Non-traditional risk factors and the risk of myocardial infarction in the young in the US population-based cohort

Chayakrit Krittanawong; Yiming Liu; Dhruv Mahtta; Bharat Narasimhan; Zhen Wang; Hani Jneid; Jacqueline E Tamis-Holland; Alam Mahboob; Usman Baber; Roxana Mehran; W H Wilson Tang; Christie M Ballantyne; Salim S Virani

doi:10.1016/j.ijcha.2020.100634

Non-traditional risk factors and the risk of myocardial infarction in the young in the US population-based cohort

Int J Cardiol Heart Vasc. 2020 Sep 23:30:100634. doi: 10.1016/j.ijcha.2020.100634. eCollection 2020 Oct.

Authors

Chayakrit Krittanawong^{1

2

3}, Yiming Liu⁴, Dhruv Mahtta¹, Bharat Narasimhan³, Zhen Wang^{5

6}, Hani Jneid^{1

2}, Jacqueline E Tamis-Holland³, Alam Mahboob¹, Usman Baber³, Roxana Mehran³, W H Wilson Tang⁷, Christie M Ballantyne¹, Salim S Virani^{1

2}

Affiliations

¹ Michael E. DeBakey VA Medical Center, Houston, TX, USA.
² Section of Cardiology, Baylor College of Medicine, USA.
³ Department of Cardiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY, USA.
⁴ National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA.
⁵ Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
⁶ Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
⁷ Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.

Abstract

Although most prevalent in elderly, myocardial infarction (MI) also affects younger adults. We sought to investigate baseline characteristics in young patients (<55 years) with MI using the National Inpatient Sample (NIS) database between 2004 and 2015. Multivariable logistic regression models were used to assess factors associated with acute myocardial infarction (AMI) in young patients. After multivariable analyses adjusted for age, sex, race, family history of atherosclerosis, body mass index (BMI), diabetes, hypertension, hyperlipidemia, chronic kidney disease, and current cigarette smoking; novel risk factors such as human immunodeficiency virus (HIV), systemic lupus erythematosus (SLE), and obstructive sleep apnea (OSA) were associated with a higher risk of developing an AMI in the young (adjusted OR for HIV 4.06; 95 CI 3.48-4.71, p < 0.001), (adjusted OR for SLE 2.12; 95 CI 1.89-2.39, p 0.04), and (adjusted OR for OSA 1.16; 95 CI 1.12-1.20, p < 0.001), respectively. Rheumatoid arthritis was associated with a lower risk of AMI (adjusted OR 0.83; 95 CI 0.76-0.89, p < 0.001). After multivariable analyses, cigarette smoking (adjusted OR 1.98; 95 CI 1.95-2.02, p < 0.001), obesity (adjusted OR 1.37; 95 CI 1.33-1.41, p = 0.003), hyperlipidemia (adjusted OR 1.07; 95 CI 1.04-1.08, p < 0.001) and a family history of CAD (adjusted OR 1.35; 95 CI 1.3-1.4, p < 0.001) were also associated with a higher risk of developing an AMI in the young. In conclusion, young patients with AMI have both traditional risk factors and non-traditional risk factors. In addition to traditional risk factors, close attention should be paid to emerging risk factors such as SLE, HIV and OSA.

Keywords: Myocardial infarction; Non-traditional risk factors; Traditional risk factors; US population-based cohort.