- Obesity and heart failure with preserved ejection fraction: pathophysiology and clinical significance
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Da Young Lee
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Cardiovasc Prev Pharmacother. 2022;4(2):70-74. Published online April 27, 2022
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DOI: https://doi.org/10.36011/cpp.2022.4.e10
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 Abstract
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- Obesity is a risk factor for heart failure and cardiovascular disease. Of particular note, over 80% of patients with heart failure with a preserved ejection fraction (HFpEF) are overweight or obese. In this study, we aimed to review the association between obesity and HFpEF. Obese patients with HFpEF exhibit a distinct phenotype. In addition to impaired left ventricular (LV) diastolic function and high filling pressures, obese patients with HFpEF possess other factors that cause elevated LV filling pressure, such as a greater dependence on plasma volume expansion, aggravated pericardial restraint, and increased ventricular interaction. Obesity can contribute to HFpEF through hemodynamic, neurohormonal, inflammatory, and mechanical mechanisms. An increased amount of body fat can induce plasma volume expansion, resulting in chamber remodeling, pericardial restraint, and ultimately elevations in LV filling pressure. Obesity can mediate the activation of sympathetic nervous system signaling and the renin-angiotensin-aldosterone system. These unique pathophysiological characteristics of individuals with both obesity and HFpEF suggest that obesity with HFpEF can be considered a specific phenotype. Future research is expected to clarify effective treatment modalities for obesity-related HFpEF.
- Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It
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HyeongKyu Park, Da Young Lee, So young Park, Jiyoung Min, Jiwon Shinn, Dae Ho Lee, Soon Hyo Kwon, Hun-Sung Kim, Nan Hee Kim
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Cardiovasc Prev Pharmacother. 2021;3(4):106-114. Published online October 31, 2021
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DOI: https://doi.org/10.36011/cpp.2021.3.e14
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Abstract
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- Background
Glycated hemoglobin (HbA1c), which reflects the patient's blood sugar level, can only be measured in a hospital setting. Therefore, we developed a model predicting HbA1c using personal information and self-monitoring of blood glucose (SMBG) data solely obtained by a patient.
Methods
Leave-one-out cross-validation (LOOCV) was performed at two university hospitals. After measuring the baseline HbA1c level before SMBG (Pre_HbA1c), the SMBG was recorded over a 3-month period. Based on these data, an HbA1c prediction model was developed, and the actual HbA1c value was measured after 3 months. The HbA1c values of the prediction model and actual HbA1c values were compared. Personal information was used in addition to SMBG data to develop the HbA1c predictive model.
Results
Thirty model training sessions and evaluations were conducted using LOOCV. The average mean absolute error of the 30 models was 0.659 (range, 0.005–2.654). Pre_HbA1c had the greatest influence on HbA1c prediction after 3 months, followed by post-breakfast blood glucose level, oral hypoglycemic agent use, fasting glucose level, height, and weight, while insulin use had a limited effect on HbA1c values.
Conclusions
The patient's SMBG data and personal information strongly influenced the HbA1c predictive model. In the future, it will be necessary to develop sophisticated predictive models using large samples for stable SMBG in patients.
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