A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Protein Signatures of Parathyroid Adenoma according to Tumor Volume and Functionality.

BACKGRUOUND: Parathyroid adenoma (PA) is a common endocrine disease linked to multiple complications, but the pathophysiology of the disease remains incompletely understood. The study aimed to identify the key regulator proteins and pathways of PA according to functionality and volume through quantitative proteomic analyses. METHODS: We conducted a retrospective study of 15 formalin-fixed, paraffin-embedded PA samples from tertiary hospitals in South Korea. Proteins were extracted, digested, and the resulting peptides were analyzed using liquid chromatography-tandem mass spectrometry. Pearson correlation analysis was employed to identify proteins significantly correlated with clinical variables. Canonical pathways and transcription factors were analyzed using Ingenuity Pathway Analysis. RESULTS: The median age of the participants was 52 years, and 60.0% were female. Among the 8,153 protein groups analyzed, 496 showed significant positive correlations with adenoma volume, while 431 proteins were significantly correlated with parathyroid hormone (PTH) levels. The proteins SLC12A9, LGALS3, and CARM1 were positively correlated with adenoma volume, while HSP90AB2P, HLA-DRA, and SCD5 showed negative correlations. DCPS, IRF2BPL, and FAM98A were the main proteins that exhibited positive correlations with PTH levels, and SLITRK4, LAP3, and AP4E1 had negative correlations. Canonical pathway analysis demonstrated that the RAN and sirtuin signaling pathways were positively correlated with both PTH levels and adenoma volume, while epithelial adherence junction pathways had negative correlations. CONCLUSION: Our study identified pivotal proteins and pathways associated with PA, offering potential therapeutic targets. These findings accentuate the importance of proteomics in understanding disease pathophysiology and the need for further research.

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture.

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.

Effect of Trap Behavior on the Reliability Instability of Metamorphic Buffer in InAlAs/InGaAs MHEMT on GaAs.

Our investigation focused on assessing the influence of the metamorphic buffer in metamorphic high-electron-mobility transistors (MHEMT) that were grown on GaAs substrates. While an MHEMT exhibited elevated off-state current levels, its direct current (DC) and radio frequency (RF) traits were found to be comparable to those of InP-based lattice-matched high-electron-mobility transistors (LM-HEMTs). However, the Pulsed I-V measurement results confirmed the presence of the fast transient charging effect, leading to a more substantial degradation in drain current observed in MHEMT. In addition, through the low-frequency noise characteristics, it was confirmed that the dominant trapping location was located in the bulk site. The slope of the 1/f noise measurement indicated that the primary trapping site was in proximity to the bulk traps. The carrier-number-fluctuation (CNF) model was employed to extract the bulk trap density (Nt). For the LM-HEMTs, the value was at 3.27 × 1016 eV-1·cm-3, while for the MHEMT, it was 3.56 × 1017 eV-1·cm-3.

Development and validation of common data model-based fracture prediction model using machine learning algorithm.

The need for an accurate country-specific real-world-based fracture prediction model is increasing. Thus, we developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort in Korea. The model includes history of fracture, age, lumbar spine and total hip T-score, and cardiovascular disease. PURPOSE: Osteoporotic fractures are substantial health and economic burden. Therefore, the need for an accurate real-world-based fracture prediction model is increasing. We aimed to develop and validate an accurate and user-friendly model to predict major osteoporotic and hip fractures using a common data model database. METHODS: The study included 20,107 and 13,353 participants aged ≥ 50 years with data on bone mineral density using dual-energy X-ray absorptiometry from the CDM database between 2008 and 2011 from the discovery and validation cohort, respectively. The main outcomes were major osteoporotic and hip fracture events. DeepHit and Cox proportional hazard models were used to identify predictors of fractures and to build scoring systems, respectively. RESULTS: The mean age was 64.5 years, and 84.3% were women. During a mean of 7.6 years of follow-up, 1990 major osteoporotic and 309 hip fracture events were observed. In the final scoring model, history of fracture, age, lumbar spine T-score, total hip T-score, and cardiovascular disease were selected as predictors for major osteoporotic fractures. For hip fractures, history of fracture, age, total hip T-score, cerebrovascular disease, and diabetes mellitus were selected. Harrell's C-index for osteoporotic and hip fractures were 0.789 and 0.860 in the discovery cohort and 0.762 and 0.773 in the validation cohort, respectively. The estimated 10-year risks of major osteoporotic and hip fractures were 2.0%, 0.2% at score 0 and 68.8%, 18.8% at their maximum scores, respectively. CONCLUSION: We developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort. These simple scoring models may help predict fracture risks in real-world practice.

In-depth proteomic signature of parathyroid carcinoma.

OBJECTIVE: Diagnosing parathyroid carcinoma (PC) is complicated and controversial that early diagnosis and intervention are often difficult. Therefore, we aimed to elucidate the protein signatures of PC through quantitative proteomic analyses to aid in the early and accurate diagnosis of PC. DESIGN: We conducted a retrospective cohort study. METHODS: We performed liquid chromatography with tandem mass spectrometry using formalin-fixed paraffin-embedded samples. For the analyses, 23 PC and 15 parathyroid adenoma (PA) tissues were collected from 6 tertiary hospitals in South Korea. RESULTS: The mean age of the patients was 52 years, and 63% were women. Proteomic expression profiling revealed 304 differentially expressed proteins (DEPs) with a cut-off of P < .05 and fold change >1.5. Among DEPs, we identified a set of 5 proteins that can discriminate PC from PA: carbonic anhydrase 4 (CA4), alpha/beta hydrolase domain-containing protein 14B (ABHD14B), laminin subunit beta-2 (LAMB2), CD44 antigen (CD44), and alpha-1-acid glycoprotein 1 (ORM1) that exhibited the highest area under the curve of 0.991 in neural network model. The nuclear percentage of CA4 and LAMB2 in immunohistochemistry was significantly lower in PC tissue than in the PA (CA4: 2.77 ± 1.96%, 26.2 ± 3.45%, P < .001; LAMB2: 6.86 ± 3.46%, 38.54 ± 4.13%, P < .001). The most enriched canonical pathways in PC included glycoprotein-6 signaling and mammalian target of rapamycin (mTOR). CONCLUSIONS: We identified key proteins differentially expressed between PC and PA using proteomic analyses of parathyroid neoplasms. These findings may help to diagnose PC accurately and elucidate potential therapeutic targets.

Residual risks of comorbidities after parathyroidectomy in a nationwide cohort of patients with primary hyperparathyroidism.

PURPOSE: Primary hyperparathyroidism (PHPT) is a common endocrine disorder with increasing incidence, while epidemiologic data in Asian population has been lacking. Therefore, we aimed to identify the incidence, prognosis, and prognostic factors of PHPT patients who underwent parathyroidectomy in Korea. METHODS: In this retrospective nationwide cohort, patients with PHPT were defined as those with diagnostic codes of PHPT and procedural codes for parathyroidectomy, excluding chronic renal failure or secondary hyperparathyroidism based on National Health Insurance Services database in Korea in 2002-2018. Main primary outcomes were all-cause mortality, cardiovascular, and cerebrovascular events. RESULTS: A total of 5561 patients were diagnosed with PHPT and had parathyroidectomy. The mean age was 54.5 years, and 71.8% were women. The age-standardized incidence was 10.1/100,000 person-year in 2018, rising from 1.7/100,000 person-year in 2002. During a mean of 5.9 years, history of cardiovascular disease, mood disorder, and genitourinary stone had increased risks of mortality with hazard ratios (HRs) of 1.59 (95% confidence interval [CI] 1.10-2.29), 1.43 (CI 1.14-1.80), and 1.40 (CI 1.09-1.80), respectively. History of hypertension, cerebrovascular disease, diabetes mellitus, and mood disorder were risk factors for cardiovascular events with HRs of 1.42 (CI 1.22-1.66), 1.29 (CI 1.05-1.58), 1.22 (CI 1.07-1.40), and 1.14 (CI 1.00-1.29), respectively. Mood disorder was a significant risk factor for cerebrovascular events (HR 1.30, CI 1.11-1.52). CONCLUSION: The incidence of PHPT patients who underwent parathyroidectomy has been rising in Korea as reported in other countries. Patients with complications, especially mood disorder, had increased cardiovascular and cerebrovascular events and mortality risks.

Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm.

BACKGRUOUND: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. METHODS: This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. RESULTS: Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. CONCLUSION: DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

Effect of Denosumab on the Change of Osteoclast Precursors Compared to Zoledronate Treatment in Postmenopausal Women with Osteoporosis.

BACKGROUND: A rapid increase in bone turnover and bone loss has been observed in response to the discontinuation of denosumab. It led to an acute increase in the fracture risk, similar to that observed in the untreated patients. We aimed to investigate the effect of denosumab on osteoclast (OC) precursor cells compared to that of zoledronate. METHODS: The study compared the effects of denosumab (60 mg/24-week) and zoledronate (5 mg/48-week) over 48 weeks in postmenopausal women with osteoporosis. From patients' peripheral mononuclear cells, CD14+/CD11b+/vitronectin receptor (VNR)- and CD14+/CD11b+/VNR+ cells were isolated using fluorescent-activated cell sorting, representing early and late OC precursors, respectively. The primary endpoint was the changes in OC precursors after 48 weeks of treatment. RESULTS: Among the 23 patients, 11 were assigned to the denosumab group and 12 to the zoledronate group (mean age, 69 years). After 48 weeks, the changes in OC precursors were similar between and within the groups. Serum C-terminal telopeptide of type I collagen levels were inversely correlated with OC precursor levels after denosumab treatment (r=-0.72, P<0.001). Lumbar spine, femur neck, and total hip bone mineral density (BMD) increased in both groups. Lumbar spine BMD increased more significantly in the denosumab group than in the zoledronate group. CONCLUSIONS: Denosumab and zoledronate treatments induced similar changes in OC precursors. During denosumab treatment, old age and suppressed bone turnover were associated with increased OC precursor cell populations. Further validation studies with prospective designs are required.

Effect of Vitamin D Supplementation on Risk of Fractures and Falls According to Dosage and Interval: A Meta-Analysis.

BACKGROUND: Although recent studies comparing various dosages and intervals of vitamin D supplementation have been published, it is yet to be elucidated whether there is an appropriate dose or interval to provide benefit regarding fracture risk. We aimed to assess the published evidence available to date regarding the putative beneficial effects of vitamin D supplements on fractures and falls according to various dosages and intervals. METHODS: We performed a meta-analysis of randomized controlled studies reporting associations between vitamin D supplementation and the risks of fractures and falls in PubMed, EMBASE, and Cochrane library. Studies with supplements of ergocalciferol or calcitriol, those with a number of event ≤10, or those with a follow-up duration of less than 6 months were also excluded. RESULTS: Thirty-two studies were included in the final analysis. Vitamin D supplementation with daily dose of 800 to 1,000 mg was associated with lower risks of osteoporotic fracture and fall (pooled relative risk [RR], 0.87; 95% confidence interval [CI], 0.78 to 0.97 and RR, 0.91; 95% CI, 0.85 to 0.98), while studies with <800 or >1,000 mg/day did not. Also, among intervals, daily administration of vitamin D was associated with the reduced risk of falls, while intermittent dose was not. Also, patients with vitamin D deficiency showed a significant risk reduction of falls after vitamin D supplementation. CONCLUSION: Daily vitamin D dose of 800 to 1,000 IU was the most probable way to reduce the fracture and fall risk. Further studies designed with various regimens and targeted vitamin D levels are required to elucidate the benefits of vitamin D supplements.

Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism.

BACKGROUND: Optimal management of primary aldosteronism (PA) is crucial due to the increased risk of cardiovascular and cerebrovascular diseases. Adrenal venous sampling (AVS) is the gold standard method for determining subtype but is technically challenging and invasive. Some PA patients do not benefit clinically from surgery. We sought to develop an algorithm to improve decision- making before engaging in AVS and surgery in clinical practice. METHODS: We conducted the ongoing Korean Primary Aldosteronism Study at two tertiary centers. Study A involved PA patients with successful catheterization and a unilateral nodule on computed tomography and aimed to predict unilateral aldosterone-producing adenoma (n=367). Study B involved similar patients who underwent adrenalectomy and aimed to predict postoperative outcome (n=330). In study A, we implemented important feature selection using the least absolute shrinkage and selection operator regression. RESULTS: We developed a unilateral PA prediction model using logistic regression analysis: lowest serum potassium level ≤3.4 mEq/L, aldosterone-to-renin ratio ≥150, plasma aldosterone concentration ≥30 ng/mL, and body mass index <25 kg/m2 (area under the curve, 0.819; 95% confidence interval, 0.774 to 0.865; sensitivity, 97.6%; specificity, 25.5%). In study B, we identified female, hypertension duration <5 years, anti-hypertension medication <2.5 daily defined dose, and the absence of coronary artery disease as predictors of clinical success, using stepwise logistic regression models (sensitivity, 94.2%; specificity, 49.3%). We validated our algorithm in the independent validation dataset (n=53). CONCLUSION: We propose this new outcome-driven diagnostic algorithm, simultaneously considering unilateral aldosterone excess and clinical surgical benefits in PA patients.

Determinants of Short-Term Weight Gain Following Surgical Treatment for Craniopharyngioma in Adults.

OBJECTIVE: Craniopharyngiomas (CPs) are associated with hypothalamic damage that causes hypothalamic obesity, however, the mechanisms underlying CP-related postoperative weight gain remain debatable. This study aimed to elucidate whether the major determinant of postoperative weight gain in patients with CP is hypothalamic injury or steroid replacement therapy. METHODS: We included 48 adult patients with CP (age ≥18 years) who underwent transsphenoidal surgery between 2010 and 2018 in a single tertiary center, and whose body weight was measured pre- and postoperatively (<120 days after the surgery). We recruited 144 age- and body mass index-matched patients with non-functioning pituitary adenoma (NFPA) as controls. RESULTS: Patients with CP experienced greater postoperative weight gain than patients with NFPA (3.0±5.1 vs. 0.1±3.6 kg, p<0.001). The prevalence of postoperative steroid use was significantly higher in patients with CP than in those with NFPA (89.6% vs. 34.0%, p<0.001). Steroid replacement therapy and CP were associated with postoperative weight gain after adjusting for covariates in overall patients (p=0.032 and 0.007, respectively). In subgroup analysis with postoperative steroid users, weight gain was significantly greater in patients with CP (n=43, 0.96±0.25 kg/month) than in patients with NFPA (n=49, 0.26±0.23 kg/month) even after adjusting for the daily steroid dose (p=0.048). CONCLUSION: Patients with CP experience greater postoperative weight gain than those with NFPA. Hypothalamic damage itself as well as steroid replacement may contribute to the postoperative weight gain in patients with CP.

Skeletal effects of combined bisphosphonates treatment and parathyroidectomy in osteoporotic patients with primary hyperparathyroidism.

INTRODUCTION: Bone loss caused by primary hyperparathyroidism (PHPT) is an indication for parathyroidectomy (PTX). However, whether adding bisphosphonates would be superior to PTX alone to increase bone mass remains unclear. We thus aimed to compare the skeletal effects of the combination treatment of bisphosphonates and PTX with PTX alone. MATERIALS AND METHODS: In this retrospective analysis, bone mineral density (BMD) changes after 1 year of combination treatment and PTX alone were compared. We also analyzed the correlation between changes in serum biochemical parameters and BMD after 1 year of treatment in both groups. RESULTS: The baseline characteristics of patients treated with PTX alone (n = 24) and combination treatment (n = 26) were comparable. BMD significantly increased after 1 year of treatment in both groups (all p < 0.001), and the increase in BMD at the femur neck was higher in the PTX alone group than in the combination group (p = 0.011). There was a decreasing trend in serum alkaline phosphatase (ALP) levels in PTX alone compared to the combination treatment group (p = 0.053). In the study cohort, lower BMD and higher ALP levels at baseline were associated with higher 1-year BMD changes at all sites. Interestingly, a significant association was found between changes in ALP and BMD at the femur neck in the PTX alone group (p = 0.003), but abolished in the combination group (p = 0.946). CONCLUSIONS: There is no additional benefit of BMD in combination treatment with bisphosphonates and PTX over PTX alone in osteoporotic patients with PHPT. Combined bisphosphonate treatment might interfere with the increase in bone mass caused by PTX.

Characteristics of germline mutations in Korean patients with pheochromocytoma/paraganglioma.

BACKGROUND: Pheochromocytomas and paragangliomas (PPGLs) are catecholamine-producing neuroendocrine tumours. PPGLs are a rare but important cause of secondary hypertension owing to their high morbidity and mortality. Patients with PPGL exhibit an increased prevalence of mutations in one of the PPGL susceptibility genes according to previous studies. We aimed to investigate the characteristics of germline mutations in the largest number of Korean patients with PPGL. METHODS: In this study, 161 patients with PPGL were evaluated. Phenotype data, including biochemical, pathological and anatomical imaging results, were collected. Germline mutations in 10 PPGL-related genes were tested by targeted next-generation sequencing (NGS), Sanger sequencing and multiplex ligation-dependent probe amplification. RESULTS: Approximately 21% of apparently sporadic PPGLs harboured germline mutations of the PPGL-related genes. The mutation carriers were younger at the first diagnosis and had more bilateral (28.6% vs 4.0%, p<0.001) and multifocal (11.4% vs 1.6%, p=0.027) PPGLs, but showed no metastatic risk (17.1% vs 11.1%, p=0.504), than non-mutation carriers. Missense mutation of SDHD p.V111I was found in this cohort of Asian patients, which was associated with unilateral pheochromocytoma with dominantly epinephrine production. CONCLUSION: This study covered the largest number of Korean patients with PPGL. To our knowledge, it is the first to compare results of targeted NGS panel with those of conventional sequencing methods in Asia. We demonstrated that the variant type, as well as the mutated gene, may determine the phenotype and prognosis of PPGLs.

Indices of ACTH-stimulated adrenal venous sampling as predictors of postsurgical outcomes in primary aldosteronism.

OBJECTIVE: This study aimed to investigate the impact of indices of adrenal venous sampling (AVS) on postsurgical outcomes in patients with primary aldosteronism (PA). DESIGN AND PATIENTS: This retrospective study determined biochemical and clinical outcomes based on ACTH-stimulated AVS parameters (lateralisation index [LI], contralateral ratio [CLR], and ipsilateral ratio [ILR]) in 251 patients with PA at 3 months after surgery. RESULTS: Modified complete biochemical success was achieved in 8 of 12 (66.7%) patients with LI = 3-4, 39 of 47 (83.0%) with LI = 4-10, and 155 of 169 (91.7%) with LI ≥ 10 (p = .004 for trend). Modified complete biochemical success was achieved in 29 of 38 (76.3%) patients with CLR ≥ 1 and ILR ≤ 2, 73 of 86 (84.9%) with CLR = 0.25-1 and ILR > 2, and 100 of 104 (96.2%) with CLR < 0.25 and ILR > 2 (p = .001 for trend). After adjusting for confounders, modified complete biochemical success was associated with an LI ≥ 10 (odds ratio [OR] = 6.32; 95% confidence interval [CI] = 1.33-29.93) using LI = 3-4 as a reference and combined CLR < 0.25 and ILR > 2 (OR = 11.49; 95% confidence interval [CI] = 2.49-53.01) using combined CLR ≥ 1 and ILR ≤ 2 as a reference. Using combined CLR ≥ 1 and ILR ≤ 2 as a reference, complete clinical success was associated with combined CLR < 0.25 and ILR > 2 (OR = 3.10; 95% CI = 1.03-9.28) and combined CLR = 0.25-1 and ILR > 2 (OR = 4.92; 95% CI = 1.64-14.76). CONCLUSION: LI ≥ 10 may be appropriate for achieving biochemical success. With ILR > 2, CLR < 0.25, and CLR < 1 may be appropriate for achieving biochemical and clinical success, respectively.

Unveiling Genetic Variants Underlying Vitamin D Deficiency in Multiple Korean Cohorts by a Genome-Wide Association Study.

BACKGROUND: Epidemiological data have shown that vitamin D deficiency is highly prevalent in Korea. Genetic factors influencing vitamin D deficiency in humans have been studied in Europe but are less known in East Asian countries, including Korea. We aimed to investigate the genetic factors related to vitamin D levels in Korean people using a genome-wide association study (GWAS). METHODS: We included 12,642 subjects from three different genetic cohorts consisting of Korean participants. The GWAS was performed on 7,590 individuals using linear or logistic regression meta- and mega-analyses. After identifying significant single nucleotide polymorphisms (SNPs), we calculated heritability and performed replication and rare variant analyses. In addition, expression quantitative trait locus (eQTL) analysis for significant SNPs was performed. RESULTS: rs12803256, in the actin epsilon 1, pseudogene (ACTE1P) gene, was identified as a novel polymorphism associated with vitamin D deficiency. SNPs, such as rs11723621 and rs7041, in the group-specific component gene (GC) and rs11023332 in the phosphodiesterase 3B (PDE3B) gene were significantly associated with vitamin D deficiency in both meta- and mega-analyses. The SNP heritability of the vitamin D concentration was estimated to be 7.23%. eQTL analysis for rs12803256 for the genes related to vitamin D metabolism, including glutamine-dependent NAD(+) synthetase (NADSYN1) and 7-dehydrocholesterol reductase (DHCR7), showed significantly different expression according to alleles. CONCLUSION: The genetic factors underlying vitamin D deficiency in Korea included polymorphisms in the GC, PDE3B, NADSYN1, and ACTE1P genes. The biological mechanism of a non-coding SNP (rs12803256) for DHCR7/NADSYN1 on vitamin D concentrations is unclear, warranting further investigations.

Applications of Machine Learning in Bone and Mineral Research.

In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.