Biomedical subjects

Min Seo Kim

Publications and source records attributed to Min Seo Kim.

3 recordsLinked to original sources

PubMed2026

Genetic evidence supports the combined targeting of lipoprotein(a) and LDL cholesterol to reduce coronary artery disease risk.

Distinct genetic mechanisms govern how lipoprotein(a) (Lp(a)) and low-density lipoprotein cholesterol (LDL-C) promote atherosclerosis. It remains unclear whether targeting both provides additive cardiovascular benefits. Here we use coding loss-of-function variants in LPA and PCSK9 and genetic scores associated with Lp(a) and LDL-C levels to evaluate the effects of lowering Lp(a) and LDL-C on coronary artery disease (CAD) risk. Among 408,039 individuals from the UK Biobank, LPA or PCSK9 loss-of-function carriers have lower CAD risk than noncarriers (odds ratio (OR) 0.91 and 0.81). Carriers of both variants have even lower CAD risk (OR 0.73). Genetic lowering of Lp(a) and LDL-C showed a stronger reduction of CAD risk (OR 0.70) than either trait individually (OR 0.85 and 0.81) in the two-factor genetic score analysis. Among statin users, Lp(a) reduction was linearly associated with CAD risk. A phenome-wide association study revealed that combined therapy was associated with cardiometabolic benefits without adverse effects. The additive benefits were replicated in 65,171 individuals from the Mass General Brigham Biobank.

Humans↗

PubMed2026

Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis in the PROACT Clinical Trials.

BACKGROUND: Coronary artery disease (CAD) polygenic risk scores (PRS) may identify individuals at elevated genetic risk "flying under the radar" in contemporary practice. The aims of the PROACT (Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis) trials are to prospectively identify these individuals, quantify subclinical coronary plaque, and slow its progression with pharmacologic interventions. OBJECTIVES: The aim of this study is to report interim feasibility and implementation findings from PROACT, a genotype-first, biobank-enabled trial, characterizing eligibility yield, callback engagement, and subclinical coronary atherosclerosis on coronary computed tomographic angiography among individuals with high CAD PRS. METHODS: Within a hospital-based biobank, adults 40 to 75 years of age with high CAD PRS, without cardiovascular disease, and not on lipid-lowering therapy were invited. The authors characterize 2,495 eligible individuals with high CAD PRS, report on the feasibility and early operational outcomes of a genotype-first callback strategy for a clinical trial in the first 1,314 invited, and describe plaque prevalence by age and sex in the first 204 participants using coronary computed tomographic angiography. RESULTS: Among 64,092 genotyped participants, 2,495 (3.9%) were eligible and had high CAD PRS despite low clinical risk (median 10-year pooled cohort equations risk for atherosclerotic cardiovascular disease 3%; Q1-Q3: 1%-8%). Recruitment showed high engagement: among 1,314 invited individuals, 283 (21.5%) opted in, and 204 (15.5%) completed baseline imaging. Compared with participants who did not opt in, those who opted in had higher specialty care engagement and lived closer to the study site. Analysis of the first 204 participants enrolled by January 31, 2025 (mean age 56.3 ± 8.5 years, 69% women), showed that despite the low clinical risk and favorable cardiovascular health (mean Life's Essential 8 score 73.3 ± 11.5 vs the U.S. average of ∼65), one-half the participants (102 of 204) had subclinical plaque. Subclinical plaque prevalence was 76.2% in men and 38.3% in women and was high across age groups. CONCLUSIONS: These exploratory findings highlight the feasibility of implementing genotype-first recruitment for prevention trials and reveal a large proportion of "silent" high-genetic risk individuals with subclinical plaque for whom pharmacotherapy could be beneficial but who remain undetected by standard clinical assessments. (Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Change in Cardiovascular Health [PROACT 1], NCT05819814; Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Intervention With Statin and Colchicine [PROACT 2], NCT05850091).

Adult↗

PubMed2025

Contributions of Common, Rare, and Somatic Genetic Variants to Incidence of Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) has a complex genetic architecture involving common, rare, and somatic variants. The association between these components requires further investigation. OBJECTIVE: To examine the individual and combined contributions of polygenic, monogenic, and somatic genetic variants to AF incidence, and develop an integrated genomic model (IGM-AF) for improved risk prediction. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used whole-genome sequence data from participants of the UK Biobank, with follow-up for AF events through hospital records, death registries, and self-report. The UK Biobank recruited participants aged 40 to 69 years in the UK between 2006 and 2010. Study data were analyzed from August 2022 to November 2024. EXPOSURES: IGM-AF comprising an AF polygenic risk score (PRS), a composite rare variant gene set (AFgeneset), and somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). Clinical AF risk was estimated using the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score. MAIN OUTCOMES AND MEASURES: The primary outcome was hazard ratios (HRs) for 5-year incident AF attributable to PRS, AFgeneset, CHIP, and their interactions. The predictive performance of IGM-AF and its components was quantified using HRs, C statistics, and reclassification indices. RESULTS: A total of 416 085 individuals (mean [SD] age, 56.6 [8.0] years; 224 642 female [54.0%]) with 30 797 AF cases were included. The PRS (HR per 1 SD, 1.65; 95% CI, 1.63-1.67; P < 1 × 10-8), AFgeneset (HR, 1.63; 95% CI, 1.52-1.75; P = 1.46 × 10-42), and CHIP (HR, 1.26; 95% CI, 1.15-1.38; P = 1.41 × 10-6) were associated with incident AF. The 5-year cumulative incidence of AF was at least 2-fold among individuals having all 3 genetic drivers (common, rare, and somatic drivers) compared with those with only 1 driver. Integration of IGM-AF with a clinical risk model (CHARGE-AF) showed higher predictive performance (C statistic, 0.80; 95% CI, 0.80-0.80) compared with IGM-AF and CHARGE-AF alone. The classification of the at-risk population for AF was improved when IGM-AF was added to CHARGE-AF (net reclassification index, 0.08; 95% CI, 0.07-0.09). CONCLUSIONS AND RELEVANCE: Results of this cohort study demonstrated the complementary value of common, rare, and somatic variants in shaping genomic AF risk. Leveraging comprehensive genetic information may enhance screening and preventive interventions for AF.

Humans↗