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Early identification of cardiovascular disease risk in women

Early identification of cardiovascular disease (CVD) risk in women can help save lives

CVD is the leading cause of death in women, taking the lives of over 300,000 women in the US in 2020.1
However, women are much less likely than men to be assessed for CVD risk based on guidelines.2

Many risk factors that are unique to women may be overlooked4

While women and men share the 3 most common risk factors for CVD—hypertension, high low-density lipoprotein-cholesterol (LDL-C), and smoking1, 5, 6—there are unique risk-enhancing factors for women at every stage of life.

Testing solutions to help assess both traditional and risk-enhancing factors for CVD risk in women

Individuals suitable for testing:

  • Patients with 1 or more traditional risk factors
  • Patients with 1 or more risk-enhancing factors unique to women (see table above)

Testing solutions for early identification of CVD risk in women

Lipid screening assesses lipoprotein and apolipoprotein to improve risk stratification, allowing you to personalize patient treatment plans more precisely

Inflammation within the artery wall is a key contributor to CVD risk; monitoring inflammatory markers may help uncover hidden CVD risk

Identification of metabolic risk at an early stage allows implementation of evidence-based strategies that can prevent or delay disease progression

Panel and profile components may be ordered separately:
Lipid Panel: Cardio IQ Cholesterol Total (91717); Cardio IQ Triglycerides (91718); Cardio IQ HDL Cholesterol (91719)
Lipid Panel with Reflex to Direct LDL: Cardio IQ Cholesterol Total (91717); Cardio IQ Triglycerides (91718); Cardio IQ HDL Cholesterol (91719). If triglyceride result is >400 mg/dL, Direct LDL Cholesterol will be performed at an additional charge

 

References

  1. CDC. Women and heart disease. Reviewed October 14, 2022. Accessed November 9, 2022. https://www.cdc.gov/heartdisease/women.htm
  2. Brown HL, et al. doi:10.1161/CIR.0000000000000582
  3. Bairey Merz CN, et al. doi:10.1016/j.jacc.2017.05.024
  4. Maffei S, et al. doi:10.1016/j.ijcard.2019.02.005
  5. Arnett DK, et al. doi:10.1161/CIR.0000000000000678
  6. Yusuf S, et al. doi:10.1016/S0140-6736(04)17018-9
  7. Lee JJ, et al. doi:10.1161/JAHA.119.012406
  8. Ayer J, et al. doi:10.1093/eurheartj/ehv089
  9. Solomon CG, et al. doi:10.1210/jcem.87.5.8471
  10. Lau ES, et al. doi:10.1016/j.jacc.2022.02.020
  11. Osibogun O, et al. doi:10.1016/j.tcm.2019.08.010
  12. Okoth K, et al. doi:10.1111/1471-0528.16692
  13. McIntyre HD, et al. doi:10.1038/s41572-019-0098-8
  14. Wu P, et al. doi:10.1161/JAHA.117.007809
  15. Wu P, et al. doi:10.1161/CIRCOUTCOMES.116.003497
  16. Okoth K, et al. doi:10.1136/bmj.m3502
  17. Carlson LE, et al. doi:10.1016/j.jaccao.2021.07.008
  18. Jurgens CY, et al. doi:10.1161/CIR.0000000000001089
  19. Moran, et al. doi:10.1161/CIRCRESAHA.121.319877
  20. Moon, et al. doi:10.1089/thy.2017.0414
  21. Razvi, et al. doi: 10.1016/j.jacc.2018.02.045 
  22. Okoth K, et al. doi:10.1136/bmj.m3502 
  23. Jiesuck Park, et al. doi:10.1136/heartjnl-2020-318764 
  24. Brown JC, et al. doi:10.1002/jcsm.12073 
  25. Barton M, et al. doi:10.1161/HYPERTENSIONAHA.108.120022 
  26. Haring B, et al. doi:10.1161/JAHA.113.000369 
  27. Tankó LB, et al. doi:10.1359/JBMR.050711