Frailty syndrome (FS) has recently attracted attention as one of the major predictors of heart failure (HF) course severity. We aimed to develop a simple tool for predicting frailty in hospitalized HF patients using routine clinical parameters. A total of 153 hospitalized patients diagnosed with heart failure with reduced ejection fraction (HFrEF) were included in the study. Presence of FS was assessed with the SHARE-FI questionnaire. Clinical and biochemical parameters were collected. Using ROC curves and logistic regression analysis, a model predicting FS presence was developed and tested. Proposed model includes five variables with following cut-off values (1 point for each variable): age > 50 years, systolic pressure on admission < 110 mmHg, total cholesterol < 4.85 mmol/L, bilirubin ≥ 15.5 mmol/L, and alanine aminotransferase ≤ 34 U/L. Receiving 5 points was considered a high risk of FS with positive and negative predictive values (NPV), 83% and 72%, respectively, and specificity of 97%. Awarding 2 points or less ruled out FS in the studied group with negative predictive value 94%. The presented novel, simple score predicts FS in HFrEF patients with routine clinical parameters and has good positive and negative predictive values. Heart failure (HF) is a growing global health problem affecting approximately 26 million people worldwide . HF is aSeveral epidemiological studies have shown that HF is associated with frailty. Frailty rates in HF patients range from 15% to 79% . FrailtyWe aimed to develop a simple tool for predicting frailty in hospitalized HF patients using routine clinical parameters.This study included 153 patients with heart failure with reduced ejection fraction (HFrEF) who were hospitalized at the 1st Department of Cardiology of Poznan University of Medical Sciences. The inclusion criteria were: HFrEF history longer than three months; left ventricular ejection fraction (LVEF) < 40%, age ≥ 18 years, and signing the informed consent form. The study was conducted according to the guidelines of the Declaration of Helsinki, and the Ethics Committee of Poznan University of Medical Sciences approved it (No. 926/14). We diagnosed FS using the standardized Survey of Health, Ageing, and Retirement in Europe—Frailty Instrument (SHARE-FI), Munich Center for the Economics of Aging (MEA), Munich, Germany . Clinica2 and LVEF using the Simpson method [Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) below 60 mL/min for three months or more. We calculated BMI using the formula BMI = weight (kg)/(height (m))n method ,17. +), potassium (K+), creatinine, B-type natriuretic peptide (BNP), N-terminal pro B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hsCRP), total bilirubin (TBIL), alanine transaminase (ALT), gamma-glutamyltranspeptidase (GGTP), total cholesterol (CholT), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), hemoglobin (Hb), serum iron, ferritin, transferrin saturation (TSAT), uric acid, fasting glucose, serum protein, and serum albumin. eGFR was calculated using both the Cockcroft–Gault (C-G) [Additionally, we evaluated the following laboratory parameters (collected at admission time) complete blood count, neutrophil to lymphocyte ratio (NLR), total iron-binding capacity (TIBC), serum sodium   and MDRDlt (C-G) .https://sites.google.com/a/tcd.ie/share-frailty-instrument-calculators/translated-calculators last accessed on 17 December 2021). It was created and validated based on the first wave of the Survey of Health, Ageing, and Retirement in Europe to provide a simple tool facilitating the evaluation of frailty status [The frailty syndrome was diagnosed using the Polish version of the SHARE-FI questionnaire, while number (%) stands for categorical variables. According to the SHARE-FI questionnaire, we compared parameters between patients with diagnosed FS and those without FS. Mann—Whitney U test was used for continuous variables, and chi-square test with Yates correction, when needed, was used for categorical variables. For continuous variables that demonstrated differences in the Mann—Whitney U test, ROC (receiver operating characteristic) curve analysis was drawn for predicting FS presence. Optimal cut-off points with the highest Youden index value were chosen. Deriving cut-off points allowed to transform continuous parameters into categorical variables with values 0 or 1. For variables that were stimulants of the FS occurrence, 1 was assigned for values of the variable greater than or equal to the cut-off point and 0 for values less than the cut-off point. Similarly, for variables that were de-stimulants of the FS occurrence, 1 was assigned for values less than or equal to the cut-off point and 0 for values greater than the cut-off value obtained from ROC analysis.p < 0.10 in the multivariable analysis. Using multivariable analysis with backward elimination, a frailty-predicting model was derived, and independent predictors of FS presence were specified. Depending on the obtained data, we aimed to simplify the model. ROC curve analysis was done for both the original and simplified model. ROC curves and their AUC (area under the curve) were compared between models to explore differences in predicting capabilities. On this basis, we determined the final shape of the model. Finally, we verified the effectiveness of the model. Logistic regression was performed using FS presence as a dependent variable, while transformed and categorical variables, which revealed significant differences between the investigated groups, were used as categorical predictors. As a part of logistic regression, a univariable analysis was performed. We included all variables with p < 0.05 as statistically significant. Statistical analysis was done using Statistica 13.3 .We considered Baseline characteristics are presented in n = 52) and those without FS (n = 101) (combined non-frail and pre-frail groups) (The variables demonstrated in  groups) .Age; systolic pressure on admission; presence of CKD; and presence of AF, BNP, NT-proBNP, eGFR C-G, total serum protein, albumin, bilirubin, ALT, total cholesterol, triglycerides, LDL, ferritin, TIBC, transferrin saturation, and serum iron were significantly different between the frail and non-frail patients, and they were chosen for model development. ROC curve analysis allowed the selection of optimal cut-off points for these variables to predict the frail status . p < 0.10, and they were included in the multivariable analysis. The backward elimination method allowed independently eliminating variables not associated with the FS presence in the multivariable model (We applied logistic regression to choose the independent predictors of the FS presence from these transformed variables and CKD and AF . All varle model . Age ≥ 5le model .p for ROC comparison = 0.65). AUC value for the original model was 0.818 (95% CI 0.750–0.885) and 0.813 (95% CI 0.745–0.881) for the simplified model. Hence, we chose the simplified model as the final model.We decided to simplify the model by allocating 1 point for every parameter with a value equal to 1 (suggesting the presence of FS) instead of the β value in the original model. ROC curve analysis was used for each model to assess the predicting capabilities of the original and simplified models . We reveThe simplified model was applied to divide patients into groups with a specified number of points , and the presence of FS was assessed in each group . Sensitivity, specificity, PPV , and NPV  were counted for specified cut-off points of the model  . Additiop-value 0.31. Meanwhile, relevance of CholT rose insignificantly (OR 3.921 (95% CI 1.135–13.546), p = 0.031 compared to OR 3.613 (1.047–12.465), and p = 0.042 without adjustment for statin therapy). Improvement in AUC was not statistically relevant: 0.824 (95% C.I. 0.757–0.891) for statin use involved vs. 0.818 (95% CI 0.750–0.885) for the original model; p for difference = 0.45. Due to lack of statistical confirmation that the addition of statin use could be beneficial, we decided not to involve it in the final model.Taking into account the fact that CholT was included in the final model, its predicting capabilities could be influenced by statin use. To assess that effect, statin use was added to the original model and in logistic regression it was insignificant; OR for statin use was 0.635 (95% CI 0.263–1.534), β = −0.455, and We have proposed a novel model to predict FS in HF patients that includes five variables. We recommend the following cut-off values: age > 50 years, SBP on admission < 110 mmHg, total cholesterol < 4.85 mmol/L, bilirubin ≥ 15.5 mmol/L, and ALT ≤ 34 U/L. This model assumes awarding points for each variable exceeding the suggested cut-off value, which eventually efficiently discriminates the patients at risk of frailty. We propose considering patients at high risk of frailty if they exceed the cut-off values in every chosen variable. Such an approach shows good positive and negative predictive values, 83% and 72%, respectively. Awarding two points or less in this score seems to rule out FS in HF patients safely. Patients with 3–4 points, however, pose a borderline group worth further investigation.The proposed model includes five variables that are readily available in every patient hospitalized due to HF. Thus, the model appears to be a simple and elegant tool very easy to apply in a busy clinical setting. Apart from its feasibility, it possesses two substantial advantages: low cost of assessing the proposed variables and their excellent availability on a patient’s examination or laboratory testing of blood samples at medical sites.The development of the discussed model required diagnosing frailty in the included patients, which was done using the SHARE-FI questionnaire . It is aApart from the relatively more straightforward tools mentioned above that concentrate on the clinical and functional domains of frailty, multidimensional tools aim to diagnose frailty using all four domains . In geneIn general, advanced age is not required to diagnose frailty among HF patients, and frailty has been observed among young adults ,27 and cp = 0.007). Michael H. Lee et al. in their meta-analysis show that ambulatory HF patients with higher BP have better outcomes with a reduction in 7% mortality and 9% hospitalization by 10-mmHg increase in SBP [SBP as a diagnostic parameter is recognized as a risk factor of cardiovascular events. According to the European Society of Cardiology (ESC) guidelines, there were no clinical trials evaluating BP targets and antihypertensive strategies in HFrEF . BP targe in SBP , which ce in SBP , with thp = 0.15); therefore, BMI is not included in the proposed novel model to predict FS in HF patients. Due to the complexity of frailty syndrome, it may not be diagnosed according to low body weight but by a complex number of parameters influencing the diagnosis of FS and regarding the social and environmental functioning of the patient [The literature describes a phenomenon called the obesity paradox in heart failure patients, pointing out that obesity may decrease mortality in this specific group ,35. This patient ,38.Ritt et al. demonstrated that frailty could be diagnosed with considerable accuracy using only laboratory parameters . Their mIn our analysis, the possibility of significant influence of statin therapy on the model based on CholT was excluded after adjustment for this parameter. Adding statin use insignificantly improved the model. Due to the methodology of the study, only parameters significant in multivariate analysis were subsumed into the model, and for that reason, statin use was rejected.Low albumin concentration is a factor of bad prognosis in HF is connected with malnourishment and FS ,46. ThisIn our model, we combined both the clinical and laboratory parameters, and to keep the model as simple as possible, we limited the number of variables to five. Similar attempts have been made, and it was demonstrated that even a model based on a relatively small number of variables could effectively predict FS . This isHowever, we must emphasize that the model we propose cannot be used to diagnose FS. Instead, this model aims to indicate patients at risk of frailty and thus requiring further diagnostics. The idea was to keep the model as simple as possible to make it easy to use in a busy clinical setting so that medical professionals would know which of their patients need special attention exceeding the usual pharmacological and non-pharmacological treatment .The study’s main limitation is that the project is a single-center study and has a limited number of patients, which might limit the generalizability of the results. Second, due to the lack of a single FS assessment method dedicated to HF patients, we chose the SHARE-FI questionnaire, which was not validated in this patient group. Altogether, although our risk score should be further validated in other cohort groups, it summarizes the most recent and complete data on HFrEF patients suspected of having FS.We propose a novel, simple score to predict FS in hospitalized HFrEF patients with good positive and negative predictive values. The score is based on routine clinical and laboratory parameters, and it can be easily applied in a busy clinical setting. Further longitudinal studies may show if the score can also be used to assess the prognosis of HFrEF patients.


Long known to be a widespread source of injury, dog bites continue to occur and can frequently be devastating. There is lasting economic and personal hardship associated with this as many attacks can be severe, especially in children. This study describes the differences between dog bites and all other bites in the trauma patient population.We included all patients from the National Trauma Data Bank (NTDB) from 2007 to 2016. All patients with an E-code for any type of bite were included. The following predictors were examined: year, age, gender, race, ethnicity, transfer, injury type, ED disposition, and ISS ≥15. Standard t-test for age, chi-squared test for all categorical variables. Wilcoxon test for non-normal quantitative variables. Statistical analyses were performed using R version 4.0.2 statistical software. “Under resourced” will be defined as Self-Pay and Medicaid. “Not known” is removed and replaced with NAs. All other insurance types are included in “Normal”.All variables tested as significant. All variables analyzed tested as significantly different between dog bites and other animal bites. When the group, pediatric subset, and adult subset are evaluated (Tables 1), pediatric patients are more likely to be dog bite patients if they are from an under resourced background . Distribution of bites are not significantly different from Low to Normal resource adult patients.Dog bites continue to be a public health problem and are more likely to be severe or fatal than bites from other animals and humans. Better methods of injury prevention and education need to be established. Children from under resourced backgrounds are at an increased risk of suffering a dog bite and all physicians should screen for and educate patients and families about this risk.


The major reason for graft loss is chronic tissue damage, as interstitial fibrosis and tubular atrophy (IF/TA), where complement activation may serve as a mediator. The association of complement activation in a stable phase early after kidney transplantation with long-term outcomes is unexplored.We examined plasma terminal C5b-9 complement complex (TCC) 10 weeks posttransplant in 900 patients receiving a kidney between 2007 and 2012. Clinical outcomes were assessed after a median observation time of 9.3 years [interquartile range (IQR) 7.5–10.6].vs. 75.5%, P < 0.003). Similarly, 10-year graft survival was lower with elevated TCC; 56.9% vs. 67.3% (P < 0.002). Graft survival was also lower when censored for death; 81.5% vs. 87.3% (P = 0.04). In multivariable Cox analyses, impaired patient survival was significantly associated with elevated TCC  along with male sex, recipient and donor age, smoking, diabetes, and overall survival more than 1 year in renal replacement therapy prior to engraftment. Likewise, elevated TCC was independently associated with graft loss  along with the same covariates. Finally, elevated TCC was in addition independently associated with death-censored graft loss  as were also HLA-DR mismatches and higher immunological risk.Elevated TCC plasma values (≥0.7 CAU/ml) were present in 138 patients (15.3%) and associated with a lower 10-year patient survival rate (65.7% Early complement activation, assessed by plasma TCC, was associated with impaired long-term patient and graft survival. Along with major improvements in posttransplant management, there has been an improvement in kidney graft survival after year 2000 . This isAfter transplantation, the innate immune system is activated by ischemia–reperfusion injury through damage-associated molecular patterns (DAMPs) released by injured cells. DAMPs are recognized by pattern recognition molecules that are present as receptors of innate immune cells either as Toll-like receptors or as soluble molecules such as upstream complement proteins . This acThe complement system plays an important role in the pathophysiology of human diseases, and therapeutic inhibition of complement is rapidly approaching the clinic . InitialAcute rejection due to DSA is dependent on systemic complement activation with positive short-term effects of complement inhibition (eculizumab) –15, but We aimed to investigate if early posttransplant complement system activation, measured as TCC in a single blood sample, was associated with decreased long-term kidney graft and overall survival in 900 consecutive kidney transplant recipients. Furthermore, the predictive ability of TCC for cause of death was investigated as a secondary outcome.Nyrefysiologisk laboratorium”, Biobank no. 266-2005-142234). Long-term outcomes were monitored on a national level in the Norwegian Renal Registry, a consent-based national medical quality registry with >99.9% individual coverage.All kidney transplants in Norway are performed at the Oslo University Hospital–Rikshospitalet. Patients were followed at the transplant center for up to 3 months. Before transfer to their local nephrology unit, an in-depth investigation including blood sample biobanking was performed in a stable phase without ongoing infection/acute rejection episodes . Written informed consent was obtained from all patients before any data and biological material were included in the Norwegian Renal Registry and the biobank. Other analyses from the study have been presented previously –27. Incl2) or impaired glucose tolerance to Tac for all patients except those with impaired glucose tolerance.All standard immunological risk patients received basiliximab and methylprednisolone induction therapy. Patients with DSA at time of transplantation, or receiving an ABO-incompatible transplant, i.e., immunologically high-risk patients, also received intravenous human immunoglobulin and rituximab pretreatment, while panel-reactive antigen (PRA)-positive (≥20%) patients received short-course antithymocyte globulin instead of basiliximab. Standard maintenance immunosuppression was a combination of a calcineurin inhibitor (CNI), mycophenolate, and prednisolone. During the study period, the preferred CNI therapy changed, according to new center preferences in January 2011 from tacrolimus (Tac) in patients younger than 50 years and cyclosporine (CsA) in older patients and those with high body mass index (BMI)  was used for TCC measurement, principally as described originally in 1985 using a monoclonal antibody (aE11) as a highly specific antibody against activated C9, exposed only in C9 incorporated TCC . The firGraft loss  and death are reported continuously to the Norwegian Renal Registry from the locally treating nephrology centers. Estimated glomerular filtration rate (eGFR) was calculated by the MDRD-4  formula .cmprsk-package and Gray’s test for the competing risk analysis of death-cause analysis).All continuous data were checked for normal distribution and presented as mean (SD) or median [interquartile range (IQR)], as appropriate. Mann–Whitney U test was applied for group comparison of independent samples. Pearson’s chi-square test was used to compare frequencies between two groups. Fisher’s exact test was used where expected cell count was less than 5. Survival rates  are presented as Kaplan–Meier actuarial survival estimates. Log-rank test was used to compare survival rates. Censoring date was February 24, 2020. Multivariable Cox regression models are based on backward stepwise selection, with statistical significance assessed by the Wald test. Included covariates were recipient age, male gender, diabetes at time of transplant, active smoking, renal replacement therapy (RRT) before transplant , deceased/living donor, cold ischemia time ≥14 h, cytomegalovirus positive to negative serostatus, any HLA-DR mismatch, higher immunological risk (PRA positivity or DSA positivity or incompatible ABO transplant or more than two previous kidney transplants), and TCC ≥0.7 CAU/ml at week 10 posttransplant. All covariates were check for the proportional hazards assumption and also for correlations with TCC values. Recipient age and time in RRT showed significant correlation with TCC levels but with low correlation coefficients  and low linear regression betas . Restricted cubic spline plots for the final multivariable models are shown in P < 0.0001) and experienced a higher frequency of deaths during the follow-up period (P = 0.0005).During the inclusion period, 1,317 adult kidney transplantations were performed in Norway, and 900 patients were included for this analysis. Exclusion causes were graft loss before the 10-week investigation (n = 22), understaffing at the Laboratory for Renal Physiology (n = 176), and other causes of not attending the 10-week investigation such as medical condition or early transfer to local nephrology units due to disabilities (n = 219). Demographics of the 417 patients not included were similar to the 900 included patients, but they had more often received a deceased donor kidney (vs. the other 762 patients (TCC <0.7 CAU/ml) (The cohort of patients investigated was divided according to the measured TCC level 10 weeks after transplantation: 138 patients with high plasma TCC (≥0.7 CAU/ml)  CAU/ml) Table 1.2 compared to 52 (40–63) ml/min/1.73 m2 in the high-range group (P = 0.12).Ten weeks after transplantation, median plasma TCC in the normal-range group (<0.7 CAU/ml) was 0.41 CAU/ml  and in the high-range group 0.87 CAU/ml . Renal function was not different between groups; median (IQR) eGFR in the normal-range group was 55 (43–66) ml/min/1.73 mThe median observation time for patient survival was 9.4 years  and for graft survival 9.1 years . In the observation period, 208 patients (23.1%) died and 97 patients (10.8%) experienced isolated graft loss.P < 0.003) with a 10-year survival rate of 65.7% (95% CI 57.7%–73.7%) compared with 75.5% (95% CI 72.2%–78.8%) (P < 0.002), with 10-year graft survival rates of 56.9% (95% CI 48.7%–65.1%) and 67.3% (95% CI 63.8%–70.8%), respectively  was associated with impaired patient survival (P = 0.09) was observed for cardiovascular disease between the two TCC groups  graft survival are shown in Univariate and multivariable Cox regression models for death-censored graft survival (isolated graft loss) are shown in Sensitivity analyses of the three Cox regression models when also adjusting for eGFR revealed that TCC remained a significant independent factor for all three outcome measures  Figure 6This study demonstrated that complement activation, measured as plasma TCC in a stable phase 10 weeks after kidney transplantation, was significantly associated with reduced long-term patient and graft survival. These novel findings point toward an important role of the complement system for long-term outcomes, moving it beyond the acknowledged pathophysiological effects and efficient complement inhibition treatment in acute humoral rejection .Activation of the complement system in several kidney diseases suggests that this part of the innate immune system has a critical role in the pathophysiology of renal damage. Despite increased understanding of the role of complement system in renal transplantation, renal inflammaging is poorly understood. Inflammaging is a systemic chronic proinflammatory status and is a risk factor for multiple chronic diseases including chronic kidney disease . The hisThe hazard ratio for death associated with elevated TCC concentration was meaningful and significant even when adjusted for acknowledged factors such as age, smoking, diabetes, and time in RRT. Obviously, an association to premature death may not be uniform with respect to the cause of death. In our analysis of cause-specific mortality, high TCC indicates the strongest impact on cardiovascular deaths, although not significantly different as was also the case for deaths from infections and malignancies. These results are in line with a previous long-term follow-up study on kidney transplant patients reporting an association of increased collectin kidney 1 (a recognition molecule for the lectin pathway) with increased cardiovascular mortality . The samGlobal complement activation is probably necessary to elicit a transplant rejection episode. The overall loss of kidney grafts was significantly higher in recipients with elevated TCC. This was found for both the composite end point of graft loss including death with a functioning graft as well as for death-censored graft losses. The isolated graft losses were also, in addition to TCC, independently associated with any HLA-DR mismatch, immunological high risk, and donor kidney status. Thus, patients positive for the abovementioned covariates are easily identifiable and could form a subgroup of kidney transplant patients suitable for future interventional studies evaluating complement inhibition and long-term graft survival.A previous study staining for C4d and C3 in protocol kidney graft biopsies, early posttransplant (1 week), revealed that local complement deposition is uneventful when only the proximal C4d fragment is found but associated with graft loss when it proceeds to C3 deposits . SimilarThe pathophysiological mechanisms of immunologically driven IF/TA are complex and not fully explored. Preclinical studies have indicated that late graft losses are dependent on renal complement component production and activation , 18, conStrengths and limitations. Strengths of the study are the large number of patients and a blood sample taken at a standardized time after transplantation, after the immediate complement activation occurred during ischemia–reperfusion with acute and transient release of TCC (e of TCC . There we of TCC , renderie of TCC . Furthere of TCC , later ae of TCC . A weaknIn conclusion, the present study showed that complement activation measured as elevated plasma TCC in an early phase after kidney transplantation predicted premature death and long-term graft loss. Whether the complement activation actually is the cause of these adverse outcomes remains to be elucidated and should lead to trials investigating complement inhibition as a therapeutic approach for these patients.The datasets analyzed in this study can be obtained from the corresponding author, who must apply for permission according to Norwegian law.The studies involving human participants were reviewed and approved by Regional Committee of Medical Research Ethics Health Region South-East. The patients/participants provided their written informed consent to participate in this study.BW, SP, AH, AÅ, and TM participated in research design. AR, KM, KH, TJ, AH, and AÅ collected patient data. JL and TM performed TCC analyses. BW and AÅ performed data analysis. BW, SP, AH, AÅ, and TM wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Overuse of short-acting beta-2 agonists (SABA), which do not treat the underlying inflammation of asthma, is linked to poor clinical outcomes such as increased exacerbation risk. This study, as part of the SABINA program, estimated the prevalence of SABA overuse and associated variables in outpatients in Germany.This retrospective study used anonymized electronic healthcare data from the Disease Analyzer database (IQVIA). A total of 15,640 patients aged ≥ 12 years with asthma who received ≥ 1 SABA prescription(s) between July 2017 and June 2018 in 924 general physician and 22 pneumologist (PN) practices were included. SABA overuse was defined as ≥ 3 prescribed inhalers (~ 200 puffs each) during the study period. The associations between SABA overuse and physician specialty, Global Initiative for Asthma (GINA) steps (based on asthma medications), age, sex, and inhaled corticosteroid (ICS)/long-acting beta agonist (LABA) use were estimated using multivariable regression for patients with probable moderate (GINA step 2) and probable severe (GINA steps 3–5) asthma.Annually, 36% of all patients (GINA steps 1–5) in general and 38% in PN practices received ≥ 3 SABA inhalers. The risk of SABA overuse was 14% higher in patients treated by a general practitioner vs. a PN; 34% and 85% higher in GINA steps 4 and 5, respectively, vs. GINA step 3; and 40% higher in male vs. female patients.SABA overuse is prevalent among patients with asthma across all GINA steps in Germany, which may indicate suboptimal asthma control. Further studies are needed to investigate the reasons behind SABA overuse. The lifetime prevalence of asthma in adults in Germany is approximately 6–9% , 2. The One of the known problems in asthma patient care is the overuse of inhaled short-acting beta-2 agonists (SABA) , 5. OverThe inclusion of reliever inhaler use in the assessment of asthma control in adults is based on the evidence that overuse of SABA medication is associated with poor symptom control , increasFactors associated with inappropriate or excessive use of SABA include male sex, low socioeconomic status, and low continuity of care. However, knowledge of these factors is based on studies performed in countries other than Germany .There are limited data on the prevalence of SABA overuse in Germany, and especially on the factors associated with it . For exaSABA use IN Asthma (SABINA) program, which has been previously described [The escribed , aims toThis study is based on data from the Disease Analyzer database (IQVIA), which compiles drug prescriptions, diagnoses, and basic medical and demographic data obtained directly and anonymously from computer systems used in the practices of general practitioners (GPs) and specialists. Diagnoses , prescriptions , and the quality of reported data were monitored by IQVIA based on multiple criteria  . In GermThis cross-sectional study included outpatients aged ≥ 12 years with an asthma diagnosis  who had received ≥ 1 SABA  prescription(s) between July 2017 and June 2018 in 924 general physician and 22 pneumologist (PN) practices. Furthermore, patients had to fulfill the observability criterion of having had at least two physician visits (not necessarily asthma-related) during the study period. Patients with chronic obstructive pulmonary disease in addition to their asthma diagnosis were excluded from the study  during the study period. The term “overuse” was appropriated from the GINA report; however, it should be noted that in GINA step 5, the use of ≥ 3 prescribed inhalers is considered as increased use but not overuse. The prevalence of SABA overuse was estimated as the proportion of patients receiving ≥ 3 prescribed inhalers out of all patients with asthma included in the study. The prevalence was calculated for the total number of patients as well as stratified by age group, sex, GINA step, physician specialty, ICS with/without LABA  use, defined comorbidities , co-therapies which are known to be associated with an increased risk for bronchospasm  –20. MoreP values < 0.05 were considered statistically significant. All analyses were carried out using SAS version 9.4 .SABA overuse analyses were of a descriptive nature. The associations between SABA overuse and physician specialty, GINA step (based on prescribed asthma medications), age, and sex were estimated using multivariable regression. Co-diagnoses and co-therapies were also included in the model. ICS/LABA use was not included in the logistic regression because the proportion of patients with ICS/LABA use was a part of the GINA definition, and 95% of patients at GINA steps 3–5 were treated with ICS/LABA. A total of 15,640 patients  with ≥ 1 SABA prescription(s) during the study period were included in the study. The mean (standard deviation) age of patients was 49 (18) years for GP patients and 56 (16) years for PN patients. The proportion of female patients was 59% in general practices and 68% in PN practices, while 21% of GP patients and 49% of PN patients were classified as being at GINA treatment step 4 or 5  and 38% of all PN patients received ≥ 3 SABA inhalers. Over the same period, 7% of GP patients and 3% of PN patients received ≥ 12 inhalers  compared with those at GINA step 1 or 2 (patients with probable mild asthma), showing poor asthma control in patients with severe asthma. This poor control was observed despite patients receiving ICS/LABA. At GINA steps 3–5, the risk of SABA overuse/increased use was even higher in patients using ICS/LABA. Patients treated by GPs were at a higher risk of overusing SABA compared with those treated by PNs. This finding could be attributed to non-familiarity of GPs with the updated GINA recommendations, which could have resulted in continued high prescribing of SABA . EducatiIn this study, SABA overuse was higher in men than in women. Several studies have investigated gender differences in asthma diagnosis and severity. Although female sex was shown to be an independent risk factor for severe asthma exacerbation among adults, , men exhAlthough SABA are as-needed inhaled medications, they often appear to be used as long-term medications for asthma control, even though this is not in accordance with the guidelines . DiffereOur findings indicate that a high proportion of patients with asthma overuse SABA in Germany. Based on similar studies, this overuse probably carries a risk of adverse outcomes. There is no valid information on the reasons for the overuse of SABA. Both patients and physicians can have an impact on this. Physicians should eliminate SABA monotherapy and explain the consequences of SABA overuse to patients. Further, physicians, especially GPs, should ensure adherence of their patients to ICS treatment, considering continued ICS therapy can reduce the overuse of SABA. Asthma education programs for patients should pay special attention to regulated SABA use. Pharmacists play an important role in the care of patients with asthma and they should explain the appropriate use of SABA to patients who collect such medication from pharmacies.The strengths of this study include the number of outpatients available for analysis and the use of real-world data, which allowed an unbiased exposure assessment  in a German cohort of patients with asthma for the first time. Moreover, we used a standardized definition of a SABA canister (~ 200 puffs/canister) to enable a more exact estimation of SABA overuse.Retrospective primary care database analyses, however, are generally limited by the validity and completeness of the data they contain. First, diagnoses and comorbidities relied solely on ICD codes used in general physician and pneumologist practices, and no information was available regarding the procedure used to diagnose asthma. Asthma severity stages were established using GINA treatment steps according to the prescriptions of asthma medications because no documented information on the diagnosis of asthma severity or the level of asthma control was available; however, it is uncertain if physicians treated patients in line with these recommendations.Second, data on the socioeconomic status (education and income) and lifestyle-related risk factors  of patients were lacking. Third, information from hospitals was not available. Fourth, receiving a prescription does not necessarily mean that the prescription was filled and used by the patient. There is a possibility that patients who received a prescription did not take the medication. In addition, information on whether patients received asthma education or written asthma action plans and their level of adherence to prescribed therapies was not captured in the study. Fifth, in the German healthcare system, a patient can receive prescriptions for asthma treatment from several doctors, for example, from both a GP and a PN; however, in this database, usually only one doctor’s prescriptions are captured. Sixth, no information on patients’ asthma training was available. Seventh, no separation of ICS/LABA therapy into reliever and controller was possible. Finally, the study was conducted in Germany, and its findings may not be extrapolated to populations in other countries due to differences in national health systems and the availability of SABA medications.In Germany, similar to other countries in the SABINA program, SABA overuse is prevalent among patients with asthma across all GINA steps, which may indicate suboptimal asthma control, suboptimal treatment practice, and suboptimal adherence to prescribed medication. Although, some original studies , 11, and


Objective: This study aimed to explore the association between uric acid (UA) and blood pressure (BP) in hypertension treatment and non-treatment groups.Methods: A cross-sectional study with 6,985 individuals from the National Health and Nutrition Examination Survey (NHANES) was performed. Multiple linear regression analysis was performed to explore the relationship of UA and BP in hypertension between the treatment group  and the non-treatment group .Results: A significantly negative association was discovered in SBP  and DBP  in the hypertension treatment group. In the hypertension non-treatment group, the associations between UA and BP including SBP, DBP were both an inverted U-shape. The inflection point of SBP and DBP was 7 and 7.5 mg/dl, respectively. For SBP, the association was positively significant  before the inflection point of 7 mg/dl. However, after the inflection point of 7 mg/dl, the association was negative . For DBP, the inflection point was 7.5 mg/dl, and the effect size was positive  before the inflection point. However, after it, the effect size was negative .Conclusion: The association between UA and BP was negative in the hypertension treatment group. In the hypertension non-treatment group, the associations between UA and BP including SBP and DBP were both an inverted U-shape. Serum uric acid (UA) is the final product of purine metabolism, and seafood, fats, and red meat are rich in it. It has been confirmed to be associated with many diseases, such as hypertension , diabeteHence, in order to investigate the association between UA and BP in hypertension, we conducted a cross-sectional study composed of 6,985 participants from the 2009–2018 National Health and Nutrition Examination Survey (NHANES) database. Moreover, all possible potential confounding factors were adjusted to evaluate the association between UA and BP in hypertension more accurately.n = 18,154), missing systolic blood pressure (SBP) and diastolic blood pressure (DBP) data , without hypertension and missing hypertension data , missing data of medicine for hypertension , and age = 80 (n = 994). The reason why people (=80 years old) were excluded was because NHANES defined all the people (≥ 80 years old) as 80 years old. In the end, 6,985 people were included in the study, and they were divided into two groups: the hypertension treatment group  and hypertension non-treatment group , normal weight (18.5~24.99 kg/m2), overweight (25~29.99 kg/m2), obesity (≥ 30 kg/m2), and not recorded. Alcohol consumption was divided into three groups based on the question: “In the past 12 months, how often did you drink any type of alcoholic beverage?” The groups were composed of drinking, no drinking, and not recorded. Diabetes was divided into four groups based on the question, “Have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?”, and were yes, no, borderline, and not recorded. Smoking was divided into three groups based on the question, “Do you now smoke cigarettes?”, and were smoking, no smoking, and not recorded. Glomerular filtration rate (GFR) was estimated by the simplified Modification of Diet in Renal Disease (MDRD) equation 186 × SC−1.154 × Age−0.203×   and diastolic blood pressure (DBP). After the participants sat down with their feet supporting the ground, they rested quietly for at least 5 min, and three blood pressure readings were obtained with a mercury sphygmomanometer. Each reading was separated by 30 s. If one or more previous readings were interrupted, the fourth reading was obtained. SBP and DBP were made up of the average of all available measurement data. Race was divided into four categories: Mexican-American, White, Black, and Other. Age was divided into four groups, which included <18, 18–44, 45–59, and ≥ 60. Body mass index (BMI) was divided into five groups, which were underweight  . The metn = 18), HDL (missing data n = 1), TC (missing data n = 1), GFR, SBP, DBP, UA, and LDL . Percentage or frequency was utilized to represent categorical variables, including gender, race, alcohol consumption (missing proportion 19.44%), diabetes (missing proportion 0.06%), smoking (missing proportion 50.15%), age, and BMI (missing proportion 1.43%). The missing data of HGB, HDL, and TC were filled in with the median, and the missing data of LDL, alcohol consumption, diabetes, smoking, and BMI were included in the data analysis separately as a group. Smooth curve fittings were used to explore the non-linear relationship between UA and SBP or DBP. We also conducted a log-likelihood ratio test of the one-line (non-segmented) model with the piecewise regression model to determine whether the threshold exists. Statistical software packages R (http://www.R-project.org) and EmpowerStats  were used for the data analyses. Statistical significance was based on the value of p < 0.05.The associations between UA and BP including SBP and DBP were explored by applying a multiple linear regression model. Mean ± SD  or median (quartile) (skew distribution) was applied to represent continuous variables, including CR, GLU, HGB  mmHg, 71.9 (12.62) mmHg, 5.89 (1.56) mg/dl, and 2.8 (0.93) mmol/L, respectively. Among the participants, 38.79% were <60 years old, 13.27% were of normal weight, 31.14% had diabetes, 17.48% were smokers, and 56.11% were alcohol drinkers. In the hypertension non-treatment group, percentage of males and females was 51 and 49%, respectively. With regard to ethnicity, 15.47% were Mexican-American, 36.33% were White, and 26.75% were Black. Overall, the mean (SD) values for CR, GLU, HGB, HDL, TC, GFR, SBP, DBP, UA, and LDL were 0.93 (0.62) mg/dl, 6.06 (2.98) mmol/L, 14.04 (1.75) mg/dl, 1.34 (0.43) mmol/L, 5.16 (1.11) mmol/L, 80.28 (27.19) ml/min/1.73 m2, 135.69 (22.12) mmHg, 78.2 (13.88) mmHg, 5.54(1.44) mg/dl, and 3.11 (0.94) mmol/L, respectively. Among the participants, 71.86% were <60 years old, 16.77% were of normal weight, 14.57% had diabetes, 29.44% were smokers, and 65.47% were alcohol drinkers. The Univariate analysis for SBP and DBP is demonstrated in A total of 6,985 participants were included in our study based on the exclusion criteria. Among them, there were 5,983 people in the hypertension treatment group and 1,002 people in the hypertension non-treatment group . In the We analyzed the independent effect of UA on SBP and DBP with three models . In modeA smooth curve fitting was used to explore the non-linear relationship between UA and SBP and DBP. From In our study, we found that in the hypertension treatment group, a linear relationship was identified between UA and BP including SBP and DBP. As the level of UA increased, SBP and DBP showed a gradual decrease. However, in the hypertension non-treatment group, the relationship was an inverted U-shape. So as far as we know, this is the first study to explore the association between UA and BP including SBP and DBP in hypertension with and without treatment in NHANES.Uric acid (UA) may play an important role in the development of hypertension through the following pathophysiological mechanisms: renal afferent artery disease, renin-angiotensin-aldosterone system (RAAS) upregulation, oxidative stress, systemic inflammation, and endothelial dysfunction , 12. HypAs mentioned before, previous studies have confirmed that UA is associated with elevated BP and the occurrence of hypertension, but the results of clinical trials are unsatisfactory. This may be a comprehensive result of the interaction between UA and antihypertensive drugs. UA can affect BP in multiple ways, and antihypertensive drugs can, in turn, affect UA levels. Another mechanism to explain this negative association may be related to hemodynamics. A decrease in BP was often accompanied by a decrease in GFR, which may lead to an increase in UA levels. In our research, we converted UA from continuous variables into categorical variables (quintiles) and found that as the level of UA increased, the GFR gradually decreased . Our resHowever, our research has some limitations. First, some variables were based on the subjective response on the questionnaire, and there may be a certain recall bias. Second, for the hypertension treatment group, the classification of hypertension and type of antihypertensive drugs were not totally identified. Lastly, the mean blood pressure obtained by measuring blood pressure continuously for a short period of time may not fully reflect the true circumstances behind blood pressure.In our study, we found that the association between UA and BP was negative in the hypertension treatment group. In the hypertension non-treatment group, the associations between UA and BP including SBP and DBP were both an inverted U-shape, and the inflection point of SBP and DBP was 7 and 7.5 mg/dl, respectively.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the ethics review board of the National Center for Health Statistics approved all NHANES protocols, and written informed consents were obtained from all participants or their proxies. The patients/participants provided their written informed consent to participate in this study.Conception and design were handled by YS and ND. Administrative support was provided by ND. Provision of study materials or patients was handled by CL, YL, and LH. Collection and assembly of data were handled by YS and ND. Data analysis and interpretation were carried out by YS and LH. Final approval of the manuscript was by all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Main problem: Soluble urokinase plasminogen activator receptor (suPAR) is an immunological risk factor for kidney disease and a prognostic marker for cardiovascular events.Methods: We measured serum suPAR levels in a total of 1,023 kidney transplant recipients either before  or at year 1 after transplantation . The association of suPAR levels and all-cause and cardiovascular mortality was evaluated by multivariable Cox regression analysis.Results: The highest suPAR tertile compared to the two lower tertiles had a significantly higher risk of all-cause mortality in both cohorts separately (cohort 1: hazard ratio (HR) 1.92, 95% confidence interval (CI) 1.20–3.08, p = 0.007; cohort 2: HR = 2.78, 95% CI 1.51–5.13, p = 0.001) and combined . The association remained significant in the subgroup of patients with normal kidney function . The increased mortality risk in patients with high suPAR levels was attributable mainly to an increased rate of cardiovascular death .Conclusion: A high suPAR level prior to and at 1 year after kidney transplantation was associated with an increased risk of patient death independent of kidney function, predominantly from cardiovascular cause.  In seTo date, no major study with long-term follow-up has investigated the value of suPAR for the prediction of cardiovascular events and cardiovascular mortality in recipients of kidney transplants. We studied the association between suPAR measured before or at year 1 after transplantation and outcomes in a total of 1,023 kidney transplant recipients.www.ctstransplant.org). SuPAR was measured 1 year post-kidney transplant in cohort 2, consisting of 549 patients aged 18 years or older who were transplanted at the Heidelberg Transplant Center from 2006 to 2015. The primary diagnosis at the time of transplant was autoimmune disease in 3.1%, disease of blood and blood forming organs in 0.9%, congenital disease in 4.4%, polycystic disease in 16.2%, diabetes in 10.0%, chronic glomerulonephritis in 26.6%, IgA nephropathy in 12.9%, interstitial nephritis in 10.2%, metabolic disease in 2.7%, vascular disease in 4.6%, and other diseases in 1.1% of the patients. In 7.3% of the cases, the original disease could not be specified.SuPAR was measured pre-kidney transplant in cohort 1 consisting of 474 patients transplanted between 1988 and 2010 with the primary diagnosis of “focal sclerosis” or “chronic glomerulonephritis” from 37 participating centers that provided a pre-transplant serum from patients reported to the Collaborative Transplant Study  and performed in accordance with the World Medical Association Declaration of Helsinki Ethical Principles in the currently valid version.Serum suPAR was measured in a blinded fashion using either the uPAR Quantikine® ELISA kit  or the suPARnostic kit  according to the manufacturer’s instructions. The lower detection limit is less than 33 and 100 pg/ml, the intra-assay variation less than 5 and 2.75%, and the inter-assay variation less than 5 and 9.17% for the uPAR Quantikine® ELISA and suPARnostic kit, respectively.The information on date and cause of patient’s death was derived from CTS basic follow up forms that are filled out by participating centers at post-transplant months 3, 6, 12 and yearly thereafter.Time to death was calculated from the serum collection date . Multivariable Cox regression analysis was performed to account for the possible influence of the following confounders separately according to cohort: cohort number, transplant year, transplant number, recipient and donor age, recipient and donor sex, donor relationship, pre-transplant human leukocyte antigen (HLA) antibodies, cold ischemia time (deceased donor), time on dialysis, HLA A + B + DR mismatches, and existence of comorbidities . Analysis in cohort 2 included the following additional variables which were not available in cohort 1: rejection treatment during first post-transplant year, 1-year serum creatinine, immunosuppressive therapy at year 1, and presence of an increased cardiovascular risk at year 1 . Survival rates were illustrated using the Kaplan-Meier method. The software package IBM SPSS Statistics  was used.n = 474) or at year 1 after transplantation . In addition to the time of serum sampling, the two cohorts differed with respect to the year of transplantation , the geographical region , the donor relationship, the recipient sex, and the donor and recipient age (p = 0.51). In cohort 2, 46.4% of patients had a serum creatinine of <130 μmol/L, 49.0% of patients a serum creatinine of 130–260 μmol/L, and 4.6% of patients a serum creatinine of >260 μmol/L at the time of suPAR measurement at year one.SuPAR levels were measured in a total of 1,023 kidney transplant recipients either before  of suPAR levels (cohort 1: hazard ratio (HR) 1.92, 95% confidence interval (CI) 1.20–3.08, p < 0.001; p < 0.001) than to death from infection or cancer . The impact of high suPAR level on all-cause mortality was more pronounced in male patients  than in female patients  than in ≥50-year-old patients . The findings were confirmed independently in female versus male, or elderly versus young patients, and most importantly, the influence of suPAR on mortality was constant in patients with different levels of kidney function . The main cause of mortality was cardiovascular death with a striking HR of 4.24 in patients with high suPAR level.In this study of 1,023 patients, serum suPAR level was a robust predictor of all-cause mortality after kidney transplantation. A high compared to normal suPAR level was associated with more than doubled risk of mortality during follow-up. This finding was consistent and independent of the time of transplantation , the primary kidney disease , the time of serum sampling , or the suPAR assay used  in patients with marginal kidney function awaiting transplantation (cohort 1), 2.78 (p = 0.001) in patients with different levels of kidney function 1 year after transplantation (cohort 2), and 5.40 (p = 0.013) when only the subgroup of patients with a serum creatinine <130 μmol/L was analyzed (cohort 2 with good kidney function). This suggests that the link between suPAR and increased mortality is not related to a decreased kidney function, but rather to an unrelated process, i.e., chronic inflammation.Several points are particularly noteworthy in our study. First, regardless of the suPAR assay used, uPAR Quantikiner et al. . The higr et al. . Howeverr et al. . In thisr et al. ,14. This® ELISA kit in cohort 1 versus suPARnostic kit in cohort 2). However, as suPAR levels were distributed similarly and not normally in both cohorts and the outcome was similar when cohorts 1 and 2 were analyzed separately, we felt that it was justified to combine both cohorts for further in depth analysis. Moreover, that suPAR levels predicted inferior outcome independent of the primary kidney disease, the time of serum sampling, or the assay, especially in the highest tertile of patients, can also be interpreted as a strength that underlines the robustness of our findings.Among the influential factors that were considered in the multivariable analysis, recipient age, suPAR level, and transplant year had the strongest impact on mortality. Pre-transplant cancer, diabetes mellitus, and other reasons of moderate or poor evaluation of the patient as candidate for transplantation (for cohorts 1 and 2), and presence of an increased cardiovascular risk at year 1 (for cohort 2 only) were also considered; however, none of them showed a significant influence. Limitations of the current study include the selection of different cohorts of patients with different underlying diseases , different timing of serum sample collection (pre-transplant in cohort 1 versus 1 year post-transplant in cohort 2), and the use of different assays for suPAR measurements  is a risk factor for cardiovascular disease and cardiovascular death in chronic kidney disease and non-chronic kidney disease populations. In hemodialysis patients, the risk of death was almost two times higher in patients with suPAR levels in the highest compared to the lowest tertile with a significantly increased risk of cardiovascular death. So far, no study examined the association of high suPAR levels with overall and cardiovascular mortality in kidney transplant recipients. In two independent cohorts with a total of 1,023 kidney transplant recipients a serum suPAR level in the highest compared to the two lower tertiles was a strong predictor of death, predominantly from cardiovascular cause. These findings were confirmed in different subcohorts. Most importantly, prediction of overall and cardiovascular death was independent of the baseline kidney function indicating that mortality may not be related to a decreased kidney function, but rather to an unrelated process such as chronic inflammation. SuPAR may help to identify kidney transplant recipients at a high risk of cardiovascular death and enable to provide them with the best follow-up and post-transplant care.


Patients in the SUA high tertile ( ≥6 mg/dL) had higher BMI (p = 0.004), lower HbA1c (p = 0.001), lower eGFR (p < 0.001) and higher rate of hypertension than low and middle tertile. After adjusting for age and sex, rapid progression of renal function was significantly associated with SUA level . This association was marginally significant when more covariates were included in the model . However, the association between tertiles of SUA and rapid decline of eGFR was not statistically significant. This study demonstrates neither a strong significant association between SUA and rapid decline of eGFR nor evidence to refuse the role of SUA levels in the increased risk of renal function decline in in T2DM patients.This study investigated the association between serum uric acid (SUA) levels with rapid decline of the estimated glomerular filtration rate (eGFR) in type 2 diabetes (T2 DM) patients. A prospective cohort study was conducted in a community-based hospital in Vietnam. We followed 405 T2DM patients with normal kidney function for five years. Rapid progression of kidney function was defined as an average annual decrease of eGFR of at least 4 mL/min/1.73 m Almost half of patients with type 2 diabetes mellitus (T2DM) have chronic kidney disease (CKD) . NephropMany risk factors for rapid decline in renal function have been determined, including hyperglycemia , hyperteThe present study aimed to investigate the association of SUA levels and risk stratification for rapid decline in eGFR over a five-year follow-up in Vietnamese patients with T2DM. Findings from this study can help to design prevention strategies and to optimize treatments for patients with T2DM.2 at baseline and were followed up for at least three years were included /Number of follow-up years. Since the mean decrease rate of eGFR in this study was 1.4 (SD 3.9) mL/min/1.73 m2 per year, rapid eGFR decline was defined as an average annual decrease of 4 mL/min/1.73 m2 which was approximately three times higher than the average rate. Rapid decline in eGFR was based on measures of creatinine level after at least three years. Rates of change were calculated by the following equation: . All variables were compared between participants with and without rapid decline in kidney function. Normality of data distribution was checked using quantile-quantile plots, histograms and Shapiro-Wilk tests . Studentp value of less than 0.05 was considered statistically significant.Multivariate logistic regression models were used to evaluate the association between SUA levels and rapid decline in eGFR. Variables selected for inclusion in final adjusted models were based on whether they were statistically different between the two groups. Moreover, factors that have been shown to be associated with either SUA levels or the rapid decline of eGFR were adjusted and retained in the models . The results were shown as odds ratios and its 95% confidence interval. In all statistical tests, a This project was approved by the Ethics Committee of Pham Ngoc Thach School of Medicine and the District 10 hospital. All participants read the participation information sheet and signed the written informed consent.Among 432 patients with normal eGFR at baseline, we excluded 27 patients from the analysis because they visited the clinic only twice during the five-year follow-up. The demographic and clinical characteristics at baseline of the 405 patients are shown in p < 0.001), lower HbA1c (p < 0.001), hypertension (p = 0.048), and higher BMI (p = 0.004). There were 121 patients (29.9%) with hyperuricemia.The baseline biochemical and laboratory characteristics of the patients are also shown in Of the 405 patients, 65 (16.0%) had rapid decline of eGFR during the follow-up period. The baseline biochemical and clinical characteristics of the patients in the two groups (rapid decline or non-rapid decline) are presented in p = 0.327). The percentage of decrease in eGFR were 7.7% and 5.3% among patients with HUA and with normal SUA level, respectively (p = 0.099).The magnitude of annual change in eGFR stratified by SUA tertiles and HUA status during follow-up is depicted in p = 0.044) in the univariate analysis (p = 0.026). After adjusting for other covariates, including, BMI, diabetes duration, HbA1c, systolic blood pressure, cholesterol, triglyceride, ACR, baseline eGFR, and diuretic use, the association between SUA and rapid kidney function decline was marginally significant . However, analysis of the association of the SUA tertiles with rapid decline of eGFR in The baseline SUA levels were significantly associated with rapid decline of eGFR  there was no clear evidence of the association between SUA levels and rapidly declining kidney function; (2) while there was a moderate number of patients with HUA , all of them were asymptomatic . The percentage of HUA in the current study seems similar to that of previously reported studies ,20. TherWe observed a prevalence of rapid decline in eGFR of 16.0% in patients with normal kidney function during five-year follow-up. The rate is alarming given that in a community-based setting most subjects had been defined as having normal kidney function and few demonstrated proteinuria. This finding may suggest rapid precipitation to end-stage renal disease. Our current study finds the rate is much higher than has been previously reported. Zoppini et al. observed that from a cohort of 1682 participants with normal renal function, 263 (15.6%) proceeded to develop rapid decline in eGFR (defined as >4% per year after 10 years) . 2), despite a previously reported association in a cross-sectional study [2 revealed that 1 mg/dL increase in baseline SUA was related to 1.27 times greater in odds of rapid eGFR decline. Particularly, 1 mg/dL increase in SUA levels over 5-years was associated with 3.77-fold risk of rapid eGFR decline  [In this prospective cohort study, we did not observe a clear association between SUA levels and rapidly declining kidney function  was higher in the higher tertiles (SUA > 5.86 mg/dL) compared to the low tertile (SUA < 5.86 mg/dL) [Although the findings demonstrated higher baseline SUA levels the greater the decrease in eGFR at follow-up, there remained no significant association with rapid decline, contrary to the findings reported in Chonchol’s study . Chang r6 mg/dL) . The lon6 mg/dL) . Of note6 mg/dL) ,30, but 6 mg/dL) . The present study has several limitations worthy of mention. First, the major limitation is a small sample size that did not allow for a full exploration of covariate influence in the outcome. However, limited by small sample size, our study demonstrates the feasibility in a small community hospital setting. Future studies including larger sample sizes are necessary to better define and understand associations and their clinical relevance. Secondly, this is an observational study carried out in a single district hospital and thus the results cannot be generalized to patients in other settings. Finally, the error of eGFR when utilizing the CKD-EPI equation demonstrated a wide dispersion with an average of 30% . This miEarly recognition of progressive loss of renal function warrants additional study of the underlying mechanisms responsible for renal failure. While progression and decline in renal function is multifactorial, this current study demonstrates neither a strong significant association between SUA and rapid decline of eGFR nor evidence to refute the role played by SUA levels in increasing the risk of renal function decline in T2DM patients. Future studies should be focused to further investigate covariates influencing renal function decline in T2DM patients.


BackgroundInterdialytic weight gain (IDWG) is a marker of higher pre-dialysis blood pressure, nutrition, and survival in hemodialysis (HD) patients. However, this relationship is incompletely characterized. In this study, we seek to define the association of IDWG/dry weight x100 (IDWG%) on blood pressure (BP), and the nutritional status of an HD population.Material and MethodsThis study was performed on 300 HD patients. The data was collected over four weeks, including total IDWG, IDWG%, and blood pressure. Normalized protein nitrogen appearance (nPNA), and serum albumin were used as markers of nutritional status. Participants were divided into three groups according to the mean of the IDWG% between two sessions of HD ; they were then compared on various aspects. Student t-test, analysis of variance (ANOVA), and linear regression analysis were used as statistical tools.ResultsThe mean (± standard deviation (SD)) age was 61.7 ± 14.2 years with 57.7% of the patients being male and 42.3% being female. The mean IDWG% for the whole studied population was 3.72% ± 1.73%. Between these three groups, a higher IDWG% was associated with younger males (p = 0.032), lower dry weight (p = 0.009), and longer duration on HD therapy (p = 0.009). IDWG% was directly associated with lower pre-dialysis serum sodium (p = 0.04), higher pre-dialysis serum creatinine (P = 0.002), and lower body mass index (BMI) (p= 0.003). Between these three groups, interdialytic variations in weight gain were not associated with increased BP. There was no significant difference between the three groups in terms of nPNA and serum albumin. ConclusionsThe most important associations of IDWG% are age, weight, pre-dialysis sodium, serum creatinine, and duration of dialysis (months). There was no association between IDWG% and increased systolic BP. IDWG% had no association with nutritional status. At all stages of chronic kidney disease (CKD), sodium and water overload may cause plasma volume expansion, left ventricular (LV) dilatation, and LV hypertrophy. This is particularly seen in dialysis patients -2. HyperInterdialytic weight gain (IDWG) is used as a parameter for fluid and salt intake between the two sessions of HD. IDWG was measured as the pre-dialysis weight prior to HD treatment, minus the prior session post-dialysis weight. It influences blood pressure and survival of HD patients -7. HowevAt the same time, IDWG is a marker of nutrition in HD patients . MalnutrHerein, we sought to identify the influence of IDWG% and not IDWG, as done in many other studies on BP and nutritional status in HD patients. IDWG% is IDWG/dry weight x100, and dry weight (DW) is the patient's weight without the excess fluid that builds up between dialysis treatments.This prospective study was conducted on 300 HD patients at four HD centers in the Provinces of Zanjan and Tehran in Iran for one month. The study was approved by the Research Ethics Committee of the Zanjan University of Medical Science . All patients signed a consent form prior to data collection.urea + 0.38 . Individuals who prescribed antihypertensive medications and with blood pressure more than 140/85 were considered to have hypertension. Serum pre-dialysis cholesterol, triglycerides, fasting glucose, hemoglobin (Hgb), albumin (Alb), calcium, phosphorus, and C-reactive protein (CRP) were measured. To evaluate the efficacy of dialysis, Kt/V was calculated. Dialysis duration , age, and gender were extracted from patients’ records and saved. The HD protocol for all patients was four hours of HD using hemophane membranes, with a blood flow rate of 300 - 350 mL/min and dialysate flow rate of 500 - 700 mL/min, bicarbonate bath, on a thrice-weekly schedule. All our patients were dialyzed with a sodium dialysis concentration of 139 mmol / L.The association of IDWG% (as a percentage of IDWG/dry body weight) on BP and nutritional status was investigated. Inclusion criteria included patients aged ≥ 16 years with more than six months on maintenance HD three times weekly. Incomplete data at analysis time were considered as exclusion criteria. Participants were divided into three groups according to IDWG%: Group A < 3%, Group B = 3% - 3.9 %, and Group C > 4% in between two sessions of dialysis. All examination and laboratory values were collected when patients reached their dry weight. Dry weight was defined to achieve an edema-free state and no orthostatic hypotension at the end of the HD session. IDWG was calculated as the pre-dialysis weight prior to HD treatment, minus the prior session post-dialysis weight. Blood pressure was measured manually by the staff of the dialysis unit shortly before the HD session, and the weight was measured before and after HD treatment. IDWG and blood pressure were averaged for pre-dialysis measurement of three hemodialysis sessions per week for four weeks and were used for statistical analysis. The serum albumin level and nPNA, which reflect the daily protein intake, were used to evaluate nutritional status. The nPNA was calculated with the equation of Bergström, nPNA = 0.45 Kt/VStatistical analysis Data were reported as mean ± standard deviation (SD) for continuous variables with normal distributions and numbers (percent) for categorical data. Demographic characteristics, laboratory data, and blood pressures were categorized into three groups of IDWG%. Differences between these groups were analyzed with analysis of variance (ANOVA) followed by Tukey’s honestly significant difference (HSD) post-hoc test or the Kruskal Wallis nonparametric test as appropriate. For categorical data, the Pearson Chi-squared test was used. IDWG or IDWG% was entered as a response variable. Spearman correlation and multivariate linear regression analysis were performed to identify associations between IDWG or IDWG% with systolic and diastolic BP and other variables, including various nutritional parameters. The following possible explanatory variables were entered into the model: age, gender, weight, height, Kt/V, serum albumin, and pre-dialysis plasma sodium concentration. Also, bivariate analyses were used to independently identify variables associated with systolic BP. Then, significant factors in bivariate analysis with a P-value less than 0.5 were entered into a multivariable model to describe the relationship between BP with IDWG% and each clinical and demographic characteristic. Statistical analyzes were performed with the IBM Statistical Package for Social Sciences (SPSS), version 20 . Two-tailed P-values < 0.05 were considered statistically significant.Three hundred HD patients were enrolled in this study, including 173 males (58%) and 127 females (42.3%) with a mean age of 61.77 ± 14.2. High blood pressure was detected in 83.7% (251 patients) and diabetes mellitus (DM) in 52% (156 patients). The mean BMI, weight gain, and IDWG% were 24.07± 4.13, 2.32 kg ± 1.1, and 3.72 %± 1.73, respectively. Table Table Table Table 2 = 0.03.Table In the present study, we found higher IDWG% was associated with younger males, lower dry weight, and longer time on HD therapy. We speculate that younger patients drink more fluid because of increased physical and social activity, leading to higher IDWG%. Our results suggest that dietary advice, including fluid restriction, should be individualized based on age and body weight. In older patients, besides volume, multifactorial elements affect the blood pressure. Rahman et al. showed that high IDWG and younger age were independent predictors of higher BP and advancing age was associated with lower BP levels in this population . We alsoOur results showed that greater IDWG and IDWG% were not associated with better nutritional status. There was no significant difference in serum albumin, serum phosphorus , and nPNA between the three groups. Some studies have reported that strict control of salt and fluids may affect nutritional status and lead to inadequate protein and calorie intake . Ipema eHecking and colleagues described that patients with high IDWG had low pre-dialysis sodium , which iThere was no significant association between IDWG and IDWG% with serum BUN, calcium-phosphorus (Ca x P) product, CRP, ferritin, anemia, Kt/V, and metabolic syndrome in studied patients. Regression analysis showed that increased hemoglobin can predict a 3% change in systolic blood pressure. We did not find an association between IDWG% and increased systolic BP or nutritional status. Our results showed that age and body weight are important factors in IDWG and IDWG%. Being young is associated with higher IDWG and its percentage. Our findings highlight the importance of personalized advice on fluid and sodium restriction.


Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. Cardiovascular diseases (CVDs) are disorders of the heart and blood vessels, including heart failure, stroke and hypertension, and represent the first leading cause of death worldwide . This delivery system relies on a complex interplay between the pumping action of the heart and the biomechanical properties of the vasculature , which could be utilized on both healthy individuals and patients with CVD. Conversely, some parameters can only be estimated via highly invasive and risky procedures (e.g. ventricular catheterization is needed to assess end-systolic and end-diastolic elastances in the ventricles), which can only be performed when required by the specific health conditions of a patient with CVD. Thus, in order to properly design a study involving multiple techniques to measure cardiovascular parameters on healthy subjects and patients with CVD that could serve as ground truth to validate our EA findings, it is important to assess beforehand which of the many model parameters may be effectively estimated by the proposed EA method. This constitutes the specific goal of the present investigation.Ground truth for the cardiovascular parameters estimated via the personalized model with the blood pressure measured via a cuff placed on the arm of the subject. When tested on three healthy individuals, the proposed EA performed better in estimating the parameters characterizing the function of the left ventricle than those of the right ventricle. The EA also performed well in estimating arterial stiffness. The satisfactory agreement between model-predicted and experimentally-measured values of blood pressure support the physiological relevance of these findings and show promise for utilizing the proposed approach as a quantitative method for noninvasive cardiovascular monitoring.The physiological relevance of the personalized solution is evaluated a posteriori by comparing the blood pressure estimated In this section 2, we outline the details on the signal acquisition (section 2.1), the physiology-based cardiovascular model (section 2.2), and the design of the EA algorithm (section 2.3).via a 3-lead configuration and the BCG was acquired with a three-axis accelerometer from Kionix with 1,000 mV/g sensitivity placed on the bed frame of a suspended bed . We note that via the following relationship:The R peaks in the ECG, located m is the mass of the subject (m = 74.2 Kg for the subject considered in this study) and a(t) is the acceleration [cm/s2] obtained by applying the following conversion to the signal aV(t) [V] actually measured by the accelerometer:where S = 1 mV, and G = 981 cm/s2  are recorded and the pulse pressure (PP) is computed as the difference between the two, so that PP = SBP−DBP. The average PP value over the six measurements for the same individual is used as a comparison with the model prediction, as illustrated in section 3.3.In addition, the blood pressure was measured The physiology-based model presented in Guidoboni et al.  is utiliIn the model, the pumping action of the ventricles is described by the pressure generatorsL and R indicate the left and right ventricles, ULO and URO are positive constants representing the pressure build-up capacity in the ventricles, and aL(t) and aR(t) represent nondimensional activation functions for the timing of ventricular contractions defined aswhere the subscripts tm = mod < Ts and aL(t) = aR(t) = 0, otherwise. In Equation (4), ta = t − Ta and tb = t − Tb, with Ta, Tb, qL, and qR positive constants. Furthermore, Ts is the length of the systolic part of the cardiac cycle, and Tc is the length of the entire cardiac cycle. Interestingly, Tc can be tailored to a specific individual by means of the distance between R peaks in the ECG. The pressure generators defined in Equation (3) are connected in series with time-varying elastances defined asfor where ELD, ELS, ERD, and ERS are positive constants.i = 2), the aortic arch (i = 3), the thoracic aorta (i = 4), the abdominal aorta (i = 5), the iliac arteries (i = 6), and the cerebral arteries (i = 14). The i labels follow those utilized in Guidoboni et al. , the end-systolic volume (ESV), the stroke volume (SV), the CO, and the ejection fraction (EF). Furthermore, the computed volume waveforms can be used to obtain the BCG waveform b is the blood density, the waveforms Vn(t) represent the blood volume occupying the cardiovascular compartment n at time t, with yn represent the distance in the head-to-toe direction between the cardiovascular compartment n, with lv, rv}, in addition to the large arteries, i.e., via the EA described in section 2.3 are assumed to be the same as those reported in Guidoboni et al. , αy = 1.0 for Subject 2 (height: 180 cm), and αy = 0.85 for Subject 3 (height: 176 cm). In so doing, we leverage the available information on a specific subject to obtain an a priori estimate for Ts, which has been estimated from the ECG recording as the average of the time interval between each R peak and the end of the following T wave.Equations (7) and (8) show that radii and lengths for each of the major arteries, namely for each refer to , 5. An arefer to . In thisi et al.  except fk = 2, …, Nc. Specifically, glv, grv, gvas, and gy are selected as in Equations , whereas gr and gl are selected via a uniform distribution within a range of ±3% of the fittest genotypes obtained upon the convergence of the EA applied to M genotypes, denoted as j = 1, …, M, that can be used to start the EA on each of the k = 1, …, Nc, individually.This procedure for determining the initial population is slightly modified when it is applied to each of the remaining selected curves tep 2: Physiological check. For each of the genotypes gj in the initial population, the corresponding phenotype pj, with j = 1, …, M, is computed via the physiology-based cardiovascular model. In order to be physiologically acceptable, we require the values of the cardiac variables in M = 300 physiologically-acceptable genetic codes is achieved.Si et al. . The rattep 3: Fitness ranking. The phenotypes pj are ranked according to their fitness, which we assume to be the similarity between the model-predicted BCG waveform k = 1, …, Nc. We remind that this is done independently for each of the Nc objective curves. In this study, the similarity is quantified by means of the Euclidean distance between the phenotypic and objective curves, which is computed as follows. The measured waveform ts ∈  as provided by the accelerometer. Since, by construction, all EA-generated curves are defined over the same time interval  as the experimentally-measured objective function ts can be easily calculated and will be denoted by d between their discrete versions SThe best 100 curves according to the fitness ranking are selected as parents for offspring generation.tep 4: Offspring generation. The offspring genotypes are produced as follows:Sp indicates the genetic code of the parent. We note that the interval of variation for tep 2. A total of λ = 3 physiologically-acceptable offsprings are produced by each parent. Finally, the physiologically-acceptable offpsrings and their parents are ranked according to their similarity to the objective curve M = 300 through the generations, and that (ii) only the fittest individuals advance from one generation to the next.where the subscript tep 5: Convergence check. The J peak and the K valley are among the most important traits characterizing the BCG waveform; they are detectable as the most prominent maximum and minimum following the R peak in the ECG .The offspring must be within the top We note that, by requiring the fitness ranking of the offspring to be high enough (second criterium), we aim at finding a solution that is close in an average sense to the measured curve while, simultaneously, maximizing the similarity with the J-K features (first criterium). If such an offspring exists, convergence is reached, otherwise, the EA continues to the next generation.A maximum limit of 30 generations has been set as a stopping criterium in case convergence is not achieved.tep 6: Solution features. Once the EA has reached convergence, the representative features of the EA solutions are computed as the average over the three best-ranked curves, namelySk = 1, …, Nc selected curves from the measured BCG.This holds for each EA run pertaining to the tep 7: A posteriori blood pressure estimation. Ultimately, the EA provides a personalized estimatefor the cardiovascular parameters summarized in via the solution of the physiology-based model described in section 2.2, yields an estimate of the distributions of blood volumes and blood pressures within the cardiovascular system of a specific subject. To evaluate the reliability of these estimates, we compare the blood pressure values measured with a cuff directly on the subject, as described in section 2.1, with the blood pressure values predicted by the model with the personalized parameters. The cuff measures the pressure at the level of the brachial artery in the arm which, however, is not explicitly included among the major arteries of our cardiovascular model (as shown in section 2.2). To address this issue, we leverage the results of the Anglo-Cardiff Collaborative Trial, which included approximately 12, 000 individuals across East Anglia and Wales in the United Kingdom . In McEniery et al. . Next, we examine the EA performance in terms of estimating various parameters in the cardiovascular model (section 3.2). Finally, the values of PPs estimated by the algorithm are compared with those measured with the cuff placed on the arm of each subject (section 3.3).k = 1, …, 5 measured experimentally (in black) and the corresponding three best-ranked via the EA (in colors) obtained for Subject 1. Analogous figures for Subjects 2 and 3 can be found in the A quantitative comparison between the J-K features in the experimental and predicted curves for each of the three subjects included in the study is summarized in and in the timings of the J and K peaks defined asRemarkably, the mean percent errors in the timings are approximately one order of magnitude lower than the percent errors in the amplitudes.L, and Ts) and the arterial Young modulus (E). Some of the parameters characterizing the right ventricle can also be estimated quite consistently , while others show marked differences among the results obtained for the five curves . Similar marked differences are displayed by the estimates for peripheral vascular resistance (R7). The values of some estimated parameters  turn out to be close to the baseline values reported in We recall that the main output of the EA algorithm is the personalized estimate of the physiological and anatomical parameters in via the proposed EA method for the three subjects included in the study is summarized in Ts, and URO satisfy this criterion for all three subjects, whereas the estimates of ELS, qL, ERS, qR, and E were satisfactory only for two out of three subjects. The value of the peripheral resistance R7 resulted to be poorly estimated in all subjects. It is also worth noticing that the results for all three subjects confirm that the ULO is estimated to be larger than URO, and that ELS (resp. ERS) is estimated to be larger than ELD (resp. ERD), thereby supporting the physiological relevance of the findings.A quantitative comparison of the cardiovascular parameters estimated Left) shows the pressure waveforms in the ascending aorta (i = 2) predicted by the cardiovascular model for the three best-ranked curves obtained by the EA performed on Right) reports the results for the brachial pressure predicted by the cardiovascular model for Subject 1 with the personalized model parameters yielded by the EA search performed on each of the k = 1, …, 5 objective curves k, whereas the black brackets indicate the maximum and minimum values. The pulse pressure, defined as the difference between systolic and diastolic values, is highlighted with solid colors in the bars.To further verify the capability of the EA algorithm to yield physiologically-meaningful solutions, we compare the brachial pressure measured experimentally with the pressure predicted by the cardiovascular model equipped with the personalized parameters provided by the EA search. The results obtained for Subject 1 are reported in y et al. , we applvia the cardiovascular model (refer to section 2.2) with the set of model parameters corresponding to the three best-ranked curves; (ii) a factor of 20±10 mmHg is added to the systolic value of the simulated central aortic pressure to obtain the systolic brachial pressure, as suggested by the population-based study of McEniery et al. , fitness evaluation , and convergence . This finding suggests that our approach could be used to obtain BCG-based cuffless blood pressure measurements, along with noninvasive estimates of central aortic pressure and valuable parameters describing cardiovascular function.In the current implementation of the algorithm, we opted for selecting refer to . This reIn the long term, this study aims at contributing to the quest for noninvasive techniques capable of providing meaningful insights into the thermodynamic efficiency of cardiac function. Without direct left ventricular inductance catheters, clinicians must rely on indirect estimations from right heart catheters or algorithms from echocardiograms, each affected by risks and limitations (Ikonomidis et al., Limitations from both the experimental and modeling viewpoints should be considered when evaluating the findings of our study. The BCG sensing modality utilized in this study is a suspended bed equipped with an accelerometer. While providing a signal that is very close to the true acceleration of the center of mass of the human body (Starr and Noordergraaf, Nc representing the number of consecutive BCG curves to be selected as objective curves. The choice of Nc = 5 adopted in this study is motivated by the need of considering multiple curves while maintaining the overall computational load affordable. Optimal choices for Nc may be explored in conjunction with the effect of breathing, which may affect the BCG curves over longer intervals.Additionally, our results show that the agreement between model-predicted and experimentally measured BCG curves is better in the systolic part than in the diastolic part of the cardiac cycle refer to . It is kThis study presented a novel combination of a physiology-based mathematical model and an evolutionary algorithm to obtain personalized estimates of cardiovascular parameters and variables of physiological interest, such as blood pressure, with the goal of developing quantitative tools for noninvasive cardiovascular evaluations based on BCG sensing. The approach proved capable of estimating many ventricular and arterial parameters with consistency when five consecutive BCG curves were selected for the subjects considered in this study. Furthermore, the good agreement between the blood pressure estimated with the model and measured experimentally with a cuff shows that the proposed approach is physiologically meaningful and may provide theoretical support to the further development of cuffless methods for blood pressure measurements (Solà and Delgado-Gonzalo, The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Institutional Review Board, Office of Research and Economic Development, University of Missouri. The patients/participants provided their written informed consent to participate in this study.NM, LS, and MZ contributed to implementing the algorithm and running the numerical simulations. GG, NM, and MZ wrote the first draft of the manuscript. All authors contributed to the conception and design of the study, contributed to manuscript revision, read, and approved the submitted version.GG would like to disclose that she received remuneration from Foresite Healthcare LLC for serving as a consultant. MS also discloses a conflict with Foresite Healthcare LLC outside the submitted work and patents licensed to Foresite Healthcare LLC. These relationships are pursuant to the University of Missouri's policy on outside activities. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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