Effect of low muscle mass on total mortality related to metabolic disease in chronic kidney disease patients.

Low muscle mass is a risk factor for mortality in patients with chronic kidney disease (CKD). However, it is not clear to what extent low muscle mass contributes to this risk, either independently or in combination with metabolic abnormalities and frailty. This study used data from the National Health and Nutrition Examination Survey 1999-2006 and 2011-2018. Low muscle mass was defined as Appendicular Skeletal Mass Index < 7 kg/m2 in men or < 5.5 kg/m2 in women. The follow-up duration was from the first anthropometric and clinical measurements to death or the last follow-up. This study enrolled 2072 patients with CKD. Low muscle mass was associated with a lower risk of metabolic abnormalities, but was associated with an elevated mortality risk. Conversely, central obesity was associated with a higher likelihood of metabolic abnormalities and frailty, yet showed no significant association with mortality risk. Subsequently conducted mediation analysis indicated that the effect of low muscle mass on mortality was direct, not mediated by frailty and metabolic abnormalities. In spite of the inverse relationship between low muscle mass and metabolic abnormalities, low muscle mass are directly associated with an increased risk of all-cause mortality. Low muscle mass may directly contribute to mortality in patients with CKD, independent of metabolic abnormalities and frailty in these patients.

The Modified S-GRAS Scoring System for Prognosis in Korean with Adrenocortical Carcinoma.

BACKGRUOUND: Adrenocortical carcinomas (ACCs) are rare tumors with aggressive but varied prognosis. Stage, Grade, Resection status, Age, Symptoms (S-GRAS) score, based on clinical and pathological factors, was found to best stratify the prognosis of European ACC patients. This study assessed the prognostic performance of modified S-GRAS (mS-GRAS) scores including modified grade (mG) by integrating mitotic counts into the Ki67 index (original grade), in Korean ACC patients. METHODS: Patients who underwent surgery for ACC between January 1996 and December 2022 at three medical centers in Korea were retrospectively analyzed. mS-GRAS scores were calculated based on tumor stage, mG (Ki67 index or mitotic counts), resection status, age, and symptoms. Patients were divided into four groups (0-1, 2-3, 4-5, and 6-9 points) based on total mS-GRAS score. The associations of each variable and mS-GRAS score with recurrence and survival were evaluated using Cox regression analysis, Harrell's concordance index (C-index), and the Kaplan-Meier method. RESULTS: Data on mS-GRAS components were available for 114 of the 153 patients who underwent surgery for ACC. These 114 patients had recurrence and death rates of 61.4% and 48.2%, respectively. mS-GRAS score was a significantly better predictor of recurrence (C-index=0.829) and death (C-index=0.747) than each component (P<0.05), except for resection status. mS-GRAS scores correlated with shorter progression-free survival (P=8.34E-24) and overall survival (P=2.72E-13). CONCLUSION: mS-GRAS scores showed better prognostic performance than tumor stage and grade in Asian patients who underwent surgery for ACC.

Elevated Fracture Risks in Patients Using Inhaled Corticosteroids: A Korean Nationwide Study.

Background: In this comprehensive retrospective nationwide cohort study, we examined the relationships between various asthma medications and bone health, utilizing data from the National Health Insurance Service database of South Korea. Methods: From 2015 to 2019, the relevant dataset included 168,611 individuals aged 66 years, among whom 8,747 were diagnosed with asthma. We focused on a subset of 6,173 patients, all 66-year-old women. Participants were categorized into four groups: nonusers of asthma medication (n=2,868), leukotriene antagonist users (n=2,281), inhaled corticosteroid (ICS) users (n=517), and those using a combination of ICS and long-acting beta-agonist (ICS+LABA) medication (n=507). The primary outcomes measured were the incidences of major osteoporotic fractures and hip fractures during the follow-up period. Results: Over 2.7 years of follow-up, 615 cases of major osteoporotic fractures and 96 cases of hip fractures were recorded. ICS users exhibited a heightened risk of both injuries, with hazard ratios of 1.38 (95% confidence interval [CI], 1.18 to 1.63; P<0.001) for major osteoporotic fractures and 1.56 (95% CI, 1.33 to 1.83; P<0.001) for hip fractures. Similarly elevated risks were observed in the ICS+LABA group. Notably, the risk associated with ICS was particularly pronounced among patients with osteopenia for both fracture types. Overall, the use of ICS, alone or in combination with LABA, in patients with asthma is associated with significantly increased risks of osteoporotic fractures, especially among those with osteopenia. Conclusion: These findings underscore the importance of considering bone health when managing asthma, especially in older patients and those with existing bone density issues.

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.

Effects of Endocrine-Disrupting Chemicals on Bone Health.

This comprehensive review critically examines the detrimental impacts of endocrine-disrupting chemicals (EDCs) on bone health, with a specific focus on substances such as bisphenol A (BPA), per- and polyfluoroalkyl substances (PFASs), phthalates, and dioxins. These EDCs, by interfering with the endocrine system's normal functioning, pose a significant risk to bone metabolism, potentially leading to a heightened susceptibility to bone-related disorders and diseases. Notably, BPA has been shown to inhibit the differentiation of osteoblasts and promote the apoptosis of osteoblasts, which results in altered bone turnover status. PFASs, known for their environmental persistence and ability to bioaccumulate in the human body, have been linked to an increased osteoporosis risk. Similarly, phthalates, which are widely used in the production of plastics, have been associated with adverse bone health outcomes, showing an inverse relationship between phthalate exposure and bone mineral density. Dioxins present a more complex picture, with research findings suggesting both potential benefits and adverse effects on bone structure and density, depending on factors such as the timing and level of exposure. This review underscores the urgent need for further research to better understand the specific pathways through which EDCs affect bone health and to develop targeted strategies for mitigating their potentially harmful impacts.

Ketonuria as an Indicator of Improvement of Renal Function in Patients with Type 2 Diabetes Receiving SGLT2 Inhibitor Treatment.

We investigated the potential association between ketonuria during treatment with sodium-glucose cotransporter-2 (SGLT2) inhibitors and its renoprotective effect in patients with type 2 diabetes. We included 192 patients who had received SGLT2 inhibitors for more than 6 months. After propensity score matching, 52 patients each were allocated into groups with or without ketonuria, respectively. The estimated glomerular filtration rate exhibited a significant improvement only in subjects with ketonuria (without ketonuria: mean difference, -0.02 mL/min/1.73 m2 [95% confidence interval (CI), -3.87 to 3.83 mL/min/1.73 m2] vs. with ketonuria: mean difference, 6.81 mL/min/1.73 m2 [95% CI, 3.16 to 10.46 mL/min/1.73 m2]; P<0.001). Improvement in estimated glomerular filtration rate at 6 months was associated with female sex and lower baseline body weight, blood pressure, and triglyceride levels in patients with ketonuria. In conclusion, the presence of ketonuria was associated with the renoprotective effect of SGLT2 inhibitors, and female sex and the absence of metabolic syndrome components may serve as additional indicators of these medications' substantial renoprotective effects in individuals with ketonuria.

Bone Loss after Solid Organ Transplantation: A Review of Organ-Specific Considerations.

This review article investigates solid organ transplantation-induced osteoporosis, a critical yet often overlooked issue, emphasizing its significance in post-transplant care. The initial sections provide a comprehensive understanding of the prevalence and multifactorial pathogenesis of transplantation osteoporosis, including factors such as deteriorating post-transplantation health, hormonal changes, and the impact of immunosuppressive medications. Furthermore, the review is dedicated to organ-specific considerations in transplantation osteoporosis, with separate analyses for kidney, liver, heart, and lung transplantations. Each section elucidates the unique challenges and management strategies pertinent to transplantation osteoporosis in relation to each organ type, highlighting the necessity of an organ-specific approach to fully understand the diverse manifestations and implications of transplantation osteoporosis. This review underscores the importance of this topic in transplant medicine, aiming to enhance awareness and knowledge among clinicians and researchers. By comprehensively examining transplantation osteoporosis, this study contributes to the development of improved management and care strategies, ultimately leading to improved patient outcomes in this vulnerable group. This detailed review serves as an essential resource for those involved in the complex multidisciplinary care of transplant recipients.

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.

A machine learning-assisted system to predict thyrotoxicosis using patients' heart rate monitoring data: a retrospective cohort study.

Previous studies have shown a correlation between resting heart rate (HR) measured by wearable devices and serum free thyroxine concentration in patients with thyroid dysfunction. We have developed a machine learning (ML)-assisted system that uses HR data collected from wearable devices to predict the occurrence of thyrotoxicosis in patients. HR monitoring data were collected using a wearable device for a period of 4 months in 175 patients with thyroid dysfunction. During this period, 3 or 4 thyroid function tests (TFTs) were performed on each patient at intervals of at least one month. The HR data collected during the 10 days prior to each TFT were paired with the corresponding TFT results, resulting in a total of 662 pairs of data. Our ML-assisted system predicted thyrotoxicosis of a patient at a given time point based on HR data and their HR-TFT data pair at another time point. Our ML-assisted system divided the 662 cases into either thyrotoxicosis and non-thyrotoxicosis and the performance was calculated based on the TFT results. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of our system for predicting thyrotoxicosis were 86.14%, 85.92%, 52.41%, and 97.18%, respectively. When subclinical thyrotoxicosis was excluded from the analysis, the sensitivity, specificity, PPV, and NPV of our system for predicting thyrotoxicosis were 86.14%, 98.28%, 94.57%, and 95.32%, respectively. Our ML-assisted system used the change in mean, relative standard deviation, skewness, and kurtosis of HR while sleeping, and the Jensen-Shannon divergence of sleep HR and TFT distribution as major parameters for predicting thyrotoxicosis. Our ML-assisted system has demonstrated reasonably accurate predictions of thyrotoxicosis in patients with thyroid dysfunction, and the accuracy could be further improved by gathering more data. This predictive system has the potential to monitor the thyroid function status of patients with thyroid dysfunction by collecting heart rate data, and to determine the optimal timing for blood tests and treatment intervention.

Sodium-Glucose Cotransporter 2 Inhibitors vs Incretin-Based Drugs and Risk of Fractures for Type 2 Diabetes.

Importance: Postmenopausal individuals with type 2 diabetes are susceptible to fractures due to the interaction of elevated blood glucose levels and a deficiency of the hormone estrogen. Despite continued concerns of fracture risks associated with sodium-glucose cotransporter 2 inhibitors (SGLT2i), existing evidence in this high-risk population is lacking. Objective: To assess the risk of fractures associated with SGLT2i vs incretin-based drugs of dipeptidyl-peptidase 4 inhibitors (DPP4i) and glucagon-like peptide 1 receptor agonists (GLP1RA), separately, in postmenopausal individuals with type 2 diabetes. Design, Setting, and Participants: This active-comparator, new-user cohort study used nationwide claims data of Korea and took place from January 1, 2013, to December 31, 2020. Postmenopausal individuals (aged ≥45 years) with type 2 diabetes were included. Exposures: New users of SGLT2i or comparator drugs. Main Outcomes and Measures: The primary outcome was overall fractures, comprising vertebral, hip, humerus, and distal radius fractures. Patients were followed up from the day after drug initiation until the earliest of outcome occurrence, drug discontinuation (90-day grace period) or switch, death, or end of the study period. After propensity score fine stratification, hazard ratios (HRs) with 95% CIs were estimated using weighted Cox models. Results: Among 37 530 (mean [SD] age, 60.6 [9.7] years) and 332 004 (mean [SD] age, 60.6 [9.9] years) new users of SGLT2i and DPP4i, respectively, a lower rate of incident overall fractures was presented with SGLT2i vs DPP4i (weighted HR, 0.78; 95% CI, 0.72-0.84). Among 111 835 (mean [SD] age, 61.4 [9.8] years) and 8177 (mean [SD] age, 61.1 [10.3] years) new users of SGLT2i and GLP1RA, respectively, no association with an increased risk of overall fractures was presented with SGLT2i vs GLP1RA (weighted HR, 0.92; 95% CI, 0.68-1.24). Results from several subgroup and sensitivity analyses presented consistent results from main analysis. Conclusions and relevance: This population-based cohort study suggests that SGLT2i was not associated with an increased rate of incident fractures compared with DPP4i and GLP1RA, separately, among postmenopausal individuals with type 2 diabetes.

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.

Chronic airway disease as a major risk factor for fractures in osteopenic women: Nationwide cohort study.

Introduction: The study aimed to demonstrate the risk factors for fractures and to develop prediction models for major osteoporotic and hip fractures in osteopenic patients using the nationwide cohort study in South Korea. Methods: The study was a retrospective nationwide study using the national screening program for transitional ages from the National Health Insurance Services database in Korea from 2008 to 2019. Primary outcomes were incident fracture events of major osteoporotic and hip fractures. Major osteoporotic and hip fracture events were defined as diagnostic and procedural codes. Patients were followed until the fragility fractures, death, or 2019, whichever came first. Results: All participants were 66-year-old females, with a mean body mass index was 25.0 ± 3.1 kg/m2. During a median follow-up of 10.5 years, 26.9% and 6.7% of participants experienced major osteoporotic and hip fractures. In multivariate analysis, a history of fracture, chronic airway disease, falls, diabetes mellitus and cerebrovascular diseases were significant risk factors for major osteoporotic (hazard ratio [HR] 2.35 for a history of fracture; 1.17 for chronic airway disease; 1.10 for falls; 1.12 for diabetes mellitus; 1.11 for cerebrovascular disease) and hip fractures (HR 1.75 for a history of fracture; 1.54 for diabetes mellitus; 1.27 for cerebrovascular disease; 1.17 for fall; 1.15 for chronic airway disease). The performances of the prediction models were area under the receiver operating curve of 0.73 and 0.75 for major osteoporotic and hip fractures. Conclusion: The study presented prediction models of major osteoporotic and hip fractures for osteopenia patients using simple clinical features.

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.

Fracture Liaison Service in Korea: 2022 Position Statement of the Korean Society for Bone and Mineral Research.

Osteoporosis and osteoporotic fractures cause socioeconomic concerns, and medical system and policies appear insufficient to prepare for these issues in Korea, where the older adult population is rapidly increasing. Many countries around the world are already responding to osteoporosis and osteoporotic fractures by adopting fracture liaison service (FLS), and such an attempt has only begun in Korea. In this article, we introduce the operation methods for institutions implementing FLS and characteristics of services, and activities of the FLS Committee for FLS implementation in the Korean Society for Bone and Mineral Research. In addition, we hope that the current position statement will contribute to the implementation of FLS in Korea and impel policy changes to enable a multidisciplinary and integrated FLS operated under the medical system.

Risk of fracture in neuromyelitis optica spectrum disorder and multiple sclerosis: a nationwide cohort study in South Korea.

PURPOSE: Interest in fractures in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) has considerably increased in the last decade. However, few studies have compared the incidence of fractures between patients with MS and NMOSD using a nationwide database. This study aimed to evaluate the differences in the risk of fracture between patients with NMOSD and MS compared to that in healthy controls using cohort data from a Korean nationwide database. METHODS: In this retrospective cohort study, data from the National Health Insurance Service (NHIS) database from January 2010 to December 2017 were analyzed. A total of 1,217/1,329 patients with MS/NMOSD free of fractures at the index date were included. Matched controls were selected based on age, sex, and the presence of hypertension, diabetes mellitus, and dyslipidemia. The mean follow-up durations after the index date were 4.40/4.08 years for patients with MS/NMOSD and 4.73/4.28 for their matched controls. RESULTS: The adjusted hazard ratios (aHRs) with 95% confidence intervals of any, hip, and vertebral fractures were 1.81 (1.43-2.28), 3.36 (1.81-6.24), and 2.01 (1.42-2.99) times higher for patients with MS than for controls, respectively, and they were 1.85 (1.47-2.34), 3.82 (2.05-7.11), and 2.84 (1.92-4.21) times higher for patients with NMOSD than for controls, respectively. No significant differences were observed in the incidence of fractures between the MS and NMOSD groups. Patients with MS/NMOSD had a 1.8-fold higher risk of fracture than matched controls, and the risk of hip fracture was especially high (3- to 4-fold higher). CONCLUSIONS: Clinicians need to regularly assess patients with MS/NMOSD for the risk of fractures and take preventative measures to reduce it.

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.