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Comparison of high polygenic risk score for hypercholesterolemia and Familial Hypercholesterolemia risk in the eMERGE IV study

Health Services and Implementation
  • Primary Categories:
    • Clinical-Adult
  • Secondary Categories:
    • Clinical-Adult & Pediatric
Introduction:
Identifying genomic risk assessments for hypercholesterolemia (HCL) can enable effective clinical interventions and improve health outcomes. The eMERGE IV study provides a risk assessment for HCL, including a cross-ancestry polygenic risk score (PRS), monogenic results for familial hypercholesterolemia (FH), family history, and clinical risk factors. We assessed the impact of having a PRS in the top 3%-ile for HCL (PRS+) or a positive monogenic result for FH (FH+) compared to not having high genomic risk (not-high-risk) on HCL-related risk factors and health outcomes.  

Methods:
Our analysis included 19,519 adults enrolled in the eMERGE IV study. Participant demographics, clinical data, and family history were collected through participant surveys or extracted from electronic health records. We used linear regression models to test the associations between the three possible genomic risk results for HCL (FH+, PRS+, or not-high-risk) and age, BMI, and most recent LDL-C levels at time of consent, calculated using the Friedewald equation. We used logistic regression models to test the associations between genomic risk results and sex; self-reported race/ethnicity (R/E); self-reported smoking history, self-reported personal history of HCL; and self-reported family history of HCL (≥1 first degree relative with HCL). Among participants with a personal history of HCL, we used logistic regression to test the association between genomic risk results and prior lipid-lowering medication use.

Results:
Participants’ mean age was 51 (range 18-75), 68% were female, and most self-reported as White (52.3%), Hispanic/Latino(a) (19.2%), or Black (13.4%). The majority (97.2%) of participants were not-high-risk for HCL, while 133 (0.7%) received FH+ results and 411 (2.1%) received PRS+ results. Among the three genomic risk groups, there were no significant differences in age, sex, self-reported R/E, or BMI. Compared to not-high-risk participants, PRS+ and FH+ participants had significantly higher LDL-C levels (p < 2.7x10-22 and p < 1.6x10-9, respectively). There were no significant differences in LDL-C levels between PRS+ (mean = 119.8 mg/dL) and FH+ participants (mean = 120.5 mg/dL; p = 0.854), yet FH+ participants were 1.7 times more likely to report a previous HCL diagnosis than PRS+ participants (p < 0.016). Despite this, participants in each of the genomic risk groups had similar rates of prior lipid-lowering medication use. Compared to not-high-risk participants, PRS+ participants were 2.4 times more likely to report a previous HCL diagnosis and FH+ participants were 4.1 times more likely, (p < 5.3x10-19; p  < 4.5x10-14, respectively). Finally, FH+ participants were 2.2 times more likely to report a family history of HCL and PRS+ participants were 1.9 times more likely, in comparison with not-high-risk participants (p < 9.0x10-11 and p < 4.8x10-6, respectively). There were no significant differences between reported family history of HCL among PRS+ and FH+ participants (p = 0.477).

Conclusion:
Participants with a high-risk PRS for HCL or a positive FH result exhibited significantly higher LDL-C levels and were more likely to have a HCL diagnosis and family history of HCL compared to those without  genomic risk. Given that PRS identifies three times more individuals at high risk compared to monogenic FH testing, Iimplementation of PRS for HCL in primary care may improve the identification of individuals who would benefit from optimization of HCL management.

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