Clinical utility of polygenic risk scores for CHD, CAD questioned
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Two polygenic risk scores, one for predicting CHD and one for predicting CAD, did not meaningfully improve risk prediction beyond conventional risk factors, according to findings published in JAMA.
CHD polygenic risk score
In an analysis of the ARIC and MESA studies, researchers determined that although a polygenic risk score was associated with 10-year CHD incidence (ARIC, HR = 1.24; 95% CI, 1.15-1.34; MESA, HR = 1.38; 95% CI, 1.21-1.58), adding it to the Pooled Cohort Equations from the American College of Cardiology and American Heart Association did not significantly increase the C statistic in either group (ARIC, change in C statistic, 0.001; 95% CI, 0.009 to 0.006; MESA, change in C statistic, 0.021; 95% CI, 0.0004 to 0.043).
Moreover, at the 10-year risk threshold of 7.5%, addition of the polygenic risk score did not provide significant reclassification improvement in either cohort (net reclassification improvement in ARIC, 0.018; 95% CI, 0.012 to 0.036; net reclassification improvement in MESA, 0.001; 95% CI, 0.038 to 0.076) beyond the clinical score.
“Given the poor discriminative performance of a polygenic risk score observed in these analyses, the clinical implications of finding a high polygenic risk score in a young person with very low absolute risk are unclear, in the absence of an identifiable risk factor such as hyperlipidemia,” Jonathan D. Mosley, MD, PhD, assistant professor of medicine at Vanderbilt University, and colleagues wrote. “Screening with a polygenic risk score could provide motivation for lifestyle modification (eg, better diet or increased physical activity), but there may be simpler ways to promote such interventions at the individual or population level.”
In other findings, the polygenic risk score also did not significantly improve calibration, compared with the clinical risk score, in either cohort.
In this retrospective study, researchers assessed the prognostic value of a previously established polygenic risk score among cohorts from the ARIC (n = 4,847; mean age, 63 years; 56% women; 100% white; 6% with diabetes) and MESA studies (n = 2,390; mean age, 62 years; 52% women; 100% white; 6% with diabetes).
CAD polygenic risk score
In an analysis of a polygenic risk score to predict CAD, the score had a lower C statistic than the Pooled Cohort Equation (0.61; 95% CI, 0.6-0.62 vs. 0.76; 95% CI, 0.75-0.77) and combining the two led to only a modest improvement in C statistic compared with the Pooled Cohort Equation alone (0.78; 95% CI, 0.77-0.79; difference, 0.02; 95% CI, 0.01-0.03), researchers reported.
Moreover, at a risk threshold of 7.5%, addition of the polygenic risk score to the Pooled Cohort Equation was tied to a net reclassification improvement of 4.4% in cases (95% CI, 3.5-5.3) and –0.4% in noncases (95% CI, –0.5 to –0.4), for an overall net reclassification improvement of 4 percentage points (95% CI, 3.1-4.9).
“[In] this study, state-of-the-art polygenic risk score only modestly improved prediction. The number of people meaningfully changing risk category and, therefore, receiving different treatment strategies based on genetic information is relatively small, with improvements mainly seen among cases reclassified to higher risk by addition of polygenic risk score to Pooled Cohort Equations, whereas no-cases had worse reclassifications (more noncases moved to the higher-risk category than were correctly reclassified to the lower-risk category),” Joshua Elliott, MBBS, MSc, of the department of epidemiology and biostatistics at the Imperial College London, and colleagues wrote. “The relative benefit of those correct vs. incorrect reclassifications in cases and noncases needs to take into account the risk-benefit profile of statins in a decision analysis and subsequent economic evaluation.”
For this observational study, researchers assessed 352,660 participants from the UK Biobank (mean age, 56 years; 58% women) who were enrolled between 2006 and 2010.
“Genotyping is already becoming a relatively inexpensive measure, requiring only a one-off assessment that can be obtained from birth,” the researchers wrote. “Germline genetic variants are therefore appealing as putative predictors of lifetime disease risk. However, the potential implementation of polygenic risk score in clinical practice needs careful evaluation.”
Too many questions, not enough research
“There are still unanswered questions that could identify selected uses of polygenic risk scores (current versions or better, more diverse versions),” Sadiya S. Khan, MD, MSc, assistant professor of medicine and preventive medicine at Northwestern University Feinberg School of Medicine, and colleagues wrote in a related editorial. “For example, none of the current studies examined use of polygenic risk scores applied much earlier in the life course. Future studies in younger adults to examine the utility of current-era and newer polygenic risk scores for CAD could also assess lifetime risk of atherosclerotic cardiovascular disease models, which are currently recommended by U.S. primary prevention guidelines to guide individualized patient-physician risk-based discussions.
“The available data do not support the clinical utility of CAD polygenic risk scores (in their current form) in middle-aged adults of European descent,” Khan and colleagues wrote. “In the meanwhile, the best approach for prevention of CAD continues to be a combination of population-wide risk factor approaches for the entire population and addition of drug therapies and lifestyle interventions according to guidelines developed by the American Heart Association and American College of Cardiology.” – by Scott Buzby
References:
Elliott JE, et al. JAMA. 2020;doi:10.1001/jama.2019.22241.
Khan SS, et al. JAMA. 2020;doi:10.1001/jama.2019.21667.
Mosley JD, et al. JAMA. 2020;doi:10.1001/jama.2019.21782.
Disclosures: Elliott, Mosley and the editorial authors report no relevant financial disclosures. Please see the studies for all other authors’ relevant financial disclosures.