Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.

Background: During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population. Objective: This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial. Methods: A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups. Results: Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21). Conclusion: These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Development of Clinical Decision Support System for Patient Blood Management in Hospital Information System.

A data pipeline was developed to send and receive patient blood management (PBM) data from all medical institutions in Korea. By incorporating the collected data with national big data, the system will be able to generate key performance index for each medical institution. The central PBM system also provides feedback to each individual medical institution.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19.

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.

COVID-19 vaccination, incidence, and mortality rates among individuals with mental disorders in South Korea: A nationwide retrospective study.

We examined COVID-19 vaccination, incidence, and mortality rates among patients with mental health disorders in South Korea from 1 January 2020 to 31 December 2021. The study found that individuals with mental disorders had higher COVID-19 incidence and mortality than those without. Patients with mood disorders had higher vaccination rates and COVID-19 incidence and mortality than those without mental disorders. In contrast, patients with schizophrenia had lower vaccination rates, slightly lower COVID-19 incidence, and higher COVID-19 mortality. Patients with mental health disorders have been vulnerable to COVID-19, and more attention should be paid to their vaccination and health needs.

Trajectories of Anxiety Symptoms for COVID-19 Patients Using Multimodal Data Collected from Telemedicine.

We designed and developed Remote Patient Monitoring (RPM) system specific for coronavirus (COVID-19) patients, and collected multimodal data. Using the collected data, we explored the trajectories of anxiety symptoms for 199 COVID-19 patients quarantined at home. Two classes were identified using latent class linear mixed model. Thirty-six patients showed an exacerbation of anxiety. Presence of initial psychological symptoms, pain on the start day of quarantine, and abdominal discomfort at one month after finishing the quarantine were associated with exacerbation of anxiety.

Implementation of Interoperable Healthcare Standards for Community Healthcare.

Building an integrated data model that includes not only clinical data but also personal health records has become increasingly important. We aimed to build a big data healthcare platform by developing a common data model that can be utilized in the healthcare field. To this end, we acquired health data from various communities to establish community care digital healthcare service models. Further, to improve personal health data interoperability, we ensured conformance to international standards, namely, the Systemized Nomenclature of Medicine Clinical Terms (SNOMED-CT) and transmission standards, namely, Health Level 7 Fast Healthcare Interoperability Resource (HL7 FHIR). Furthermore, FHIR resource profiling was designed to transmit and receive data, following the HL7 FHIR R4 guidelines.

The Association of Acute Signs and Symptoms of COVID-19 and Exacerbation of Depression and Anxiety in Patients With Clinically Mild COVID-19: Retrospective Observational Study.

BACKGROUND: To date, the association between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety in patients with clinically mild COVID-19 has not been evaluated. OBJECTIVE: This study was designed to assess the correlation between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety in patients with clinically mild COVID-19 at a residential treatment center in South Korea. METHODS: This retrospective study assessed 2671 patients with COVID-19 admitted to 4 residential treatment centers operated by Seoul National University Hospital, South Korea, from March 2020 to April 2022. Depression and anxiety were assessed using the 2-item Patient Health Questionnaire (PHQ-2) and 2-item Generalized Anxiety Disorder (GAD-2) scale, respectively. The exacerbation of depression and anxiety symptoms was identified from the differences in PHQ-2 and GAD-2 scores between admission and discharge, respectively. The patients' clinical characteristics, including acute signs and symptoms of COVID-19, GAD-2 and PHQ-2 scores, were obtained from electronic health records. Demographic characteristics, a summary of vital signs, and COVID-19 symptoms were analyzed and compared between the patient groups with and those without exacerbated PHQ-2 and GAD-2 scores using the chi-square test. We applied logistic regression to identify the association between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety. RESULTS: Sleep disorders were associated with exacerbated depression (odds ratio [OR] 1.09, 95% CI 1.05-1.13) and anxiety (OR 1.1, 95% CI 1.06-1.14), and the sore throat symptom was associated with exacerbated anxiety symptoms (OR 1.03, 95% CI 1.00-1.07). Patients with abnormal oxygen saturation during quarantine were more likely to have exacerbated depression (OR 1.27, 95% CI 1.00-1.62), and those with an abnormal body temperature during quarantine were more likely to experience anxiety (OR 1.08, 95% CI 1.01-1.16). As anticipated, patients who experienced psychological symptoms at admission were more likely to experience depression (OR 1.91, 95% CI 1.52-2.41) and anxiety (OR 1.98, 95% CI 1.54-2.53). Meanwhile, the PHQ-2 and GAD-2 scores measured at admission revealed that lower the score, higher the possibility of exacerbation of both depression (OR 0.15, 95% CI 0.11-0.22) and anxiety (OR 0.13, 95% CI 0.10-0.19). CONCLUSIONS: Results from this study suggest the importance of further interventions for patients with abnormal oxygen saturation, abnormal body temperatures, sore throat, and sleep disorder symptoms or initial psychological symptoms to mitigate the exacerbation of depression and anxiety. In addition, this study highlights the usability of short and efficient scales such as the PHQ-2 and GAD-2 in the assessment of the mental health of patients with clinically mild COVID-19 symptoms who were quarantined at home during the pandemic era.

Building and implementing a contactless clinical trial protocol for patients with COVID-19: A Korean perspective.

Introduction: To effectively manage patients with coronavirus disease 2019 (COVID-19) while minimizing contact between medical staff, clinical trial protocol that facilitates contactless patient management was designed to predict deterioration of disease condition and monitor mental health status. Methods: Through consultation with infectious disease specialists and psychiatrists, this study identified main clinical indicators related to respiratory and non-respiratory outcomes, and mental health. Telehealth devices that could collect relevant data indicators were explored. The following three modes were identified: wearable devices, video calls, and online questionnaires. Clinical trial protocol was implemented to patients confirmed with COVID-19 infection and admitted to Seongnam residential treatment centers between September 1, 2021 and December 30, 2021. Data were collected from wearable devices, video calls, online questionnaires, and from electronic health records. Participant satisfaction was assessed through an online survey at the time of discharge. Results: In total, 120 asymptomatic and mildly symptomatic COVID-19 patients participated in this trial. Seven types of physiological and life log data were collected from 87 patients using wearable devices, video and audio recordings, and online mental health-related questionnaire. Most participants were satisfied with the overall trial process, but perceived difficulties in using telehealth devices. Conclusion: This trial collected simultaneously generated multimodal patient data using various telehealth devices in a contactless setting for COVID-19 patients. Data collected in this study will be used to build a remote patient management system based on the prediction algorithms.

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker.

Background: Depression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview. Methods: A total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student's T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model. Results: A total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800. Conclusion: The possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants' words during an interview show significant potential as an objective and diagnostic marker through machine learning.

Association between higher variability in kidney function and long-term mortality.

AIM: We evaluated whether estimated glomerular filtration rate variability in the general population could be associated with all-cause mortality. METHODS: Health examination data from 7842 individuals aged >20 years who visited for health check-ups at least thrice at ≥6-month intervals between May 1, 1995 and November 30, 2010 were collected. Estimated glomerular filtration rate variability was defined as the coefficient of variation of the estimated glomerular filtration rate, that is, standard deviation/mean value multiplied by 100. The study population was divided into three groups based on the coefficient of variation tertiles, and the mortality risks were compared across groups. RESULTS: The mean duration from the final visit to the outcome was 10.3 ± 2.9 years. The mean coefficient of variations of estimated glomerular filtration rate variability from the lowest to the highest variability group were 5.1 ± 1.8%, 9.0 ± 1.0%, and 14.4 ± 3.9%, respectively. There was a 1.3 times higher risk of mortality in the group with the highest variability (hazard ratio: 1.300, 95% confidence interval: 1.013-1.669) after adjustment. The findings were similar in patients with diabetes and those >60 years old (hazard ratio: 1.635, 95% confidence interval: 1.076-2.483; hazard ratio: 1.585, 95% confidence interval: 1.107-2.269). CONCLUSION: Higher estimated glomerular filtration rate variability was associated with increased 10-year mortality in the general population. This variability was very small, but considering the patients' long-term prognoses, it was significant.

Estimation of low-density lipoprotein cholesterol levels using machine learning.

BACKGROUND: Low-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerides (TG) or non-fasting states. We aimed to propose a novel method to estimate LDL-C using machine learning. METHODS: Using a large, single-center electronic health record database, we derived a ML algorithm to estimate LDL-C from standard lipid profiles. From 1,029,572 cases with both standard lipid profiles (total cholesterol, high-density lipoprotein-cholesterol, and TG) and direct LDL-C measurements, 823,657 tests were used to derive LDL-C estimation models. Patient characteristics such as sex, age, height, weight, and other laboratory values were additionally used to create separate data sets and algorithms. RESULTS: Machine learning with gradient boosting (LDL-CX) and neural network (LDL-CN) showed better correlation with directly measured LDL-C, compared with conventional methods (r = 0.9662, 0.9668, 0.9563, 0.9585; for LDL-CX, LDL-CN, Friedewald [LDL-CF], and Martin [LDL-CM] equations, respectively). The overall bias of LDL-CX (-0.27 mg/dL, 95% CI -0.30 to -0.23) and LDL-CN (-0.01 mg/dL, 95% CI -0.04-0.03) were significantly smaller compared with both LDL-CF (-3.80 mg/dL, 95% CI -3.80 to -3.60) or LDL-CM (-2.00 mg/dL, 95% CI -2.00 to -1.94), especially at high TG levels. CONCLUSIONS: Machine learning algorithms were superior in estimating LDL-C compared with the conventional Friedewald or the more contemporary Martin equations. Through external validation and modification, machine learning could be incorporated into electronic health records to substitute LDL-C estimation.

Satisfaction and Usability of an Information and Communications Technology-Based System by Clinically Healthy Patients With COVID-19 and Medical Professionals: Cross-sectional Survey and Focus Group Interview Study.

BACKGROUND: Digital health care is an important strategy in the war against COVID-19. South Korea introduced living and treatment support centers (LTSCs) to control regional outbreaks and care for patients with asymptomatic or mild COVID-19. Seoul National University Hospital (SNUH) introduced information and communications technology (ICT)-based solutions to manage clinically healthy patients with COVID-19. OBJECTIVE: This study aims to investigate satisfaction and usability by patients and health professionals in the optimal use of a mobile app and wearable device that SNUH introduced to the LTSC for clinically healthy patients with COVID-19. METHODS: Online surveys and focus group interviews were conducted to collect quantitative and qualitative data. RESULTS: Regarding usability testing of the wearable device, perceived usefulness had the highest mean score of 4.45 (SD 0.57) points out of 5. Regarding usability of the mobile app, perceived usefulness had the highest mean score of 4.62 (SD 0.48) points out of 5. Regarding satisfaction items for the mobile app among medical professionals, the "self-reporting" item had the highest mean score of 4.42 (SD 0.58) points out of 5. In focus group interviews of health care professionals, hospital information system interfacing was the most important functional requirement for ICT-based COVID-19 telemedicine. CONCLUSIONS: Improvement of patient safety and reduction of the burden on medical staff were the expected positive outcomes. Stability and reliability of the device, patient education, accountability, and reimbursement issues should be considered as part of the development of remote patient monitoring. In responding to a novel contagious disease, telemedicine and a wearable device were shown to be useful during a global crisis.

Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples.

PURPOSE: To illustrate what is inside the so-called black box of deep learning models (DLMs) so that clinicians can have greater confidence in the conclusions of artificial intelligence by evaluating adversarial explanation on its ability to explain the rationale of DLM decisions for glaucoma and glaucoma-related findings. Adversarial explanation generates adversarial examples (AEs), or images that have been changed to gain or lose pathologic characteristic-specific traits, to explain the DLM's rationale. DESIGN: Evaluation of explanation methods for DLMs. PARTICIPANTS: Health screening participants (n = 1653) at the Seoul National University Hospital Health Promotion Center, Seoul, Republic of Korea. METHODS: We trained DLMs for referable glaucoma (RG), increased cup-to-disc ratio (ICDR), disc rim narrowing (DRN), and retinal nerve fiber layer defect (RNFLD) using 6430 retinal fundus images. Surveys consisting of explanations using AE and gradient-weighted class activation mapping (GradCAM), a conventional heatmap-based explanation method, were generated for 400 pathologic and healthy patient eyes. For each method, board-trained glaucoma specialists rated location explainability, the ability to pinpoint decision-relevant areas in the image, and rationale explainability, the ability to inform the user on the model's reasoning for the decision based on pathologic features. Scores were compared by paired Wilcoxon signed-rank test. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC), sensitivities, and specificities of DLMs; visualization of clinical pathologic changes of AEs; and survey scores for locational and rationale explainability. RESULTS: The AUCs were 0.90, 0.99, 0.95, and 0.79 and sensitivities were 0.79, 1.00, 0.82, and 0.55 at 0.90 specificity for RG, ICDR, DRN, and RNFLD DLMs, respectively. Generated AEs showed valid clinical feature changes, and survey results for location explainability were 3.94 ± 1.33 and 2.55 ± 1.24 using AEs and GradCAMs, respectively, of a possible maximum score of 5 points. The scores for rationale explainability were 3.97 ± 1.31 and 2.10 ± 1.25 for AEs and GradCAM, respectively. Adversarial example provided significantly better explainability than GradCAM. CONCLUSIONS: Adversarial explanation increased the explainability over GradCAM, a conventional heatmap-based explanation method. Adversarial explanation may help medical professionals understand more clearly the rationale of DLMs when using them for clinical decisions.

Association and Prediction of Subclinical Atherosclerosis by Nonalcoholic Fatty Liver Disease in Asymptomatic Patients.

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing in the general population. This study evaluated the association between NAFLD and significant coronary stenosis in asymptomatic adults and evaluated sex-based differences. Methods: We performed a retrospective cross-sectional study in participants without previous cardiovascular diseases who visited the Seoul National University Hospital Health Promotion Center for a health checkup between January 1, 2010, and December 31, 2015. NAFLD was diagnosed on sonography, while coronary artery stenosis (CAS) was assessed on coronary computed tomography angiography (CCTA). Results: We obtained 3,693 participants who met the inclusion criteria, and 3,449 of them had no significant stenosis. Among the participants with significant stenosis, the prevalence of NAFLD was 59.4% (145 patients). The prevalence of NAFLD was 47.26% in male participants, which was higher than that in female participants. The association between NAFLD and significant CAS persisted after adjusting for age, body mass index, glycated hemoglobin, and Framingham risk factors. The correlation between NAFLD and significant coronary stenosis appeared to be stronger in women than in men, but the absolute risk was higher in men than in women. Conclusion: NAFLD was strongly associated with CAS. We should be alert about an increased cardiovascular risk in patients with NAFLD and more intensively provide primary prevention by performing tests to detect subclinical atherosclerosis.

Information Technology-Based Management of Clinically Healthy COVID-19 Patients: Lessons From a Living and Treatment Support Center Operated by Seoul National University Hospital.

BACKGROUND: South Korea took preemptive action against coronavirus disease (COVID-19) by implementing extensive testing, thorough epidemiological investigation, strict social distancing, and rapid treatment of patients according to disease severity. The Korean government entrusted large-scale hospitals with the operation of living and treatment support centers (LTSCs) for the management for clinically healthy COVID-19 patients. OBJECTIVE: The aim of this paper is to introduce our experience implementing information and communications technology (ICT)-based remote patient management systems at a COVID-19 LTSC. METHODS: We adopted new electronic health record templates, hospital information system (HIS) dashboards, cloud-based medical image sharing, a mobile app, and smart vital sign monitoring devices. RESULTS: Enhancements were made to the HIS to assist in the workflow and care of patients in the LTSC. A dashboard was created for the medical staff to view the vital signs and symptoms of all patients. Patients used a mobile app to consult with their physician or nurse, answer questionnaires, and input self-measured vital signs; the results were uploaded to the hospital information system in real time. Cloud-based image sharing enabled interoperability between medical institutions. Korea's strategy of aggressive mitigation has "flattened the curve" of the rate of infection. A multidisciplinary approach was integral to develop systems supporting patient care and management at the living and treatment support center as quickly as possible. CONCLUSIONS: Faced with a novel infectious disease, we describe the implementation and experience of applying an ICT-based patient management system in the LTSC affiliated with Seoul National University Hospital. ICT-based tools and applications are increasingly important in health care, and we hope that our experience will provide insight into future technology-based infectious disease responses.

Effects of smoking habit change on hospitalized fractures: a retrospective cohort study in a male population.

We examined effects of smoking habit change on fracture risk in men. Long-term quitters and never smokers showed decreased risk for overall fractures, lumbar fractures, and other site fractures. Short-term quitters did not show decreased risk. Longer time since smoking cessation may lead to decreased fracture risk in men. PURPOSE: Cigarette smoking is a well-known modifiable risk factor of osteoporosis and fractures. This study investigated the effects of change in smoking habits on risks of all types of fractures in men using a nationwide health claims database. METHODS: Retrospective study was performed using the Korean National Health Insurance Service-National Sample Cohort Data. Cox proportional hazards regression analyses were performed to estimate risks of all types of hospitalized fractures, hip fractures, lumbar fractures, and other site fractures (all other fractures excluding the lumbar and hip areas). RESULTS: Compared to continued smokers, long-term quitters and never smokers showed decreased risk for all types of fractures (adjusted hazard ratio (aHR) 0.83, 95% confidence interval (CI) 0.78-0.88 and aHR 0.84, 95% CI 0.80-0.89, respectively). According to skeletal site, long-term quitters and never smokers showed decreased risk for lumbar fractures (aHR 0.82, 95% CI 0.68-0.98 and aHR 0.85, 95% CI 0.73-0.99, respectively) and other site fractures (aHR 0.83, 95% CI 0.78-0.89 and aHR 0.85, 95% CI 0.81-0.90, respectively). Hip fractures were decreased in never smokers (aHR 0.77, 95% CI 0.62-0.94). Short-term quitters did not show decreased risk for fractures. CONCLUSIONS: Longer time since smoking cessation in men may lead to decreased risk for fractures, especially lumbar and other site fractures. Physicians should counsel patients at risk for fractures both to quit smoking and to maintain abstinence from smoking. Further studies may be required to help comprehend how smoking cessation can affect fracture risk.

Association of Concurrent Changes in Metabolic Health and Weight on Cardiovascular Disease Risk: A Nationally Representative Cohort Study.

Background The combined effect of transitions of metabolic health and weight on cardiovascular disease (CVD) remains unclear. We aimed to examine the association of concurrent changes of metabolic health and weight on CVD over time. Methods and Results The study population consisted of 205 394 from the Korean National Health Insurance Service. Metabolic health was determined by fasting serum glucose, total cholesterol, and blood pressure levels, while obesity was determined by body mass index. All participants were divided into either metabolically healthy nonobese (MHNO), metabolically healthy obese, metabolically unhealthy nonobese, or metabolically unhealthy obese for each of the first (2002-2003) and second (2004-2005) health screening periods, after which participants were followed-up for CVD from 2006 to 2015. Cox proportional hazards regression was used to determine adjusted hazard ratios (aHRs) and 95% CIs. Among initial MHNO participants, those who became metabolically healthy obese (aHR, 1.25; 95% CI, 1.10-1.41), metabolically unhealthy nonobese (aHR, 1.23; 95% CI, 1.15-1.31), and metabolically unhealthy obese (aHR, 1.34; 95% CI, 1.12-1.61) had elevated risk for CVD compared with those who remained MHNO. Conversely, improving metabolic health and obesity were associated with reduced CVD risk among initially metabolically unhealthy nonobese to secondary MHNO (aHR, 0.79; 95% CI, 0.73-0.84), metabolically unhealthy obese to MHNO (aHR, 0.68; 95% CI, 0.58-0.81), and metabolically unhealthy obese to metabolically healthy obese (aHR, 0.73; 95% CI, 0.66-0.80) participants. Conclusions Changes toward metabolically unhealthy or obese states resulted in increased CVD risk. Improving metabolic health along with reducing weight may lead to decreased risk of CVD.

Oral bisphosphonate use and the risk of female breast, ovarian, and cervical cancer: a nationwide population-based cohort study.

Bisphosphonate use was not associated with the risk of female breast, ovarian, or cervical cancer. The results according to bisphosphonate type or concurrent drug uses were not associated with the cancer risk. The protective effect of bisphosphonate use on female breast cancer was significant in the low comorbidity group. PURPOSE: Despite the antitumor mechanisms, the effect of bisphosphonates on the risk of cancer is still unclear. We investigated the association between oral bisphosphonate use and the development of female breast, ovarian, and cervical cancer. METHODS: We accomplished a population-based cohort study using the National Health Insurance Services (NHIS) database. A total of 204,525 participants were included in a cohort, and we identified the incident cases of each cancer from 2007 to 2013. We assessed cumulative bisphosphonate exposure from 2003 to 2006 using the defined daily dose (DDD) system. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were presented to assess the association between bisphosphonate use and cancer incidence using multivariate Cox proportional hazard regression models. Subgroup analyses were performed to assess cancer development according to risk factors and concurrent drug use. RESULTS: There was a total of 1547, 266, and 370 incident cases of female breast, cervical, and ovarian cancer, respectively, during the study period of 1,367,294 person-years. Bisphosphonate exposure was not significantly associated with risk of female breast (adjusted HR (aHR), 0.78; 95% CI, 0.60-1.02), ovarian (aHR, 1.30; 95% CI, 0.82-2.07), nor cervical cancer (aHR, 0.70; 95% CI, 0.44-1.12). Further subgroup analyses also revealed no statistically significant effects of bisphosphonate use with various risk factors and concurrent drug use. CONCLUSIONS: Our study showed no significant associations between bisphosphonate exposure and female breast, cervical, and ovarian cancer. In the future, large prospective studies or a meta-analysis would be needed to verify the associations.

Osteoarthritis Affects Health-Related Quality of Life in Korean Adults with Chronic Diseases: The Korea National Health and Nutritional Examination Surveys 2009-2013.

BACKGROUND: Osteoarthritis (OA) is a chronic disease that commonly afflicts the elderly. This disease reduces the health-related quality of life (HRQoL) and causes a significant social burden. Whether the effect of coexisting chronic conditions on HRQoL varies according to the presence of OA remains unclear. Therefore, this study aimed to investigate this notion. METHODS: A total of 13,395 participants were identified from the 2009-2013 Korean National Health and Nutrition Examination Survey for analysis. HRQoL was assessed using the European quality of life-5 dimensions (EQ-5D) index. Patients with OA were defined as those diagnosed by a physician or those who displayed both, symptoms and radiological findings consistent with OA at the time of the survey. Associations between OA and 8 chronic conditions were tested using regression analysis. RESULTS: The EQ-5D index was lower in patients with OA than in those without (mean difference, -0.145; 95% confidence interval [CI], -0.138 to -0.151; P<0.001). Most patients with OA and chronic conditions showed a lower score than those without. EQ-5D was particularly lower in OA patients with hypertension, dyslipidemia, stroke, and renal failure. The estimated ß coefficient for the interaction term was significant in renal failure (-0.034; 95% CI, -0.055 to -0.012), after adjusting for demographic and socio-economic variables. CONCLUSION: OA significantly affects HRQoL of Korean elderly individuals alone or when combined with other conditions. OA combined with renal failure is particularly detrimental. These results indicate the importance of managing OA, which is an underestimated disease in public health surveys.

Genome-Wide Association Study of Bone Mineral Density in Korean Men.

Osteoporosis is a medical condition of global concern, with increasing incidence in both sexes. Bone mineral density (BMD), a highly heritable trait, has been proven a useful diagnostic factor in predicting fracture. Because medical information is lacking about male osteoporotic genetics, we conducted a genome-wide association study of BMD in Korean men. With 1,176 participants, we analyzed 4,414,664 single nucleotide polymorphisms (SNPs) after genomic imputation, and identified five SNPs and three loci correlated with bone density and strength. Multivariate linear regression models were applied to adjust for age and body mass index interference. Rs17124500 (p = 6.42 × 10(-7)), rs34594869 (p = 6.53 × 10(-7)) and rs17124504 (p = 6.53 × 10(-7)) in 14q31.3 and rs140155614 (p = 8.64 × 10(-7)) in 15q25.1 were significantly associated with lumbar spine BMD (LS-BMD), while rs111822233 (p = 6.35 × 10(-7)) was linked with the femur total BMD (FT-BMD). Additionally, we analyzed the relationship between BMD and five genes previously identified in Korean men. Rs61382873 (p = 0.0009) in LRP5, rs9567003 (p = 0.0033) in TNFSF11 and rs9935828 (p = 0.0248) in FOXL1 were observed for LS-BMD. Furthermore, rs33997547 (p = 0.0057) in ZBTB and rs1664496 (p = 0.0012) in MEF2C were found to influence FT-BMD and rs61769193 (p = 0.0114) in ZBTB to influence femur neck BMD. We identified five SNPs and three genomic regions, associated with BMD. The significance of our results lies in the discovery of new loci, while also affirming a previously significant locus, as potential osteoporotic factors in the Korean male population.