Background: The development of left ventricular hypertrophy in primary hypertension increases cardiovascular mortality and morbidity. Reversal of left ventricular hypertrophy through therapeutic control of blood pressure reduces the risk of adverse cardiovascular incidents.
Objective:  In our study, we explored for the determinants of left ventricular hypertrophy regression.
Methods: A cohort (n=217) of patients with hypertensive left ventricular hypertrophy was identified by screening consecutive patients in medical outpatient unit. The primary inclusion criteria were (i) Blood pressure more than140/90 mm of Hg (ii) Left Ventricular Mass Index more than 115 and 95 gm/m2 for males and females respectively. Left Ventricular Mass Index was determined by echocardiography at the time of recruitment and after 24 weeks of standard pharmacotherapy. The change in Left Ventricular Mass Index was modelled using multiple linear regression with both categorical and continuous explanatory variables. The effect of drug therapy on change in Left Ventricular Mass Index was tested in the model with dummy coded variables for the treatment categories.
Results: In simple linear regression, the variables significantly correlating with change in Left Ventricular Mass Index were baseline Left Ventricular Mass Index , change in systolic blood pressure , change in mean blood pressure , baseline systolic blood pressure , age  and diabetes . The best fit model (r2=0.408) retained baseline Left Ventricular Mass Index , change in systolic blood pressure  and diabetes  as the significant predictors. Introduction of treatment effect into the model non-significantly increased the fit of the model .
Conclusions: Pre-treatment Left Ventricular Mass Index and reduction in systolic blood pressure were the major determinants of left ventricular hypertrophy regression. We also observed that there is lesser left ventricular hypertrophy regression in diabetic patients, warranting future research to explore glycaemic control as a modifiable factor in left ventricular hypertrophy reversal.  Left ventricular hypertrophy (LVH), which develops as a consequence of increased blood pressure (BP), independently increases cardiovascular mortality and morbidity. It occurs in 36-41% of hypertensive patients2,3. Statistically, the mass of the left ventricle has a direct bearing on the incidence of cardiovascular accidents1. Adequate control of blood pressure retards the progression of the disease and regresses LVH, attenuating cardiovascular adversities1,4-7. Quantitatively, the decrease in blood pressure induces a proportionate decrease in Left Ventricular Mass (LVM)8,9. However, statistical models in contemporary literature could not completely explain the variability in LVM reduction. The gap in knowledge warrants further exploration of factors that may influence LVH regression. In our study, the predictors of LVM reduction were first identified individually by bivariate correlation. Using the significant correlates, we constructed a best fit multiple linear regression model by stepwise forward selection (SFS) method. We observed that 41 % of the variability in change in LVM was explained by the model. Our work can be a useful tool for health care professionals in assessment of response to therapy in primary hypertension.Essential hypertension is a major non-communicable chronic disease in our society and its incidence is expected to increase in near future. Progression of the disease leads to systemic involvement affecting vital organs including heart, brain and kidney. Association of the disease with increased incidence of cardiovascular events has been definitely provenThe study was carried out at the medicine out-patient unit of a tertiary care centre in Central India.The study was enacted after obtaining ethical approval of the Institutional Human Ethics Committee, All India Institute of Medical Sciences, Bhopal.2 in males and females respectively. The exclusion criteria were 1. Renal pathology of any aetiology; 2. Pregnancy; 3. Pre-menopausal women willing to conceive; 4. Congestive heart failure NYHA class II- IV; 5. Morphological, congenital or electrical heart disease; 6. Left ventricular systolic dysfunction; 7. Bilateral renal artery stenosis; 8. Secondary hypertension; 9. Hepatic pathology of any aetiology. Only patients who provided voluntary informed consent were recruited in the research work. The data of 217 patients was available for analysis on completion of the study.Consecutive patients at the medicine outpatient unit were screened. Patients of either gender between 35 to 85 years old and blood pressure above 140/90 mm of Hg who had LVH were included in the study. LVH is defined as Left Ventricular Mass Index (LVMI) more than 115 and 95 g/m10. All patients (N=217) received standard care for essential hypertension as per the existing guidelines. Both treatment-naïve and previously treated patients were recruited. Anti-hypertensive therapy comprised of Angiotensin converting enzyme inhibitors (ACEIs)/ Angiotensin receptor blockers (ARBs) monotherapy or combination of ACEIs/ARBs with the other two first line anti-hypertensive drugs viz. calcium channel blockers (CCBs) and thiazide diuretics (TD). On the basis of treatment regimen, four groups were constituted (Group A (n=70)- ACEIs/ARBs, Group B (n=48)- ACEIs/ARBs+CCB, Group C (n=51)- ACEIs/ARBs+TD, Group D (n=48- ACEIs/ARBs+CCB+TD). The dependent variable in the study was the change in LVMI following uninterrupted treatment for a period of six months.The data for the regression analysis was part of a larger non-randomized, open label, parallel group, prospective study designed to assess the quantum of LVM regression in hypertensive LVH following medical therapy10.The standard protocol followed in this study for the echocardiographic estimation of LVM and measurement of blood pressure is described in our previously published workThe MLR model was constructed using SPSS (version 22). The response variable in the model was the quantum of change in LVMI following standard medical therapy. Both categorical and continuous predictors were tested for presumed addition as a significant variable in the model. Medical therapy comprising of four groups (Group A to Group D) was coded into four hierarchical treatment levels with Group A as the reference. At the end, the treatment levels were incorporated in the model as independent variables to test confounding effect due to medical therapy. The systematic method to develop the model is elaborated under the following steps.The strength of correlation between change in LVMI (response variable) and the determinants of LVH reversal (the Predictors) was tested using bivariate linear regression. The categorical predictors were recoded into “0” and “1” and then tested for correlation. For instance, sex was represented as “1” and “0” for men and women respectively. In this preliminary step, the cut-off threshold probability was kept high  to identify the maximum number of predictors for the dependent variable.The threshold for feeding the correlates in the MLR model was also kept low (P≤ 0.1), to facilitate the inclusion of maximum number of variables in the model. The final inclusion of a significant variable in the MLR model was ascertained using SFS method. The method involves sequentially adding the predictors in decreasing order of the strength of correlation. After every addition, the fate of a predictor was decided using its significance level and increase in explained variability of the model.A predictor was deleted from the model on basis of pre-set conditions after each iteration of SFS method. The cut off value of the level of significance for discarding a predictor from the model was set at > 0.1. A predictor was also deleted if there was multicollinearity between them. Multicollinearity was checked by correlation testing between a pair of variables. If the Pearson correlation coefficient was greater than 0.6, the stronger predictor was preferably retained in the model. We also checked for multicollinearity using Variance Inflation Factor (VIF). We set the cut off VIF at >3 to reject a variable2) value was derived from the model. R2 denotes the fraction of the total variation in the outcome variable that can be explained by the regressors in the best fit model.The final MLR model was created using those predictors only that remained after multiple iterations of the SFS method. The model was enacted with the preserved significant predictors. The coefficient of determination  on change in LVMI. The four treatment categories (Group A to D) were converted into explanatory variables by coding into dummy variables using “0” and “1”. The reference Group A was coded as “0” only. The TE model was run with the four explanatory variables added en bloc by “Enter” method. R2 value and the statistical significance of the combined model were noted. The change in significance levels of intergroup difference in treatment effect was also noted.TE can be a confounding factor for the variables in the best fit model. We checked the confounding effect by combining the two models. The dummy variables for treatment categories were forced into the best fit model as explanatory variables. The change in R11.The MLR model was cross validated by both the Hold-out and Leave-one-out methodt=145) and the validation set (nv=72). An MLR model predictive of change in LVMI was developed with the nt and was used to predict the change in LVMI in the nv. Bivariate linear correlation between the actual and the modelled values of change in LVMI in the validation set was observed. 95% confidence interval of the slope of the regression line was calculated and the acceptable upper limit of the margin of error was kept at 10%. A P-value of < 0.05 was deemed statistically significant.The study population (n=217) was divided into two successive sets-the training set  are shown in 2=0.408) with baseline LVMI , change in systolic BP  and diabetes  as predictors (The proportion of explained variability (PEV) with baseline LVMI as the single predictor was 37.9% (r=0.616). PEV increased to 39.7% when change in systolic BP (SBP) was added as an explanatory variable in the model (P<0.05 for both the variables). When change in mean BP (MBP), baseline SBP (bSBP) and age were sequentially added to the model, PEV changed insignificantly (< 1.0%). These three variables which were statistically significant in simple linear regression became insignificant in the MLR model (P>0.05). However, baseline LVMI and change in SBP continued to be statistically significant at each step (P<0.05). There was an increase of 1.1% in PEV upon addition of diabetes as a predictor and the variable showed statistical significance (P=0.05). We also found that change in MBP and bSBP had very strong correlation with change in SBP (P=0.767 and 0.679 respectively) leading to redundancy. Therefore, in the MLR model, change in MBP and bSBP were dropped, while baseline LVMI, change in SBP and diabetes were retained. Further, we checked for interaction between the significant predictors by introducing their double products into the model. All such interaction were found to be statistically non-significant. In this way, the best fit model was finalized  with significant TE difference between Group A and Group D (P=0.002)  . Upon fot was 23.92 ±23.55 gm/m2. The mean values of the actual and the modelled change in LVMI in the nv was 16.54±15.63 and 20.46±13.40 gm/m2 respectively. The Pearson coefficient for bivariate correlation between the actual and the modelled values of change LVMI in the nv was 0.59 (P<0.001) (The mean change in LVMI in the nP<0.001) . The mar2 respectively. The Pearson coefficient for bivariate correlation between the actual and the modelled values of change LVMI in the two sets was 0.64 (P<0.001) (The mean of change in LVMI in the whole set and leave-one-out set was 21.47 ±17.67 and 21.47±13.73 gm/mP<0.001) . The marRegression of LVH is the surrogate marker for the efficacy of antihypertensive therapy in reducing cardiovascular events. Therefore, the factors determining LVH regression are of immediate clinical interest. In our work, we explored the tentative variables that could have a possible relation with the quantum of LVH regression. We incorporated the significant variables into a single multiple linear regression model. The objective of multiple linear regression modelling is to integrate the significant regressors in a single viable model to achieve the best possible prediction for the outcome of interest. It seeks to get rid of the spareable predictors and optimizes the effect of the individual regressors into a common final value. Validated models can help in rapid and correct assessment of clinical and diagnostic outcomes. We developed the MLR model predictive for LV mass regression using stepwise forward selection method.12. However, it is known that hypertensives with diabetes have greater LV mass13-15. Diabetics also show lesser regression of LVH with the same decrease of blood pressure compared to non-diabetics. In the Losartan Intervention For Endpoint (LIFE) Reduction in Hypertension Study, the regression in LVH was studied in 9193 hypertensives (of which 1195 were diabetics) using electrocardiography (ECG) Cornell voltage-duration product criteria. At the end of 5 years, the decrease in LVH by the ECG criteria was less in diabetics (P<0.001)16.Our Best Fit Model included the most important variables and excluded the redundant variables. The three variables that found place in the Best Fit Model were baseline LVMI , change in systolic BP  and diabetes . The model explained 40.8% of the variability in change in LVMI. We compared our results with a similar study conducted by Konstantinos et al. The researchers had developed an MLR model by “stepwise backward method” which could explain 36% of the variability in change in LVMI. Baseline LVMI and change in systolic BP were the common predictors. In addition, the study reported “impaired left ventricular geometry and blunted nocturnal blood pressure fall before treatment” as significant predictors of LVH regression. The researchers did not find any correlation with diabetes17.In our study, we used echocardiography to measure LV mass and got similar finding that LVH regression was less in diabetics (unstandardized coefficient= -4.64). The Aliskiren in Left Ventricular Hypertrophy (ALLAY) trial which employed magnetic resonance for cardiac mass also reported that diabetes mellitus (P = 0.043), change in systolic blood pressure (P = 0.005) and pre-treatment LVMI (P < 0.001) are significant predictors of LVH regression17. In the ACCORD BP trial, intensive and standard BP lowering (target SBP of <120 and <140 mmHg respectively) were compared in 4733 diabetic patients (SBP 130-80 mm of Hg) not receiving more than 3 drugs for hypertension. There was greater regression of already existing LVH and also lesser development of new LVH. It was assumed that lower risk of LVH will have the potential to reduce cardiovascular mortality and morbidity18. The SPRINT trial was a randomized multicenter trial conducted on 8164 hypertension patients without diabetes which also compared the outcomes of intensive BP lowering with standard BP lowering. It not only evaluated the risk of LVH but also compared the rates of CVD events in the two arms. Similar to the ACCORD BP trial, it was found that there was greater chance of regression of previous LVH and lesser chance of development of new LVH with intensive BP lowering. The risk of cardiovascular accidents was found to be 24% lower  in the intensive BP lowering group. However, the researchers found that the reduction in CVD events was over and above of what could be assigned to the favorable effect of LVH regression. It was concluded that intensive BP reduction had cardiovascular benefit that go beyond the effect of hemodynamic stress on the heart19. A metanalysis of randomized controlled trials performed by Schmieder et al in 1996 observed that the decrease in LVMI was related with the decrease in blood pressure 8.The outcome of another metanalysis by Jennings et al similarly concluded that there is a “strong relationship between changes in blood pressure and LVH regression”9.However, further studies are warranted to explore the effect of glycemic variability in regression of hypertensive LVH. In a cohort of patients having concurrent diabetes and hypertension, it was found that more intensive lowering of BP causes greater regression of LVH20,21. We studied the role of clinical factors in inducing left ventricular regression primary hypertension patients receiving ACE inhibitor/ ARB based regimen. The treatment arms in our observational study were non-randomized, naturally occurring groups based on convenience sampling and the treatment effects are not transferable across study cohorts. An MLR model is a viable alternative method to find out the predictors of the outcome of interest in such a non-experimental study. In our model, we did not find any novel predictor for LVH regression. However, we have elaborated a systematic approach towards construction of the predictive model. We tested and statistically controlled confounding due to treatment effect. This enabled us to identify the deterministic factors independent of the treatment categories. We also performed an inter-group comparison (one way ANOVA) between the four groups with respect to change in BP and LVMI (results shown in our previous work 10). We found that in mutually exclusive groups and not meriting statistical comparison, combination therapy brings greater reduction in BP as well as greater regression of LV mass. It is such that the maximum regression is seen in the group on combination of three drugs. When change in BP was introduced as a covariate (ANCOVA method), there was no statistically significant difference between the groups10.Our MLR model also indicates that the single most important factor amenable to intervention is the reduction in systolic blood pressure. Adequate control of blood pressure will not only retard the progression of the disease but also regress left ventricular hypertrophy, reducing the rate of cardiovascular complications. However, the drug regimens practiced achieving these therapeutic objectives are empirical based on limited studies. Amongst the different antihypertensives, ACE inhibitors and ARBs are considered first line drugs for therapy in hypertensive heart diseases. Inhibition of the Renin-angiotensin aldosterone system by ACE inhibitors/ ARBs is the most accepted theory that explains their beneficial effect21-23.The results of ANCOVA and MLR model are in congruence with each other. Both analyses indicate that BP control is the modifiable factor amenable to treatment in regression of hypertensive LVH. The proportion of change in LV mass that can be attributed to either drug or BP reduction are inter-related and inseparable because BP reduction itself varies as a function of the drug class and dose. Therefore, the objective of antihypertensive therapy would be to control blood pressure through rational use of the recommended drugs. This observation asserts the recommendations of recent guidelines of prompt shifting to combination therapy of antihypertensives for better control of blood pressure and long-term prognosis. The results also reinforce the recommendation of existing guidelines that triple drug therapy should be used to reach the goal of target blood pressure whenever indicatedWhile pre-treatment left ventricular mass is the major determinant of left ventricular hypertrophy regression, the only factor amenable to standard treatment is reduction in systolic blood pressure. Our study also shows that there is lesser left ventricular hypertrophy regression in diabetics warranting future research to explore glycaemic control as a modifiable factor in left ventricular hypertrophy reversal.


Patients with chronic kidney disease (CKD), especially those who are hemodialyzed (HD), are at significantly high risk of contracting cardiovascular disease and having increased mortality. This study aimed to find potential death predictors, the measurement of which may reflect increased mortality in HD patients, and then combine the most promising ones in frames of a simple death risk assessment model. For this purpose, HD patients ( Chronic kidney disease (CKD), defined as a progressive loss of functional nephrons leading to a gradual reduction in the glomerular filtration rate (GFR) and increased urinary albumin excretion, has become a global public health problem  and a reEpidemiological studies have shown that in 2017, the global incidence of CKD was 9.1%. Whereas it increased in all age groups by 29.3% between 1990 and 2017, the age-standardized global incidence has not changed significantly. CKD caused 1.2 million deaths in 2017 and has been found to be the 12th most common cause of death globally. Moreover, 7.6% of all CVD deaths have been linked to impaired kidney function. Overall, deaths from CKD or CVD attributed to CKD have accounted for 4.6% of all deaths. Between 1990 and 2017, global mortality from CKD at all ages increased by 41.5%. In contrast, age-standardized CKD mortality has remained stable, contrary to the declined age-standardized mortality worldwide for CVD, cancer, and chronic obstructive pulmonary disease [It has been proven that the incidence and severity of CVD increases with a decrease in the GFR. Coronary artery disease (CAD) in CKD has shown a pattern of diffuse multi-vessel involvement with calcification in the coronary arteries in kidney failure patients . In the In the face of such facts, special attention has been paid to understanding the mechanisms leading to the worsening of CVD in CKD and the increasing mortality rate in CKD.This topic has turned out to be challenging because of the multifaceted nature of the disorders in which the kidney is involved. Accumulating evidence has suggested that the kidneys are not only the target organs of many diseases, but that they can strikingly exacerbate or trigger systemic patho-physiological processes through their complex functions and influence the homeostasis of the whole organism. Due to its essential role in circulation, the kidney has often been revealed to be a target organ for systemic vascular, hemodynamic, metabolic, and inflammatory disorders. The involvement of the kidneys in many pathological processes of non-renal origin has been found to worsen CKD as well as the prognosis .Traditional CV risk factors have been found to be very commonly observed in CKD patients, and their contribution to CVD has been revealed to be especially important in the early CKD stages. Hypertension, dyslipidemia, insulin resistance, diabetes, and smoking, due to their impact on the large and smaller renal vessels, have been revealed to affect both the CV and cerebrovascular system, but also the progression of CKD . HyperteThe unique nature of CVD in CKD is constantly suggested ,11,12,13Apart from the role of the traditional risk factors, two primary mechanisms have been distinguished as contributing to CVD development in CKD. The first mechanism is directly based on the kidney action, which may release hormones, cytokines, and enzymes in response to injury or failure, thus contributing to the characteristic vascular changes. The second mechanism has relied on CKD mediators and hemodynamic changes leading to cardiac damage . FurtherMuch research has been performed to date to find variables that could be used to predict CV mortality in hemodialyzed (HD) patients. Many potential markers have been indicated. However, so far, none of these are strong enough and could be recommended. Recently, Hansrivijit et al., in their systematic review, indicated a total of seven predictive factors as excellent: N-terminal pro-brain natriuretic peptide (NT-proBNP), BNP, soluble urokinase plasminogen activator receptor (suPAR), augmentation index, left atrial reservoir strain, C-reactive protein (CRP), and systolic pulmonary artery pressure. Another seventeen predictive factors were found to be within acceptable ranges, and the authors classified them into the following subgroups: predictors for the non-dialysis population, echocardiographic factors, comorbidities, and miscellaneous . It showOur study aimed to create a model for assessing the risk of death in HD patients with a history of acute myocardial infarction (AMI) in the previous year, based on a 2-year follow-up. HD patients were tested to identify potential markers; their collective assessment could reflect the mortality observed in this patient group.A total of 103 people were enrolled in the study, including 71 consecutive HD patients with AMI in the last year (HD group) and treated in 2016–2018 at the Nephrology Outpatient Clinic and the Clinical Hospital’s Dialysis Center, H. Święcickiego in Poznań; 32 healthy participants (control group) served as a comparative group.In the HD group, the duration of the HD sessions was at least 12 h/week; standard bicarbonate dialysis fluids and polysulfone low-flux dialyzers were used. HD procedures were performed on each patient three times a week via an arteriovenous fistula from their own or artificial vessels. The blood flow during hemodialysis was 200–350 mL/min, with an average dialysis fluid flow of 500 mL/min. The most common cause of the kidney failure was hypertensive nephropathy  prior to the second HD in the week of scheduled routine blood tests.All samples were stored at −80 All assessed laboratory parameters were performed using standard methods, in accordance with the manufacturer’s instructions. For more detailed information, concerning methods that were used, see ClinicalTrials.gov Identifier: NCT05214872.-the complete blood count, glucose (Glu), total protein (TP), albumin (ALB), creatinine, urea; parameters of lipid metabolism: total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), potasium (K), sodium (Na), magnesium (Mg); parameters of iron metabolism: iron concentration, total iron binding capacity (TIBC), the unsaturated iron binding capacity (UIBC), and ferritin concentration; activity of alanine transaminase (ALT), aspartate transaminase (AST), and alkaline phosphatase (ALP); parameters of calcium and phosphate metabolism: total and ionized calcium, phosphate (PO-estimated GFR (eGFR)—according to the KDIGO 2012 recommendations—was calculated based on the Modification of Diet in Renal Disease (MDRD) formula: eGFR = 186 × (creatinine concentration (mg/dL)) − 1.154 × (age) − 0.203 × (0.724) for the female gender;-serum concentration of the selected inflammatory markers: neopterin and interleukin 18 (IL-18); oxidative stress parameters: advanced oxidation protein products (AOPP), advanced glycation ends products (AGE), carboxymethyl-body mass index (BMI) (kg/m-carotid intima-media thickness (IMT) was measured by The Accuson CV 70 system (Siemens) with a 10 MHZ transducer. Two longitudinal projections were assessed . The distal 1cm of the common carotid artery just proximal to the bulb was measured by means of a computer analysis system .The following parameters were determined in all studied groups:For non-invasive cardiological examinations, the Portapres TM device , Pulse Trace 2000TM , the SphygmoCor MxTM tonometer , the Colin CBM-7000 TM device , and the Acuson CV 70 ultrasound machine  were used. The system was equipped with a vascular head operating at 10 MHz.https://atcormedical.com/technology/sphygmocor/ (accessed on 7 December 2021). The values of the parameters that were taken into account in order to find the best predictive model are included in the The evaluation of the variables using the non-invasive devices was based on advanced algorithms for analyzing pulse waveforms and EKG curves. The SphygmoCor system  used derived the central aortic pressure waveform from cuff pulsations recorded at the brachial artery. Analysis of the waveforms provided the values of the key parameters, including the central systolic pressure (cSP), central pulse pressure (cPP), and indices of arterial stiffness such as augmentation pressure (cAP) and augmentation index (cAI). Moreover, Pulse Trace TM enabled the assessment of the following: reflection index (RI), vascular stiffness index (SI), peripheral pulse pressure (pPP), and peripheral pulse pressure/central pulse pressure (pPP/cPP ratio). A more detailed explanation of the methods of determining the individual variables can be found at The study was carried out in accordance with the Declaration of Helsinki of the World Medical Association and approved by the Bioethics Committee at Poznan University of Medical Sciences (decision No. 274/16 of 3 March 2016). All included study participants fulfilled the criteria and completed the study. They were fully informed about the study, and all of them gave written informed consent before the enrollment.Detecting the most important differences in variable levels between hemodialyzed patients who died and those who survived within two years of follow-up;Checking whether differences found are unique to deceased hemodialyzed patients or if distortions are specific for all hemodialyzed subjects by comparing to the control group;Selecting the most strongly differentiating variables found in the previous steps;Finding significant and strong correlations within selected variables for hemodialyzed patients;Model building and testing;Model diagnostics and results analysis.In order to find factors associated with death risk, exploratory statistical analysis was performed. Based on the obtained results and the current state of knowledge, variables for building and testing the generalized linear model (GLM) were selected. The performed statistical analysis and model building can be divided into the following steps:All steps were performed on Python 3.8. The following packages were used to execute statistical analysis: numpy-1.19.5, pandas-1.2.4, statmodels-0.12.2, scipy-1.3.3. Data and results were visualized on Python with the help of the matplotlib-3.1.2 and seaborn-0.11.0 packages.Basic information concerning the analyzed groups of patients is presented in Basic information about the analyzed patients are summarized in The results of all performed analyses and comparisons between HD-D and HD-A, HD-D and control, and HD-A and the control group are included in the The full list of variables along with statistical selection flow is presented in The strongest and most significant differences have been found for albumin, 3-NT, IL-18, AOPP, NT-proBNP, and phosphates, which may suggest the importance of kidney failure-associated factors such us inflammation, malnutrition, and nitrosative and oxidative stress.All mentioned parameters, except for albumin, were elevated in the HD-A and HD-D groups. It is also worth noting that for almost every parameter, much higher variance was present in the group of HD patients see . This waThe correlation between variables selected in the previous step helps to evaluate them as potential death predictors, especially if taken in mutual complementation see . All choThe best GLM model to differentiate patients who died from those who survived relied on 4 variables: Interleukin-18, 3-NT, ALB, and phosphates. At this stage, it was necessary to exclude 4 patients belonging to the HD-A group due to a lack of IL-18 (3 patients) or phosphates (1 patient) measurements assigned to them in the dataset. The model can be summarized with the following equation:Y is associated with a high risk of death, while values below zero correspond to decreased risk. The model was able to correctly assign 95.5% of the patients in the HD group to HD-D and HD-A accordingly. The model mean accuracy was 0.8893 ± 0.0617, and the random sampling of the data estimated the train (80%) and test (20%) sets; cross-validation was repeated 20 times. General information about the model can be found in The positive value of the model’s result denoted with the letter p > 0.05), deletion of this variable worsens the model.An evaluation of the chosen predictor variables has been summarized in According to During residual analysis, a strong outlier patient was detected. Based on the obtained results, it can be concluded that the deletion of this outlier will not have any significant impact on the final model. In order to double check the model, the outlier patient was deleted from the dataset and a residual analysis was performed; results are presented in The statistical approach that helped to obtain the model is limited by the number of dead patients. Although the detected relationships seem to be clear and strong, it is reasonable to expect some sampling bias. It is reasonable to suppose that this model could be easily adopted in clinical practice and used to prevent patient death, if possible.The pathogenesis of CKD-related CVD is complex and multifactorial, primarily as renal dysfunction is caused by various disturbances, including autoimmune, metabolic, oncological, and vascular diseases.Today, there is no doubt that the diagnosis of CKD poses an increased risk of CVD . AlthougThe results of our research are another voice indicating that in CKD, looking for the causes of increased CV-related morbidity and mortality solely in the group of traditional factors commonly considered as CV will only be in vain. The present study shows that the causes of CV-related mortality in HD patients are complex and should be considered in the context of the contribution of not one singe factor, but of the entire system of relationships between various factors. This mixture of dependencies is quite a challenge.Based on the results of the statistical analysis, we discovered that the four parameters—IL-18, 3-NT, phosphate, and ALB—when taken together in one proposed model, could predict 95.5% of the CV-related death over a 2-year follow-up among the hemodialyzed patients included in this study. These four parameters represented the main problems of HD patients: inflammation, oxidative stress, malnutrition, and calcium-phosphate disorders.Among the unique features of CKD patients, chronic inflammation is one of the key issues. On the one hand, the inflammatory process is the body’s protective response to infection and tissue damage, which leads to vasodilatation and the recruitment of leukocytes, plasma proteins, and fluid into the affected tissue. On the other hand, chronic inflammation may be a maladaptive and poorly controlled response  that contributes to many complications, including cardiovascular ones. It is worth noting that chronic inflammation occurs in a large proportion of the CKD population and is increasingly associated with renal function deterioration .The inflammasome—an intracellular platform created in response to pathogen-associated molecular patterns (PAMP) and danger-related molecular patterns (DAMP)—by converting procaspase-1 into active caspase-1, activates pro-inflammatory cytokines such as interleukin 1-roptosis ,20. Receroptosis . NLRP3 iroptosis . As discroptosis , in a moroptosis . The actroptosis .In our proposed predictive CV-mortality model, the inflammatory process was represented by IL-18, a cytokine that can regulate inflammation at multiple checkpoints . Multifuteinases . As the teinases . Althougteinases ,30, manyteinases ,31,32. TIn the previous study by Formanowicz et al. , in a 2-In this study, both HD groups have shown significantly higher levels of IL-18 in the serum than in the control group. There are at least several possible explanations for this condition. First, IL-18 is a medium molecule and protein-bound uremic toxin that is difficult to remove with any of the current dialysis strategies; hence, the observed accumulation of IL-18 in dialysis patients. Second, the dialysis procedure activates the monocyte/macrophage network and synthesizes many inflammatory cytokines, explaining the observed increase in serum IL-18. Moreover, IL-18 has been associated with the instability of the atherosclerotic plaque, which might have also increased its concentration in the studied patients. Additionally, nitric oxide (NO) has been revealed to suppress the secretion of IL-1aspase-1 . Hence, Our previous results  confirmegression ,35. In tOne of the most significant causes of CVD in CKD is increased oxidative stress and the insufficient effectiveness of antioxidant systems. The redox imbalance negatively affects all components of the kidneys, i.e., the renal circulatory system, glomeruli, renal tubules, and interstitial tissue. Furthermore, HD, the method of renal replacement therapy, bothers the redox balance. An additional source of oxidative stress is the treatment—heparin use, which increases the activity of MPO. The issues of oxidative stress in the context of kidney disease and the effects on the cardiovascular system are discussed in detail in .In addition to the significance of oxidative stress, the role of nitrosative stress, characterized by the overproduction of NO, has also been emphasized . It is rReactive nitrogen species (RNS), similar to ROS, are characterized by a significant oxidizing potential and a short half-life, resulting from their ability to react with other molecules. One of the products of RNS action on proteins is 3-NT, which is most often formed by the indirect modification of the tyrosine residue. At the first stage, the attack of ONOOmolecule . Enzymesthe cell .In the present study, the accumulation of 3-NT has been found in both HD groups when compared to control, and a significant difference between HD-A and HD-D has been disclosed. However, different results were obtained by Massy et al. , who evaKidney damage has also been shown to be associated with nitrosative stress in animal models. In rats, after 5/6 kidney nephrectomies, the accumulation of 3-NT has been observed in the rest of the kidney tissue as well as in other organs . At the same time, a decrease in the concentration of iNOS and endothelial NOS (eNOS) in the kidneys, heart, and aorta has been observed, suggesting disturbances in the synthesis of NO, resulting in vasoconstriction . CardiovRecent research findings have suggested the existence of a potential link between the formation of NO-derived oxidants and the development of CAD ,47. It hr et al.  were theIn the current study, for the first time, we discovered that 3-NT, the representative molecule of nitrosative stress, could be a promising marker of CV mortality.Malnutrition is one of the most serious CKD complications. The global incidence of malnutrition in kidney disease is 28–54%,  and it iMalnutrition in CKD is associated with an increased incidence of CVD and the rapid progression of atherosclerotic organ damage. Moreover, it has been proven that along with the deterioration of kidney function, there is an enhanced secretion of pro-inflammatory cytokines, resulting in increased resting energy consumption . This leads to the intensification of catabolism and malnutrition .In general, the causes of malnutrition in HD patients are varied and include numerous factors that interact with each other, among which the following are highlighted: (1) reduced energy or protein intake; (2) coexistence of chronic diseases and overlapping acute diseases; (3) the HD procedure itself acting as a catabolic stimulus; (4) possible loss of water-soluble vitamins and nutrients into the dialysate during HD; 5) the significantly altered intestinal microbial flora; (6) diagnostic or therapeutic procedures that can reduce nutrient intake or cause net protein breakdown; (7) chronic blood loss; (8) hormonal derangements with particular emphasis on insulin resistance and an impaired balance between pro-inflammatory leptin and anti-inflammatory adiponectin; (9) metabolic products that accumulate in renal failure and may contribute to wasting; (10) loss of renal metabolic function; and (11) accumulation of toxic substances taken from the environment. All of these factors taken together may promote oxidative, nitrosative, and carbonyl stress, anemia, acidosis, and chronic inflammatory processes [ the signThe relationship between serum albumin concentration and increased mortality among HD patients was first discovered by Goldwasser ; numerouIn this study, both HD groups showed significantly lower serum albumin levels than the control group. Moreover, HD-D patients disclosed a significantly lower serum albumin concentration than the HD-A group. The median serum albumin concentration in HD-A was 3.85 g/dL, which, according to the results of various studies on HD patients, made the albumin level a significant biomarker of increased mortality in this group. It should be mentioned that no assessments have been performed to check the nutritional status of the patients studied; only BMI was calculated. BMI values revealed that in the HD-D group, more people were overweight than in HD-A. The observed hypoalbuminemia could be a result of the influence of many factors and inflammation in both groups, since albumin acts as a negative acute-phase reactant and impacts the nutrition status in some particular studied patients.Phosphate homeostasis in the human organism is directly influenced by calcitriol, PTH, fibroblast growth factor 23 (FGF-23), and Klotho protein. CKD is considered as a Klotho-deficiency state and a premature aging disease . The kidHyperphosphatemia, due to its contribution in hypertension, vascular calcification, calcification of heart valves, atherosclerosis, left ventricular hypertrophy, and myocardial fibrosis , has beeIn our current study, significantly higher phosphate levels have been observed in HD patients compared to the control group. Additionally, serum phosphates differentiated the HD-D and HD-A groups, which confirms previous reports on the importance of hyperphosphatemia. These results clearly show that special emphasis should be placed on reducing hyperphosphatemia as it is by far one of the most important modifiable mortality factors in CKD.In addition, apart from the previously presented markers that were selected for the predictive model, the most significant differences between the HD-A and HD-D groups were found for the NT-proBNP and AOPP serum concentration. In this study, both HD groups showed significantly higher levels of Nt-proBNP compared to healthy people, which is not surprising because people who survived AMI in the previous year were included in the study. It should be emphasized that the HD-D patients revealed the highest concentrations of Nt-proBNP, which proves their poor cardio-vascular system condition.On the other hand, AOPP, known biomarkers of the oxidative proteins modification , also tuInterestingly, the 2-year CV mortality predictive model that has been discovered in this study does not contain parameters directly indicative of renal dysfunction, such as urea, creatinine, or eGFR. Surprisingly, the urea concentration was even lower in HD-D vs. HD-A. Furthermore, changes in the lipid metabolism parameters did not explain the increased mortality in the HD-D group. In addition, the results obtained from non-invasive cardiac examinations proved to be insufficient predictive support. However, there were some differences between HD-A and HD-D, mainly in the central rather than the peripheral blood pressure . HD-D patients, when compared to HD-A, revealed a thicker atherosclerotic plaque , which were mostly more unstable . It should be emphasized that both HD groups had a significantly larger atherosclerostic plaque than the control group, and in both HD groups, hsCRP in serum was significantly higher than in the control group.Moreover, in the study by Wang et al., a positive correlation between hsCRP and the area of the coronary plaque was found, suggesting an important relationship between hsCRP and the local inflammatory process . It coulIn summary, inflammation, oxidative stress/nitrosative stress, malnutrition, and calcium-phosphate imbalance are closely related processes seen in HD patients. In addition to the hypotheses put forward in the mid-1980s, which linked the exposure of whole blood to cellulose membranes with increased IL-1 synthesis as the cause of HD disorders, it was not until the late 1990s that the first reports of uremia and its association with CVD, PEW, and worse survival outcomes appeared. What at the time was called the “new” risk factor has now evolved into an established, non-traditional risk factor. The results that have been obtained in this study may indicate an advantage of non-traditional risk factors over traditional risk factors in predicting CV-related mortality in HD patients. Not all parameters differentiating the HD group were finally included in the predictive model that we proposed; however, the addition of the rest, namely AOPP and NT-proBNP, has not changed the predictive power of the proposed model, which does not exclude their importance.It has been discovered that four interrelated processes, such as immuno-inflammatory processes, nitrosative and oxidative stress, malnutrition, and calcium-phosphate disorders, may be essential, especially if they are taken into account together, in predicting CV-related mortality in HD patients with a history of AMI in the previous year prior to enrollment in the study.Moreover, the results of our studies have shown that it is most likely impossible to use only one marker to predict cardiovascular risk in hemodialyzed patients, mainly due to the complexity of the mechanisms underlying the increased cardiovascular risk in this group. Understanding the interplay between kidney and heart is still quite a challenge, and routinely used markers, known as traditional markers, are most often not very efficient at predicting mortality in this remarkable group of patients.


The literature involving HRS is rapidly evolving and newer tests and updated definitions have been proposed which allows timely identification and treatment. Here, we will discuss the definition, pathophysiology, prevention, diagnosis, and treatment of HRS.The development of acute kidney injury (AKI) is one of the most frequent complications in patients with cirrhosis. AKI due to volume depletion is the most common etiology and hepatorenal syndrome (HRS) is the second most common cause of AKI in these patients. HRS is the extreme form of kidney injury in patients with cirrhosis, which is caused due to a reduction in renal blood flow unresponsive to volume expansion. Hepatorenal syndrome (HRS) is one of the most common and severe complications of cirrhosis, which occurs due to functional reduction in circulatory volume to kidneys which is unresponsive to volume expansion. Historically, two subtypes of HRS are described. HRS type 1 is acute in onset (less than two weeks) and heralds a poor prognosis. HRS type 2 is more chronic in nature and is associated with less severe kidney dysfunction characterized by refractory ascites resistant to diuretics. The literature and understanding of the pathophysiology regarding HRS is rapidly evolving and we now have a more detailed understanding of the disease. Over the years, the definition of HRS has evolved and now allows for early diagnosis and treatment with subsequent improvement in overall outcomes. Diagnosis of HRS requires exclusion of other causes of kidney injury, including intrinsic causes such as acute tubular necrosis and postrenal causes. Management includes treatment with vasoconstrictors and albumin. Liver transplantation is the only curative treatment of HRS.Definition of HRSHRS is one of the severe complications occurring in patients with advanced cirrhosis, with high mortality if not recognized early and treated promptly.  It occurAcute renal impairment occurs in about 27%-53% of patients admitted to the hospital with decompensated cirrhosis and ascites [However, serum creatinine is not a reliable predictor and often underestimates the degree of kidney damage in cirrhotic patients. Malnutrition and sarcopenia with reduced hepatic creatinine production, increased renal tubular secretion of creatinine, increased volume of distribution due to volume overload, and inaccurate measurement of serum creatinine by calorimetric method due to elevated serum bilirubin often leads to falsely lower serum creatinine level . In pracIn patients with cirrhosis, a rise in creatinine (by 0.3 mg/dl or 50% from baseline) is associated with significant morbidity and economic loss due to a longer hospital stay, admission to intensive care unit, multiple organ failure, and 90-day hospital mortality . The revPathophysiologyThe main hypothesis regarding the development of HRS in the last 20 years has been the “splanchnic arterial vasodilation theory”. Cirrhosis is characterized by reduced systemic vascular resistance due to splanchnic vasodilation . In the Recently, a new theory has been proposed regarding the development of HRS. The development of HRS involves circulatory dysfunction as well as systemic inflammation. Bacterial translocation in cirrhotics lead to activation of bacterial products such as lipopolysaccharides, flagellin and bacterial DNA. Similarly, damaged hepatocytes release intracellular components including heat shock protein, double-stranded genomic DNA, adenosine triphosphate, etc. Their release leads to the release of pro-inflammatory cytokines like interleukin-6 (IL-6), tumor necrosis factor-a (TNF-a), interleukin 1b (IL-1b), etc., which are responsible for the development of renal impairment in patients with cirrhosis, acute on chronic liver failure and acute liver failure. Similarly, hepato-adrenal syndrome leads to a decrease in arterial pressure but an increase in renin and nor-epinephrine, nephropathy secondary to accumulation of intratubular bile casts, intra-abdominal hypertension in patients with refractory ascites have been implicated in the development of HRS .Risk factors and preventionPredictors of HRS include hyponatremia, high plasma renin activity, liver size, and severity of ascites. The prevalence of unprecipitated HRS-AKI is only 1.8% ,17. LargAlbumin infusion (6-8 grams per liter of fluid removed) post large-volume paracentesis (defined as paracentesis > 5 liters) significantly reduces the risk of development of HRS-AKI and improves the overall survival . This prManagement of HRSWhen a patient with liver disease has renal dysfunction, it should be differentiated whether it is AKI, CKD or AKD. Once AKI has been diagnosed, the cause should be investigated and management started immediately. Diuretics should be held. Beta-blockers should be stopped . PrecipiIf no obvious cause is found, treatment should be started with 20%-25% albumin at 1g/kg/day  for two days. At the same time, intra-renal and post-renal etiologies should be ruled out with urine studies and renal ultrasonography. Acute tubular necrosis (ATN) should be distinguished from HRS-AKI. Determination of fractional excretion of sodium (FeNa) and urinary sodium is often unreliable in distinguishing ATN from HRS-AKI. However, Fractional excretion of urea (FeUrea) can be used to differentiate HRS-AKI from ATN in patients who take diuretics .Since the definition of HRS-AKI does not rule out the possibility of kidney parenchymal damage, several urinary biomarkers reflecting tubular damage have been studied to differentiate ATN from HRS-AKI. Neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), and liver type fatty acid-binding protein (L-FABP) are few urinary biomarkers studied for this purpose. Compared to the patients without ATN, their value was found to be significantly higher in patients with ATN. This could be useful in differentiating causes of AKI in patients with cirrhosis .The diagnosis of AKI-HRS should be considered if the above measures do not lead to improvement in renal function and specific therapy with vasoconstrictor and albumin should be initiated immediately. Early treatment of the HRS-AKI results in higher reversal rates and improved outcomes. Treatment of AKI-HRS includes vasoconstrictor in addition to albumin which should be initiated as soon as possible. Several randomized controlled trials have demonstrated that treatment with terlipressin plus albumin, led to significant improvement in renal function in patients with HRS type 1. The survival rate was better in patients who responded to treatment with terlipressin and albumin -32. ThesNow, let us discuss the individual agents used in the management of HRS.VasoconstrictorsThe use of vasoconstrictors leads to splanchnic vasoconstriction and reduces the portal pressure which in turn leads to an increase in effective arterial blood volume and renal blood flow. Renal perfusion increases with improvement in mean arterial pressure. This increase in mean arterial pressure is associated with a higher rate of HRS reversal .The vasoconstrictors used are terlipressin, norepinephrine, and the combination of octreotide and midodrine which are all used in combination with albumin infusion. Terlipressin is a synthetic vasopressin analog with predominant vasopressin 1A receptor effects, mild vasopressin 1B receptor effect, and also has modest vasopressin 2 receptor effects. Acting on vasopressin 1A agonist, it constricts the splanchnic vessels . ActivatTerlipressin can be administered as both intravenous boluses and as a continuous infusion. Intravenous boluses can be given at 0.5 mg-1 mg every four to six hours to 2 mg every four hours. Continuous infusion can be given at 2 mg/day to 12 mg/day. Continuous infusion of octreotide has fewer side effects such as diarrhea, ischemia, and circulatory overload . Thus, aNor-epinephrine, which is given via central line at a dose of 0.5-3 mg/h is a systemic vasoconstrictor. It works through the activation of α-1 adrenergic receptors located on vascular smooth muscle cells. Its efficacy is similar to terlipressin with rates of HRS-AKI reversal ranging between 39%-70%, as shown in small randomized controlled trials -40. It iMidodrine is an oral α-1 agonist and has a mechanism of action similar to nor-epinephrine. Midodrine, when used alone does not improve renal function in patients with HRS . OctreotThe role of vasoconstrictors for the management of type 2 HRS or HRS-NAKI remains unclear as most of the studies have been done in HRS-AKI or type 1 HRS. AlbuminInfusion of albumin is the standard of care in the treatment of HRS. It should be used in combination with vasoconstrictors: Terlipressin, nor-epinephrine and midodrine, and octreotide. Albumin serves multiple roles in the management of HRS. It is a plasma volume expander and has positive cardiac inotropic effects . The dosInformation about the treatment of HRS II or HRS-NAKI is very limited. Vasoconstrictive agents and albumin have been used in the treatment of HRS 2 and improvement in renal function has been observed as well. However, there is about a 50% recurrence rate after cessation of treatment. Trial of terlipressin may be helpful when selecting cirrhotic patients with renal failure as a bridge to transjugular intrahepatic portosystemic shunt or liver transplantation . Since tThe medical treatment of HRS is summarized in Table Transjugular intrahepatic portosystemic shunt (TIPS)The benefit of TIPS is mainly in patients with diuretics refractory ascites or patients who cannot tolerate diuretics and patients with uncontrolled variceal hemorrhage ,49. Its Renal replacement therapy (RRT)RRT can be used as a bridge to liver transplantation in patients with HRS-AKI who have volume overload, uremia, or metabolic derangements refractory to medical treatment. However, RRT alone does not improve survival . The morLiver transplantationLiver transplantation is the treatment of choice for patients with HRS-AKI. The functional nature of hepatic renal syndrome means that the kidney is expected to recover following successful liver transplantation. However, recovery of renal function is not universal and is dependent on multiple factors including age, co-morbid conditions like diabetes mellitus, unrecognized intrinsic kidney disease, and the most important being duration of kidney injury . Serum cCurrently, in the United States, a listing policy now exists for simultaneous liver-kidney transplant based on prior consensus recommendations and includes the duration of AKI, need for dialysis, evidence of metabolic disease , and evidence of CKD .Emerging therapySerelaxin is a recombinant human relaxin-2, which increases the renal blood flow by reducing renal vascular resistance and also reverses endothelial dysfunction . A randoHRS is a serious complication in patients with liver disease and has a poor prognosis. Improvements have been achieved in understanding the pathophysiology, diagnosis, and management of hepatorenal syndrome in recent years. In addition to the splanchnic and circulatory system changes, inflammation has been recognized as a major factor in the development of HRS. With more understanding, there is hope for earlier identification and introduction of new therapies to improve the outcome. However, the focus should be given to the prevention of HRS with timely treatment of infection and the use of albumin when needed. For patients diagnosed with HRS-AKI, terlipressin or nor-epinephrine plus albumin are the only effective treatment. Although, currently not approved for treatment in the United States, with promising data from different trials, it is hoped that Federal Drug Administration will approve terlipressin for clinical use in HRS. Liver transplant still remains the optimal treatment and the prospect of simultaneous liver-kidney transplantation is evolving.


We analyzed the site and signal intensity of immunohistochemical (IHC) staining of stimulator of interferon genes (STING) using kidney tissues at the time of diagnosis in patients with MCD. Patients with MCD were divided into high (n = 6) and low-intensity (n = 14) subgroups according to the signal intensity. Urinary mtDNA levels were elevated in the MCD groups more than in the MHC group (p < 0.001). Time-averaged proteinuria and frequency of relapses during the follow-up period were higher in the high-intensity than in the low-intensity subgroup . Mitochondrial injury may be associated with MCD pathogenesis, and the signal intensity of STING IHC staining at the time of diagnosis could be used as a valuable prognostic marker in MCD.We hypothesized that minimal change disease (MCD) pathogenesis may be associated with mitochondrial injury, and that the degree of mitochondrial injury at the time of diagnosis may serve as a valuable prognostic marker. We compared urinary mitochondrial DNA (mtDNA) at the time of diagnosis in patients with MCD and age- and sex-matched healthy controls (MHC) ( Minimal change disease (MCD) is the cause of nephrotic syndrome in approximately 10% to 15% of adults .T-cell dysregulation has been proposed as a major contributor to podocytopathy ,4,5, andThe kidney is an organ with high energy demand and has abundant mitochondria. Mitochondrial dysfunction is associated with the pathogenesis of several kidney diseases ,17,18,19We hypothesized that the MCD pathogenesis may be associated with mitochondrial injury, and that STING staining signal intensity in the kidney tissue of patients with MCD at diagnosis may be a valuable biomarker for predicting treatment outcomes. We examined whether urinary mtDNA was elevated in patients with MCD compared with age- and sex-matched patients with IgAN and healthy controls (MHC). We also compared the STING IHC staining and signal intensity between patients with MCD and age- and sex-matched IgAN patients, and assessed the correlation with STING IHC staining signal intensity and treatment outcomes in patients with MCD.n = 10 each). MHC were included if they had no history of specific diseases, drugs, or any abnormalities on health checkups at the time of enrollment. All participants provided written informed consent.This was a combined analysis of a prospective multicenter and retrospective single-center cohort study. To evaluate mitochondrial injury using urinary mtDNA copy numbers, we prospectively enrolled patients with MCD, age- and sex-matched healthy controls (MHC) and patients with IgAN as controls from the Cohort for Biomarker Inquiry of Renal Aggravation (COBRA) cohort, which collects clinical information, kidney tissue, serum, and 24 h-collected urine samples from patients with glomerulonephritis at Soonchunhyang University Seoul, Bucheon, and Cheonan Hospitals from May 2019 to December 2020 (n = 7), or difficulty in differentiating them from focal segmental glomerulosclerosis   were diagnosed with MCD. We excluded 12 patients who were followed for <3 years due to follow-up loss and 11 patients whose pathology showed combined diseases, including IgAN, diabetic nephropathy, and hypertensive nephrosclerosis  . FinallySince IgAN is the most common primary GN worldwide, and the only known GN in which mitochondrial injury is related to its pathogenesis and clinical outcomes in a previous study , it was We collected data on demographics and comorbidities, including a history of diabetes mellitus and hypertension. We obtained the body mass index and mean arterial pressure at the time of kidney biopsy. The type, duration, and total dose of administered medications, including immunosuppressive agents, angiotensin-converting enzyme (ACE) inhibitors, and angiotensin II receptor blockers (ARBs) during the follow-up period were reviewed. We obtained laboratory data at every follow-up visit. The eGFR was determined from serum creatinine values using the Chronic Kidney Disease Epidemiology Collaboration equation . ProteinUrinary copy numbers of the mtDNA genes nicotinamide adenine dinucleotide dehydrogenase subunit-1 (ND1) and cytochrome C oxidase subunit III (COX3) were measured by quantitative real-time polymerase chain reaction (qPCR) using 24-h urine collection samples in MHC from MHC and prospectively enrolled patients with MCD and IgAN at the time of kidney biopsy.DNA was isolated from urine samples (1.75 mL) using DNA isolation kits . DNA concentrations were analyzed using a NanoDrop spectrophotometer . We performed qPCR using ND1 primers (forward 5′-AGTCACCCTAGCCATCATTCTACT-3′ and reverse 5′-GGAGTAATCAGAGGTGTTCTTGTGT-3′) and COX3 primers (forward 5′-AGGCATCACCCCGCTAAATC-3′ and reverse 5ʹ-GGTGAGCTCAGGTGATTGATACTC-3ʹ) , and 20 ng of template DNA/sample. The PCR conditions were as follows: 95 °C for 10 min, 40 cycles of 95 °C for 15 s, and 60 °C for 60 s. We corrected mtDNA copy numbers to the nuclear control gene, RNAse-P  using human genomic DNA for the standard curve and calculated mtDNA copy numbers using Copy Caller software version 2.0  and expressed it as an mtDNA/nDNA ratio ,25.We analyzed the STING IHC stain and signal intensity in the kidney tissue of retrospectively enrolled MCD and matched IgAN patients. Kidney tissue was fixed with Mildform 10N , and sections cut at a thickness of 3 mm were incubated with the appropriate primary antibody and processed with the Vectastain Elite ABC HRP kit . The signal was detected by incubation with an ImmPACT diaminobenzidine peroxidase substrate (Vector Laboratories). The IHC stain site was evaluated for the entire kidney tissue, including the glomerulus and tubulointerstitium, and signal intensity was categorized from negative to 3+ by a single pathologist. Retrospectively collected patients with MCD were divided into high and low-intensity subgroups according to a signal intensity of STING IHC staining in glomeruli higher or lower than 2+, respectively.In a retrospective study to assess the correlation between STING IHC staining signal intensity and treatment outcomes in patients with MCD, we analyzed within-subgroup differences in the mean annual rate of eGFR decline, rate of complete remission (CR) or partial remission (PR), time to remission, rate of relapse, and time to first relapse. CR was defined as proteinuria < 0.3 g per 24 h and PR defined as proteinuria > 0.3 g but <3.5 g per 24 h or a decrease in proteinuria by at least 50% from the baseline value at the time of kidney biopsy and <3.5 g per 24 h. A relapse was defined as proteinuria > 3.5 g per 24 h after CR or PR, according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines .2 tests or Fisher’s exact tests for categorical variables, as appropriate. One-way ANOVA or the Kruskal–Wallis test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables, as appropriate analysis methods, were used to compare the differences between the three groups. After one-way ANOVA, Bonferroni correction was used for post-hoc analysis. Urinary mtDNA copy numbers were log-transformed, and differences between groups were compared using the Mann-Whitney test. All statistical tests were two-sided, and p-values of less than 0.05 were considered statistically significant. All analyses were performed using SPSS version 25.0 for Windows  or Graphpad Prism5 .Descriptive characteristics of the study population are reported as means ± standard deviations for continuous variables and as frequency counts with percentages for categorical and binary variables. The differences between the two groups were compared via Mann–Whitney and Wilcoxon signed-rank tests for continuous variables and either χWhen we prospectively evaluated mitochondrial injury using urinary mtDNA copy numbers, we observed that proteinuria levels were higher in the MCD group than in the IgAN group. Both the MCD and IgAN groups showed higher proteinuria levels than the MHC group. More patients in the MCD and IgAN group were hypertensive and treated with ARBs or ACE inhibitors than in the MHC group. There were no significant differences in other baseline characteristics between the MCD, IgAN, and MHC groups .When we retrospectively studied the characteristics of STING IHC staining, we observed that baseline eGFR and proteinuria were higher in the MCD than in the IgAN group, but other baseline characteristics did not differ between the groups .Among the enrolled patients, all patients with MCD and IgAN who were treated with corticosteroids were treated after the final diagnosis via kidney biopsy.10ND1/nDNA and log10COX3/nDNA were significantly higher in the MCD and IgAN groups than in the MHC group. Log10ND1/nDNA and log10COX3/nDNA tended to be higher in the IgAN group than in the MCD group, but there was no significant difference between the groups  and low-intensity (n = 14) subgroups according to a signal intensity of STING IHC staining in glomeruli higher or lower than 2+, respectively. There was no significant difference in baseline characteristics, including eGFR and proteinuria, between the subgroups  and 290 (39.5%) patients were diagnosed with MCD and IgAN, respectively. At the time of study design, we planned to analyze only patients with pure MCD who had sufficient follow-up. Pathologists other than the ones who initially diagnosed the patients reviewed the pathological findings. From among the patients initially diagnosed primarily with MCD, we excluded those whose pathology showed combined diseases or those in whom MCD could not be clearly distinguished from focal segmental glomerulosclerosis. In addition, patients who were followed up for <3 years due to follow-up loss were excluded. As mentioned above, since IgAN is the most common primary GN worldwide, and the only known GN in which mitochondrial injury is related to its pathogenesis and clinical outcomes in a previous study , it was The current study had several limitations. First, one of the major limitations was that we did not use kidney tissue and 24-h collected urine samples from the same patient. The COBRA cohort, a prospective GN cohort, was launched less than two years ago, so it was too short to confirm the long-term clinical outcomes of enrolled patients in this cohort. Therefore, we used prospectively collected urine samples from the COBRA cohort to measure urinary mtDNA levels only. To compare the long-term clinical outcomes according to the STING IHC staining intensity, IHC staining was performed using kidney tissues at the time of diagnosis from retrospectively selected patients with sufficient long-term clinical data for at least three years among patients diagnosed with MCD during the planned study period. Second, as mentioned above, the small sample size and the short observation period pose limitations in generalizing the results of this study to all patients with MCD and may reduce the reliability of the results. Third, there may have been a lead-time bias because the registered patients were diagnosed at different times during the disease. If the degree of mitochondrial injury was milder as the diagnosis was made early in the disease, the STING IHC staining intensity could have been weaker. Early immunosuppressant administration in these patients may affect the clinical outcomes. Considering that it is difficult to repeatedly perform a kidney biopsy, which is an invasive diagnostic method, additional experimental studies using animal models are necessary to confirm the change in mitochondrial injury degree during the natural course of MCD. Fourth, it is an important limitation that systemic mtDNA levels are not measured, as was the limitation of other GN studies using urinary mtDNA ,27. The In conclusion, the current study results suggest that mitochondrial injury may be associated with the MCD pathogenesis, and evaluation of the signal intensity of STING IHC staining during diagnosis could be valuable in determining the prognosis of MCD. Further studies with larger cohorts and long-term follow-up durations are needed to clarify this.


Evaluating left ventricular function through instantaneous left ventricular deformation parameters might not always be accurate for patients with high fluctuations in blood pressure value due to afterload dependence. Myocardial work (MW) is a more advanced tool that combines global myocardial longitudinal strain (GLS) with LV (left ventricular) systolic pressure. The purpose of this study was to investigate the effect of blood pressure changes on MW indices in the population with normal blood pressure and hypertension in a day. A total of 117 participants (34 control subjects and 83 hypertensive patients) underwent echocardiographic measurements at rest, twice a day. Simultaneously, the brachial blood pressure was also measured. LV pressure‐strain loop (PSL) was used to calculate global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE). The differences in the GLS and MW indices between the groups were compared, and the correlation of blood pressure changes with the changes in GLS and MW indices were evaluated. Compared to the control group, the hypertensive group showed higher GWI, GCW, and GWW but lower GLS and GWE. Absolute changes in blood pressure, GLS, and MW indices in hypertensive patients were significantly higher than that of the control subjects. Blood pressure changes had significant univariate correlation with changes in GLS and MW indices. In conclusion, significant fluctuations in blood pressure could induce changes in MW indices to preserve left ventricular systolic function. Repeated assessment of MW indices is necessary for hypertensive patients with large blood pressure fluctuations. The exclusion criteria included: (a) suboptimal image quality; (b) left ventricular ejection fraction (LVEF) < 50%; (c) atrial fibrillation or other severe arrhythmias; (d) moderate or severe valvular stenosis; (e) intracardiac shunt. Four patients having systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 110 mmHg with no associated symptoms were treated immediately with nifedipine, while the others did not take any drugs before and during the test. After the test, patients were treated with antihypertensive drugs according to their blood pressure values. Patients with systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 110 mmHg displaying associated symptoms were not included in the study. Based on the selection criteria, we enrolled healthy controls without structural heart disease or cardiovascular risk factors and hypertensive patients. The study was approved by the Ethics Committee of Scientific Research of Shandong University Qilu Hospital (KYLL‐202011–100) and conducted as per the Declaration of Helsinki. All patients and control subjects were informed about the study. According to the 2020 International Society of Hypertension (ISH) and Global Hypertension Practice Guidelines, the hypertensive patients were subdivided into Grade 1 hypertension (140–159/90–99 mmHg) and Grade 2 hypertension (≥160/100 mmHg).2.2Transthoracic echocardiographic imaging was obtained using a Vivid E95 machine equipped with an M5S 3.5 MHz transducer . Echocardiography of patients was performed by the same experienced sonographers twice a day between 9 am and 5 pm with an interval of over 4 hours. The patients were at rest with left side decubitus. The standard imaging windows and measurements were obtained according to the current guidelines of the American Society of Echocardiography and the European Association of Cardiovascular Imaging.2 in men and LVMI > 95 g/m2 in women. LV geometry was categorized as follows: (a) Normal geometry: no LVH and RWT < 0.43; (b) Concentric remodeling: no LVH and RWT ≥0.43; (c) Eccentric hypertrophy: LVH and RWT < 0.43; (d) Concentric hypertrophy: LVH and RWT ≥0.43.The cardiac structural and functional measures included left atrium longitudinal dimension (LA‐l), right atrium longitudinal dimension (RA‐l), interventricular septal end‐diastolic dimension (IVSd), right ventricular end‐diastolic dimension (RVDd), left ventricular diameter diastole (LVDd), left ventricular end‐diastolic posterior wall thickness (PWTd), relative wall thickness (RWT), left atrium volume index (LAVI), left ventricular mass index (LVMI), late mitral inflow velocity (A), early mitral inflow velocity (E), peak early diastolic mitral annular velocity (e’), and left ventricular ejection fraction (LVEF). The LVEF was calculated using the biplane Simpson's method while the Left ventricular mass (LVM) was derived from the Devereux formula, and the left atrial volume (LAV) was measured at the LV end‐systole. LVMI and LAVI were obtained by correcting the LVM and LAV through body surface area (BSA). According to the 2018 ESC/ESH Guidelines for the management of arterial hypertension, LV hypertrophy (LVH) was defined as LVMI > 115 g/mSimultaneous with echocardiography, blood pressure values were measured on the right brachial artery using an automated blood pressure monitor . Smoking and drinking coffee, tea, or alcohol and exercise were prohibited for 30 minutes before measuring BP. Patients did not fast for lunch but were asked to eat a light and not a full meal. Before the procedure, patients were asked to empty their bladder and relax for 3–5 minutes. During the BP measurements, the patients remained quiet and were in the supine position. The BP measurements were taken thrice with 1–2 minute intervals, and the average value was used for the analyses.2.3To evaluate the GLS using STE, the standard imaging windows were acquired from the three apical views  at frame rates between 50 and 80 frames/s. The myocardial motion in the region of interest was automatically tracked using the Automated Function Imaging software (EchoPAC Version 203). If necessary, the region of interest was adjusted by correcting the edge or width of the endocardium. According to the standardized 17‐segment heart model,2.4MW was quantified by the noninvasive PSL, which integrated the brachial blood pressure into the LV strain parameters using the EchoPAC software. The area inside the PSL served as an indicator of the myocardial work index. In the process of LV ejection, the constructive and waste work of the myocardial segments were analyzed, and the global values were calculated as an average of all segment values.The closing and opening times of the aortic and mitral valves were measured through the apical long‐axis view using 2D‐echocardiography. Based on the LV strain, brachial blood pressure, and the valvular event times, the following LV myocardial work parameters were calculated by the software:Global myocardial work index : the myocardial work, expressed as the area of LV‐PSL, was calculated starting from the closure of the mitral valve till the opening of the mitral valve.Global constructive myocardial work : the “positive” work performed by the LV segments contributing to the LV ejection (shortening during systole and lengthening during isovolumic relaxation).Global wasted myocardial work : the “negative” work performed by the LV segments not contributing to the LV ejection (lengthening during systole and shortening during isovolumic relaxation).Global myocardial work efficiency : the percentage of myocardial work, calculated as the ratio of GCW to the sum of GCW and GWW.2.5Two experienced sonographers re‐measured 30 randomly selected participants to assess the repeatability. In the process, sonographers were blinded to the clinical data as well as to each other's results. A month later, the images were analyzed again by the same sonographer to assess the intra‐observer variability. The same images were also analyzed by both sonographers to assess the inter‐observer variability.2.6t‐test was used to compare the data between two groups, while one‐way analysis of variance (ANOVA) with Tukey's honestly significant difference (HSD) post‐hoc tests  or Games‐Howell HSD post‐hoc tests  were performed to compare multigroup data. Analysis of covariance and multivariable linear regression was used to adjust the influence of confounding variables. Correlations between the parameters were assessed by Pearson's correlation test. All tests were two‐tailed with 95% confidence intervals (CI), and P‐values < .05 were considered statistically significant. Intra‐class correlation coefficients (ICC) and Bland‐Altman plots were used to assess the inter‐and intra‐observer variability.All data were collected, statistically analyzed, and tabulated using the SPSS 25 software  and Med Calc 19.04 . Visual checks and the Shapiro‐Wilk test were applied to test normality. Continuous variables were presented as mean ± SD or mean± SE. Categorical data variables were shown as number (n) and percentage (%). The Chi‐square test was used to compare the categorical data. For continuous data, the Student's 33.1P < .05). No significant inter‐group differences were observed in the heart rate (HR), sex, and history of diabetes. In terms of cardiac structure, hypertensive patients were more likely to have larger dimensions of the atrium, ventricle, and interventricular septal along with higher LAVI and bigger LVMI . Regarding the left ventricular function, hypertensive groups displayed significantly higher E/e’ but showed lower E/A and LVEF . Compared to the controls, hypertensive patients showed a higher percentage of LVH, eccentric hypertrophy, and concentric hypertrophy. The clinical characteristics of the study participants are shown in Table The study cohort included 34 control subjects with no history of hypertension and 83 hypertensive patients, of whom 45 (54.22%) had Grade 1 hypertension and 38 (45.78%) had Grade 2 hypertension. Patients with hypertension tended to have higher age, body mass index, and body surface area . Among hypertensive patients, GWI, GCW, and GWW were higher in Grade 2 group than that in the Grade 1 group. However, GLS and GWE did not reach significant differences within the hypertensive groups. With the adjustments applied to age, BSA, and BMI at the baseline, no obvious difference was observed in GLS between hypertensive patients and the controls. All information is presented in Tables The GWI, GCW, and GWW were found to be higher in the hypertensive groups, while GLS and GWE were found to be lower compared to the control group . Among controls, only GCW was greater in a higher SBP state compared to the baseline SBP , GLS, and MW indices in hypertensive groups were significantly higher than those of the control group. However, no significant differences were observed in the changes of GLS and MW indices within the hypertensive groups Table . When ad3.3Figure 3.4P < .05)  Table . In the 3.5Excellent intra‐observer and inter‐observer variabilities were observed while measuring the MW parameters Table . For the4Our study described the response of MW indices and GLS to the altered blood pressure in hypertensive patients and healthy control subjects. The main findings of this study were as follows: Firstly, in hypertensive patients, a decrease was observed in the GLS while an increase was seen in the GWI with rising blood pressure with no significant changes observed in the controls. Secondly, compared to the controls with smaller LV afterload changes, the absolute changes in GLS and MW indices in hypertensive patients with higher LV afterload changes were higher. Thirdly, a significant correlation was found between the changes in the LV afterload associated variables  and changes in the GLS and MW indices.Evaluating cardiac systolic function has always been a crucial task in the clinical practice of cardiology. GLS reflects the subendocardial function, which is susceptible to wall stress, ischemia, and fibrosis, through the speckle‐tracking algorithm.The left ventricular MW indices have been used for the assessment of patients with hypertension. Chan and associates found that the patients with Grade 2 hypertension showed significantly higher GWI and GCW, while in patients with Grade 1 hypertension, these parameters only tended to increase compared to the controls.Despite MW having great potential for clinical applications, only a few studies have explored the effect of blood pressure fluctuations on myocardial work within one day. We observed an interesting phenomenon in the study, wherein for hypertensive patients, a rise in blood pressure led to a decrease in the GLS and an increase in GWI within one day. A possible explanation for this contradictory phenomenon might be a compensatory mechanism. Firstly, a significant increase in LV afterload might have led to increased wall stress and reduced deformation. Low curvature and high fiber stress make the longitudinal deformation very sensitive to the changes in blood pressure.Additionally, we found that GCW became significantly greater with an increase in SBP. In normal subjects, an augmentation of GCW was not observed along with changes in GWW and GWE, which indicated that the fluctuations of afterload in the physiological range led to an absolute increase in GCW but no changes in GWW and GWE. However, in people with hypertension, substantial augmentations of blood pressure not only increased the effective work but also caused an increment in the GWW. The increase in GWW may be related to the uncoordinated contractions caused by increasing wall stress. Since the increasing GCW could only partially offset GWW, a significant decline was observed in the GWE within the normal range.Compared to the controls, the changes in MW indices were higher in hypertensive patients. The possible reason for this may be significantly higher fluctuation of blood pressure in hypertensive patients than those of the controls. Along this line, we found that the changes in SBP, DBP, and MBP showed significant but weak correlations with the changes in MW indices. Elevated blood pressure can be balanced by the myocardial self‐regulation mechanism , but it may also increase the stiffness and the oxygen consumption of the myocardium. In the long run, chronically raised blood pressure may eventually promote myocardial fibrosis and LV remodeling, further causing LV failure. In hypertensive patients, instantly assessing the left ventricular function parameters may not be a sound clinical evaluation, and repeated observations of GLS and MW indices might be necessary.4.1This study had several limitations. Firstly, it was single‐center research with a relatively small sample size. Secondly, although the pre‐existing left ventricular pathological changes such as hypertrophy, abnormal contraction, and dilatation may disturb the effect of changes in afterload, we could not perform a more detailed subgroup analysis since it was difficult with the limited sample size. In the subsequent work, we plan to confirm our results in a multicenter study with large sample size.5In healthy people without structural heart disease or cardiovascular risk factors, MW is relatively insusceptible to oscillations within the physiological range of blood pressure. However, in a population of hypertensive patients, a significant increment in blood pressure may lead to an increase in GWI and GCW to preserve left ventricular systolic function. Additionally, the increased GWW cannot be balanced by GCW, which results in a significant reduction of GWE. Repeated assessments of GLS and MW indices are necessary for hypertensive patients who display high fluctuations in blood pressure.Xinhao Li, Mei Zhang, Mengmeng Li, and Yu Zhang: conception, design, and analysis and interpretation of data. Xinhao Li, Quande Liu, Wuyun Bao, and Xiaoyu Wan: drafting of the manuscript or revising it critically for important intellectual content. Xinhao Li and Mei Zhang: final approval of the manuscript submitted.