of-interest policies. Dr Mehta reported receiving grants Novartis and Amgen paid to the Virginia Commonwealth University outside the submitted work. No other disclosures were reported.

Funding/Support: Dr Bhatia is supported by grant 1K08HL166962 from the National Institutes of Health (NIH). Dr Razavi is supported by grant F32HL172499 from the National Heart, Lung, and Blood Institute (NHLBI). Dr Alebna is supported by grant T32HL149645T32, postdoctoral training grant from the NHLBI. Dr Tsimikas is supported by grants R01 HL159156 and HL170224 from the NHLBI. The MESA study was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the NHLBI, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Data Sharing Statement: See Supplement 2.

Additional Contributions: The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

Additional Information: This research has been conducted using the UK Biobank Resource under application numbers 100584 and 101614.

^✉

Corresponding author.

PMCID: PMC12138797 PMID: 40465279

Key Points

Question

Does the addition of lipoprotein(a) [Lp(a)] improve risk prediction when added to the new American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) equations?

Findings

In this analysis of 2 prospective cohort studies, atherosclerotic cardiovascular disease (ASCVD) event rates were accurately predicted by the PREVENT equations among individuals with elevated Lp(a) levels. The addition of Lp(a) to the PREVENT equations modestly improved risk prediction, particularly among lower-risk individuals.

Meaning

The PREVENT equations perform well for risk prediction overall, including among individuals with elevated Lp(a); Lp(a) is independently associated with ASCVD, so the addition of Lp(a) to the PREVENT equations may improve risk prediction, particularly among specific subgroups.

Abstract

Importance

Lipoprotein(a) [Lp(a)] is independently associated with atherosclerotic cardiovascular disease (ASCVD) risk but is not included in the new American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) equations for CVD risk assessment.

Objective

To evaluate the performance of these equations in individuals with elevated Lp(a).

Design, Setting, and Participants

Cohort study involving 314 783 participants from the multicenter Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2018; n = 6670) and the population-based UK Biobank (UKB, 2006-2022; n = 308 113) without known cardiovascular disease with available Lp(a) measurements. Analyses were conducted March 25, 2025.

Exposure

Elevated Lp(a) level of 125 nmol/L or higher.

Main Outcomes and Measures

Coronary heart disease (CHD), ASCVD, heart failure (HF), and total CVD. Participants were categorized as low (<5%), borderline (5% to <7.5%) intermediate (7.5% to <20%), and high (≥20%) risk of each outcome. Ten-year observed event rates were calculated, and the association between elevated Lp(a) and outcomes overall and by risk category was evaluated in age- and sex-adjusted Cox proportional hazards models. Improvement in risk prediction with the addition of elevated Lp(a) was evaluated using continuous and categorical net reclassification improvement (NRI) (using the above cut points).

Results

Among the 314 783 participants (mean [SD] age, 62.1 [10.2] years and 3523 females [53%] in MESA; mean [SD] age, 56.3 [8.1] years; 169 648 females [55%] in the UKB), observed 10-year ASCVD event rates generally fell within the bounds of predicted risk categories regardless of Lp(a) level, although participants with elevated Lp(a) had higher event rates than did those with nonelevated Lp(a) (hazard ratio [HR], 1.30; 95% CI, 1.22-1.38) with similar results for CHD, HF, and total CVD. For CHD, the strongest association was among low-risk individuals (P for interaction = .31). The addition of elevated Lp(a) values to PREVENT modestly improved ASCVD risk prediction (category-free NRI, 0.058; 95% CI, 0.043-0.065; categorical NRI, 0.006, 95% CI, 0.004-0.011) with the greatest improvement in borderline-risk; when Lp(a) was evaluated continuously, the greatest improvement in prediction was among individuals at low risk. For CHD, the greatest improvement in prediction was in low- and high-risk individuals.

Conclusions and Relevance

In this analysis of 2 cohort studies, the novel PREVENT equations performed well for risk prediction overall, including among individuals with elevated Lp(a). However, Lp(a) values remain independently associated with higher risk, and Lp(a) may improve personalized risk assessment, particularly among specific subgroups.

This analysis of 2 cohort studies assesses whether adding lipoprotein(a) levels to the AHA PREVENT equations improved risk prediction of atherosclerotic cardiovascular disease.

Introduction

Lipoprotein(a) (Lp[a])] is a low-density lipoprotein (LDL)-like lipoprotein that is genetically determined, present at elevated levels (>50 mg/dL or >125 nmol/L) in approximately 20% of the general population, and associated with atherosclerotic cardiovascular disease (ASCVD), aortic valvular disease, and heart failure (HF).^1,2 The 2019 American College of Cardiology and American Heart Association (ACC/AHA) primary prevention guidelines endorsed the use of a risk calculator for ASCVD based on the pooled cohort equations (PCEs) to risk stratify individuals and guide decisions regarding therapies such as statins, aspirin, and treatment for hypertension.³ Multiple international societies have recently recommended universal Lp(a) screening, including the European Society of Cardiology and European Atherosclerosis Society,⁴ the Canadian Cardiovascular Society,⁵ and the National Lipid Association.⁶ The 2018 ACC/AHA blood cholesterol management guidelines do not make a recommendation regarding universal Lp(a) screening but list elevated Lp(a) as a risk-enhancing factor for those at intermediate risk.⁷ However, Lp(a) was not included as a component of the PCE, and it has previously been shown that the addition of Lp(a) to the PCE improves risk prediction.⁸

Recently, new equations for cardiovascular risk assessment in primary prevention, the AHA Predicting Risk of Cardiovascular EVENT (PREVENT) equations, were developed, validated, and published. These equations incorporate multiple novel elements including risk stratification from a younger age, a broader assessment of metabolic risk factors, adjustment for statin use, removal of race and ethnicity, focus on social determinants of health, and prediction of multiple outcomes including coronary heart disease (CHD), ASCVD, HF, and total cardiovascular disease (CVD).⁹ However, the equations did not account for Lp(a) levels, and while it is well established that elevated Lp(a) level is associated with increased risk of CHD, ASCVD, and HF, it is currently unknown how the PREVENT equations perform in individuals with an elevated Lp(a) and whether the addition of Lp(a) values to the equations improves risk prediction. This may be particularly relevant because the PREVENT equations do not incorporate race or ethnicity, and Lp(a) levels are known to vary by ancestry.

Given the high prevalence of elevated Lp(a) and the potential adoption of the PREVENT equations for risk prediction in upcoming guidelines, this study evaluated the performance of the PREVENT risk scores among individuals with elevated Lp(a) in 2 large multiethnic populations. This cohort study evaluated observed 10-year risk by predicted 10-year risk categories among individuals with elevated Lp(a) and for improvement in risk prediction with the addition of Lp(a) values to the PREVENT equations in the Multi-Ethnic Study of Atherosclerosis (MESA) and the UK Biobank (UKB).

Methods

Study Population

The design of MESA has been described.¹⁰ Briefly, MESA is a prospective cohort study of individuals free of known cardiovascular disease at baseline; 6814 participants were recruited between July 2000 and 2002 from 6 centers across the US, and adjudicated cardiovascular events are available through 2018. Participants were characterized at baseline through standardized questionnaires, physical examinations, and laboratory measurements on fasting blood samples. Lp(a) level was measured using a latex-enhanced turbidimetric immunoassay (Denka Seiken) and reported in milligrams per deciliter. Elevated Lp(a) was defined as higher than 50 mg/dL. Race and ethnicity were determined by self-report. For the analysis of MESA, participants without follow-up for cardiovascular events, with missing covariates for the PREVENT equations calculation, and without Lp(a) measurement were excluded.

The UKB is a prospective cohort study of more than 500 000 individuals in the UK, recruited from 2006 through 2010. Lp(a) measurements were performed using an immunoturbidimetric assay (Randox Laboratories) and reported in nanomoles per liter. Elevated Lp(a) was defined as higher than 125 nmol/L. Race and ethnicity were determined by self-report; other was defined as self-report of multiracial. For the analysis of the UKB, participants with missing covariates for PREVENT, prebaseline events, or without Lp(a) measurement were excluded.

For both studies, all participants provided written informed consent when recruited to the original studies. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines were followed.

AHA PREVENT Equations

For the primary analyses, the base 10-year equations were used for coronary heart disease (CHD, defined as fatal and nonfatal myocardial infarction [MI]), ASCVD (CHD and fatal or nonfatal stroke), and HF and total CVD (defined as ASCVD and HF) according to previously published methods.¹¹ The optional predictors of hemoglobin A_1c and zip code were not available at the first MESA visit, but the urine albumin-creatinine ratio was and this was included in a secondary analysis using the enhanced 10-year equations. The majority of UKB participants lacked data on at least 1 of the optional variables; therefore, enhanced equations were not calculated in this cohort. Accuracy of the coding for the PREVENT equations was confirmed by using the sample cases and calculations provided in the original description of the PREVENT equations.¹¹

Outcomes

In MESA, CHD was defined as fatal and nonfatal MI; ASCVD, as CHD and fatal or nonfatal stroke; and total CVD, as ASCVD and HF. Events were centrally adjudicated by MESA. In the UKB, composite outcomes were defined similarly using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes (I61-I63 for stroke; I21-I22, for MI; I50 for HF). These outcomes align with the outcomes used for the development of the PREVENT equations.¹¹

Statistical Methods

Baseline characteristics were compared by Lp(a) level for each study using the respective thresholds for elevated Lp(a). Continuous variables were compared using t tests or Mann-Whitney tests, as appropriate, and categorical variables were compared using χ² tests. Participants were categorized by 10-year predicted risk by the PREVENT equations (<5%, 5% to <7.5%, 7.5% to <20%, and ≥20%) for stratified analyses. Analyses were performed in MESA and UKB separately and in a pooled cohort of the 2 studies (elevated Lp[a]) defined as >125 nmol/L). Although there is no exact conversion factor between milligrams per deciliter and nanomoles per liter for Lp(a),¹² a conversion factor of 2.5 was used to convert levels in MESA to nanomoles per liter for comparison between studies and for pooled analyses. For the overall population and across risk categories, 10-year observed event rates were calculated by totaling the number of first events in each group, divided by the total person-time in each group (until the first event, end of follow-up, or death) and multiplied by 365 × 10. The association between elevated Lp(a) levels and risk of CHD, ASCVD, HF, and total CVD (overall and stratified by risk group) was assessed in a time-to-event analysis up to 10 years using Cox proportional hazards regression models with adjustment for age and sex. The multiplicative interaction between elevated Lp(a) and risk category for each outcome was evaluated in similar models. Improvement in risk prediction with the addition of elevated Lp(a) values to the PREVENT equations was assessed using the Harrel C index and by evaluating for continuous and categorical net reclassification improvement (NRI, using thresholds of 5%, 7.5%, and 20%). As a sensitivity analysis, the primary analysis was performed using alternative risk thresholds (3%, 5%, and 7.5%) because these categories have not yet been defined for the PREVENT equations and other thresholds may ultimately be used.¹³ Additional sensitivity analyses were conducted evaluating for improvement in risk prediction with the addition of Lp(a) levels as a continuous variable and using study-specific 90th and 95th percentile thresholds for elevated Lp(a). No significant nonlinearity was detected in the association between Lp(a) level as a continuous variable and ASCVD risk.

All analyses were performed using R version 4.3.1. A 2-tailed P value <.05 was considered statistically significant.

Results

After excluding participants with a prebaseline event (n = 4), no follow-up (n = 31), or missing Lp(a) levels (n = 109), the study population was 6670 for MESA. There were 6623 participants (mean [SD] age, 62.1 [10.2] years; 3523 females [53%]; 791 Chinese [12%]; 1474 Hispanic [22%]; 1832 non-Hispanic Black [28%]; 2573 non-Hispanic White [39%]); with all covariates for the base equations for CHD, ASCVD, and total CVD, and 6595 for the enhanced equations. There were 6640 participants with all covariates for the base HF equations and 6612 for the enhanced equations. Those with an Lp(a) level higher than 125 nmol/L were more often women and more often Black with higher prevalence of hypertension and diabetes, as well as higher total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein (LDL)–C levels. There was no difference in estimated CHD, ASCVD, or total CVD risk by the base or enhanced PREVENT equations, but predicted risk for HF was higher in those with elevated Lp(a) (Table 1).

Table 1. Population Characteristics by Lipoprotein(a) Level in the Multi-Ethnic Study of Atherosclerosis and the UK Biobank.

	Multi-Ethnic Study of Athersclerosis				UK Biobank
	Overall (n = 6670)	Lipoprotein(a)^a		P value	Overall (n = 308 113)	Lipoprotein(a)		P value
	Overall (n = 6670)	≤125 nmol/L (n = 5341)	>125 nmol/L (n = 1329)	P value	Overall (n = 308 113)	≤125 nmol/L (n = 273 825)	>125 nmol/L (n = 34 288)	P value
Age, mean (SD), y	62.1 (10.2)	62.0 (10.3)	62.4 (10.1)	.26	56.3 (8.1)	56.3 (8.1)	56.5 (8.0)	<.001
Sex, No. (%)
Female	3523 (53)	2734 (51)	789 (59)	<.001	169 648 (55)	150 216 (55)	19 432 (57)	<.001
Male	3147 (47)	2607 (49)	540 (41)	<.001	138 465(45)	123 609 (45)	14 856 (43)	<.001
Race and ethnicity, No. (%)^c
Chinese	791 (12)	717 (13)	74 (6)	<.001	1100 (0.4)	1052 (0.4)	48 (0.1)	<.001
Hispanic	1474 (22)	1260 (24)	214 (16)
Non-Hispanic Black	1832 (28)	1200 (23)	632 (48)		5361 (2)	4423 (1.6)	938 (2.8)
Non-Hispanic White	2573 (39)	2164 (41)	409 (31)		288 481 (95)	256 343 (95)	32 138 (95)
South Asian					6661 (2.2)	6141 (2.3)	520 (1.25)
Other					1972 (0.6)	1790 (0.7)	182 (0.5)
BMI, mean (SD)	28.3 (5.5)	28.2 (5.4)	28.8 (5.7)	<.001	27.3 (4.7)	27.3 (4.7)	27.4 (4.7)	.001
Hypertension, No. (%)	2985 (45)	2322 (44)	663 (50)	<.001	114 948 (37)	101 702 (37)	13 246 (39)	<.001
Diabetes, No. (%)	829 (12)	640 (12)	189 (14)	.03	14 245 (5)	12 738 (5)	1507 (4)	.03
Current smoking, No. (%)	866 (13)	673 (13)	193 (15)	.07	31 810 (10)	28 251 (10)	3559 (10)	.70
Hypertension medication use, No. (%)	2475 (37)	1920 (36)	555 (42)	<.001	78 864 (26)	69 788 (26)	9076 (27)	<.001
Baseline statin use, No. (%)	984 (15)	721 (14)	263 (20)	<.001	38 368 (13)	33 470 (12)	4898 (14)	<.001
Systolic blood pressure, mean (SD), mm Hg	127 (22)	126 (21)	128 (22)	.002	140 (20)	140 (20)	140 (20)	<.001
Cholesterol, mean SD, mg/dL
Total	194 (36)	192 (35)	203 (37)	<.001	222 (43)	221 (43)	228 (43)	<.001
HDL-C	51 (15)	50 (15)	54 (15)	<.001	56 (15)	56 (15)	57 (15)	<.001
LDL-C	117 (31)	115 (31)	126 (32)	<.001	139 (33)	138 (33)	144 (33)	<.001
Lp(a), median IQR, nmol/L	43 (19-101)	31 (15-60)	192 (154-253)	<.001	21 (10-62)	17 (9-43)	155 (139-172)	<.001
Urine albumin-creatinine, mean (SD), mg/g	26 (157)	24 (133)	34 (230)	.05
10-y Risk, median IQR, %^b
CHD (base)	2.8 (1.3-5.4)	2.8 (1.3-5.4)	2.9 (1.4-5.4)	.33	2.0 (1.0-3.5)	2.0 (1.0-3.5)	2.0 (1.1-3.4)	<.001
CHD (enhanced)	2.4 (1.1-4.7)	2.4 (1.1-4.7)	2.4 (1.2-4.6)	.52
ASCVD (base)	5.3 (2.5-9.9)	5.3 (2.5-9.8)	5.4 (2.7-9.9)	.17	3.9 (2.0-6.4)	3.9 (2.0-6.4)	3.9 (2.1-6.4)	<.001
ASCVD (enhanced)	4.7 (2.2-8.7)	4.6 (2.2-8.7)	4.9 (2.4-8.7)	.27	-
HF (base)	4.6 (1.9-10.1)	4.5 (1.8-10.0)	5.1 (2.0-10.5)	.013	2.7 (1.2-5.0)	2.7 (1.2-5.0)	2.8 (1.3-4.0)	<.001
HF (enhanced)	3.9 (1.5-8.7)	3.8 (1.5-8.6)	4.2 (1.7-8.9)	.013
Total CVD (base)	8.8 (4.1-16.9)	8.7 (4.0-16.9)	9.1 (4.4-16.9)	.34	5.9 (3.1-9.8)	5.8 (3.0-9.8)	5.9 (3.2-9.7)	.07
Total CVD (enhanced)	7.6 (3.5-14.7)	7.5 (3.4-14.9)	7.9 (3.7-14.3)	.46

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Abbreviations: ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index, calculated as weight in kilograms divided by height in meters squared; CHD, coronary heart disease; CVD, cardiovascular disease; HDL-C, high-density lipoprotein-cholesterol; HF, heart failure; LDL-C, low-density lipoprotein-cholesterol; Lp(a), lipoprotein(a); PREVENT, Predicting Risk of Cardiovascular Disease Events.

SI conversion factor: To convert total cholesterol, HDL-C, and LDL-C from mg/dL to mmol/L, multiply by 0.0259.

^{^a}

The conversion factor used for Lp(a) of 50 mg/dL to be equivalent to 125 nmol/L is 2.5.

^{^b}

Base refers to base PREVENT equations, and enhanced refers to PREVENT equations with optional predictors.

^{^c}

Race and ethnicity were determined by self-report, and other was defined as self-reported multiracial.

In the UKB, there were 308 113 participants (mean [SD] age, 56.3 [8.1] years; 169 648 females [55%]; 1100 Chinese [0.4%]; 5361 non-Hispanic Black [2%]; 288 481 non-Hispanic White [95%]; 6661 South Asian [2.2%]) after excluding those with missing variables needed to calculate the PREVENT score (n = 51 700), prebaseline events (n = 22 049), or missing Lp(a) (n = 120 269). There were 4438 participants without data regarding race or ethnicity. Among those with Lp(a) levels higher than 125 nmol/L, there was a higher prevalence of hypertension but a lower prevalence of diabetes. Total cholesterol, HDL-C, and LDL-C levels were also higher in the group with elevated Lp(a) values, which also had higher estimated CHD, ASCVD, and HF risk by the PREVENT equations. Although these differences were statistically significant, they are unlikely to be of clinical significance (Table 1).

In the pooled cohort, the 10-year observed ASCVD event rates were higher for participants with Lp(a) values higher than 125 nmol/L than for those whose values were 125 nmol/L or lower overall and across all risk categories. Event rates fell within the bounds of estimated risk categories by the PREVENT equations with the exception of those with an estimated risk of 5% to less than 7.5% and Lp(a) values of 125 nmol/L or lower (Figure 1). Elevated Lp(a) was associated with higher ASCVD risk overall (hazard ratio [HR], 1.30; 95% CI, 1.22-1.38) and across risk categories (P for interaction = .69; Figure 2). Similar results were seen for total CVD. For CHD, elevated Lp(a) was associated with higher risk overall (HR, 1.42; 95% CI, 1.32-1.53) with the numerically strongest association among low-risk individuals, but interaction testing was negative (P for interaction = .31). For HF, elevated Lp(a) was marginally associated with higher risk overall (HR, 1.08; 95% CI, 1.00-1.17; eTable 1 in Supplement 1). There was no significant interaction between elevated Lp(a) and race or ethnicity of ASCVD risk (P = .97). When alternative risk thresholds were used, results were similar: An elevated Lp(a) was associated with increased ASCVD and CHD risk across all risk categories (eTable 2 in Supplement 1).

Figure 1. — Results for 10-year atherosclerotic cardiovascular disease (ASCVD) event rates by predicted ASCVD risk category and lipoprotein(a) [Lp(a)] level are given for pooled cohorts, the Multi-Ethnic Study of Atherosclerosis (MESA), and the UK Biobank (UKB). For MESA, 50 mg/dL is equivalent to 125 nmol/L using a conversion factor of 2.5 for Lp(a). PREVENT indicates Predicting Risk of Cardiovascular Disease Events.

Figure 2. — For the Multi-Ethnic Study of Athersclerosis (MESA), 50 mg/dL is equivalent to 125 nmol/L using a conversion factor of 2.5 for lipoprotein(a). ASCVD indicates atherosclerotic cardiovascular disease; HR, hazard ratio; PREVENT, Predicting Risk of Cardiovascular Disease Events; UKB, the UK Biobank.

In MESA, the 10-year observed event rates for ASCVD were higher for Lp(a) levels higher than 125 nmol/L compared with levels of 125 nmol/L or less overall and across risk categories, but all event rates fell within the bounds of estimated risk by the PREVENT equations, except for those with Lp(a) levels higher than 125 mmol/L and with a risk of 5% to less than 7.5% (Figure 1). Lp(a) levels higher than 125 nmol/L were associated with higher risk of ASCVD (Figure 2), CHD, and total CVD but not HF (eTable 3 in Supplement 1). In the UKB, Lp(a) levels higher than 125 nmol/L were associated with higher 10-year ASCVD events overall and for each risk category except for 20% or higher, and event rates fell within the predicted range by PREVENT score except for those with Lp(a) levels of 125 nmol/L or less and risks of between 5% and less than 7.5% and 7.5% and less than 20% and for those with Lp(a) levels higher than 125 nmol/L and 20% or higher risk (Figure 1). Elevated Lp(a) levels were associated with higher ASCVD (Figure 2), CHD, and total CVD risk overall (eTable 4 in Supplement 1).

In the pooled cohorts, the addition of elevated Lp(a) levels to the PREVENT equations resulted in significant category-free NRI for all outcomes including ASCVD (NRI, 0.058; 95% CI, 0.043-0.065; Table 2) and CHD (NRI, 0.079; 95% CI, 0.064-0.098; eTable 5 in Supplement 1). The greatest NRI was noted for individuals with a borderline-risk of ASCVD (Table 2), high-risk followed by low-risk of CHD, high-risk of HF and intermediate-risk of total CVD (eTable 5 in Supplement 1). Significant categorical NRI was observed for ASCVD (NRI, 0.006; 95% CI, 0.004-0.011; Table 2) and total CVD (NRI, 0.003; 95% CI, 0.001-0.006; eTable 5 in Supplement 1). When using alternative risk thresholds, categorical NRI was significant for ASCVD (NRI, 0.004; 95% CI, 0.000-0.008), CHD (NRI, 0.007; 95% CI, 0.001-0.011), and HF (NRI, 0.002; 95% CI, 0.000-0.005; eTable 6 in Supplement 1). When evaluating the studies individually, significant category-free NRI was observed for CHD, ASCVD, and total CVD in both studies. Improvement was also seen by categorical NRI for CHD in the UKB cohort (eTable 7 in Supplement 1). When Lp(a) level was evaluated continuously, there was significant category-free (NRI, 0.073; 95% CI 0.051-0.080) and categorical NRI (NRI, 0.006; 95% CI, 0.004-0.010) for ASCVD overall, with the greatest improvement among those with less than 5% risk (NRI, 0.099; 95% CI, 0.086-0.121). Significant category-free and categorical NRI were also noted for ASCVD when using 90th or 95th percentile thresholds (eTable 8 in Supplement 1).

Table 2. Model Performance With Addition of Lipoprotein(a) to the American Heart Association PREVENT Net Reclassification in Pooled Cohorts for Atherosclerotic Cardiovascular Disease^a.

Atherosclerotic cardiovascular disease risk	Category-free, net reclassification improvement (95% CI)
Atherosclerotic cardiovascular disease risk	Overall	Events	Nonevents
Overall	0.058 (0.043 to 0.065)^b	−0.718 (−0.732 to −0.711)	0.776 (0.774 to 0.779)
Risk, %
<5	0.052 (0.025 to 0.067)^b	−0.725 (−0.750 to −0.709)	0.776 (0.774 to 0.780)
5 to <7.5	0.062 (0.044 to 0.078)^b	−0.710 (−0.731 to −0.691)	0.771 (0.767 to 0.776)
7.5 to <20	0.058 (0.045 to 0.080)^b	−0.721 (−0.734 to −0.699)	0.779 (0.774 to 0.784)
≥20	0.051 (0.003 to 0.161)^b	−0.650 (−0.723 to 0.784)	0.700 (−0.661 to 0.737)
Categorical	0.006 (0.004 to 0.011)^b	0.007 (0.005 to 0.013)	−0.001 (−0.002 to −0.001)

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^{^a}

For Predicting Risk of Cardiovascular Disease Events (PREVENT), the base 10-year equations were used.

^{^b}

Net reclassification improvement.

Adding elevated Lp(a) to the PREVENT base equations did not result in overall improvement in the C index for ASCVD (Table 3) or any other outcome in the pooled cohort or the individual studies (eTable 9 in Supplement 1). Similar results were seen when evaluating Lp(a) levels continuously or using 90th or 95th percentile thresholds (eTable 10 in Supplement 1). When added to the enhanced equations in MESA, elevated Lp(a) levels did not result in improvement in the C index for any outcome but did result in significant NRI for CHD (eTable 11 in Supplement 1).

Table 3. Model Performance With the Addition of Lipoprotein(a) to the American Heart Association Prevent (C Index) in Pooled Cohorts for Atherosclerotic Cardiovascular Disease^a.

Atherosclerotic cardiovascular disease risk	C Index (SE)
	Pooled			Multi-Ethnic Study of Atherosclerosis			UK Biobank
	PREVENT alone	PREVENT + Lp(a) >125 nmol/L	Change	PREVENT alone	PREVENT + Lp(a) >125 nmol/L^b	Change	PREVENT alone	PREVENT + Lp(a) >125 nmol/L	Change
Overall	0.73 (0.00)	0.73 (0.00)	0	0.74 (0.01)	0.74 (0.01)	0	0.73 (0.00)	0.73 (0.00)	0
Risk, %
<5	0.68 (0.01)	0.68 (0.01)	0	0.66 (0.03)	0.67 (0.03)	0.01	0.68 (0.01)	0.68 (0.01)	0
5 to <7.5	0.54 (0.01)	0.55 (0.01)	0.01	0.51 (0.03)	0.55 (0.04)	0.04	0.54 (0.01)	0.55 (0.01)	0.01
7.5 to <20	0.58 (0.01)	0.58 (0.01)	0	0.60 (0.02)	0.60 (0.02)	0	0.58 (0.01)	0.58 (0.01)	0
≥20	0.60 (0.03)	0.59 (0.03)	−0.01	0.59 (0.05)	0.57 (0.05)	−0.02	0.61 (0.04)	0.62 (0.03)	0.01

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Abbreviation: Lp(a), lipoprotein(a).

^{^a}

For Predicting Risk of Cardiovascular Disease Events (PREVENT), the base 10-year equations were used.

^{^b}

The conversion factor used for Lp(a) of 50 mg/dL to be equivalent to 125 nmol/L is 2.5.

Discussion

In this study of 2 large, multiethnic cohorts of individuals free of known cardiovascular disease at baseline, the PREVENT equations accurately predicted risk categories for multiple CVD outcomes for individuals with and without elevated Lp(a). However, elevated Lp(a) was associated with higher risk of CHD, ASCVD, HF, and total CVD overall and across PREVENT risk categories. Although only modest improvement was noted in risk prediction, the addition of elevated Lp(a) may be useful for personalized risk assessment, particularly among lower-risk individuals.

The PCE has been the standard for ASCVD risk assessment for the past decade in the US. Although the PREVENT equations have not yet been incorporated into new guidelines, they were designed to supplant the PCE. Much work is ongoing to understand their strengths and limitations. Prior studies have demonstrated improvement in risk prediction with the addition of Lp(a) values (either continuously or using a threshold) to conventional risk factors, the PCE, Systematic Coronary Risk Evaluation, Framingham Risk Score, and the Reynolds Risk Score (RRS) in several different populations.^{8,14,15,16,17,18} In these prior studies, any improvement in the C index or the Harrell C statistic has been modest. The highest overall C index was seen for CVD in the Bruneck study¹⁶ with the addition of Lp(a) level to the RRS (0.778). Most recently, this was evaluated in MESA with the PCE. The PCE accurately predicted ASCVD risk categories for individuals with Lp(a) levels higher than 50 mg/dL, but Lp(a) remained associated with increased risk (HR, 1.27; 95% CI, 1.00-1.61). The addition of Lp(a) levels higher than 50 mg/dL to the PCE resulted in improvement in ASCVD risk prediction with a category-free NRI of 0.0963, with the greatest improvement noted among participants at low and borderline risk (<7.5%). The C index with or without the inclusion of elevated Lp(a) was 0.739 (SE, 0.011) of CVD, and increased from 0.735 (SE, 0.014) to 0.737 (SE, 0.013) for CHD.⁸ The present study is the first, to our knowledge, to perform this analysis with the new PREVENT equations. Similar results were noted for ASCVD to the prior MESA study, with greater independent risk associated with elevated Lp(a) (HR, 1.34; 95% CI, 1.07-1.67) and similar category-free NRI (0.092). Additionally, these results were externally validated in a separate population from the UKB and in a pooled analysis. In addition to ASCVD and CHD, modest improvement in risk prediction was also noted for HF and total CVD; evaluation of these outcomes is a novel aspect of the PREVENT equations and the current study. There were also notable findings in specific risk category subgroups.

Prior studies have evaluated for improvement in risk prediction with the addition of other biomarkers to the PCE. In a prior study of MESA data, there was no improvement in the C index with the addition of Lp(a), ankle brachial index, apolipoprotein B, chronic kidney disease, family history of MI, high-sensitivity C-reactive protein, or waist circumference and fasting glucose values. The exception was coronary artery calcium scoring, which improved the C index by 0.017. Continuous NRI was significant for elevated Lp(a) levels (0.0963), comparable with high-sensitivity C-reactive protein (0.1273), and less pronounced than family history of MI (0.1934), waist circumference and fasting glucose levels (0.2120), and coronary artery calcium score (0.5768).⁸ In the present study, as noted above, the improvement in ASCVD prediction in MESA was similar (0.0917). In a prior study of 4 cohort studies, even non–HDL-C (0.004) and diabetes (0.009) led to relatively modest increases in the C index when added to other traditional risk factors for CHD.¹⁹ Addition of a polygenic risk score to the PCE was shown in one study to improve the C index by 0.004 with a categorical NRI of 0.024 and category-free NRI of 0.0162 for CHD.²⁰ In our study, the category-free NRI for CHD was 0.141 in MESA and 0.079 in the pooled cohorts. Thus, the lack of improvement in the C index with the addition of Lp(a) levels to the PREVENT equations and the significant NRI are in line with the generally modest improvement in risk prediction with the addition of individual biomarkers to risk scores, with coronary artery calcium being the most notable exception. Additionally, independently increased risk does not necessarily translate into better utility for risk prediction.

One of the novel aspects of the PREVENT equations is the exclusion of race and ethnicity whereas the PCE was developed with specific scores for White and Black individuals. This was done with an underlying premise that race is a social construct, not a biological one. However, Lp(a) levels are known to vary by race and ethnicity,²¹ and this variation may partially explain differences in risk estimates that were previously accounted for by race and ethnicity.²² In our study, however, there was no significant interaction between race and ethnicity and the PREVENT equations for predicting risk of ASCVD. Findings were limited by the smaller sample size for non-White racial and ethnic groups, and further studies are needed with larger numbers of racial and ethnic subgroups.

This study has important clinical implications. First, Lp(a) testing currently occurs at very low rates.²³ Although Lp(a) is not yet a target for specific medical therapy, it is at the minimum associated with increased long-term ASCVD risk, and noted to be a risk enhancer in clinical practice guidelines.²⁴ Additionally, knowledge of Lp(a) levels is actionable and may guide aggressive risk-factor modification and use of lipid-lowering and antiplatelet therapy.²⁵ The notable findings that elevated Lp(a) was most strongly associated with increased CHD risk in low-risk individuals may argue for more widespread Lp(a) testing to personalize risk assessment. Second, a recent study using data from the National Health and Nutrition Examination Survey observed that use of the PREVENT equations resulted in lower risk estimates and a reduction in the number of individuals meeting current risk thresholds for statin therapy compared with the PCE.²⁶ The impact of this on clinical outcomes remains to be seen, but our findings show that Lp(a) levels may be useful for personalized risk assessment for all outcomes on top of the PREVENT equations, and thus aligns with recent recommendations for universal Lp(a) testing.^6,27 Because risk categories for clinical decision-making with the PREVENT equations have not yet been established, we evaluated alternative risk categories using cut points of 3%, 5%, and 7.5% risk. We demonstrated consistent findings with these thresholds, particularly increased ASCVD risk across thresholds, with greater CHD risk in lower-risk individuals. Third, the PREVENT equations perform well in individuals with elevated Lp(a) levels, and thus can be used for accurate risk prediction broadly in this population. Further studies are needed to better assess the enhanced PREVENT equations, and the equations for 30-year risk in association with Lp(a) levels.

Limitations

This study has several limitations. We focused primarily on the base 10-year equations due to lack of full covariate data for the enhanced equations. There was also lack of sufficient follow-up to evaluate the 30-year equations. However, MESA, which started recruitment in 2000, is approaching 30 years of follow-up. MESA was used to derive the PREVENT equations; however, it was 1 of 25 datasets used and contributed a relatively small portion of the participant data. Lp(a) levels in MESA were converted to nanomole per liter to allow comparison and pooling with the UKB; however, it has previously been noted that such a conversion is imperfect.²⁸ Although the PREVENT equations incorporate statin therapy, a minority of participants in this study had baseline statin use; thus, further studies are needed to understand the use of PREVENT equations for statin users. Recent evidence suggests that the PREVENT equations significantly reclassify individuals into lower-risk categories. However, the relatively small number of high-risk participants in both MESA and UKB limits the ability to fully assess the impact of Lp(a) levels on the performance of the PREVENT equations in high-risk patients. Risk thresholds used in this study were based on those currently recommended for the PCE and other potential thresholds; however, guidelines have not yet been released to define these thresholds for the PREVENT equations, and these are likely to vary by outcome. Another limitation is that the UKB is a study of the UK population and may not represent the population in the US; however, the results of the UKB analysis are generally consistent with those of MESA.

Conclusions

In 2 multiethnic cohorts, the novel AHA PREVENT equations performed well for risk prediction among individuals with and without elevated Lp(a). However, Lp(a) measures remain independently associated with higher risk, particularly in lower-risk individuals. These findings suggest that more widespread testing using Lp(a) values may aid in further personalizing risk assessment.

Supplement 1.

eTable 1. Ten-Year Events Stratified by AHA PREVENT Risk Category in Pooled Cohorts

eTable 2. Ten-Year Events Stratified by AHA PREVENT Risk Category in Pooled Cohorts using Alternative Risk Thresholds

eTable 3. Ten-Year Events Stratified by AHA PREVENT Risk Category in MESA

eTable 4. Ten-Year Events Stratified by AHA PREVENT Risk Category in UKB

eTable 5. Model Performance with Addition of Lp(a) to AHA PREVENT (NRI) in Pooled Cohorts for other Outcomes

eTable 6. Model Performance with Addition of Lp(a) Level to AHA PREVENT Score for Pooled Cohorts using Alternative Risk Thresholds (Categorical NRI)

eTable 7. Model Performance with Addition of Lp(a) Level to AHA PREVENT Score for Individual Studies (NRI)

eTable 8. Model Performance with Addition of Lp(a) to AHA PREVENT (NRI) in Pooled Cohorts for ASCVD with Alternate Lp(a) Measures

eTable 9. Model Performance with addition of Lp(a) to AHA PREVENT (C-index) in Pooled Cohorts for other Outcomes

eTable 10. Model Performance with addition of Lp(a) to AHA PREVENT (C-index) in Pooled Cohorts for ASCVD with Alternate Lp(a) Measures

eTable 11. Model Performance with Addition of Lp(a) Level to AHA PREVENT Score using Enhanced Equations in MESA

jamacardiol-e251603-s001.pdf^{(447.4KB, pdf)}

Supplement 2.

Data Sharing Statement

jamacardiol-e251603-s002.pdf^{(15.1KB, pdf)}

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