Yonsei University · Medicine
Professor Namki Hong's research lab specializes in clinical and translational research in endocrinology and metabolic diseases, with a focus on aging, osteoporosis, sarcopenia, and cardiovascular risk in older adults. The lab integrates advanced imaging techniques—such as DXA-based radiomics—and machine learning to improve risk prediction for fractures and metabolic disorders. It also investigates biomarkers like PTHrP and GGT in relation to cancer-associated wasting and metabolic syndrome, aiming to identify early indicators of disease progression. The lab emphasizes data-driven, interdisciplinary approaches combining clinical expertise with computational modeling to address unmet needs in geriatric and metabolic health.
Figures are computed from collected data and may differ slightly.
Although elevated serum gamma-glutamyl transferase activity (GGT) has been linked with metabolic risk factors for sarcopenia, including non-alcoholic fatty liver disease, adiposity, and insulin resistance, whether GGT independently associated with sarcopenia and sarcopenic obesity has not yet been investigated. We analyzed cross-sectional data of 3,193 community-dwelling adults (42.2% men, age 63.4 ± 8.7) aged ≥50 years from the Fifth Korean National Health and Nutrition Examination Survey, 2010
Serum PTHrP levels predicted cancer-associated WL independent of the presence of hypercalcemia, inflammation, tumor burden, and other comorbidities.
Dual-energy X-ray absorptiometry (DXA)-based bone mineral density testing is standard to diagnose osteoporosis to detect individuals at high risk of fracture. A radiomics approach to extract quantifiable texture features from DXA hip images may improve hip fracture prediction without additional costs. Here, we investigated whether bone radiomics scores from DXA hip images could improve hip fracture prediction in a community-based cohort of older women. The derivation set (143 women who sustained
Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem fro
Ezetimibe-statin combination therapy was associated with greater cardiovascular benefits in patients with diabetes than in those without diabetes. Our findings suggest that ezetimibe-statin combination therapy might be a useful strategy in patients with diabetes at a residual risk of MACEs.
Participants will be observed actively and passively every 4-5 years and the first follow-up will be completed in 2020. The KURE data set has strength in comprehensive physical function assessments, quantifiable imaging data sets using CT and detailed information regarding the social support networks of participants from a large community-based elderly Korean population.
Chest X-ray-based deep leaning model improved detection of sarcopenia, which merits further investigation.
The severity of cortisol excess was associated with greater improvement of L3 VFA, VSR and SMD 1 year after adrenalectomy. These CT-based markers may allow more objective assessment of treatment benefit at earlier stage.
Sex-specific jump power cut-offs were found to detect the presence of either sarcopenia or dysmobility syndrome in older adults independent of Asian or Caucasian ethnicity.
A novel role of leucine-rich repeat-containing 17 (LRRc17), an LRR protein secreted by osteoblasts, as a negative regulator of receptor activator of NF-κB ligand-induced osteoclast differentiation was found. However, the clinical association between LRRc17 and osteoporotic fracture (OF) has not yet been investigated. We hypothesized that low circulating plasma level of LRRc17 might serve as an independent and additive risk factor for OF, including vertebral fractures (VF) and non-vertebral fract
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