Hidden Cardiovascular Phenotypes in Apparently Low risk Cardiology Outpatients

Abstract

Background: Patients classified as low risk in routine cardiology outpatient practice are generally assumed to have minimal cardiovascular vulnerability. However, conventional risk assessment focuses primarily on overt clinical events and may overlook early biological, electrical, functional, and structural abnormalities. Whether a limited set of clinically key associated factors an explain subclinical cardiovascular vulnerability in apparently low-risk outpatients remains unclear. Methods: This retrospective observational study included 476 adults aged 18–75 years evaluated in a cardiology outpatient clinic. Patients with established cardiovascular disease or major cardiac comorbidities were excluded. Routine outpatient data encompassing clinical characteristics, laboratory findings, electrocardiography, exercise treadmill testing, and transthoracic echocardiography were reviewed. Subclinical cardiovascular vulnerability was defined as the presence of at least one abnormality across inflammatory, electrical, functional, or structural domains. Multivariable modeling was performed with emphasis on identifying clinically dominant determinants of subclinical cardiovascular phenotypes. Results: Subclinical cardiovascular vulnerability was identified in 30.0% of patients. Although multiple clinical, biochemical, and functional parameters were associated with this phenotype, the majority of the clinically relevant signal was driven by a limited subset of variables. Current smoking, ischemic ST-segment depression during exercise testing, and elevated hs-CRP consistently demonstrated the strongest associations, while other factors contributed more modestly to the overall risk profile. Conclusion: A substantial proportion of cardiology outpatients categorized as low risk exhibit subclinical cardiovascular vulnerability detectable through routine outpatient assessment. While several factors are associated with this phenotype, a small number of key associated factors appear to account for most of the clinically actionable risk. A focused, phenotype-oriented interpretation of simple outpatient markers may improve risk refinement and help identify individuals who warrant closer cardiovascular surveillance despite apparently low estimated risk.

Keywords

• subclinical cardiovascular phenotypes
• risk misclassification
• smoking
• exercise testing
• inflammation; primary prevention

Ethical Statement

The study protocol was reviewed and approved by the ethics Committee (meeting date: December 3, 2025; decision number: 163). The study was conducted in accordance with the principles of the Declaration of Helsinki.

Görünürde Düşük Riskli Kardiyoloji Ayaktan Hasta Popülasyonunda Subklinik Kardiyovasküler Fenotipler

Abstract

Amaç: Rutin kardiyoloji poliklinik pratiğinde düşük riskli olarak sınıflandırılan hastaların kardiyovasküler açıdan minimal risk taşıdığı varsayılmaktadır. Ancak geleneksel risk değerlendirme yaklaşımları çoğunlukla belirgin klinik olaylara odaklanmakta ve erken dönemdeki biyolojik, elektriksel, fonksiyonel ve yapısal anormallikleri gözden kaçırabilmektedir. Görünürde düşük riskli hastalarda sınırlı sayıda klinik olarak baskın belirtecin subklinik kardiyovasküler kırılganlığı açıklayıp açıklayamayacağı net değildir. Bu çalışmada, düşük riskli kardiyoloji polikliniği hastalarında subklinik kardiyovasküler kırılganlığın ve bunu belirleyen baskın klinik sinyallerin değerlendirilmesi amaçlandı. Yöntemler: Bu retrospektif gözlemsel çalışmaya, bir kardiyoloji polikliniğinde değerlendirilen 18–75 yaş arası 476 erişkin hasta dahil edildi. Önceden tanımlı kardiyovasküler hastalığı veya majör kardiyak komorbiditesi olan hastalar çalışma dışı bırakıldı. Klinik özellikler, laboratuvar bulguları, elektrokardiyografi, efor testi ve transtorasik ekokardiyografi verileri içeren rutin poliklinik kayıtları incelendi. Subklinik kardiyovasküler kırılganlık; inflamatuvar, elektriksel, fonksiyonel veya yapısal alanlardan en az birinde anormallik saptanması olarak tanımlandı. Subklinik kardiyovasküler fenotiplerin klinik olarak baskın belirleyicilerini ortaya koymak amacıyla çok değişkenli analizler yapıldı. Bulgular: Hastaların %30,0’unda subklinik kardiyovasküler kırılganlık saptandı. Birden fazla klinik, biyokimyasal ve fonksiyonel parametrenin bu fenotiple ilişkili olduğu gözlenmekle birlikte, klinik açıdan anlamlı ilişkinin büyük bölümü sınırlı sayıdaki değişken tarafından belirlendi. Güncel sigara kullanımı, efor testinde iskemik ST-segment depresyonu ve yüksek hs-CRP düzeyleri en güçlü ilişkileri gösterirken, diğer faktörlerin genel risk profiline katkısı daha sınırlı düzeyde kaldı. Sonuç: Düşük riskli olarak sınıflandırılan kardiyoloji polikliniği hastalarının önemli bir kısmında, rutin poliklinik değerlendirme ile saptanabilen subklinik kardiyovasküler kırılganlık mevcuttur. Bu fenotip ile çeşitli faktörler ilişkili olsa da, klinik olarak eyleme dönüştürülebilir riskin büyük kısmı sınırlı sayıdaki baskın sinyal tarafından açıklanmaktadır. Basit poliklinik belirteçlerinin fenotip odaklı yorumlanması, risk sınıflandırmasının daha hassas yapılmasına katkı sağlayabilir ve görünürde düşük tahmini riske rağmen daha yakın kardiyovasküler izlem gerektiren bireylerin belirlenmesine yardımcı olabilir.

Keywords

• Subklinik kardiyovasküler fenotipler
• Kardiyovasküler riskin yanlış sınıflandırılması
• Sigara kullanımı;
• Efor testi
• İnflamasyon; Birincil Korunma.

References

1. 1.Roth GA, Mensah GA, Johnson CO, et al. Globalburden of cardiovascular diseases and risk factors,1990-2019. J Am Coll Cardiol. 2020;76:2982-3021.
2. 2.Piepoli MF, Hoes AW, Agewall S, et al. 2016European guidelines on cardiovascular diseaseprevention in clinical practice. Eur Heart J.2016;37:2315-2381.
3. 3.Arnett DK, Blumenthal RS, Albert MA, et al. 2019ACC/AHA guideline on the primary prevention ofcardiovascular disease. Circulation. 2019;140:e596-e646.
4. 4.Blaha MJ, Cainzos-Achirica M, Greenland P. Thelegacy of the Multi-Ethnic Study of Atherosclerosis(MESA). J Am Coll Cardiol. 2016;68:2587-99.
5. 5.Libby P, Ridker PM, Hansson GK. Inflammation inatherosclerosis. J Am Coll Cardiol. 2009;54:2129-38.
6. 6.Naghavi M, Libby P, Falk E, et al. From vulnerableplaque to vulnerable patient. Circulation.2003;108:1664-72.
7. 7.Greenland P, Blaha MJ, Budoff MJ, et al. Coronarycalcium score and cardiovascular risk. J Am CollCardiol. 2018;72:434-47.
8. 8.Myers J, Prakash M, Froelicher V, et al. Exercisecapacity and mortality among men referred forexercise testing. N Engl J Med. 2002;346:793-801.

9. 9.Mora S, Ridker PM. Justification for the use ofstatins in primary prevention. Circulation.2006;114:165-73.
10. 10.Ridker PM, Danielson E, Fonseca FAH, et al.Rosuvastatin to prevent vascular events in men andwomen with elevated C-reactive protein. N Engl JMed. 2008;359:2195-207.
11. 11.Emerging Risk Factors Collaboration. C-reactiveprotein, fibrinogen, and cardiovascular diseaseprediction. Lancet. 2010;375:132-40.
12. 12. Katz MH. Multivariable analysis: a practical guidefor clinicians. 2nd ed. Cambridge: CambridgeUniversity Press; 2011.
13. 13.Budoff MJ, Dowe D, Jollis JG, et al. Diagnosticperformance of 64-multidetector row coronary computed tomography angiography. N Engl J Med. 2008;358:1336-45.
14. 14.Nasir K, Rubin J, Blaha MJ, et al. Interplay ofcoronary artery calcification and inflammation inasymptomatic individuals. Circ Cardiovasc Imaging.2012;5:573-81.
15. 15. Khera R, Pandey A, Ayers CR, et al. Contemporaryrisk stratification beyond traditional risk scores.Circulation. 2020;142:2219-30.
16. 16.Steyerberg EW. Clinical prediction models: apractical approach to development, validation, andupdating. New York: Springer; 2019.
17. 17.Moons KGM, Altman DG, Reitsma JB, et al.Transparent reporting of a multivariable predictionmodel for individual prognosis or diagnosis(TRIPOD). Ann Intern Med. 2015;162:W1-W73.
18. 18.U.S. Department of Health and Human Services.The health consequences of smoking: 50 years ofprogress. A report of the Surgeon General. Atlanta(GA): U.S. Department of Health and HumanServices; 2014.
19. 19.Ambrose JA, Barua RS. The pathophysiology ofcigarette smoking and cardiovascular disease. J AmColl Cardiol. 2004;43:1731-7.
20. 20.Lauer MS, Froelicher ES, Williams M, Kligfield P.Exercise testing in asymptomatic adults. Circulation. 2005;112:771-6.
21. 21.Gibbons RJ, Balady GJ, Bricker JT, et al. ACC/AHA2002 guideline update for exercise testing.Circulation. 2002;106:1883-92.
22. 22.Ridker PM. Inflammatory biomarkers andcardiovascular risk: hs-CRP. Circulation.2003;107:363-9.
23. 23.Ridker PM, Buring JE, Shih J, et al. Prospectivestudy of C-reactive protein and risk of futurecardiovascular events. Circulation. 1998;98:731-3.
24. 24.Danesh J, Wheeler JG, Hirschfield GM, et al. C-reactive protein and other circulating markers ofinflammation in the prediction of coronary heartdisease. N Engl J Med. 2004;350:1387-97.
25. 25.Harrell FE Jr. Regression modeling strategies.2nd ed. New York: Springer; 2015.
26. 26.Budoff MJ, Nasir K, McClelland RL, et al. Coronarycalcium predicts events better with inflammationmarkers. J Am Coll Cardiol. 2009;53:345-52.
27. 27.Blaha MJ, Rivera JJ, Budoff MJ, et al. Associationbetween obesity, hs-CRP, and coronary arterycalcification. Atherosclerosis. 2011;216:129-34.
28. 28.Ioannidis JPA. Why most discovered trueassociations are inflated. Epidemiology.2008;19:640-8.
29. 29.Rothman KJ, Greenland S, Lash TL. Modernepidemiology. 3rd ed. Philadelphia: LippincottWilliams & Wilkins; 2008.
30. 30.Pearson TA, Blair SN, Daniels SR, et al. AHAguidelines for primary prevention of cardiovasculardisease. Circulation. 2002;106:388-91.