Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Background: Polycyclic aromatic hydrocarbons are generated from the incomplete combustion of organic materials such as fossil fuels and wood. These compounds, often inhaled, cause respiratory issues like asthma and chronic obstructive pulmonary disease due to oxidative stress and inflammation. Pohang, South Korea’s iron and steel hub, experiences significant polycyclic aromatic hydrocarbons emissions from extensive industrial activities and heavy truck movements. This study investigates the link between urinary polycyclic aromatic hydrocarbons metabolites and respiratory diseases among Pohang residents, considering their cumulative exposure and duration of residency. Methods: Utilizing data from the National Environmental Research Institute’s health impact assessment conducted between 2012 and 2015, this study included 1,189 participants from Pohang. Urinary concentrations of polycyclic aromatic hydrocarbons metabolites served as exposure indicators. The study assessed the correlation between these metabolite levels and self-reported respiratory diseases, considering variables such as duration of residence, smoking status, and occupational exposures. Statistical analyses involved logistic regression and fixed-effect meta-analysis. Results: The findings indicate a significant association between elevated levels of polycyclic aromatic hydrocarbons metabolites and an increased risk of respiratory diseases, particularly among those in the highest exposure quartile. The study also found a notable dose-response relationship, emphasizing the heightened risk with longer residency durations near the industrial complex. Despite yearly fluctuations, the trend suggests a persistent risk associated with high polycyclic aromatic hydrocarbons exposure. Conclusions Elevated exposure to polycyclic aromatic hydrocarbons near industrial areas such as the Pohang Industrial Complex is significantly associated with an increased prevalence of respiratory diseases. The study underscores the importance of implementing public health strategies to reduce polycyclic aromatic hydrocarbons exposure, especially in industrial regions. Future research should broaden the scope of polycyclic aromatic hydrocarbons metabolites analyzed and consider comprehensive exposure histories to better understand the relationship between polycyclic aromatic hydrocarbons exposure and respiratory health outcomes.

Background: Low compliance (LC) with lifestyle modification is a very common obstacle in obesity management. The purpose of the current study was to investigate the effectiveness of obesity management according to compliance with a lifestyle-modification program. Methods: The “Change 10 Habits” program was administered four times over 12 weeks. Eighty-seven participants were divided into LC and high compliance (HC) groups for analysis after intervention. Then, to assess the program’s effectiveness based on compliance, we conducted t-tests and linear regression modeling. Results: In week 12, the scores of two dietary habits—specifically, “eat three meals regularly, adequate amount” and “do not eat after 9:00 PM”—were significantly higher in the HC group than in the LC group. Changes in leg and total body fat percentages were significantly improved in the HC group (−0.2%±0.3% vs. 0.9%±0.3%, P< 0.05; −0.1%±0.3% vs. 1.1%±0.5%, P<0.05, respectively). The body mass index was also significantly lower in the HC group than in the LC group (26.7±1.8 kg/m2 vs. 27.7±2.1 kg/m2 , P<0.05) at final follow-up. Finally, the systolic blood pressure, triglyceride, and very-low-density lipoprotein cholesterol values of the HC group also decreased significantly (from 117.9±12.2 to 114.3±15.0 mmHg, P<0.05; from 121.7±74.9 to 105.7±60.9 mg/dL, P<0.05; and from 24.3±15.0 to 21.1±12.2 mg/dL, P<0.05, respectively). Conclusion HC with the study program effectively improved the dietary habits, body fat composition, blood pressure, and lipid profile of adults with mild obesity.

Background: Low compliance (LC) with lifestyle modification is a very common obstacle in obesity management. The purpose of the current study was to investigate the effectiveness of obesity management according to compliance with a lifestyle-modification program. Methods: The “Change 10 Habits” program was administered four times over 12 weeks. Eighty-seven participants were divided into LC and high compliance (HC) groups for analysis after intervention. Then, to assess the program’s effectiveness based on compliance, we conducted t-tests and linear regression modeling. Results: In week 12, the scores of two dietary habits—specifically, “eat three meals regularly, adequate amount” and “do not eat after 9:00 PM”—were significantly higher in the HC group than in the LC group. Changes in leg and total body fat percentages were significantly improved in the HC group (−0.2%±0.3% vs. 0.9%±0.3%, P< 0.05; −0.1%±0.3% vs. 1.1%±0.5%, P<0.05, respectively). The body mass index was also significantly lower in the HC group than in the LC group (26.7±1.8 kg/m2 vs. 27.7±2.1 kg/m2 , P<0.05) at final follow-up. Finally, the systolic blood pressure, triglyceride, and very-low-density lipoprotein cholesterol values of the HC group also decreased significantly (from 117.9±12.2 to 114.3±15.0 mmHg, P<0.05; from 121.7±74.9 to 105.7±60.9 mg/dL, P<0.05; and from 24.3±15.0 to 21.1±12.2 mg/dL, P<0.05, respectively). Conclusion HC with the study program effectively improved the dietary habits, body fat composition, blood pressure, and lipid profile of adults with mild obesity.

Background: Low compliance (LC) with lifestyle modification is a very common obstacle in obesity management. The purpose of the current study was to investigate the effectiveness of obesity management according to compliance with a lifestyle-modification program. Methods: The “Change 10 Habits” program was administered four times over 12 weeks. Eighty-seven participants were divided into LC and high compliance (HC) groups for analysis after intervention. Then, to assess the program’s effectiveness based on compliance, we conducted t-tests and linear regression modeling. Results: In week 12, the scores of two dietary habits—specifically, “eat three meals regularly, adequate amount” and “do not eat after 9:00 PM”—were significantly higher in the HC group than in the LC group. Changes in leg and total body fat percentages were significantly improved in the HC group (−0.2%±0.3% vs. 0.9%±0.3%, P< 0.05; −0.1%±0.3% vs. 1.1%±0.5%, P<0.05, respectively). The body mass index was also significantly lower in the HC group than in the LC group (26.7±1.8 kg/m2 vs. 27.7±2.1 kg/m2 , P<0.05) at final follow-up. Finally, the systolic blood pressure, triglyceride, and very-low-density lipoprotein cholesterol values of the HC group also decreased significantly (from 117.9±12.2 to 114.3±15.0 mmHg, P<0.05; from 121.7±74.9 to 105.7±60.9 mg/dL, P<0.05; and from 24.3±15.0 to 21.1±12.2 mg/dL, P<0.05, respectively). Conclusion HC with the study program effectively improved the dietary habits, body fat composition, blood pressure, and lipid profile of adults with mild obesity.

Elevated blood cholesterol and triglyceride levels induced by secondary causes are frequently observed. The identification and appropriate handling of these causes are essential for secondary dyslipidemia treatment. Major secondary causes of hypercholesterolemia and hypertriglyceridemia include an unhealthy diet, diseases and metabolic conditions affecting lipid levels, and therapeutic side effects. It is imperative to correct secondary causes prior to initiating conventional lipid-lowering therapy. Guideline-based lipid therapy can then be administered based on the subsequent lipid levels.

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs.1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANSmid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.