1.A Position Paper on Lipoprotein(a) From the Lipoprotein(a) Task Force of the Korean Society of Lipid and Atherosclerosis: Current Evidence, Clinical Applications, and Future Directions
Youngwoo JANG ; Jang Hoon LEE ; Sang-Guk LEE ; Hun Jee CHOE ; Sang Min PARK ; In-Kyung JEONG ; Byung Jin KIM ;
Journal of Lipid and Atherosclerosis 2026;15(1):2-25
Lipoprotein(a) [Lp(a)] is a genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve stenosis (CAVS), with plasma levels largely unaffected by lifestyle modification or conventional lipid-lowering therapy. Although international guidelines increasingly recognize Lp(a) as a risk-enhancing factor, in many Asian populations thresholds for high Lp(a) and treatment strategies remain undefined.This Korean position paper, developed by the Lp(a) Task Force of the Korean Society of Lipid and Atherosclerosis, presents an evidence-based summary of the pathophysiology, clinical relevance, and therapeutic landscape surrounding Lp(a), with a focus on Koreanspecific data. It reviews the genetic architecture of Lp(a), ethnic variability in concentrations, and its mechanistic roles in inflammation, thrombosis, and calcification. Based on large Korean cohorts, a 3-tiered classification is proposed of normal (<30 mg/dL), borderline high (30–49 mg/dL), and high (≥50 mg/dL), harmonizing global thresholds with local data. The document also highlights the limitations of current Lp(a) assays in Korea, and calls for standardized, isoform-insensitive testing. Novel therapeutics, including antisense oligonucleotides, small interfering RNAs, and small molecular inhibitors, have shown promising Lp(a)-lowering effects, with multiple phase 3 trials currently ongoing, or in planning. Given the unmet clinical need, the paper recommends incorporating Lp(a) into cardiovascular risk assessment, and calls for Korean-specific longitudinal studies, national screening strategies, and participation in clinical trials. These efforts will help clarify Lp(a)-associated risk in Korean patients and guide the adoption of future targeted therapies.
2.Bisphosphonates as a Tacrolimus-Sparing Strategy in Kidney Transplantation: Insights from a Retrospective Analysis
Hee Byung KOH ; Hyo Jeong KIM ; Ga Young HEO ; Namki HONG ; Yaeji LEE ; Seung Hwan SONG ; Hoon Young CHOI ; Chan-Young JUNG ; Hyung Woo KIM ; Jaeseok YANG ; Kyu Ha HUH ; Chung Mo NAM ; Beom Seok KIM
Yonsei Medical Journal 2026;67(1):17-26
Purpose:
Due to chronic toxicity, tacrolimus-sparing is an important issue in kidney transplant recipients (KTRs). Several studies have shown that bisphosphonate use is associated with favorable graft outcomes in KTRs. We investigated whether the association between tacrolimus trough levels (TTLs) and graft outcomes differed according to bisphosphonate use in KTRs.
Materials and Methods:
We conducted a retrospective study encompassing 1441 KTRs who were administered tacrolimus-based immunosuppressants. The primary exposure was a time-dependent cross-product of TTLs (low TTLs vs. normal-high TTLs with a reference of 6 ng/mL) and bisphosphonate use. Two primary outcomes were evaluated: overall graft loss (death or conversion to kidney replacement) and an estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m 2 .
Results:
During the median follow-up of 6.1 (3.4–9.7) years, overall graft loss occurred in 157 (10.9%) patients. Cox regression revealed that normal-high TTLs without bisphosphonate use were associated with a reduced risk of overall graft loss [adjusted hazard ratio (aHR), 0.65; 95% confidence interval (CI), 0.45–0.95] compared to low TTLs without bisphosphonate use. The use of bisphosphonate in conjunction with normal-high TTLs correlated with an even lower risk of overall graft loss (aHR, 0.25; 95% CI, 0.08–0.80) compared with low TTLs without bisphosphonate use. In patients with low TTLs, bisphosphonate use was associated with a reduced risk of overall graft loss compared with non-use (aHR, 0.20; 95% CI, 0.09–0.43). Similar trends were observed in the eGFR outcome.
Conclusion
The use of bisphosphonate was associated with favorable graft outcomes, even with low TTLs. Incorporating bisphosphonate into a conventional immunosuppressant regimen may potentially reduce tacrolimus requirement.
3.Machine Learning Prediction of Attachment Type From Bio-Psychological Factors in Patients With Depression
Yoon Jae CHO ; Jin Sun RYU ; Jeong-Ho SEOK ; Eunjoo KIM ; Jooyoung OH ; Byung-Hoon KIM
Psychiatry Investigation 2025;22(4):412-423
Objective:
Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms.
Methods:
Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost).
Results:
Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor.
Conclusion
Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.
4.Combination of Aβ40, Aβ42, and Tau Plasma Levels to Distinguish Amyloid-PET Positive Alzheimer Patients from Normal Controls
Seungyeop BAEK ; Jinny Claire LEE ; Byung Hyun BYUN ; Su Yeon PARK ; Jeong Ho HA ; Kyo Chul LEE ; Seung-Hoon YANG ; Jun-Seok LEE ; Seungpyo HONG ; Gyoonhee HAN ; Sang Moo LIM ; YoungSoo KIM ; Hye Yun KIM
Experimental Neurobiology 2025;34(1):1-8
Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.
5.Korean Practice Guidelines for Gastric Cancer 2024: An Evidence-based, Multidisciplinary Approach (Update of 2022 Guideline)
In-Ho KIM ; Seung Joo KANG ; Wonyoung CHOI ; An Na SEO ; Bang Wool EOM ; Beodeul KANG ; Bum Jun KIM ; Byung-Hoon MIN ; Chung Hyun TAE ; Chang In CHOI ; Choong-kun LEE ; Ho Jung AN ; Hwa Kyung BYUN ; Hyeon-Su IM ; Hyung-Don KIM ; Jang Ho CHO ; Kyoungjune PAK ; Jae-Joon KIM ; Jae Seok BAE ; Jeong Il YU ; Jeong Won LEE ; Jungyoon CHOI ; Jwa Hoon KIM ; Miyoung CHOI ; Mi Ran JUNG ; Nieun SEO ; Sang Soo EOM ; Soomin AHN ; Soo Jin KIM ; Sung Hak LEE ; Sung Hee LIM ; Tae-Han KIM ; Hye Sook HAN ; On behalf of The Development Working Group for the Korean Practice Guideline for Gastric Cancer 2024
Journal of Gastric Cancer 2025;25(1):5-114
Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.
6.Machine Learning Prediction of Attachment Type From Bio-Psychological Factors in Patients With Depression
Yoon Jae CHO ; Jin Sun RYU ; Jeong-Ho SEOK ; Eunjoo KIM ; Jooyoung OH ; Byung-Hoon KIM
Psychiatry Investigation 2025;22(4):412-423
Objective:
Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms.
Methods:
Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost).
Results:
Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor.
Conclusion
Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.
7.Machine Learning Prediction of Attachment Type From Bio-Psychological Factors in Patients With Depression
Yoon Jae CHO ; Jin Sun RYU ; Jeong-Ho SEOK ; Eunjoo KIM ; Jooyoung OH ; Byung-Hoon KIM
Psychiatry Investigation 2025;22(4):412-423
Objective:
Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms.
Methods:
Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost).
Results:
Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor.
Conclusion
Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.
8.Combination of Aβ40, Aβ42, and Tau Plasma Levels to Distinguish Amyloid-PET Positive Alzheimer Patients from Normal Controls
Seungyeop BAEK ; Jinny Claire LEE ; Byung Hyun BYUN ; Su Yeon PARK ; Jeong Ho HA ; Kyo Chul LEE ; Seung-Hoon YANG ; Jun-Seok LEE ; Seungpyo HONG ; Gyoonhee HAN ; Sang Moo LIM ; YoungSoo KIM ; Hye Yun KIM
Experimental Neurobiology 2025;34(1):1-8
Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.
9.Machine Learning Prediction of Attachment Type From Bio-Psychological Factors in Patients With Depression
Yoon Jae CHO ; Jin Sun RYU ; Jeong-Ho SEOK ; Eunjoo KIM ; Jooyoung OH ; Byung-Hoon KIM
Psychiatry Investigation 2025;22(4):412-423
Objective:
Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms.
Methods:
Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost).
Results:
Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor.
Conclusion
Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.
10.Machine Learning Prediction of Attachment Type From Bio-Psychological Factors in Patients With Depression
Yoon Jae CHO ; Jin Sun RYU ; Jeong-Ho SEOK ; Eunjoo KIM ; Jooyoung OH ; Byung-Hoon KIM
Psychiatry Investigation 2025;22(4):412-423
Objective:
Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms.
Methods:
Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost).
Results:
Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor.
Conclusion
Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

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