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Korean Journal of Family Medicine ; : 106-115, 2019.
Article in English | WPRIM | ID: wpr-738866

ABSTRACT

BACKGROUND: This study evaluated factors independently associated with electrocardiographic left ventricular hypertrophy (ECG-LVH) in subjects who were normotensive on clinical measurement and had no prior history of hypertension. METHODS: This cross-sectional study analyzed cases and controls in the Comprehensive Medical Examination Center of Hallym University Sacred Heart Hospital. Eligible case participants presented ECG-LVH according to the Sokolow- Lyon or Cornell criteria, were normotensive on clinical measurement, and had never received a diagnosis of hypertension. The control group comprised subjects with normal sinus rhythm who were normotensive on clinical measurement with no history of hypertension. RESULTS: A multiple logistic regression model showed male sex, age and systolic blood pressure to be positively related to the presence of ECG-LVH. A positive relation of smoking and regular exercise; an inverse relation of pulse rate to the presence of ECG-LVH were found only in men. An inverse relation of uric acid level was found only in women. Detailed analyses of relatively healthy and young men according to whether or not to exercise regularly showed that positive relations of age and systolic blood pressure; an inverse relation of obesity to the presence of ECG-LVH were apparent in the non-regular exercise group but not in the regular exercise group. In the regular exercise group, only pulse rate showed significant (inverse) association with the presence of ECG-LVH. CONCLUSION: The varying risk factor profiles associated with ECG-LVH according to sex and the participation in regular exercise may help to elucidate the ECG-LVH in clinical normotensives with no prior history of hypertension.


Subject(s)
Female , Humans , Male , Blood Pressure , Cardiovascular Diseases , Cross-Sectional Studies , Diagnosis , Electrocardiography , Heart , Heart Rate , Hypertension , Hypertrophy, Left Ventricular , Logistic Models , Obesity , Risk Factors , Smoke , Smoking , Uric Acid
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