We conducted this study to explore a novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID‐19).The current study was a retrospective, multicenter, observational study. The clinical data of COVID‐19 patients at admission were collected. Patients were randomly divided into training set and testing set (70% vs. 30% of patients). Independent risk factors were identified via logistic regression analysis.9/L at admission , blood urea nitrogen ≥7 mmol/L at admission , estimated glomerular filtration rate ≤90 ml/min/1.73 m2 at admission , activated partial thromboplastin time ≥37 s , D‐dimer ≥ 0.5 mg/L  and procalcitonin ≥0.5 μg/L . The area under curve of ROC curve was 0.773 . The risk score had robustness and generalizability after validation. Cardiovascular complications were significantly associated with poorer survivals (log‐rank test: p < .001).Finally, 1207 patients were included. Ten independent risk factors associated with cardiovascular complications were identified in training set: male , age ≥ 60 years old , cough , chronic heart disease , lymphocyte count ≤1.1 × 10We developed and validated a novel risk score, which has a promising predictive capacity for cardiovascular complications in COVID‐19 patients. p < .001).We developed and validated a novel risk score, which has a promising predictive capacity for cardiovascular complications in coronavirus disease 2019 (COVID‐19) patients. Cardiovascular complications were significantly associated with poorer survival (log‐rank test:  However, extrapulmonary manifestations or injury should not be ignored as well. It has been reported that pre‐existing cardiovascular diseases are associated with worse prognosis in COVID‐19 patients.Meanwhile, it is also of considerable value to identify risk factors for predicting potential cardiovascular complications at an early stage, which could guide and optimize the therapies for COVID‐19 under the circumstance of limited healthcare resources. However, related studies are still insufficient up to now. Early prediction models that combine clinical features to identify COVID‐19 patients at high risk of cardiovascular events remain poorly defined and challenging to investigate. The current retrospective, multicenter, observational study was conducted aiming to develop and validate a novel risk score used for predicting cardiovascular complications, and also to assess the relationship between these complications and prognosis among COVID‐19 patients.22.1This retrospective, multicenter, observational study of laboratory‐confirmed COVID‐19 patients was conducted in accordance with the amended Declaration of Helsinki and approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (No. 2020‐272). Written informed consent was waived because of the urgent need to collect clinical data and retrospective observational design.Clinical data of hospitalized patients from two major COVID‐19 designated hospitals  in Wuhan city, and 36 COVID‐19‐designated hospitals in Sichuan province, China between January 14 and March 9, 2020 were collected and analyzed. All included patients were randomly divided into training set and testing set (70% percent vs. 30% of patients). The training set was used to develop a risk score and a testing set was applied to validate the robustness and generalizability of the risk score.All patient data were anonymously recorded to ensure confidentiality. Two doctors reviewed the medical records of all patients independently. Any disagreement was resolved through the third doctor and team discussion until consensus reached.2.2All patients enrolled in this study were diagnosed with confirmed COVID‐19 according to World Health Organization interim guidance.The following exclusion criteria were used: (1) under 18 years old; (2) being pregnant; (3) died or having missing baseline data on admission; (4) recovering from cardiac arrest/cardiopulmonary resuscitation.Demographic characteristics, basic vital signs, symptoms and signs, comorbidities, chest computed tomography (CT) scan images, and laboratory examinations data were retrospectively collected from electronic medical records. All these baseline data were recorded at admission or within 24 h after admission to hospitals. Continuous variables were categorized for further analysis. The threshold value of each continuous variable was determined by the clinically relevant cut‐off value, or upper limit or lower limit of the normal range. Two doctors completed the data collection independently.2.3The occurrence of cardiovascular complications was considered if any of the following appeared during hospitalization: (1) acute myocardial injury; (2) acute myocardial infarction (AMI), including non‐ST elevation or ST elevation MI; (3) new or worsening HF; (4) de novo arrhythmia; (5) deep vein thrombosis (DVT) or pulmonary embolism (PE).This composite endpoint has been used in previous studies that evaluated the cardiovascular complications of pneumonia.Likewise, two doctors reviewed and checked the diagnosis of cardiovascular complications independently. Any disagreement was resolved through the third doctor and team discussion until consensus reached.2.4t tests for normally distributed continuous variables, the Mann–Whitney U test for non‐normally distributed continuous variables, and χ2 test or Fisher's exact test for categorical variables.Data were analyzed by using IBM SPSS Statistics version 23.0 (SPSS). Data were expressed as mean ± standard deviation or median (interquartile range) for continuous variables, as well as counts and percentages for categorical variables. The data were tested by the Kolmogorov–Smirnov normality test and Bartlett's test for homogeneity of variance. The difference between the two groups was tested using a two‐tailed independent Student's p < .10 were included in univariate and multivariate logistic regression analysis to identify independent risk factors. The odds ratio (OR) and confidence interval (CI) were used to evaluate risk factors. The score for each independent risk factor was assigned as an integer value close to the regression coefficient. The total risk score of each patient is the sum of each single score. To assess the accuracy of risk score as a predictor of cardiovascular complications of COVID‐19, the receiver operating characteristics (ROC) curve was conducted and the area under the ROC curve (AUC) was reported. The optimal cut‐off point of the risk score was based on the Youden's index of ROC curve, corresponding to the maximum joint sensitivity and specificity.Variables with p < .05 was considered statistically significant.After that, the variables required for calculating the risk score were collected, calculated, and tested for the testing set. The performance of risk score in training set, testing set, and overall patients were compared. We also conducted survival analysis in all patients using the Kaplan–Meier method and log‐rank test to explore the impacts of cardiovascular complications on the prognosis of COVID‐19 patients. 33.1A total of 1240 patients confirmed with COVID‐19 were retrospectively enrolled in the study. Ultimately, 33 patients were excluded according to the exclusion criteria, and 1207 patients were analyzed. Among them, 845 patients  were randomized to training set and 362 patients  were included in the testing set . There were also some other significant differences between patients with or without cardiovascular complications in terms of fever (p = .031), cough (p = .002), weakness/fatigue (p = .035), unconsciousness (p = .036), chronic heart disease (p < .001), diabetes mellitus (p = .036).In the training set, 122 (14.4%) patients were found to have cardiovascular complications. Among them, 98 patients had acute myocardial injury and 5 patients further developed AMI. A total of 8 patients had new or worsening HF and 30 patients had de novo arrhythmia. Additionally, only one patient was diagnosed with DVT. The rate of male and the median age of patients with complications were both significantly higher than those of patients without complications , neutrophil counts , aspartate aminotransferase , and so on; however, lower lymphocyte counts , platelet counts , estimated glomerular filtration rate , and so forth. The detailed baseline characteristics of the patients are shown in Table Furthermore, some differences in laboratory examinations were also demonstrated. Compared with patients without cardiovascular complications, patients with cardiovascular complications had higher white blood cell counts , blood urea nitrogen ≥7 mmol/L at admission , eGFR ≤ 90 ml/min/1.73 m2 at admission , activated partial thromboplastin time (APTT) ≥ 37 s , D‐dimer ≥ 0.5 mg/L , and procalcitonin ≥0.5 μg/L . The details and corresponding score for each risk factor are showed in Table The factors with p < .01; Figure As a result, the total risk score for each patient varied from 0 to 23 points. Finally, an optimal cut‐off value of 7.5  was identified to predict cardiovascular complications according to ROC curve and AUC was 0.773  patients were found to have cardiovascular complications. The accuracy of the risk score in the testing set  was similar to that in the training set. Furthermore, the AUC of the risk score in overall patients (training set plus testing set) was 0.766 . The optimal cut‐off value is also 7.5 . The results are summarized in Table p < .001) in the Kaplan–Meier curves, which showed that cardiovascular complications had considerable adverse impacts on the prognosis of COVID‐19 patients (Figure In addition, cardiovascular complications were significantly associated with poorer survival (log‐rank test: s Figure .4To our knowledge, this is the first predictive tool used for predicting cardiovascular complications among COVID‐19 patients at admission to hospital. Ten independent risk factors at admission were identified and the total score varied from 0 to 23 points for each patient. The risk score has also been validated. A higher total score is correlated to increased risk of cardiovascular complications which might lead to significantly poor prognosis of COVID‐19 patients. Therefore, early prediction of cardiovascular complications is important and necessary.Wei et al.According to previous reports, cardiovascular complications were common clinical manifestations and could increase the health burden of SARS and influenza in the last decades.Previous studies have demonstrated that myocardial injury occurred in about 10% of patients with COVID‐19.In the risk score, some factors have been widely confirmed to be connected with cardiovascular events, including male, older age, chronic heart disease, prolonged APTT, elevated D‐dimer, and so on. Their predictive values for cardiovascular complications have also been demonstrated in community‐acquired pneumonia.We found that some risk factors in the current study were also applied in previous similar prediction models for critical illness, admission to ICU, or mortality of COVID‐19 patients. For instance, Gong et al.Given the lack of specific antiviral agents for SARS‐CoV‐2 and significant adverse effects of cardiovascular complications on the prognosis of patients, early identification of COVID‐19 patients at high risk of cardiovascular complications, timely intervention, and protection of target organ are important and essential. In the current study, all risk factors are easy to obtain at admission to hospital and this risk score has been well developed and validated with promising predictive capacity. It might help clinicians make optimal treatment decisions for patients who are prone to develop cardiovascular complications, and help researchers explore more detailed systemic damage and pathophysiological mechanisms of COVID‐19 in the future.There are several limitations to our study. First, it was a retrospective observational study with potential unavoidable selection bias. Second, the sample size was relatively moderate, and the number of patients was not equal between groups with or without cardiovascular complications. Third, the drugs and therapies before admission might have considerable impacts on our results. Then, we only recorded cardiovascular complications before discharge without follow‐up. The long‐term damage of SARS‐CoV‐2 to cardiovascular systems and related risk factors remain to be explored. Further well‐designed, multicenter, long‐term studies with better comparability are warranted to clarify the characteristics of these risk factors and verify our conclusions.59/L, blood urea nitrogen ≥7 mmol/L, eGFR ≤ 90 ml/min/1.73 m2, APTT ≥ 37 s, D‐dimer ≥ 0.5 mg/L and procalcitonin ≥0.5 μg/L. This risk score has a promising predictive capacity for cardiovascular complications which could significantly impair the prognosis of COVID‐19 patients. Our conclusions need to be further confirmed in further studies.We developed and validated a novel risk score, which is based on 10 risk factors at admission: male, age ≥ 60 years, cough, chronic heart disease, lymphocyte count ≤1.1 × 10The authors declare that there are no conflict of interests.Dong Huang, Huan Yang, He Yu, Ting Wang, Rong Yao, and Zongan Liang conceived the idea, designed, and supervised the study, drafted the manuscript, and had full access to all of the data and took responsibility for the integrity of the data. Dong Huang and Huan Yang collected data. Dong Huang and Huan Yang analyzed data and performed the statistical analysis. All of the authors reviewed and approved the final version of the manuscript.


To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms  to cross-verified human annotation. We provide a set of videos—carefully annotated by several human raters—of three widely used behavioral tests . Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost. Accurate analysis of rodent behavior is crucial when assessing treatment efficacy in preclinical research. The rapid development of new tools and molecular interventions in rodents, as well as the growing number of available transgenic mouse lines, increase the need to accurately and efficiently detect and quantify rodent behavior , 2. TypiRecently, major advances in machine learning have given rise to the first descriptions of unsupervised analyses of behavior, revealing the stunning temporal and structural complexity of rodent behavior –16. HoweA detailed description of all procedures is found in C57BL/6J (C57BL/6JRj) mice  were obtained from Janvier (France). Mice were maintained in a temperature- and humidity-controlled facility on a 12-h reversed light–dark cycle (lights on at 08:15 am) with food and water ad libitum. Mice were housed in groups of 5 per cage and used for experiments when 2.5–4 months old. For each experiment, mice of the same age were used in all experimental groups to rule out confounding effects of age. All tests were conducted during the animals’ active (dark) phase from 12–5 pm. Mice were single housed 24 h before behavioral testing in order to standardize their environment and avoid disturbing cage mates during testing , 34. AllL × W × H]) consisted of four transparent Plexiglas walls and a light gray PVC floor. Animals were tested for 10 min under dim lighting (4 lux). Distance, time in center, supported rears and unsupported rears were recorded.OFT took place inside sound insulated, ventilated multi conditioning chambers . The open field arena (45 × 45 × 40 cm [L × W), elevated 61.5 cm. Light intensity in the open arms was at 19–21 lux. All EPM tests were 10 min in duration. Distance, velocity, time in zone (open/closed arms + center) and head dips were recorded.The EPM was made from gray PVC, with arms measuring 65.5 × 5.5 cm  filled to 17 cm with 17.9–18.1 °C water for 6 min.EthoVision XT14 was used to acquire all forced swim and elevated plus maze videos and to analyze all of the open field videos. The automatic animal detection settings were used for all tests, slight tuning of these settings was performed using the fine-tuning slider in the automated animal detection settings to ensure the animals could be tracked throughout the entire arena. We ensured that there was a smooth tracking curve and that the centerpoint of the animal remained stable before analysis took place.https://github.com/ETHZ-INS/DLCAnalyzer).DeepLabCut 2.0.7 was used to track 13 body points and several points of the various arenas Fig. . The dat2) was defined as the center of the arena. To automatically distinguish supported from unsupported rears, we empirically determined the area in which mice could not perform a supported rear. Thus, all rears within 12.5 cm of the walls were considered supported rears, while rears in the rest of the field were considered unsupported rears. Rearing was defined as an interruption of a beam in the z-axis for a minimum of 150 ms. If another rear was reported within 150 ms of the initial rear, it was counted as part of the initial rear.Locomotion was tracked using an infrared beam grid; an additional beam grid was raised 6.5 cm above the locomotion grid to measure rearing. The central 50% . Values of points with low likelihood (>0.95) were removed and interpolated using the R package “imputeTS” (v 2.7). The speed and acceleration of each point was determined by integrating the animal’s position over time. Points of interest relating to the arenas were tracked and median XY coordinates were used to define the arenas in silico. The pixel-to-cm conversion ratio for each video was determined by comparing the volume of the arena in silico in px2 to the measured size of the arena in cm2. Zones of interest were calculated from the arena definitions using polygon-scaling functions. Detailed descriptions of how individual behaviors were computed can be found in X and Y coordinates of DLC-tracking data were imported into R Studio (v 3.6.1) and processed with custom scripts , as well as all videos (https://zenodo.org/record/3608658). The machine learning approach is described in the In order to create a training dataset, 20 videos of the OFT were manually labeled (using VIA video annotator ), indicaWe used a Dell XPS 8930 workstation , 512GB SSD, 2TB HDD, Nvidia GTX 1080 GPU) to implement the DLC-based approach, and to train the machine learning classifiers. We investigated the labeling, training, and analysis times of networks that use different numbers of labeled points. It takes an experienced experimenter ~5 min to label 20 frames with 18 points of interest . Using the same computer described above, the network then trains overnight (ca. 11 h), and a 10-min video  is analyzed in ca. 9 min , whereas all comparisons between more than two groups were performed using one-way ANOVAs in order to identify group effects. Significant main effects were then followed up with post-hoc tests (Tukey’s multiple comparison test). We also report the coefficient of variation (CV) in order to show the dispersion of the data around the mean.Data were tested for normality and all comparisons between normally distributed datasets containing two independent groups were performed using unpaired Our goal was to compare the tracking performance of DLC to commercial solutions using three of the most popular rodent behavior tests in basic neuroscience research: the open field test, the elevated plus maze, and the FST. Robust tracking was previously demonstrated using DLC  and otheF = 331.9, P < 0.0001, CV = DLC:12.24%, EthoVision: 11.03%, TSE: 16.83%). TSE reported a similar value to that of DLC and EthoVision for time in center   available to us from another study, which were either normally reared in our facility , or they were reared with high social exchange . We tested these mice in adulthood in the open field test. When tracking with the DLC-based approach, we found that the social exchange group had higher locomotor activity  = 4.34, q = 0.004, multiple testing adjustment using Benjamini and Yekutieli correction) and spent more time in the center of the open field , and the “all-in-one” TSE Multi Conditioning system. We tested 20 mice in the TSE Multi Conditioning System’s OFT arena, the videos acquired from these tests were then analyzed using EthoVision XT14 and DLC. In the OFT, simple tracking parameters such as distance traveled and time spent in zone (center) were comparable between DLC and EthoVision. However, TSE’s Multi Conditioning system reported a significantly different mean distance traveled  Fig. . The vasThe FST and EPM analyses could not be scored using the TSE Multi Conditioning System, since the EPM/FST apparatus is not compatible with its “all-in-one” setup. We therefore acquired videos of 29 mice performing the FST, and 24 mice performing the EPM using EthoVision, which were later analyzed using DLC. Using DLC and EthoVision XT14, we found no significant differences regarding distance traveled in the FST or EPM  or time in zones   = 23.82, P < 0.0001, CV head dips = R1:50.71 %, R2:46.38 %, R3:45.54 %, DLC:47.03 %, EthoVision:28.33%), with differences between all groups and EthoVision , 11.50(R2), 10.83(R3), 9.60(DLC), df = 20, p < 0.0001), but no differences between human annotation and DLC. To test whether these differences have direct biological relevance, we injected a small cohort of mice with either 3 mg/kg yohimbine, an alpha2-adrenoceptor antagonist known to trigger anxiety through increased noradrenaline release in the brain [t = 3.68, df = 10, p = 0.004; DLC: t = 3.21, df = 10, p = 0.009), showing the expected decrease in exploratory behavior associated with increased anxiety. In contrast, EthoVision failed to detect a significant group effect    Fig. . This isng) Fig. , presumar = 0.95, p < 0.001, Fig. To further demonstrate the versatility of our approach, we extended our tracking analysis to two tests that rely on accurate tracking of the animal in slightly more complex environments, the 3 Chamber Sociability Test (3CST) and the Barnes Maze. Neither of these tests are available for static systems like the TSE setup, but can be scored with EthoVision. The 3CST assesses sociability in mice, by measuring how much social approach/interaction a freely moving mouse displays toward an enclosed conspecific . We demoWe then leveraged the increased flexibility and accuracy of our approach to quantify changes in head angle, using tracking data from nose, neck, and tailbase see Fig. , which ir = 0.97), thus outperforming the manual cutoff (Pearson’s r = 0.89) and EthoVision (Pearson’s r = 0.95). The classifier also outperformed EthoVision when compared to the other two raters , strongly suggesting that individual human raters are less reliable than well-calibrated automated approaches.So far, we have demonstrated that manually defined parameters can be used to automatically determine distinctive behaviors based on custom-defined criteria and simple post-hoc scripts to analyze tracking data generated by DLC. However, we found that using this approach for more complex behaviors was labor intensive, arbitrary and sometimes inaccurate, as exemplified by the fact that this ad-hoc approach could not outperform the floating analysis performed by EthoVision see Fig. . Therefoers Fig. . Notablyers Fig. . Interesers Fig. , the corr = 0.968, p = 0.032) and unsupported rears  also in females  = 9.547, p < 0.0001) with differences between the human raters and EthoVision , the machine learning-based behavioral classifiers and EthoVision , and between EthoVision and TSE . We observed significant group differences between the number of supported rears reported by EthoVision, TSE, and the human and machine learning classifiers  = 104.5, p < 0.0001). Post-hoc tests reveal significant differences between the human raters and EthoVision , and between the human annotators and the TSE system . Similarly, the machine learning classifiers reported significantly different results to those reported by EthoVision  and the TSE system . The TSE system and EthoVision were also in disagreement . Again, no significant difference was detected between the performance of the humans or machine learning classifiers, which were highly correlated  and to EthoVision’s behavior recognition module Figs.  and S10.ted Fig. . We concThis report shows that DeepLabCut (DLC) video tracking combined with simple post analyses can detect and quantify behavioral data as well as—if not better than—commercial solutions. Moreover, we developed supervised machine learning approaches that use features extracted from the DLC-tracking data to score complex, ethologically relevant behaviors. We demonstrate that the machine learning classifiers approach human accuracy, while outperforming commercial systems at a fraction of the cost. We show that the flexibility and accuracy of our approach can increase statistical power by reducing variability, and that it can be readily applied to a wide variety of different setups and more specialized measurements.Scoring complex ethological behaviors with human-like accuracy is an important step forward in the analysis of behavior , 45–48. Beyond the behaviors investigated here, researchers are of course interested in many different behaviors such as stretching, grooming, or social interactions. While TSE cannot detect any of these behaviors, EthoVision can detect them with some degree of accuracy , after tAnother key advantage of using pose estimation software to generate the input data for machine learning classifiers is that it offers increased tracking flexibility by enabling users to define and record the parameters of interest themselves. In contrast, commercial systems have unnecessary constraints or paywalls in place. Although our training set did not contain sufficient instances of grooming behavior to train a classifier (only 20–30 events in our entire training dataset), a recent report used transgenic mice that show an over-grooming phenotype to train machine learning algorithms to accurately quantify grooming behavior, vastly outperforming EthoVision . Recentlr = 0.96 instead of r = 0.90 for floating [Regarding human scoring, our annotators were all trained at the same time by an expert behaviorist, and reached a consensus about what constituted each behavior before beginning to score the videos. In the case of notable discrepancies between annotators, the annotator in question was trained again, re-blinded and given the opportunity to re-score the videos again. This reduced inter-rater variability that can arise from differences in the definitions of the behaviors even within a given lab, or from subtle differences in human judgment , 10, 11.floating ). AlthouAs behavioral analysis moves more toward video tracking as opposed to reliance on beam grids, recent developments in unsupervised behavioral identification approaches have widened the horizons of what was previously thought possible , 2. ApprThis project was funded by the ETH Zurich (JB and BG), the ETH Project Grant ETH-20 19-1 (JB and BG), the SNSF Grants 310030_172889/1 (JB) and CRSII5-173721 (BG), the Forschungskredit of the University of Zurich FK-15-035 (JB), the Swiss Data Science Center C17-18, (BG), the Vontobel-Foundation (JB), the Novartis Foundation for Medical Biological Research (JB), the EMDO-Foundation (JB), the Olga Mayenfisch Foundation (JB) and the Betty and David Koetser Foundation for Brain Research (JB), and two Neuroscience Center Zurich Project Grants Oxford/McGill/Zurich Partnership (JB and BG). Open access funding provided by ETH Zurich. The authors declare no conflict of interest.Supplementary DataSupplementary Material and Methods


UB values using the GOD–POD–UnaG method highly correlated with the iDB/A ratio in both <5% and ≥5% DB/TB ratio sera , whereas a low correlation (r = 0.428) occurred for ≥5% DB/TB ratio sera using the GOD–POD method. Our GOD–POD–UnaG method can measure UB levels regardless of the presence of DB.The glucose oxidase–peroxidase (GOD–POD) method used to measure serum unbound bilirubin (UB) suffers from direct bilirubin (DB) interference. Using a bilirubin-inducible fluorescent protein from eel muscle (UnaG), a novel GOD–POD–UnaG method for measuring UB was developed. Newborn sera with an indirect bilirubin/albumin (iDB/A) molar ratio of <0.5 were classified into four groups of DB/total serum bilirubin (TB) ratios , and the correlation between the UB levels and iDB/A ratio was examined. Linear regression analysis was performed to compare UB values from both methods with the iDB/A ratio from 38 sera samples with DB/TB ratio <5% and 11 samples with DB/TB ratio ≥5%. The correlation coefficient ( An increase in serum unconjugated bilirubin  levels, most importantly free bilirubin not bound to albumin , is associated with the development of serious brain injury in newborns, called bilirubin encephalopathy ,2. In JaAn automated analyzer designed to measure UB serum/plasma levels was developed in 1982  using the glucose oxidase and peroxidase (GOD–POD) method ,7. To daIn 2013, a bilirubin-inducible fluorescent protein from eel muscle (UnaG) was cloned, which is characterized as having high affinity and specific binding to iDB rather than DB . In newbThe aims of our study involve further delineating the strengths of the GOD–POD–UnaG method with respect to the established GOD–POD method. First, we studied clinical serum samples to determine how much the DB/TB ratio would affect UB values when using the GOD–POD method (Study A). Next, we tested whether the GOD–POD–UnaG method could be used for UB measurements in newborn sera, regardless of those with a high DB/TB ratio (Study B).n = 239), 5–10% (n = 29), 10–20% (n = 17), and ≥20% (n = 16). The correlation (slope and correlation coefficient) between UB values and the iDB/A ratio in each group is shown in In this study, we collected clinical data from 345 newborn patients. To evaluate the validity of UB values, 301 samples with an indirect bilirubin/albumin (iDB/A) ratio from 0.1 to 0.5 were analyzed when the linear correlation between the UB value and iDB/A ratio was maintained  in the DB/TB <5% group (outliers) was compared between each group. In comparison to the DB/TB ratio <5% group, in which 14% of samples were above the upper limit line of the 95% CI, the other groups had a significantly higher proportion of samples above this upper limit, with 31% of the 5–10% group, 65% of the 10–20% group, and 81% of the ≥20% group (n = 38) or a high DB/TB ratio  for analysis. The low DB/TB ratio samples were obtained from 30 newborn patients , with a median age at sampling of five days (1–19 days), median TB level of 13.0 mg/dL (3.1–18.2 mg/dL), median DB level of 0.2 mg/dL (0.1–0.3 mg/dL), and median UB level of 0.48 µg/dL (0.04–0.87 µg/dL).A total of 49 serum samples from 34 newborn patients were classified as having a low DB/TB ratio  and high DB/TB ratio samples (n = 11). When the GOD–POD method was used, the 38 samples with a low DB/TB ratio showed a significant correlation , whereas the 11 samples with a high DB/TB ratio did not show any correlation . In contrast, when the GOD–POD–UnaG method was employed, the 38 samples with a low DB/TB ratio showed a significant correlation  and the 11 samples with a high DB/TB ratio also showed a significant correlation  (n = 38). Employing the GOD–POD–UnaG method resulted in samples plotted near the regression line of low DB/TB ratio serum (n = 38) regardless of the DB/TB ratio  . More spTB ratio .p = 0.0011). For example, in Sample 1, although the DB/TB ratio was 8.2%, the UB values determined from the GOD–POD and GOD–POD–UnaG methods were quite different . We then analyzed the correlation between UB values determined from the GOD–POD and GOD–POD–UnaG methods. The 38 samples with low DB/TB ratio showed a significant correlation  between the two methods, whereas the 11 samples with high DB/TB ratio resulted in lower correlation . The UB values determined with the GOD–POD method were higher than those determined with the GOD–POD–UnaG method  can now survive. Neonatologists are encountering an increase in opportunities to treat and care for newborn patients exhibiting high DB/TB ratios in NICUs . These nAnalysis of the correlation between UB and the iDB/A ratio was limited to serum with an iDB/A ratio <0.5; the relationship between the two certainly remains linear when the iDB/A ratio <0.5 , i.e., tThe UB-Analyzer (UA-2) that uses the GOD–POD method can measure serum TB and UB levels . UB is rBecause UnaG emits very intense fluorescence and accurate measurements are hindered by an internal shielding effect when a high concentration of iDB is present, we diluted the solutions 800-fold. However, if we consider the equilibrium relationship between bilirubin and albumin, it is important to use a more concentrated solution for the UB measurement requiring the reaction with POD ; an 800-In clinical practice, a newborn patient with a high serum DB/TB ratio may require phototherapy and exchange transfusion for treatment, which is administered based on the UB values measured by the GOD–POD method; however, if judged by the results from the GOD–POD–UnaG method, this patient does not need such treatments. Out of the 11 high DB/TB ratio serum samples in A limitation of this study was the number of cases examined. However, we were able to show clearly from both clinical and fundamental science perspectives that the GOD–POD method affects the UB levels when the DB/TB ratio in patient serum is ≥5%. As the GOD–POD–UnaG method in this study involved manual labor and the use of expensive microplate readers, we were unable to examine numerous serum samples. Going forward, an automated device needs to be developed to perform GOD–POD–UnaG measurements with a larger number of newborn sera to verify these results and provide a clinically feasible alternative to the GOD–POD method. Finally, because other novel UB measurement methods using a fluorescence sensor or potentiometric sensor are developed ,16, furtWe conducted a multicenter retrospective study using the clinical data of serum TB and DB values in newborns admitted at the NICU at Kobe University Hospital, Kakogawa City Hospital, and Hyogo Prefectural Kobe Children’s Hospital, Japan, from 2011 to 2014. The study protocol was approved by a central institutional review board at Kobe University Hospital . Formal written informed consent was not required due to the retrospective nature of the study, which used anonymized data generated from our regular practice. This study protocol had an announcement on our website.We retrospectively analyzed the blood test values of newborn patients admitted for jaundice management that exhibited TB levels ≥5.0 mg/dL . A total of 345 patients, including 270 patients with DB <0.5 mg/dL and 75 patients with DB ≥0.5 mg/dL were analyzed.Serum UB levels were measured using the UB-Analyzer (UA-2). TB and DB measurements were performed using the bilirubin oxidase method  or Nescoat VL T-bil and Nescoat VL D-bil kits ) or the vanadic acid oxidation method ). The IatroLQ D-bil kit detects only conjugated bilirubin as DB. Meanwhile, the Nescoat VL D-bil kit and Direct Bilirubin E-HA Test detect conjugated bilirubin and 𝛅-bilirubin as DB. Serum iDB concentrations were calculated using the formula (iDB) = (TB) − (DB) with 1 mg/dL = 17.1 μM. Serum albumin was measured using a modified bromocresol purple method .When the serum DB level can be regarded as almost zero and the TB/albumin (TB/A) molar ratio is between 0.1 and 0.5, the TB/A ratio is linearly correlated with serum UB . In the g rpm for 10 min in the dark, and then the sera were stored at −20 °C in the dark until used. UnaG was provided by the Brain Science Institute, RIKEN, Japan [For study B, blood samples were obtained from newborns for routine laboratory tests for a variety of medical reasons at the NICU in Kobe University Hospital. Residual blood samples after performing regular laboratory tests were immediately centrifuged at 3000× N, Japan . The stuThe UB-Analyzer (UA-2) was used in accordance with the recommended manufacturer instructions. Briefly, 1000 μL of phosphate buffered saline (PBS) with glucose was first mixed with 20 μL of artificial iDB solution or sera (51-fold dilution). The reaction was then initiated with the addition of 25 μL GOD–POD solution. The initial velocity of total bilirubin oxidation was monitored by absorbance spectroscopy, and then automatically calculated as the UB value .According to the original protocol for the UB-Analyzer using the GOD–POD method, 1000 μL of PBS with glucose was mixed with 20 μL of artificial iDB solution or sera (51-fold dilution). The reaction was then initiated with the addition of 25 μL GOD–POD solution (reaction) or PBS (control) at 37 °C. After 20 s, the reactions were quenched with 555 μL of a 1.0% ascorbic acid solution . We confirmed that the concentration of ascorbic acid has no impact on the measurement of the iDB level . A mixtur) were calculated using JMP 13.0.0 . Correlations and differences were deemed statistically significant when p < 0.05.Statistical analyses were performed with the Mann–Whitney nonparametric rank test to compare the two independent datasets, which are expressed as the median (range) . RegressWe clearly demonstrated that UB levels using the GOD–POD method can be affected when the DB/TB ratio >5%, suggesting that DB should be easily oxidized by POD and lead to an overestimation of the UB value. Importantly, we developed the GOD–POD–UnaG method as a novel UB measurement method. This method can measure the UB levels in newborn sera, regardless of the presence of DB, highlighting an attractive alternative method to the conventional GOD–POD assay, especially for the prognosis of newborns with high DB serum.The GOD–POD–UnaG method was registered with the international patent office on 12 June 2020 (registration number: 6716108).


MiRNAs can be used as promising diagnostic biomarkers of heart failure, while lncRNAs act as competing endogenous RNAs of miRNAs. In this study, we collected peripheral blood monocytes from subjects with or without HF to explore the association between certain lncRNAs, miRNAs and HF. Heart failure patients with preserved or reduced ejection fraction were recruited for investigation. ROC analysis was carried out to evaluate the diagnostic values of certain miRNAs and lncRNAs in HF. Luciferase assays were used to study the regulatory relationship between above miRNAs and lncRNAs. LncRNA overexpression was used to explore the effect of certain miRNAs in H9C2 cells. Expression of miR‐30c was significantly decreased in the plasma and peripheral blood monocytes of patients suffering from heart failure, especially in these with reduced ejection fraction. On the contrary, the expression of lncRNA‐CASC7 was remarkably increased in the plasma and peripheral blood monocytes of patients suffering from heart failure. Both miR‐30c and lncRNA‐CASC7 expression showed a promising efficiency as diagnostic biomarkers of heart failure. Luciferase assays indicated that miR‐30c played an inhibitory role in lncRNA‐CASC7 and IL‐11 mRNA expression. Moreover, the overexpression of lncRNA‐CASC7 suppressed the expression of miR‐30c while evidently increasing the expression of IL‐11 mRNA and protein in H9C2 cells. This study clarified the relationship among miR‐30c, lncRNA‐CASC7 and IL‐11 expression and the risk of heart failure and showed that lncRNA‐CASC7 is potentially involved in the pathogenesis of HF via modulating the expression of miR‐30c. In fact, lncRNAs are pervasively distributed in the genome of humans and many other species.Cancer Susceptibility Candidate 7 (CASC7) is an lncRNA of about 9 kb in length.In a study carried out by Panizo et al,In a study carried out by Lai et al, miR‐30e was reported to play a cardio‐protective role in HF via the miR‐30e/Beclin‐1 signalling axis, thus preventing the apoptosis of cardiomyocytes induced by autophagy, and the inhibition of apoptosis and autophagy of cardiomyocytes by miR‐30e contributes to the increased cardiac functions in cardiotoxicity mediated by doxorubicin and ACE2.It has been found that three lncRNAs  are competing endogenous RNAs of miR‐30c, which can be used as a diagnostic biomarker of HF.22.1A total of 186 participants were recruited in our study. These 186 patients consisted of 62 subjects without heart failure (control group), 62 participants diagnosed with heart failure and preserved ejection fraction (HF + pEF group) and 62 participants diagnosed with heart failure and reduced ejection fraction (HF + rEF group) and all those participants were enrolled in Affiliated Hospital of Guilin Medical University during February 2015 to March 2018. 5 mL peripheral blood samples were collected from each subject. Then, the demographic and clinicopathological characteristics of the participants, such as their age, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, body mass index, hypertension, diabetes mellitus, dyslipidaemia, ischaemic heart disease, atrial fibrillation, brain natriuretic peptide (BNP), echocardiography, ejection fraction, interventricular septum, posterior wall, left ventricular mass index (LVMI), left atrial volume index (LAVI), deceleration time, e’ (lat), peak E and the ratio of transmitral early peak velocity to E' (E/e’), were collected and compared among the three groups. This study was approved by the Ethics Committee of Affiliated Hospital of Guilin Medical University and informed consent has been obtained upon the initiation of this study.2.2The collected peripheral blood samples were treated using a commercial kit to separate plasma and isolate peripheral blood monocytes. Then, the expression of several miRNAs, that is miR‐30c, miR‐146a, miR‐221, miR‐328 and miR‐375, as well as the expression of several lncRNAs, that is lncRNA‐CCAT1, lncRNA‐CASC7 and lncRNA‐AK017368, in the plasma and peripheral blood monocytes was measured using real‐time PCR. In brief, all samples were treated with a mirVana kit  to isolate total RNA, which was then mixed with reagents in a kit of Universal complementary DNA synthesis (Thermo Fisher Scientific), a SYBR Green master mix (Applied Biosystems) and a primer sets specifically designed for PCR. The real‐time PCR was performed using 96‐well plates on 7900HT real‐time PCR instrument (Invitrogen). The final calculation of relative expression of miR‐30c , miR‐146a , miR‐221 , miR‐328 , miR‐375 , lncRNA‐CCAT1 , lncRNA‐CASC7 , lncRNA‐ AK017368  and IL‐11 mRNA  were done using the ΔΔCt method, and all results were normalized to the expression of endogenous control U6  and GAPDH . We have performed the validation test for SYBR green‐based qPCR following the method described previously.2.32 and saturated humidity. When the cultured H9C2 cells entered the phase of logarithmic growth, they were trypsinized and made into a single cell suspension. Then, for each one of three lncRNAs to be tested, that is lncRNA‐CASC7, lncRNA‐CCAT1 and lncRNA‐ AK017368, the H9C2 cells were divided into two groups, that is 1. Negative control (NC) and 2. p‐lncRNA‐CASC7; 1. NC and 2. p‐lncRNA‐CCAT1; and 1.NC and 2. p‐lncRNA‐ AK017368. In the next step, the cells in various groups were transfected with respective pcDNA 3.1 plasmids of lncRNAs at 30nM using Lipofectamine 3000 (Invitrogen) in accordance with the standard transfection protocol provided by the manufacturer. All transfected cells were harvested at 48 hours after transfection to analyse the expression of target genes.H9C2 cells, a cell line of embryonic rat cardiomyocytes derived from embryonic BD1X rat heart tissues, were acquired from ATCC and maintained in a modified DMEM media  supplemented with appropriate antibiotics and 10% foetal bovine serum. The cultured cells were incubated at 37°C in 5% CO2.4Target Scan software was utilized in this study to search for potential miR‐30c binding sites located in lncRNA‐CCAT1, lncRNA‐CASC7 and lncRNA‐AK017368. Then, based on the sequences obtained from the Target Scan analysis, the sequences of above 3 lncRNAs containing the miR‐30c binding sites were cloned into pcDNA luciferase vectors to obtain wild‐type lncRNA‐CCAT1, lncRNA‐CASC7 and lncRNA‐AK017368 vectors. At the same time, using a commercial site‐directed mutagenesis assay kit , site‐directed mutagenesis was carried out to generate a single mutation in the miR‐30c binding sites located in lncRNA‐CCAT1, lncRNA‐CASC7 and lncRNA‐AK017368, respectively. In the next step, H9C2 cells were co‐transfected with miR‐30c mimics and wild‐type or mutant vectors of lncRNA‐CCAT1, lncRNA‐CASC7 or lncRNA‐AK017368. At 48 hours after transfection, the luciferase activity of transfected cells in each group was assayed using a Bright Glo luciferase assay system (Promega) in accordance with the standard protocol provided by the manufacturer.2.5Cell samples were collected and rinsed two times using cold PBS , which were subsequently lysed with a protease inhibitor RIPA buffer . The isolated protein samples were then separated using 10% SDS‐PAGE and blotted onto PVDF membranes (Thermo Fisher Scientific). After being incubated with anti‐IL‐11  primary antibodies and HRP secondary antibodies  following a conventional protocol, the PVDF membranes were developed using an ECL kit (Thermo Fisher Scientific) and β‐actin was used as the internal reference for the densitometric analysis.2.6http://www.biosoft.hacettepe.edu.tr/easyROC/). All statistical analyses were carried out using SPSS version 21.0 statistical software. All statistical differences were compared using a level of significance of 0.05.One‐way analysis of variance (ANOVA) was used to compare statistical differences among various groups. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic values of various miRNA and lncRNA candidates. The effects of potential confounding factors such as age, gender, NYHA class, BNP was evaluated using multiple variate analysis. The statistical power of analysis is 0.97  to modulate the intracellular activity of their target miRNAs.Interestingly, miRNAs can regulate the expression of genes responsible for heart fibrosis. For example, both miR‐30 and miR‐133 are down‐regulated during HF. Since miR‐30 and miR‐133 can suppress the expression of CTGF, the reduced expression of miR‐30 and miR‐133 promotes the fibrosis of heart tissues in HF.Several miRNAs can regulate the expression of Pak1 and Cdc42. For example, The suppression of miR‐30c expression in cardiomyocytes increased the expression of Pak1 and Cdc42, indicating that miR‐30c is an important regulator of Pak1 and Cdc42 expression.Abonnenc et alIL‐11 is implicated in hematopoiesisHowever, there are limitations of our study. On one hand, the samples used in our study were the patient plasma samples and PBMCs, while it would be more convincible if myocardial tissues could be collected and used in this study. On the other hand, the sample size was limited, which will influence the accuracy of our study. Therefore, in our future study, larger sample size should be applied with more appropriate patient tissue, that is myocardial tissue, should be collected for investigation.5In summary, the expression of CASC7 was high while the expression of miR‐30c was low in plasma and peripheral blood monocytes collected from HF patients. Furthermore, miR‐30c can be used as a diagnostic biomarker of HF, while CASC7 is a competing endogenous RNA of miR‐30c. CASC7 can indirectly regulate IL‐11 expression via miR‐30. The results of this study provided a new perspective for lncRNA‐directed diagnosis and treatment of HF.The authors declare that they have no conflict of interest.Yu‐li Xu: Conceptualization ; Formal analysis ; Project administration ; Writing‐original draft . Yang Liu: Conceptualization ; Software ; Supervision ; Writing‐original draft . Ru‐ping Cai: Investigation ; Methodology ; Supervision . Shi‐rong He: Investigation ; Software ; Visualization . Ri‐xin Dai: Investigation ; Visualization . Xi‐heng Yang: Investigation ; Software . Bing‐hui Kong: Investigation ; Methodology . Zhen‐bai Qin: Resources ; Software ; Visualization . Qiang Su: Conceptualization ; Funding acquisition ; Project administration ; Supervision ; Writing‐review & editing .Not applicable.This study was approved by the Ethics Committee of Affiliated Hospital of Guilin Medical University and informed consent has been obtained upon the initiation of this study.


Posaconazole therapy may lead to increased serum estradiol levels and development of gynecomastia. Early detection by endocrine hormone measurements may help preventing gynecomastia. Posaconazole therapy may lead to increased serum estradiol levels and development of gynecomastia. Early detection by endocrine hormone measurements may help preventing gynecomastia. Gynecomastia has not previously been reported in posaconazole therapy. Substitution of posaconazole by voriconazole reversed blood pressure and serum potassium while gynecomastia remained unchanged. Early detection by endocrine measurements may help preventing gynecomastia.Adverse drug effects account for up to 20% of all cases of gynecomastia.The currently available azole antifungal agents used for systemic treatment are not fully selective, and besides blocking fungal ergosterol synthesis by inhibiting lanosterol 14α‐demethylase (CYP51), they can cause adverse effects by interfering with human steroidogenic cytochrome P450 (CYP) enzymes. This can lead to adverse drug‐drug and drug‐hormone interactions. The best‐studied example is the imidazole‐based antifungal compound ketoconazole, well known to cause gynecomastia, with an incidence of 4%‐8% at lower doses (200‐400 mg/day) and up to about 21% at higher doses (800‐1200 mg/day).Ketoconazole may alter estradiol and testosterone levels through multiple mechanisms. It was found to inhibit the adrenal 11β‐hydroxylase (CYP11B1), increasing the 11‐deoxycortisol/cortisol ratio by 15‐ to 80‐fold.The newer triazole antifungals  have not been reported to cause similar endocrinologic manifestations, despite nearly 30 years of clinical experience with these triazoles. A new formulation of posaconazole (delayed‐release tablets) has recently become available with resultant increases in serum drug concentrations compared to the previously prescribed oral solution formulation.2Coccidioides complement fixation titer of 1:32 and a C‐reactive protein level of 254 mg/L. He was placed on oral fluconazole 600 mg/day. Over the next 8 weeks, he developed xerosis, cheilitis, and alopecia and refused further fluconazole therapy. He was subsequently transitioned to posaconazole delayed‐release tablets 300 mg/d. At return visit 5 months later, he was symptom‐free and the patient's coccidioidal complement fixation titer had decreased to 1:8. However, he had developed new onset of gynecomastia, supported by a mammogram showing benign fibrotic tissue, along with hypertension . The mammogram did, however, not reveal any tumor. The normal human chorionic gonadotropin (hCG), follicle‐stimulating hormone (FSH), luteinizing hormone (LH), testosterone, and prolactin levels  showing either elevated 11‐deoxycortisol, indicating CYP11B1 inhibition, or an increased cortisol to cortisone ratio, a biomarker of decreased 11β‐HSD2 activity,Nevertheless, substitution of posaconazole by voriconazole reversed the adverse effect on blood pressure, serum potassium, and endocrine hormones as seen in previous cases.In conclusion, we report the first case of posaconazole‐induced gynecomastia and review the known adrenal enzymatic pathways responsible for the development of drug‐induced gynecomastia. Future work further delineating the exact pathophysiologic mechanism(s) behind posaconazole‐induced gynecomastia should be undertaken to more fully understand what might be an underreported phenomenon.None declared.GRT and PNS: assessed patient data. GRT, PNS, and AO: designed the concept of the manuscript, performed the literature search, defined the intellectual content, and wrote the manuscript.This study was approved by the institutional review board of the University of California, Davis School of Medicine, Davis, USA. The patient gave consent to the study but was lost for follow‐up.


DKK1 gene encodes an extracellular inhibitor of the Wnt pathway with an important role in bone tissue development, bone homeostasis, and different critical aspects of bone biology. Several BMD genome‐wide association studies (GWASs) have consistently found association with SNPs in the DKK1 genomic region. For these reasons, it is important to assess the functionality of coding and regulatory variants in the gene. Here, we have studied the functionality of putative regulatory variants, previously found associated with BMD in different studies by others and ourselves, and also six missense variants present in the general population. Using a Wnt‐pathway‐specific luciferase reporter assay, we have determined that the variants p.Ala41Thr, p.Tyr74Phe, p.Arg120Leu, and p.Ser157Ile display a reduced DKK1 inhibitory capacity as compared with WT. This result agrees with the high‐bone‐mass (HBM) phenotype of two women from our cohort who carried mutations p.Tyr74Phe or p.Arg120Leu. On the other hand, by means of a circularized chromosome conformation capture‐ (4C‐) sequencing experiment, we have detected that the region containing 24 BMD‐GWA variants, located 350‐kb downstream of DKK1, interacts both with DKK1 and the LNCAROD  in osteoblastic cells. In conclusion, we have shown that some rare coding variants are partial loss‐of‐function mutations that may lead to a HBM phenotype, whereas the common SNPs associated with BMD in GWASs belong to a putative long‐range regulatory region, through a yet unknown mechanism involving LNCAROD. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.The  The challenge of current genetics is to establish the underlying functional basis of all these associations.For the last two decades, genome‐wide association studies (GWASs) have been a powerful tool to identify genes associated with complex diseases. Regarding osteoporosis, more than 500 loci associated with BMD have been determined. However, these loci only explain 20% of the BMD variability; few have been attributed a clear functional mechanism.1 The  Moreover, rare and penetrant mutations causing Mendelian bone phenotypes have been described in Wnt pathway genes, such as the loss‐of‐function and gain‐of‐function mutations in the LRP5 coreceptor, causing the two diametrically opposed phenotypes of osteoporosis‐pseudoglioma syndrome and high bone mass (HBM), respectively. The Wnt pathway is finely regulated by several extracellular inhibitors, among which sclerostin and DKK1 stand out. These two proteins prevent the formation of the heterotrimeric complex LRP5/6‐FZD‐Wnt by forming other heterotrimeric complexes, together with LRP5 and LRP4 (in the case of sclerostin) or with LRP5 and Kremen1/2 (in the case of DKK1). DKK1 binds both the first and third β‐propeller domains of LRP5, and it is in the first β‐propeller domain where mutations causing the HBM phenotype are clustered. Different studies have highlighted the important role of the DKK1 protein in the development of skeletal tissue, in bone homeostasis and in different critical aspects of bone biology. In mice, the homozygous Dkk1 KO is lethal at birth and displays severe defects in head formation that result in anterior truncations. In contrast, the heterozygous KO, the hypomorphic (doubleridge), the tamoxifen inducible at 7 weeks, or the osteolineage‐specific mouse models show an increase in BMD and an increase in bone formation. By studying the allelic combination of null or doubleridge mice , a gene dose‐dependent inverse correlation with BMD was observed. On the other hand, transgenic overexpression of the Dkk1 gene in osteoblasts produces a relative decrease in the number of osteoblasts, thus producing a decrease in bone formation. In the last decade, thanks to the direct effect on the inhibition of osteoblastogenesis and the indirect activation of osteoclastogenesis, sclerostin and DKK1 have become interesting targets for the anabolic treatment of osteoporosis. In particular, monoclonal antibodies against DKK1 stimulate bone formation in younger animals and to a lesser extent in adult animals, and enhance fracture healing.One of the most enriched pathways in genome‐wide significant signals for BMD is the Wnt pathway. Polymorphisms in several of its genes have been associated with BMD and fracture risk.10 MorDKK1 variants. In a previous study, we resequenced the transcribed regions and the intronic flanks of the gene in two patient groups with opposed and extreme BMD values from the Barcelona osteoporosis (BARCOS) cohort of postmenopausal women, and found three interesting variants in potentially functional regions: a rare missense variant , a rare 3′UTR (untranslated region) variant (rs74711339), and a common variant predicted to affect splicing (rs1569198), which was also found nominally associated with femoral neck BMD in the complete cohort. In addition to these variants, a region between DKK1 and MBL2 is relevant because many GWASs on BMD found strong associations with SNPs clustered in it and not with SNPs inside the DKK1 gene.Given the importance of DKK1 in bone physiology and pathology, it is essential to reach a comprehensive understanding of the functional roles of the e cohort.35 In DKK1, as well as to determine if the region harboring the genome‐wide significant SNPs is actually regulating the DKK1 gene, the MBL2 gene, or both. We carried out functional studies specific for each variant  gene.In this study, our objective was to test the underlying functionality of selected variants found in 2. The human fetal osteoblast 1.19 (hFOB) cell line and human medulla‐derived mesenchymal stem cells (MSCs) were used for 4C‐seq assays. The hFOB 1.19 cell line was obtained from ATCC (ATCC CRL‐11372) and grown in DMEM:F12 (1:1) medium without phenol red (Gibco–Life Technologies), supplemented with 10% FBS and 0.3 mg/mL geneticin (Gibco–Life Technologies) at 34°C and 5% of CO2. MSCs were kindly provided by Dr. José Manuel Quesada Gómez from Instituto Maimónides de Investigación Biomédica, Hospital Universitario Reina Sofía, Córdoba, Spain. These cells were grown in α‐MEM medium (Gibco–Life Technologies), supplemented with 10% FBS, 1% p/s, and 1x glutamax (Gibco–Life Technologies) at 37°C and 5% of CO2. Human primary osteoblasts (hOBs) used for expression quantitative trait loci (eQTLs) assays are described in Roca‐Ayats and colleagues.The human osteosarcoma cell line Saos‐2 was used for luciferase reporter assays and 4C‐seq assays. It was obtained from the American Type Culture Collection (ATCC HTB‐85) and grown in DMEM , supplemented with 10% FBS  and 1% penicillin/streptomycin , at 37°C and 5% of COa, Spain.42 TheWnt1‐V5 expression vector, mesdc2 expression vector, human WT LRP5 expression vector, pRL‐TK, pGL3‐OT reporter, and human DKK1‐FLAG expression vector are described in Balemans and colleagues. The mutations p.Ala41Tyr, p.Tyr74Phe, p.Pro84Leu, p.Ala106Thr, p.Arg120Leu, and p.Ser157Ile were introduced into the expression vector DKK1‐FLAG using the QuickChange Site‐Directed Mutagenesis kit , following the manufacturer's instructions. A 300‐bp fragment of the DKK1 3′UTR containing the SNP rs74711339 in a central position was cloned  into the pmirGLO dual‐luciferase miRNA target expression vector . Constructs were cloned using the XhoI and SdaI restriction sites. All primers used are detailed in Supplementary Information Table The mouse lleagues.44 The5 Saos‐2 cells per well were cultured in 6‐well plates, 24 hours before the transfection. For the Wnt pathway reporter assay, we cotransfected: mouse Wnt1‐V5 (64 ng), mesdc2 (128 ng), human WT LRP5 (128 ng), pRL‐TK (160 ng), pGL3‐OT reporter (1600 ng). and DKK1 (48 ng). When necessary, the empty pcDNA3 vector was used to equalize the total amount of DNA transfected in each experiment . For the miRNA reporter assay, we transfected at a 1:1 ratio, the pGFP vector and either the empty pmirGLO or with the DKK1‐3′UTR fragment with one or the other allele . Fugene HD was used following the manufacturer's instructions. Forty‐eight hours after transfection, cells were lysed and the luciferase activities of Photinus pyrali and Renilla reniformis were measured using a Glomax Multi + luminometer (Promega), following the instructions of the dual‐luciferase reporter assay system (Promega).Two luciferase gene reporter assays were performed: one to test the inhibitory activity of WT and mutant DKK1 proteins (Wnt pathway reporter assay) and the other to test the effect of the 3′UTR variant rs74711339 (3′UTR reporter assay). For both experiments, 3 × 105 Saos‐2 cells per well were cultured in 6‐well plates, 24 hours before the transfection. We transfected 2 μg of the DKK1 expression plasmid . For the negative control, we transfected 2 μg of the empty pcDNA3 vector. Fugene HD was used following the manufacturer's instructions. After 24 hours, the medium was changed, reducing from 2 to 1 mL of DMEM, without FBS or antibiotics. Forty‐eight hours after transfection, the supernatant (conditioned medium) of each condition was collected and were concentrated using 10 K Amicon Ultra filters . Extracellular proteins (concentrated conditioned medium) were quantified using the Pierce BCA Protein Assay kit . Proteins from concentrated conditioned medium (5 μg/lane) were separated by electrophoresis in an SDS‐PAGE and transferred to a hydrophobic polyvinylidene fluoride transfer membrane (Millipore). The ab109416 antibody against DKK1  was used. The images were developed using a peroxidase‐conjugated secondary antibody  for DKK1 antibody. All the experiments were performed in three independent biological replicates.For the Western blot assay, 3 × 10HMBS gene expression was used as normalizing control, and fold changes were calculated by relative quantification, using the second derivative method. Primers are summarized in Supplementary Information Table DNA was extracted from cultured hOBs using the Wizard Genomic DNA Purification kit (Promega), according to the manufacturer's instructions. The concentration of the purified DNA was analyzed on a spectrophotometer . Genotypes for rs1569198, rs74711339, and rs1373004 were assessed by Sanger sequencing using the BigDye Terminator v3.1  in the genomics facilities of the Universitat de Barcelona. Primers  were designed using the Primer3 Input 0.4.0 (Supplementary Information Table hOB cDNA from homozygous women for the rs1569198 SNP was amplified using two primers (Fw: TCCGAGGAGAAATTGAGGAA and R: TCCATGAGAGCCTTTTCTCC). The forward primer is located at the 3′end of exon 3 and the reverse primer is located at the 5′ end of exon 4. This pair of primers would amplify a 254‐bp amplicon in the case of WT splicing, and a 296‐bp amplicon in the case of alternative splicing. 4‐bp cutters were used as primary (DpnII) and secondary (Csp6I) restriction enzymes. For each cell line, a total of 1.6 μg of library was PCR amplified . Samples were sequenced with Illumina Hi‐Seq technology according to standard protocols at the Genomics Service of the Centro Nacional de Investigaciones Cardiovasculares . 4C‐seq data were generated as described previously. Briefly, raw sequencing data were demultiplexed and mapped to the corresponding reference genome (GRCh37). Reads located in fragments flanked by two restriction sites of the same enzyme, in fragments smaller than 40 bp or within a window of 10 kb around the viewpoint were filtered out. 4C‐seq data were normalized by the total weight of reads within ±2 Mb around the viewpoint.Circularized chromosome conformation capture‐sequencing (4C‐seq) experiment was carried out at the Functional Genomics Service of the Centro Andaluz de Biología del Desarrollo . 4C‐seq libraries were generated from Saos‐2, hFOB 1.19 and hMSC cell lines as described previously.46 4‐bhttps://github.com/Nikoula86/AFourC.git), following and adapting previously described pipelines. Briefly, we assumed that the 4C signal profile relative to the viewpoint v with coordinate xv on chromosome N is formed by three independent contributions: a constant background level, a negative exponential representing the monotonic decay of the 4C signal with the genomic distance from the viewpoint,) and N gaussians representing the interaction peaks:4C‐seq data were analyzed with the AFourC software , and p < 0.05 was considered significant. GraphPad  was used for graphic representation. Each experiment included a minimum of three replicates and was repeated independently in three separate experiments.The ratio of the Firefly and Renilla luciferase measurement was calculated . For each mutant, the test factor had the following levels: Empty (refers to the luciferase activity resulting from the endogenous Wnt pathway), Active , Inhibitor , and Mutant . We analyzed the data distribution and heteroscedasticity via the Shapiro‐Wilk test for normality and the Breusch‐Pagan test, respectively. We normalized replicates per day using the DKK1‐WT inhibition to reduce the nuisance factor. Then, we assessed the differences between luciferase activities through the Kruskal‐Wallis test. A post hoc test for multiple comparisons of groups was performed according to pairwise comparisons using the Wilcoxon rank sum test, adjusting p < 0.05 was considered significant. GraphPad (Graphpad Prism 8) was used for graphic representation. Each experiment included three replicates and was repeated independently in four separate experiments.The ratio of the Firefly and Renilla luciferase measurement was calculated (RLUs). We analyzed the data distribution and homogeneity of variances via the Shapiro‐Wilk test for normality and the Bartlett test, respectively. We normalized replicates per day using the empty vector activity to reduce the nuisance factor. We assessed the differences between luciferase activities through an ANOVA test. The Tukey HSD (honestly significant difference) test was used to perform the post hoc test for multiple group comparisons, testing the effect between all the conditions in the miRNA assay. Statistical analyses were performed using R software v. 3.5.3 , and  This function carries out an association analysis between SNPs and a dependent variable (DKK1 expression levels) under five different genetic inheritance models: codominant, dominant, recessive, overdominant, and log‐additive.For the study of the eQTLs, the WGassociation function in R software v.3.5.3 was used.51 ThiWnt1, mesdc2, and LRP5, together with a TCF/LEF‐luciferase reporter, in Saos‐2 cells resulted in an approximate fivefold stimulation of the endogenous Wnt pathway as previously described. Likewise, addition of WT DKK1 resulted in a one‐third inhibition of the pathway , p.Tyr74Phe, a 23.2% inhibition of the Wnt pathway , p.Arg120Leu, a 15.6% inhibition , and p.Ser157Ile, a 22.2% inhibition . No significant differences were observed for the remaining two missense variants , producing a protein with 16 additional amino acids, encoded by the 3′ end of intron 3. However, this alternative transcript has not been detected in primary osteoblast cDNA from 12 women homozygous for the G allele  are cis‐eQTLs of DKK1 in these cells. None of the tested SNPs resulted as eQTLs in our samples  areDKK1 , and we applied an algorithm to discern the significant contacts . To test their possible functionality, we have performed a luciferase reporter assay specific for the canonical Wnt pathway and we have determined that mutations p.Ala41Thr, p.Tyr74Phe, p.Arg120Leu and p.Ser157Ile affect DKK1 inhibitory capacity whereas p.Pro84Leu and p.Ala106Thr did not. Interestingly, none of the variants tested are located in the CRD2 domain (amino acids 189–263) known to bind to the LRP5 first and third β‐propeller to exert the inhibition . We propose that the loss‐of‐function of the protein with the p.Arg120Leu mutation may be contributing to the HBM phenotype of this woman, through insufficient inhibition of the Wnt pathway. In fact, this variant modifies the N‐terminal cysteine‐rich domain , is the consistently predicted as deleterious, according the four most commonly used in silico tools .58 Thihttps://sift.bii.a-star.edu.sg/; Table  Its frequency in the general population is 2.56 of every 10,000 individuals, but its effect in terms of bone mass is still unknown, as it happens for the p.Ser157Ile variant. The latter also displayed lower inhibitory activity, but in this case it may be a consequence of reduced secretion of the protein to the extracellular space  and the functional effect identified here is consistent with the cosegregation in the HBM family.p.Ala41Thr is the variant with the second highest loss of inhibitory capacity (37.8%) and also predicted as deleterious by SIFT software  and no interactions between this GWA‐SNP–rich region and neighboring genes have been reported. We undertook a 4C‐seq assay to determine its interactions across the genome in three bone cell types, and found that the GWA‐SNP–rich region is interacting with both DKK1 and regions that harbor several newly described transcripts of LNCAROD. Interestingly, this lncRNA has been recently shown to enhance DKK1 transcription, requiring chromosomal proximity between the A‐ROD and the DKK1 loci. Our 4C‐seq data are in agreement with these results because we found interactions with both DKK1 and LNCAROD, which suggests a regulatory role for the GWA‐SNP–rich region. It should be noted that in the study by Ntini and colleagues, they only considered short forms of LNCAROD, whereas the last build of the genome (hg38) includes new longer versions, overlapping with the GWA‐SNP–rich region , probably corresponding to this human region, and displays a drastic drop in Dkk1 expression levels (between 35% and <1% of WT), while keeping the levels of neighboring Mbl2 and Prkg1 genes intact. In this sense, it would be interesting to verify if LNCAROD is underexpressed in this mouse model, which would confirm the role of this lncRNA in DKK1 regulation. Our results clearly show for the first time that this region is relevant for the regulation of DKK1, and it will be very interesting to assess the specific role of the different SNPs in this process.A group of 30 SNPs associated with bone parameters in 10 different GWASs exists in a 3.3‐Mb window in 10q21.1,41 of This work has two main limitations. One of them is the complex luciferase assay used for the study of missense mutations. This method involves the cotransfection of several vectors, which implies high variability. It is possible that some of the variants tested, expected to have a small effect, show differences in activity below the sensitivity of the reporter gene assay we have used. Regarding the eQTL study, the main limitation is the sample size. Because of difficulties in obtaining appropriate samples, we only were able to use 45 primary osteoblasts, which precluded testing SNPs with low allele frequency. It would be useful to have a bank of primary osteoblasts with a much higher sample size to get information on different eQTLs important for bone; this information is lacking in current databases.DKK1 in HBM phenotype and osteoporosis determination. Four missense variants in DKK1 affect its inhibitory capacity, consistent with the HBM phenotype of a few individuals bearing these changes. Additionally, genomic interactions exist between a region rich in BMD‐GWA‐SNP and the DKK1 gene, and also with a lncRNA known to enhance DKK1 expression. These functional data open a way to study the mechanisms of DKK1 regulation, which may define novel therapeutic targets for bone diseases.In conclusion, for the first time we show functional evidence for the relevance of Service on Advisory Board: DO: Kyowa Kirin; XN: Amgen, UCB; ADP: Amgen, UCB.Honoraria or royalties for books or publications or for lectures (speaker fees), or participating in a speakers bureau: DO: Speaker fees in a couple of talks for Kyowa Kirin; XN: Speaker fees from Amgen, Lilly; ADP: Lilly, Amgen, UCB, Gilead, Theramex.Research grants, direct salary support or other financial support from commercial entities: DO: Research grant funded by Kyowa Kirin.Stock holdings and/or stock options in pharmaceutical, medical: ADP: Active Life Sci.The other authors declare no conflicts of interest.Núria Martínez‐Gil: Conceptualization; data curation; formal analysis; investigation; methodology; writing‐original draft; writing‐review and editing. Neus Roca‐Ayats: Investigation; methodology; writing‐review and editing. Nurgül Atalay: Investigation; methodology; writing‐review and editing. Marta Pineda‐Moncusí: Data curation; formal analysis; writing‐review and editing. Natalia Garcia‐Giralt: Investigation; methodology; writing‐review and editing. Wim van hul: Conceptualization; writing‐review and editing. Eveline Boudin: Investigation; methodology; writing‐review and editing. Diana Ovejero: Investigation; methodology; writing‐review and editing. Leonardo Mellibovsky: Investigation; methodology; writing‐review and editing. Xavier Nogués: Investigation; methodology; writing‐review and editing. Adolfo Diez‐Perez: Investigation; methodology; writing‐review and editing. Daniel Grinberg: Conceptualization; funding acquisition; supervision; writing‐original draft; writing‐review and editing. Susanna Balcells: Conceptualization; funding acquisition; supervision; writing‐original draft; writing‐review and editing.https://github.com/Nikoula86/AFourC.git. All data and material will be available upon request.The software package and the 4C data files used to make the statistical analysis, are publicly available at https://publons.com/publon/10.1002/jbm4.10423.The peer review history for this article is available at Appendix S1: Supplementary InformationClick here for additional data file.