Lee J, et al. Endocrinol Metab. 2013 Sep;28(3):180-191. http://dx.doi.org/10.3803/EnM.2013.28.3.180

Background: Metabolic syndrome is a cluster of conditions that occur together, increasing the risk of cardiovascular disease. However, the relationship between metabolic syndrome and dementia has remained controversial. Using nationwide population cohort data, we investigated the association between metabolic syndrome and dementia, according to the dementia type. Methods: We analyzed data of 84,144 individuals, in the aged group of more than 60 years, between January 1, 2009, to December 31, 2009, at Gangwon province by using the information of the (Korean) National Health Insurance Service. After eight years of gap, in 2017, we investigated the relationship between metabolic syndrome and dementia. We classified Dementia either as dementia of the Alzheimer type (AD) or vascular dementia (VD). AD and VD were defined as per the criteria of International Classification of Disease, Tenth Revision, Clinical Modification codes. Multiple logistic regression analyses examined the associations between metabolic syndrome or five metabolic syndrome components and dementia. Analyses included factors like age, sex, smoking, alcohol, physical inactivity, previous stroke, and previous cardiac disease. Results: Metabolic syndrome was associated with AD (OR = 11.48, 95% CI 9.03–14.59), not with VD. Each of five components of metabolic syndrome were also associated with AD. (high serum triglycerides: OR = 1.87, 95% CI 1.60–2.19; high blood pressure: OR = 1.85, 95% CI 1.55–2.21; high glucose: OR = 1.77, 95% CI 1.52–2.06; abdominal obesity: OR = 1.88, 95% CI 1.57–2.25; low serum high-density lipoprotein cholesterol: OR = 1.91, 95% CI 1.63–2.24) However, among components of metabolic syndrome, only the high glucose level was associated with VD. (OR = 1.26, 95% CI 1.01–1.56) body mass index (BMI), fasting glucose, and smoking were also associated with AD. (BMI: OR = 0.951, 95% CI 0.927–0.975; fasting glucose: OR = 1.003, 95% CI 1.001–1.005; smoking: OR = 1.020, 95% CI 1.003–1.039) A history of the previous stroke was associated with both AD and VD. (AD: OR = 1.827, 95% CI 1.263–2.644; VD: OR 2.775, 95% CI 1.747–4.406) Conclusions: Metabolic syndrome was associated with AD but not with VD. Patients with metabolic syndrome had an 11.48 times more likeliness to develop AD compared to those without metabolic syndrome. VD was associated only with several risk factors that could affect the vascular state rather than a metabolic syndrome. We suggested that the associations between metabolic syndrome and dementia would vary depending on the type of dementia.

The aim of this study is to compare Friedewald-estimated and directly measured low density lipoprotein cholesterol (LDL-C) values and assess the concordance in guideline risk classification between the two methods. The data were derived from the 2009 to 2011 Korea National Health and Nutrition Examination Survey. We included subjects with triglyceride (TG) levels < 400 mg/dL. Analysis was done for 6,454 subjects who had all lipid panels- total cholesterol, directly measured LDL-C, high density lipoprotein cholesterol (HDL-C), and TG. The subjects ranged in age from 10 to 87 years old. The mean age was 41.5 ± 17.3 years. For subjects with TG < 400 mg/dL, overall concordance in guideline risk classification was 79.1%. The Friedewald formula tended to underestimate LDL-C more at higher TG or lower HDL-C levels. Especially, the percent of subjects who were misclassified into a lower risk category was 31% when TG were 200 to 299 mg/dL; and 45.6% when TG were 300 to 399 mg/dL. A greater underestimation of LDL-C occurred at higher TG and lower Friedewald-estimated LDL-C levels. Of subjects with a Friedewald-estimated LDL-C < 70 mg/dL, 55.4% had a directly measured LDL-C ≥ 70 mg/dL when TG were 200 to 399 mg/dL. The Friedewald equation tends to underestimate LDL-C in highrisk subjects such as hypertriglyceridemia and hypo-HDL-cholesterolemia. For these individuals accurate assessment of LDL-C is crucial, and therefore additional evaluation is warranted.

Lee J, et al. Yonsei Med J. 2017 Mar;58(2):326-338. https://doi.org/10.3349/ymj.2017.58.2.326

Objective: This study aimed to investigate thyroid hormone (TH) status and its relationship with myocardial function as well as clinical and biochemical parameters in stress cardiomyopathy (CMP).Methods: Forty-five patients with stress CMP (the patient group), 31 patients without stress CMP (the control II group), and 58 healthy subjects (the control I group) were included. Sick euthyroid syndrome (SES) was defined as low total triiodothyronine (T₃) with normal TSH levels.Results: In the patient group at admission, prevalence of SES was 62.2%. Compared with the control I group, the patient group had a decrease in left ventricular ejection fraction (LVEF) and systolic blood pressure (BP) and an increase in troponin-I, CK-MB, and B-type natriuretic peptide (BNP) levels. Total T₃ levels were reduced, and anti-thyroid peroxidase antibody (anti-TPO Ab) positivity, C-reactive protein (CRP) and cortisol levels were elevated. Total T₃ levels were associated with acute physiology and chronic health evaluation II (APACHE II) score, LVEF, systolic BP, and cortisol levels in multivariate analysis. In the control II group, total T₃ levels were not associated with any variables. In the SES (n=28) and myocardial dysfunction (MDys, n=27) subgroups, increased APACHE II score and BNP levels as well as decreased LVEF and systolic BP were significant. Total T₃ levels were reduced, and CRP, cortisol and catecholamines levels were elevated. In the MDys subgroup, anti-TPO Ab positivity and titer were increased.Conclusion: These results suggest that total T₃ levels may be associated with myocardial contractility, clinical severity, and cortisol levels. Thyroid autoimmunity may influence myocardial contractility in stress CMP.

Background: Coronary artery calcium score (CACS) is a reliable predictor for future cardiovascular disease risk. Although deep learning studies using computed tomography (CT) images to predict CACS have been reported, no study has assessed the feasibility of machine learning (ML) algorithms to predict the CACS using clinical variables in a healthy general population. Therefore, we aimed to assess whether ML algorithms other than binary logistic regression (BLR) could predict high CACS in a healthy population with general health examination data. Methods: This retrospective observational study included participants who had regular health screening including coronary CT angiography. High CACS was defined by the Agatston score ≥ 100. Univariable and multivariable BLR was performed to assess predictors for high CACS in the entire dataset. When performing ML prediction for high CACS, the dataset was randomly divided into a training and test dataset with a 7:3 ratio. BLR, catboost, and xgboost algorithms with 5-fold cross-validation and grid search technique were used to find the best performing classifier. Performance comparison of each ML algorithm was evaluated with the area under the receiver operating characteristic (AUROC) curve. Results: A total of 2133 participants were included in the final analysis. Mean age and proportion of male sex were 55.4 ± 11.3 years and 1483 (69.5%), respectively. In multivariable BLR analysis, age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10–1.15, p < 0.001), male sex (OR, 2.91; 95% CI, 1.57–5.38, p < 0.001), systolic blood pressure (OR, 1.02; 95% CI, 1.00–1.03, p = 0.019), and low-density lipoprotein cholesterol (OR, 1.00; 95% CI, 0.99–1.00, p = 0.047) were significant predictors for high CACS. Performance in predicting high CACS of xgboost was AUROC of 0.823, followed by catboost (0.750) and BLR (0.585). The comparison of AUROC between xgboost and BLR was significant (p for AUROC comparison < 0.001). Conclusions: Xgboost ML algorithm was found to be a more reliable predictor of CACS in healthy participants compared to the BLR algorithm. ML algorithms may be useful for predicting CACS with only laboratory data in healthy participants.