IL‐1B, CD‐38, CSF1R, PPARG, NR1H3, DDO, HDC, DPYS and CAD) were identified. Furthermore, by comparing the RNA‐Seq results with the GSE30566 dataset, we found that the biomarker KCND3 was the only up‐regulated gene in DKD patients. These biomarkers may have potential application for the therapy and diagnosis of DKD, as well aid in determining the mechanisms underlying DKD.Diabetic kidney disease (DKD) is the leading cause of end‐stage renal disease. However, because of shared complications between DKD and chronic kidney disease (CKD), the description and characterization of DKD remain ambiguous in the clinic, hindering the diagnosis and treatment of early‐stage DKD patients. Although estimated glomerular filtration rate and albuminuria are well‐established biomarkers of DKD, early‐stage DKD is rarely accompanied by a high estimated glomerular filtration rate, and thus there is a need for new sensitive biomarkers. Transcriptome profiling of kidney tissue has been reported previously, although RNA sequencing (RNA‐Seq) analysis of the venous blood platelets in DKD patients has not yet been described. In the present study, we performed RNA‐Seq analysis of venous blood platelets from three patients with CKD, five patients with DKD and 10 healthy controls, and compared the results with a CKD‐related microarray dataset. In total, 2097 genes with differential transcript levels were identified in platelets of DKD patients and healthy controls, and 462 genes with differential transcript levels were identified in platelets of DKD patients and CKD patients. Through Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, we selected 11 pathways, from which nine potential biomarkers ( Based on platelet RNA sequencing of venous blood platelets, we identified 2097 genes with differential transcript levels in platelets of patients with diabetic kidney disease compared to patients with chronic kidney disease and 462 genes with differential transcript levels compared to healthy controls, and found nine potential biomarkers, with KCND3 identified as the most promising biomarker for clinical diagnosis of diabetic kidney disease. AUCarea under the curveBPbiological processesCCcellular componentsCKDchronic kidney diseaseDEGdifferentially expressed geneDKDdiabetic kidney diseaseDMdiabetes mellituseGFRestimated glomerular filtration rateESRDend‐stage renal diseaseGBMglomerular basement membraneGDTLsgenes with differential transcript levelsGOGene OntologyGPIglycosylphosphatidylinositolHChealthy controlsJAK-STATJanus kinase-signal transducer and activator of transcriptionKEGGKyoto Encyclopedia of Genes and GenomesLXR αliver X receptor αMFmolecular functionmTORmammalian target of rapamycinNDKDnon-diabetic kidney diseasePPARperoxisome proliferator-activated receptorPPIprotein–protein interactionRNA-SeqRNA sequencingROCreceiver operating characteristicTNFR1/2tumor necrosis factor receptors ½Chronic kidney disease (CKD) is highly prevalent, with 850 million individuals suffering from the disease and one in 10 adults possibly being at risk worldwide . The maiDiabetic kidney disease occurs in patients with diabetes mellitus (DM). Different from primary CKD or non‐diabetic kidney disease in patients with DM, the clinical diagnosis of DKD is based on the presence of proteinuria and impaired kidney function in the setting of diabetes, with a distinct histopathological pattern of glomerular basement membrane (GBM) thickening, mesangial matrix expansion, nodular glomerulosclerosis and arteriolar hyalinosis , 8, 9. Cet al.[Advances in techniques make it possible for researchers to identify new biomarkers in DKD. Accordingly, a transcriptome analysis of kidney samples of DKD patients was performed by Woroniecka et al. aiming tet al., 12, 13.et al., 15. Comet al., 17. RNAet al..However, there are few studies employing RNA‐Seq of platelets in the blood of patients with CKD and DKD. A study has shown that there is overactivation of platelets in patients with diabetes, and the subsequent released platelet products can cause damage to the blood vessel wall and microcirculatory bed (including the kidney) . FurtherIL‐1B, CD‐38, CSF1R, PPARG, NR1H3, DDO, HDC, DPYS and CAD. Furthermore, we compared the RNA‐Seq data with the microarray data set related to CKD. KCND3 is the only up‐regulated genes in DKD patients. In sum, we have identified many potential molecular targets and signal pathways, providing a potential and comprehensive theoretical basis for the treatment and diagnosis of DKD patients.In the present study, RNA‐Seq was performed to characterize the transcriptome profiles in the platelets of healthy individuals and patients with CKD and DKD. In total, 2097 genes with differential transcript levels (GDTLs) were identified in HCs compared to DKD patients, and 462 GDTLs were identified in CKD patients compared to DKD patients. To evaluate the changes in gene splicing and signaling pathways, we carried out Gene Ontology (GO) terminology to predict the potential function of these DEGs, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to evaluate the potential regulatory pathways of these DEGs. We identified 11 pathways and nine potential biomarkers: The overall research route is oulined in Fig. g for 10 min at 4 °C and then centrifuged again to further remove hemocytes. The supernatant was transferred to a 1.5‐mL Eppendorf tube and centrifuged at 2880 g for 20 min to obtain white precipitate platelets. Phosphate‐buffered saline solution was used to wash the platelets, which were subsequently re‐suspended in RNAlater  and then frozen at −20 °C.Blood samples drawn from peripheral veins were stored in EDTA‐containing tubes. Platelet‐rich plasma was first obtained via centrifugation at 114 Total platelet RNA was extracted using the RNeasy Micro Kit  in accordance with the manufacturer’s instructions. Total RNA was qualified and quantified using model 2100 Bioanalyzer  and a Qubit RNA BR assay kit , respectively. RNA with RIN ≥ 7 was considered high‐quality.In accordance with the manufacturer’s instructions, poly‐(A)‐containing mRNA was purified using oligo (dT) magnetic beads (Thermo Fisher Scientific), as well as Oligotex mRNA Kits (Qiagen). Then, the purified products were fragmented at 94 °C for 15 min. The first‐strand cDNA was synthesized using the fragmented and primed mRNA, as well as ProtoScript reverse transcriptase  and the program was 25 °C for 10 min, 42 °C for 50 min and 70 °C for 15 min. The second‐strand cDNA was synthesized using the first‐strand cDNA and NEBNext second‐strand synthesis enzyme mix, and the program was 16 °C for 1 h. Then, the end repair/dA‐tail was performed by using NEBNext End Prep Enzyme Mix (New England Biolabs) and the program was 20 °C for 30 min, followed by 65 °C for 30 min. Adaptor ligation was also performed using dA‐Tailed cDNA, blunt/TA Ligase master mix and diluted NEBNext adaptor, and the program was 20 °C for 15 min, followed by 37 °C for 15 min. After end repair process and ligation of adapters, the cDNA fragments were amplified by using PCR Master Mix (Thermo Fisher Scientific). The PCR products were quantified using a Qubit DNA HS Assay Kit . The fragment size was detected using a model 2100 bioanalyzer chip (Agilent) and the concentration was analyzed using the KAPA kit . The mixed library was stored at −80 °C.r, version 3.3.0, and r‐studio, version 0.99.902 . Analysis of differential expression was carried out using the edgeR package, version 1.10.1 (https://bioconductor.org), for colorectal cancer and control groups. The read counts were corrected and were changed to counts‐per‐million, translated in the log2 format and multiplied by the TMM‐normalization factor determined by the calcNormFactors‐function of edgeR [P < 0.05).The FASTQ‐files of raw data were aligned in RNA‐Seq, as reported previously . Brieflyof edgeR . The star package clusterProfiler.GO is a structured, controlled vocabulary for systematic analysis of gene function at the molecular and cellular levels . KEGG isGSE30566 were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) and normalized with GeneSpring software (Agilent) as log2 values. The normalized data were compared with our platelet RNA‐Seq data to select co‐DEGs using VENNY, version 2.1 (https://bioinfogp.cnb.csic.es/tools/venny). GO and KEGG enrichment analyses were performed again to functionally cluster the co‐GDTLs.Raw data from the microarray dataset https://version11.string‐db.org) [To identify and understand functional protein interactions in human cells, as well as to systematically investigate the molecular mechanism of disease and identify new drug targets, we used STRING database (‐db.org)  to searct‐test was performed using spss, version 21.0 . Receiver operating characteristic (ROC) curve drawing, the Pearson related test and other data visualizations were all carried out on the r studio platform. Data are presented as the mean ± SD of triplicate samples.Student’s http://www.bioinformatics.babraham.ac.uk/projects/fastqc) evaluating the quality of sequencing reads, the results showed that the percentage of bases with Phred values were > 20 and 30 in the total bases, the values of Q20 (%) and Q30 (%) were more than 90%, the percentage of unidentifiable bases was less than or equal to 0.02%, and the sum of bases G and C accounts for about 50% of the total bases, indicating that the data output was well qualified. The result of reference sequence alignment showed that the total mapping percentage was more than 80%, whereas the data percentage of multiple mapping was < 10%, indicating that the data of this study was better than the reference genome and met the requirements of the next analyses.The platelet transcripts of 10 healthy individuals, three CKD patients and five DKD patients were quantitatively analyzed by the RNA‐Seq technique. On average, each of the 18 libraries produced 38.62 million reads. With FastQC , cellular components (CC) and molecular function (MF) terms. As shown in Fig. To explore the pathways involved in the development of CKD and DKD, the GDTLs were submitted to KEGG analysis. In the DKD and HC groups, we selected six meaningful pathways, which were Hematopoietic cell lineage, Mammalian target of rapamycin (mTOR) signaling pathway, Type I DM, Ubiquitin mediated proteolysis, Regulation of autophagy and Glycosylphosphatidylinositol (GPI)‐anchor biosynthesis. In the DKD and HC groups, we selected five meaningful pathways, which were Alanine, Aspartate and glutamate metabolism, Pyrimidine metabolism, peroxisome proliferator‐activated receptor (PPAR) signaling pathway, Janus kinase‐signal transducer and activator of transcription (JAK‐STAT) signaling pathway and Histidine metabolism. The first 15 pathways in both groups are listed in Fig. GSE30566 dataset, a microarray analysis dataset for identifying gene expression profiles in human CKD, which included 53 renal biopsy samples from CKD patients and eight normal renal biopsy samples from healthy individuals. Among this set, 909 down‐regulated and 80 up‐regulated signatures were identified. We carried out co‐expression analysis with this dataset compared to our data from the platelet RNA‐Seq. Finally, by drawing the Venn diagram for analysis, we visualized the co‐expression gene KCND3   Fig. . Some resyndrome , 30.KCND3 changes in all datasets is shown in Fig. GSE30566), 17 GDTLs (overlapping HC and DKD group and GSE30566) and 71 GDTLs (overlapping the two groups), as shown in Fig. A bar chart of IL‐1B, CD‐38, CSF1R, PPARG, NR1H3, DDO, HDC, DPYS and CAD. The results showed that, except for KCND3, the area under the curve (AUC) values of the other eight key biomarkers were all > 0.8, which means that the biomarkers we selected have good applicability.Next, ROC analysis Fig.  was usedr > 0.6 and r ≤ −0.6 represent a significant positive correlation and negative correlation, respectively. We found that, in the HC and DKD groups, there was a positive correlation between CD‐38 and NR1H3 (r = 0.67) and a negative correlation between CSF1R and KCND3 (r = −0.61). In addition, in the CKD and DKD groups, we found that DPYS was positively correlated with HDC (r = 0.72) and ODD (r = 0.79). Moreover, protein–protein analysis (PPI) analysis is an effective way of identifying new drug or therapeutic targets for various diseases [In addition, we carried out Pearson rank correlation analysis Fig.  among thdiseases , 32. TheIL‐1B and CD‐38 to be down‐regulated in CKD patients and HCs compared to DKD patients, respectively, which makes them the potential biomarkers with respect to DKD progression. This result suggests that our RNA‐Seq analyses are reliable.Although eGFR and albuminuria have been the main biomarkers for DKD diagnosis in the clinic, DKD patients in the very early stage are always ignored. One reason is that changes of eGFR in these patients are not so remarkable . AlthougIL‐1 family play an important role in CKD [IL‐1R2, IL‐1R1 and IL‐1B in platelets in the DKD group was significantly higher than that in the HC group. IL‐1B is a major inflammatory cytokine produced by a variety of cells, including renal parenchyma cells and infiltrating cells, and is the mediator of acute and chronic inflammation [IL‐1B expression is accompanied by the inhibition of kidney inflammation [By comparing the data of platelet RNA‐Seq between DKD and HCs, we found some interesting pathways, such as Hematopoietic cell lineage, mTOR signaling pathway, Type I DM, Ubiquitin mediated proteolysis, Regulation of autophagy and GPI‐anchor biosynthesis. These pathways may provide a reference for the treatment of DKD. Diabetic glomerulosclerosis is still the cause of CKD in most patients with type 1 and type 2 diabetes . Studiese in CKD . Our stuammation . In the ammation , 48.IL‐2RA, IL‐20RA, IL‐15RA and IL‐5RA was significantly increased, whereas the expression of ILF was significantly decreased in DKD group compared to the CKD group . LXR α comprises a sterol‐regulated transcription factor that plays an important role in atherosclerosis by integrating cholesterol homeostasis and immunity [PPARG gene in the platelet of DKD group was significantly increased, whereas the expression of NR1H3 was significantly decreased, with a |log2 fold change| > 4.78 for both. This shows that is reasonable to propose PPARG and NR1H3 as important targets for distinguishing between DKD and CKD.In the platelet RNA‐Seq data of DKD vs. CKD, we found that, in the JAK‐STAT signaling pathway, the expression of oup Fig. . The proimmunity . Studiesimmunity . In the KCND3 is the only up‐regulated gene in DKD patients compared to CKD, HCs or CKD‐related dataset GSE30566, which makes it a key target for the diagnosis and treatment of DKD patients. Although the AUC value of KCND3 is lower than 0.8, Pearson rank correlation analysis showed the significance of KCND3 to be an important biomarker (with r = −0.61). In the ventricle, the KCND3 gene encodes voltage‐gated rapid inactivation of the main pore‐forming α subunit of the type A potassium channel, and participates in a rapid cardiac transient outward potassium current (Ito), which plays a major role in the early repolarization phase 1 of cardiac action potential [KCND3 focus on cerebellar ataxia, Brugada syndrome and long QT syndrome [KCND3 in the kidney. Our study shows that KCND3 is the key biomarker for diagnosis of DKD patients, which needs to be confirmed in further research.As shown in Fig. otential . Most ofsyndrome . HoweverThe present study depends on the RNA‐Seq of venous blood platelets in DKD or CKD patients. Despite the rapid development of sequencing techniques, the blood samples of DKD patients have been in the spotlight for many years. The search for new biomarkers of DKD has centered primarily on identifying analyses in urine and blood that improve the prediction of later established end points, and then the differentially expressed microRNAs and epigenetic modifications in kidney tissues , 54. We IL‐1B, CD‐38, CSF1R, PPARG, NR1H3, DDO, HDC, DPYS and CAD. In addition, we have also annotated and analyzed the differential transcript profiles in RNA‐Seq and microarray datasets and found biomarker KCND3 to be the only up‐regulated gene in DKD patients. In‐depth studies are necessary to reveal the function and mechanism of these potential genes in DKD diagnosis and therapies.In summary, we have comprehensively identified 2097 GDTLs in the venous blood platelets of HCs vs. DKD and 462 GDTLs in platelets of CKD vs. DKD for the first time, and identified nine potential biomarkers for DKD patients: The datasets generated during and/or analyzed during the present study are available from the corresponding author upon reasonable request.XLZ and HMJ conceived and designed the research. All authors conducted the experiments and analyzed data. BLZ and XHY wrote the manuscript and all authors commented on previous versions of the manuscript. All authors have read and approved the final version of the manuscript submitted for publication.The authors declare that they have no conflicts of interest.


We explored the use of the augmentation index (AI) based on pulse wave analysis (PWA) in the pulmonary circulation as a measure of wave reflection and arterial stiffness in individuals with and without pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH). Right heart catheterization was performed using a pressure and Doppler flow sensor–tipped catheter to obtain simultaneous pressure and flow velocity measurements in the pulmonary artery in 10 controls, 11 PAH patients, and 11 CTEPH patients. PWA was applied to the measured pressure, while wave intensity analysis (WIA) and wave separation analysis (WSA) were performed using both the pressure and velocity to determine the magnitudes and timings of reflected waves. Type C (AI < 0) pressure waveform dominated in controls and type A (AI > 12%) waveform dominated in PAH patients, while there was a mixture of types A, B, and C among CTEPH patients. AI was greater and the inflection time shorter in CTEPH compared to PAH patients. There was a poor correlation between AI and arterial wave speed as well as measures of wave reflection derived from WIA and WSA. The infection point did not match the timing of the backward compression wave in ~50% of the cases. In patients with type C waveforms, the inflection time correlated well to the timing of the late systolic forward decompression wave caused by ventricular relaxation. In conclusion quantifying pulmonary arterial wave reflection and stiffness using AI based on PWA may be inaccurate and should therefore be discouraged. We characterized the pressure waveforms in the pulmonary arteries in individuals with and without pulmonary hypertension. Pulse wave analysis (PWA) is unreliable in the pulmonary circulation due to the challenges related to reliable identification of an inflection point and indices derived from PWA may not be indicative of wave reflection or arterial stiffness, particularly when type C pressure waveforms are common. Assessing wave reflection in the pulmonary artery based on PWA should therefore be discouraged. In addition to the steady flow resistance, the pulsatile component of the right ventricular load is also important. Arterial pulse pressure is determined by the force of ventricular ejection, arterial compliance and wave reflection; the latter occurs as a consequence of changes in the energy transmission properties of the vessels, e.g., branching or arterial stiffening, causing an admittance mismatch. Reflected waves arriving in the pulmonary artery during systole impose an additional load on the contracting ventricle , which is applied to the measured pressure waveform  was determined as the time difference between the foot of the upslope of the pressure waveform and the inflection point.Pulse wave analysis, WIA, and WSA were performed using custom‐written Matlab software (MathWorks). Pressure and velocity signals were ensemble‐averaged using the m Figure  as previm Figure . The firm Figure . Augmentm Figure . In accom Figure , pressur2.3dP) and velocity (dU). The local wave speed (c), a measure of pulmonary arterial stiffness was calculated using the sum of squares method  and backward (WI–) components  and backward (Pb) pressures in accordance with WSA  corresponding to the energy density over a cardiac cycle squared. Peak Pf and Pb were quantified after subtracting the diastolic pressure. Wave reflection was quantified by the wave reflection index (WRI), defined as the ratio of the backward compression wave (BCW) to forward compression wave (FCW) energy, and by the ratio of peak Pb to Pf. The timing of peak BCW (TBCW), peak forward decompression wave (TFDW) and peak Pb (TPb) were determined with respect to the foot of the measured pressure. The travel time of the reflected wave (ΔT) was defined as the time difference between the peak of FCW to BCW.Separated waves derived from WIA were quantified by the cumulative area under each wave (J/m2.4SD or median (25%–75% quartile) as appropriate. Differences between the controls, PAH patients and pre‐PEA CTEPH patients were compared using one‐way ANOVA  followed by a Bonferroni test (or a Dunn's test) to control the familywise error rate. Chi‐square tests and Fisher's exact tests were used for categorical data. Student's t tests or Wilcoxon's rank‐sum test (non‐parametric data and groups with n < 5) were used to assess differences between post‐PEA CTEPH patients and the control group. Paired Student's t test or the Wilcoxon signed‐rank test were used to compare differences between the data from the main and branch PAs and between pre‐ and post‐PEA data in CTEPH patients. The relationship between variables were examined by Pearson's correlation or, where appropriate, Spearman's correlation analysis. Strength of association was estimated by calculating the standardized beta‐coefficient derived from a multilevel regression analysis with disease category and individuals as random effects. Bland‐Altman plots were used to assess the agreement between the inflection time and TBCW and TFDW. The level of significance was set at p < 0.05. All statistical analyses were performed using Stata 13 (StataCorp).Results are expressed as mean ± 33.1Participant characteristics are shown in Table The morphology of the pressure waveforms of the controls differed from the PH patients as shown by the more obvious dicrotic notch and the more convex downslope, contrasting with the rapid downstroke of the PH patients. In approximately 50% of the participants, a discernible inflection point could be identified . In another case, a convincing inflection point was not evident  in both the main and branch PAs, AI was significantly greater  was delayed in the main PA in CTEPH (pre‐PEA) patients compared to controls and PAH patients.Wave intensity analysis revealed three distinctive systolic waves in PH patients Figure : an initls Table . The difPb decreased, consistent with an overall decrease in pulmonary pressures. The decrease in BCW energy, WRI and Pb/Pf were small and not statistically significant  and a weak negative correlation in the branch PA .Comparisons between the parameters derived from PWA, WIA, and WSA were conducted in all participants followed by subgroup analysis restricted to PH patients with 0 ≤ AI ≤ 50% . AI correlated poorly with the local arterial wave speed in the main PA Figure , while tr = 0.21, p = 0.25), while AI was poorly correlated to Pb/Pf in the main , TBCW  and TPb  in both the main and branch PA  and it remained so when restricted to patients with 0 ≤ AI ≤ 50%. TPb was poorly correlated to ΔT and  and TBCW . Examining the relationship between the parameters derived from PWA, WIA, and WSA using a multilevel regression model yielded similar results.There was a weak, but statistically significant correlation between AI and WRI in the branch PA Figure , but notA Figure . In contTi coincided closely with TBCW (±30 ms) in 19% (main PA) and 45% (branch PA) of the cases  and 67% (branch PA) of the patients  and ΔT . However, it was strongly and significantly correlated to the timing of peak FDW  in the PH patients, while in control subjects, the inflection point generally appeared earlier than TFDW  with type C pressure waveforms, the inflection time correlated poorly to W Figure . AlthougW Figure .4We characterized the pressure waveforms in the main and branch pulmonary arteries in individuals with and without pulmonary hypertension. Type C pressure waveforms dominated in controls and type A waveform dominated in PAH patients, while there was a mixture of types A, B and C among CTEPH patients (pre and post‐PEA). When restricted to PH patients with 0 ≤ AI ≤ 50%, we found that AI was greater and inflection time shorter in pre‐PEA CTEPH patients compared to PAH patients, consistent with previous studies  PAH patients. If augmentation pressure is attributed to reflected waves, an AI over 50% would be non‐physiological as it implies a reflected wave greater in magnitude than the incident wave  were observed in pre‐PEA CTEPH patients compared to PAH patients using WIA and WSA. Also, as previously shown , therefore, AI and A poor correspondence between AI and measures of wave reflection is not surprising. In the systemic circulation, the contour of the pressure waveform and pulse pressure amplification alters with heart rate (Wilkinson et al., 5The major limitation of the present study and previous studies that employed PWA in the pulmonary artery (Castelain et al., Ensemble averaging waveform data was necessary to minimize noise and artefacts in the waveforms especially for velocity data collected from PH patients, where flow has been shown to be highly disturbed. Ensemble averaging pressure data might obscure inflection points; however, this approach is widely used and inspection of individual waveforms suggested this was not a problem.6Pulmonary arterial pressure waveforms differed between individuals without pulmonary vascular disease and patients with PAH and CTEPH. However, PWA is unreliable in the pulmonary circulation due to the challenges related to reliable identification of an inflection point and indices derived from PWA may not be indicative of wave reflection or arterial stiffness, particularly when type C pressure waveforms are common. Assessing wave reflection in the pulmonary artery based on PWA should therefore be discouraged.None.J.S., A.D.H., U.S., and C.M designed the study; J.S., L.S.H., C.M. and S.M. acquired the data; J.S. and A.D.H. analyzed the data; J.S., A.D.H., U.S. and L.S.H. interpreted the results and J.S. drafted the manuscript. All the authors have contributed to revising the manuscript critically for intellectual content and approved the final version.


Fibroblast growth factor23 (FGF23) is elevated in CKD and has been associated with outcomes such as death, cardiovascular (CV) events and progression to Renal Replacement therapy (RRT). The majority of studies have been unable to account for change in FGF23 over time and those which have demonstrate conflicting results. We performed a survival analysis looking at change in c-terminal FGF23 (cFGF23) over time to assess the relative contribution of cFGF23 to these outcomes.We measured cFGF23 on plasma samples from 388 patients with CKD 3-5 who had serial measurements of cFGF23, with a mean of 4.2 samples per individual. We used linear regression analysis to assess the annual rate of change in cFGF23 and assessed the relationship between time-varying cFGF23 and the outcomes in a cox-regression analysis.2, median baseline cFGF23 was 162 relative units/ml (RU/ml) (IQR 101-244 RU/mL). Over 70 months (IQR 53-97) median follow-up, 76 (19.6%) patients progressed to RRT, 86 (22.2%) died, and 52 (13.4%) suffered a major non-fatal CV event. On multivariate analysis, longitudinal change in cFGF23 was significantly associated with risk for death and progression to RRT but not non-fatal cardiovascular events.Across our population, median baseline eGFR was 32.3mls/min/1.73mIn our study, increasing cFGF23 was significantly associated with risk for death and RRT.The online version contains supplementary material available at 10.1186/s12882-021-02528-2. Fibroblast growth factor-23 (FGF23) is a regulatory hormone produced by osteocytes and osteoblasts; it plays a significant role in phosphate homeostasis . FGF23 iNumerous studies have investigated the relationship between baseline FGF23 and clinical outcomes in CKD. FGF23 has been shown to associate with risk for death –10 and cThere is growing understanding of the underlying pathophysiological mechanisms. FGF23 causes reversible hypertrophy of cardiac myocytes in mice  and in mElevated levels of FGF23 have been associated with risk for progression of kidney disease and renal replacement therapy , 25, 26 Although these clinical studies have identified that FGF23 is an important risk factor in CKD, the majority do not account for change in FGF23 over time. There is individual variability in FGF23 levels but studies in both healthy participants  and CKD Three studies have sought to investigate change in FGF23 over time and the effect on risk for death in CKD patients , 7, 8. TOur study examined cFGF23 levels over time in a cohort of 388 patients selected from the Salford Kidney Study (SKS) to analyse the relationship between change in cFGF23 and the clinical outcomes of death, renal progression and cardiovascular events.2 are eligible for recruitment. Patients are managed in accordance with standard clinical practice guidelines and are followed up until death or initiation of renal replacement therapy . Demographic data are recorded at baseline and annually during study follow up. Blood samples are drawn for standard clinical tests and additional samples stored for subsequent biomarker analyses. Three hundred eighty-eight patients had repeated measurements of cFGF23, with a mean of 4.2 measurements per individual. Time zero was defined as the baseline sample date and follow-up was until death, initiation of renal replacement therapy, or April 2014. Date and cause of death was obtained from the Office of National Statistics (ONS), date of initiation of renal replacement therapy (RRT) was obtained from the electronic patient records. Major adverse cardiovascular events were recorded during study follow-up and defined as myocardial infarction, coronary angiogram plus angioplasty or stenting, coronary artery bypass graft surgery or stroke. Any patient reported events were verified against primary care and hospital records to ensure accuracy of reporting.The Salford kidney study (SKS), Salford, UK is a prospective observational study to investigate outcomes in kidney disease. Three hundred eighty-eight subjects recruited between 2004 and 2013 who had sufficient stored baseline and serial plasma samples were selected for this analysis. Details of the SKS cohort  have been published previously –35. In bAll biomarker analyses were performed on lithium-heparin plasma samples that had been stored at − 80 °C. cFGF23 was measured in 2014 using a 2nd generation two-site enzyme linked immunosorbent assay (ELISA) supplied by Immutopics  detecting intact FGF23 and its c-terminal fragments. eGFR was calculated from IDMS calibrated creatinine measurements using the 4-variable Modified Diet in Renal Disease (MDRD) study equation .p-value of the difference between the groups was estimated by Kruskal Wallis H test. Categorical variables were expressed as numbers (%) and p-value of the difference between the groups being calculated by the Chi-square test.The annual rate of change of cFGF23 (delta cFGF23) and the annual rate of change in estimated glomerular filtration rate (delta eGFR) were calculated by linear regression analysis using all available values between study baseline and endpoint. The cohort was split into four quartiles based on delta cFGF23 values The baseline characteristics and outcomes were compared across these cFGF23 quartiles. Continuous variables were expressed as median (IQR), and p-value < 0.05 was taken to be statistically significant. All analysis was performed using SPSS- Version 24 registered to the University of Manchester.Univariate and multivariate Cox-regression analysis was used to evaluate the strength of association between the cFGF23 quartiles and the outcomes RRT, all-cause mortality prior RRT, and non-fatal cardiovascular events. The univariate analysis included all baseline characteristics, while multivariate models incorporated factors that were significant in the univariate models in a stepwise approach.. Kaplan Meier curves were used to demonstrate the difference in survival patterns between the quartiles with the log-rank test used to test the statistical significance. Throughout the analysis, a 2. The median study follow up was 70 months (IQR 53 to 97 months).Baseline demographics for the cohort stratified according to quartiles of delta cFGF23 are presented in Table Three hundred eighty-eight patients had 3 or more serial measurements of cFGF23. The mean number of measurements per individual was 4.2  with a median time of 1.1 years between successive measurements. Across the whole cohort the median baseline level of cFGF23 was 162 RU/ml (IQR 101-244 RU/mL).There was a high rate of progression to RRT in our study, with 76 (19.6%) of patients reaching this endpoint. There were 86 (22.2%) deaths prior to initiation of RRT. During follow-up, 13.4% of the cohort suffered at least one non-fatal major cardiovascular event.p = 0.002, p < 0.001 and p < 0.001 respectively) (Table p = 0.001) and renal replacement therapy , although there remained a strong inverse correlation between change in eGFR and cFGF23. There was no significant association between delta cFGF23 and non-fatal cardiovascular events in our analysis . Results of the Cox regression analyses in respect to these outcomes are shown in Table When quartiles of delta cFGF23 were compared to clinical outcomes, there was a significant difference in risk for death, RRT and delta eGFR between those in the highest and lowest quartiles Additional file 2: Supplementary Table 2. Cox-regression model for renal replacement therapy (univariate model)Additional file 3: Supplementary Table 3. Cox-regression model for cardiovascular event (univariate model)


Diabetic kidney disease (DKD) is one of the most common complications in type 2 diabetes mellitus (T2D) and a major cause of morbidity and mortality in diabetes. Despite the widespread use of nephroprotective treatment of T2D, the incidence of DKD is increasing, and it is expected to become the fifth cause of death worldwide within 20 years. Previous studies have demonstrated that GLP-1 receptor agonists (GLP-1 RA) have improved macrovascular and microvascular outcomes independent of glycemic differences, including DKD. GLP-1Ras’ improvement on kidney physiology is mediated by natriuresis, reduction in hyperfiltration and renin-angiotensin-aldosterone system (RAAS) activity and anti-inflammatory properties. These findings translate into improved clinical outcomes such as an enhanced urine albumin-to-creatinine ratio (UACR) and a reduction in renal impairment and the need for renal replacement therapies (RRT). In this article, we review the role of GLP-1RAs on the mechanisms and effect in DKD and their clinical efficacy. Control of cardiovascular risk factors, such as dyslipidemia, obesity, smoking and changes in other modifiable factors based on strict blood pressure control, use of RAAS inhibitors and adequate glycemic treatment, has resulted in improvement in the incidence in T2D related myocardial infarction, stroke, amputations and mortality ,2,3. How65 years . All thiCurrent standards of diabetes care recommend metformin as initial therapy, after life style modification in patients with T2D. Recently, the guidelines have included in the algorithm for choosing the second line of treatment the criteria of an increased cardiovascular risk, cardiovascular disease or renal complications . In the The GLP1RAs are included in the incretin-based therapies as well as dipeptidyl peptidase 4 (DPP-4) inhibitors. GLP1 is a peptide secreted during ingestion and its mean effect is reflected on glucose metabolism by insulinotropic signals mediated by GLP1 receptors, which are highly expressed on ß-pancreatic cells . This hoThe first drug from the GLP-1RAs’ class was exenatide, which was approved by the US Food and Drug Administration (FDA) in April 2005 for the treatment of T2D. It consists in a 39-aminoacid peptide made by a substitution of an Ala8Gly of the exendin-4 hormone found in the saliva of the Gila monster. This modified chemical structure confers resistance to the degradation of DPP-4 . From th2, post-hoc analysis demonstrated the safety in patients with CKD [The lack of trials in DKD has limited the experience of pharmacokinetic and clinical data with GLP-1AR in these group of patients with T2D. Therefore, caution or discontinuation was recommended when renal function is severely impaired . Howeverwith CKD ,22,23, uIn this review, we focus on the role of GLP-1 (and GLP-1-RAs) in the physiology of the gut-renal axis, the renoprotective mechanisms of this incretin therapies and the renal outcomes obtained in the most recognized clinical studies using GLP1-RAs in patients with T2D.As mentioned above, GLP-1 and GIP are two incretin hormones responsible for the reduction in blood glucose levels in response to nutrient ingestion. In fact, humans submitted to an oral glucose load showed a much greater increase in plasma insulin levels than those infused with intravenous glucose administration . This phNevertheless, not only a gut-pancreas connection has been demonstrated but also a contribution to the physiological control of water and electrolyte balance upon meal ingestion. The regulation of the gut-renal axis is mediated by multiple pleiotropic actions in different locations such as the central nervous system, adjusting thirst, intestinal co-transporters to control fluid and electrolyte absorption and secretion and also on the kidney, by the stimulation of renal tubular excretion and/or reabsorption of fluid and electrolytes ,26. As tThe role of GLP1 is not only limited on tubular effects but also on changes on hemodynamics. For example, a postprandial hyperfiltration induced after ingestion of a high protein meal by an increase in renal blood flow was identified. This mechanism contributes in part of sodium and solutes homeostasis after ingestion by increasing the pressure and vasodilation of the afferent arteriole through nitric oxide . From a There are several experimental studies that evaluate the effects of GLP1 and GLP1-RAs on the renal metabolism and shed light on the main mechanisms responsible for changes in eGFR, a reduction in albuminuria and other renal outcomes seen in clinical studies. In this section, we will explain in detail the main mechanisms underlying the attenuation of DKD by GLP1-RAs.The main renoprotective effect of incretin-based therapies such GLP1-RAs is mediated by regulation of the glucose metabolism. Indeed, the most known effect of the GLP1 gut derived hormone is the reduction in hyperglycemia in T2D which is severely impaired or lost on those patients ,33. Its  placebo . Other a placebo ,37. Anot placebo .Various reports have shown that T2D is associated with chronic low-grade inflammation, which is linked with oxidative stress, proliferation and fibrosis that affect kidney function and morphology . ExperimAnother main molecular target implicated in oxidative stress and progression to DKD is the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB). The activation of this pro-inflammatory protein complex is induced by hyperglycemia and attenuates the therapeutic effects of the GLP-1R agonist by its downregulation . The benGLP-1 has been previously demonstrated to induce renal sodium excretion and increase urine flow in experimental and clinical studies in healthy and T2D individuals ,45,46. TIt is well known that the mechanism of hyperfiltration is a prime disruptor in DKD, especially when it is accompanied by albuminuria and renal function decline, which is observed in advance stages of the disease . The GLPIn rodent models, GLP1-RA mediated the increase in endothelial nitric oxide synthase (eNOS) activity and expression, and nitric oxide (NO) production was described. This NO increase produces a direct vasodilation effect mainly on preglomerular arterioles that increases the glomerular filtration rate (GFR) ,55. AddiOn the other hand, other clinical and animal studies suggest that GLP1-RAs’ therapies reduce glomerular hyperfiltration and provide renoprotection in DKD. In experimental studies in diabetic rodents, 4–8 weeks of administration of liraglutide and linagliptin significantly reduced glomerular hyperfiltration . In clinThese discrepancies on GLP1-RAs’ effects on renal hemodynamics may be explained by differences in the characteristics of the studied population, the dose administrated and the posology or differences in study designs.GLP1RAs treatment points to a clinically relevant lowering effect on blood pressure. These results can be partially explained by indirect effects on weight loss, an increase in natriuresis and the regulation of RAAS ,61,62. MGLP1-RAs have a main glucose lowering effect by stimulating β-cell pancreatic secretion and inhibiting glucagon production. Strict control of dyslipidemia was shown to have a beneficial effect on DKD . This meAbdominal obesity is related with T2D and the associated chronic complications such as DKD . SeveralOther conventional glucose-lowering treatments such as insulin or sulphonylureas increases body weight ; howeverp < 0.001). This means that, in absolute terms, semaglutide obtained a reduction in 15.3 kg with respect to the baseline  [p < 0.001) and −1.69 kg , respectively [In fact, semaglutide shines as a weight-loss therapy, reaching a decline of 12.4% compared with a placebo in 68 weeks  . Other Gectively ,79.www.clinicaltrials.gov accessed on 25 August 2021). It is important to remark that the data provided about GLP1RAs’ effect on the kidney are obtained of studies that were designed to aim cardiovascular events, so renal effects were obtained as secondary outcomes—A literature search was conducted in the following databases for the period through 1 January 2005 to 1 April 2021: Medline, Elsevier, Embase and Scopus. Only randomized, double-blind, placebo-controlled trials published that include renal results as primary or secondary outcomes were considered. Ongoing trials were identified using the US National Institutes Health Clinical Trials Registry (p = 0.004). No differences in the doubling of the serum creatinine, initiation of RRT or renal death were observed. On post hoc analysis, liraglutide showed a reduction in the risk of major adverse CV events and all-cause mortality in comparison to a placebo in patients with DCKD (eGFR < 60 mL/min/1.73 m2 and UACR > 30 mg/g) [In this study, 9340 participants with T2D and high cardiovascular risk were assigned to liraglutide 1.8 mg subcutaneous injection vs. placebo and were followed 3.84 years. The renal outcomes studied included new-onset persistent macroalbuminuria, persistent doubling of serum creatinine, KF and death due to renal disease. All the renal events were grouped on first statistical analysis, and a reduction in its incidence was observed as HR 0.78 (95% CI: 0.67–0.92), and the main contribution of this effect was the reduction in new onset macroalbuminuria HR 0.74  ,81.p = 0.05)); the main effect was driven by new onset macroalbuminuria. Concretely, UACR ratios at the end of treatment with respect to baseline were 0.973 with semaglutide 0.5 mg, 0.858 with semaglutide 1.0 mg and 1.302 with the placebo. Other outcomes studied were persistent doubling of the serum creatinine level, creatinine clearance <45 mL/min/1.73 m2 and need for RRT. No differences were observed in KF or renal death. The baseline characteristics of the patients were T2D patients with established cardiovascular or chronic kidney disease; 25.2% had an eGFR moderately decreased (30–59 mL/min/1.73 m2) and 2.9% severely decreased (<30 mL/min/1.73 m2), and 12.7% had macroalbuminuria at screening [Semaglutide subcutaneous treatment showed a reduction in the incidence of new or worsening nephropathy vs. a placebo in a SUSTAIN-6 trial  with semaglutide 0.5 mg and 0.68 (95% CI 0.59 to 0.78) for semaglutide 1.0 mg versus placebo. A decrease in eGFR were observed at the start of the study in patients with normal kidney function (and in those with mild kidney impairment with semaglutide 1.0 mg in SUSTAIN 6), but no differences were found at the end of the study between semaglutide and the placebo. No increase in the risk of kidney adverse events with semaglutide was seen .2. Subjects were followed up for 15.9 months. In the results, no statistical differences were observed in the renal outcomes studied . No renal outcomes were analyzed in PIONEER-5, but safety was demonstrated for individuals with a moderate eGFR decrease (30–59 mL/min/1.73 m2) [The main objective of this trial was the evaluation of cardiovascular safety of oral semaglutide. At baseline, 31283 participants with high cardiovascular risk, ≥50 years of age and a cardiovascular and/or chronic disease established were included. It is important to note that 26.9% of the patients had an eGFR < 60 mL/min/1.73 m1.73 m2) ,83.2 and ≤75 mL/min/1.73 m2, and the UACR between 300 and 5000 mg/g or eGFR ≥ 25 mL/min/1.73 m2 and ≤50 mL/min/1.73 m2 and the UACR between 100 and 5000 mg/g. The dose administrated will be titrated from 0.25 mg to 1 mg, and the estimated finish date will be in August 2024 [The FLOW trial is the first and ongoing study with renal outcome as the primary objective, defined as persistent EGFR decline (≥50% from the start point), reaching KF or renal death from CV or kidney disease. The enrollment of 3508 participants is estimated in a randomized, quadruple-blind, placebo-controlled trial that evaluates the effect of semaglutide on the progression of renal disease in patients with CKD and T2D. The inclusion criteria are eGFR ≥ 50 mL/min/1.73 must 2024 .2; p = 0.005 vs. insulin glargine) and dulaglutide 0.75 mg  than with insulin glargine (31.3 mL/min/1.73 m2). Dulaglutide treatment was associated with a significantly smaller decline in eGFR compared with insulin glargine over 52 weeks. The association between dulaglutide treatment and reduced eGFR decline was most evident in participants with macroalbuminuria [Renal outcomes were assessed in AWARD-7: this study included 577 patients with T2D and moderate-to-severe CKD treated with insulin and the maximum dose of an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. The response to once-weekly injectable dulaglutide 1.5 mg, 0.75 mg, versus daily insulin glargine was evaluated. The primary outcome was HbA1c at 26 weeks. Renal outcomes observed were only secondary objectives, as the estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR). The effects of dulaglutide on UACR reduction were not statistically significantly different from that of insulin glargine (22.5% (95% CI −35.1 to −7.5)) with dulaglutide 1.5 mg (20.1% (95% CI −33.1 to −4.6)) with dulaglutide 0.75 mg (95% CI 13.0% (−27.1 to 3.9) with insulin glargine). At 52 weeks, eGFR was higher with dulaglutide 1.5 mg (34.0 mL/min/1.73 muminuria .p = 0.0004). New onset macroalbuminuria was the only renal outcome to be statistically significant when studied separately ; no differences were observed in the sustained decline in eGFR ) and chronic renal replacement therapy ) [In this multicenter, randomized, double-blind and placebo-controlled trial were included 371 participants with at least 50 years of age, with T2D with CV risk factors or a previous CV event. A weekly subcutaneous injection of dulaglutide (1.5 mg) was compared with a placebo. The primary outcome on this study was the composite of the following events: non-fatal myocardial infarction, non-fatal stroke or death from cardiovascular causes (including unknown causes). On a follow-up of 5.4 years, the renal outcome  occurred in 17.1% of participants sorted in the dulaglutide treatment branch and in 19.6% in the placebo group ) .The EXSCEL 2017 trial assessed the cardiovascular safety of exenatide in 14.752 patients of whom 73.1% had previous cardiovascular disease, and were followed for a median of 3.2 years. The primary outcome composite of the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction or nonfatal stroke occurred in 11.4% in the exenatide group and in 12.2% in the placebo group . Exenatide therapy was not associated with significant differences in eGFR decline, RRT or renal death. However, based on the data obtained, macroalbuminuria occurred less in the exenatide group (2.2%) compared to the placebo (2.5%) (HR 0.87 (95% CI: 0.70–1.07)) .In HARMONY, the incidence of major adverse cardiovascular events in patients aged 40 years and older with T2D and CV disease occurred in 338 of 4731 (7%) patients that received a subcutaneous injection of albiglutide and in 428 (9%) of 4732 patients in the placebo group . They only examined decline in the estimated glomerular filtration rate as a parameter of kidney outcome, and there were not significant differences in the treatment group versus the placebo. No other renal outcomes were studies .2) in 25% of the participants included in the lixisenatide group and 22% treated with the placebo. In ELIXA, a prespecified analysis of the percentage change in UACR from baseline was employed, and showed a modest difference over the placebo  [p = 0.0174) and a lower UACR change rate in patients treated with lixisenatide with micro-  or macroalbuminuria . All these results were adjusted to HbA1c changes, demonstrating a reduced UACR progression beyond glycemic control [Patients with T2D and a recent acute coronary event  were analyzed. This included 6068 patients in a double-blinded placebo-controlled randomized to assess differences in the incidence of cardiovascular death, myocardial infarction, stroke or hospitalization for an unstable angina in individuals treated with a once-daily subcutaneous injection of lixisenatide versus a placebo. In the baseline characteristics, there was a preexisting eGFR decline (<60 mL/min/1.73 m= 0.004) . In a po control .DKD remains the leading cause of chronic kidney disease, kidney failure and need for RRT ,5. The cThere exists several pre-clinical studies tested on animals that demonstrate the beneficial effects of GLP1-RAs on kidney function. In fact, there is evidence of a direct GLP-1 effect on glomerular homeostasis and tubular ion excretion, mechanisms implicated on hyperfiltration and the main histopathological cause of DKD ,49,51. ODue to the evidence previously exposed, the development of new large clinical trials in the T2D population with DKD is urgently needed to analyze the main effect of GLP1-RAs on the kidney as primary objectives. In fact, there are currently ongoing several clinical trials with GLP1-RA with renal outcomes as the main hypothesis: the effect of liraglutide, lixisenatide and semaglutide on renal complications, such as eGFR decrease or albuminuria progression, is being further studied . This new perspective offers the possibility to reduce the incidence of patients with moderated-advanced stages of CKD as well as the need for RRT or kidney transplant, resulting in a decrease in hospitalizations and deaths due to renal causes and, consequently, a reduction in sanitary costs without sacrificing the quality of life of patients with CKD.


In systolic chronic heart failure, a heterogeneous blood volume (BV) regulation can be found with plasma volume expansion in many cases, possibly leading to pseudoanemia. Little is known about the volume status after heart transplantation (HTX). So far, anemia of HTX recipients was solely investigated using hemoglobin-concentration that may be misleading in a clinical context. The objective of the study was whether a difference in plasma volume and red cell volume can be observed in clinically stable heart transplant recipients compared to matched control subjects. Secondary, the aim was to describe anemia in the long-term after HTX based on quantitative data.Blood volume and its constituents red cell volume and plasma volume were quantified using an abbreviated carbon monoxide rebreathing method (aCORM) with focus on its primary measure total hemoglobin mass (Hbmass) and coincidental anemia in 36 (7 women) heart transplant recipients. For comparison, a matched control group of 46 (5 women) healthy subjects was selected.Neither Hbmass nor blood volumes were significantly different in HTX patients compared to matched healthy control group subjects. The prevalence of anemia 6.3 ± 4.3 years after transplantation was 19%. Hbmass and red cell volume were significantly lower in anemic HTX patients compared to non-anemic patients while plasma volume was not expanded. Various immunosuppressant regimens did not have an effect on Hbmass, plasma volume or red cell volume.There was no difference in blood volumes and Hbmass between HTX patients and control subjects. The pathophysiologic blood volume regulation in chronic heart failure does not seem to be longer active in long-term HTX recipients. However, in the long-term after HTX, anemia occurs in a considerable number of patients as true anemia without a clear association with immunosuppression.https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00006078.German registry for clinical studies, DRKS00006078. Registered 09 May 2014,  In chronic heart failure (CHF), the composition of blood volume (BV) continues to be of interest to fully understand pathophysiological adaptations , 2. QuanVolume overload in CHF is typically described as plasma volume (PV) expansion, conceivably leading to hemodilution and pseudoanemia –8. It isOn the other hand, anemia after heart transplantation was suggested to have prognostic value , 13 but Clinicians frequently use radioactively labeled albumin for assessment of intravascular volumes , 10. In The objective of the study was whether a difference in PV and RCV can be observed in clinically stable long-term heart transplant recipients compared to matched control subjects applying aCORM. Secondary, the aim was to describe coincidental anemia in the long-term after HTX based on quantitative BV data.In a cross-sectional, controlled approach, Hbmass, RCV and PV were measured in 39 patients (8 women) after heart transplantation. The patients (Group HTX) were recruited upon their routine follow-up visit in the transplantation unit of our heart center after providing signed informed consent. Inclusion criteria were a period of at least 6 months since transplantation and clinical stability without any sign for organ rejection. Exclusion criteria were hemodynamic instability, acute coronary syndrome, anemia with a hemoglobin concentration ([Hb]) ≤ 8 g/dl, active bleeding, active malignancy, limited life expectancy < 1 year, noncardiac chronic renal disease, uncontrolled lung disease, acute infection and chronic inflammatory disease. Associated data such as echocardiography and laboratory results, medication and functional status were recorded in the context of the visit. The subjects were deemed “stable” based on their routine follow up visit free of cardiac symptoms and without any sign for organ rejection.A healthy control group (Group CON) with no obvious medical condition and no established medication was matched retrospectively by age, body weight and height from our database of reference subjects yielding 46 subjects (5 women) whose Hbmass and BV were previously measured using the aCORM in our laboratory. This group was selected only for the comparison of Hbmass and BV data. The same exclusion criteria applied to the control group.The study was approved by the ethics committee of our University Hospital (31/14) and is in line with the latest revised form of the Declaration of Helsinki. The study is registered in the German registry for clinical studies (DRKS-ID: DRKS00006078).Venous blood from HTX patients was analyzed for [Hb] and Hct, proBNP, iron, ferritin, and transferrin saturation as well as plasma levels of immunosuppressive agents among other values. Vitamin B12 was not ordered on a routine basis. In CON, venous [Hb] and Hct were available in the database from the time of application of aCORM.Hbmass and subsequently, blood volumes were derived applying aCORM . As CO h− 1 × [1 + (0.003661 × current temperature)].K = current barometric pressure × 760CO = COadministered (COsystem + lung (after disconnection) + COexhaled (after disconnection)).Madministered = CO volume administered into the spirometer.COsystem + lung (after disconnection) = CO concentration in the spirometer × .COexhaled (after disconnection) = end-tidal CO concentration × alveolar ventilation × time.COΔHbCO% = difference between baseline HbCO and HbCO in the blood samples after CO administration.− 1)1.39  Hüfner’s number . Alveolar ventilation and lung residual volume were approximated based on the subject’s characteristics.Alveolar ventilation and lung residual volume were approximated based on the subject’s characteristics.Units: Hb: g; current barometric pressure: mmHg; current temperature: °Celsius; CO: ml; CO concentration: ppm.Intravascular volumes  can be then calculated using the following formulas :\docume1). As venous [Hb] was unavailable in four HTX patients, it was inferred from capillary [Hb] by a factor based on regression analysis of venous and capillary [Hb] in the remaining HTX subjects. Based on the recommendation of the International Council for Standardization in Hematology (ICSH), intravascular volumes and Hbmass were adjusted to body surface area (BSA)  and Hct were used for determination of MCHC. For RCV calculation, Hct was corrected to whole-body Hct by the factor 0.91  (Hct1). ea (BSA)  to prese formula .For both groups, the expected normal blood volumes were calculated according to the gender-specific ICSH formulae  to analyAnemia was defined according to the definition of the World Health Organization (WHO) with [Hb] < 13 g/dl in men and [Hb] < 12 g/dl in women . GFR wasEF was determined through the modified biplane Simpson’s rule  if possiSAS JMP 9.0 and Graphpad Prism 6.0f were used for data administration and statistical analyses. A two-tailed t-test was used to test for group differences. A two-tailed chi-square test was used for the analysis of contingency tables regarding anemia. An ordinary one-way ANOVA was used to test the effect of the immunosuppressant regimen on Hbmass and blood volumes. The prevalence of anemia was analyzed using Fischer’s exact test. The alpha level was set at 0.05.In HTX, three patients (one woman) had to be excluded due to insufficient test performance (e.g. lack of understanding of the instructions) and leakages at the mouthpiece. In the final analysis, 36  HTX patients and 46  CON subjects were included. All subjects tolerated aCORM well. There were no adverse cardiac effects, such as chest pain, dizziness or shortness of breath, and no signs of CO toxicity such as headaches or visual disturbances. The [COHb] before CO rebreathing was 1.1 ± 0.7% in HTX and 1.0 ± 0.9% in CON. At the time “6 min” after the commencement of CO rebreathing [COHb] increased to 6.3 ± 1.3% in HTX and 6.8 ± 1.2% in CON.The baseline characteristics of both groups including gender-specific data are displayed in Table Hbmass as well as RCV were not different between HTX and CON, neither in the absolute nor BSA-adjusted analysis Table . PV was p = 0.997), RCV (p = 0.989), PV (p = 0.370) and BV (p = 0.594). When analyzing HTX patients with (67%) or without (33%) RAAS-inhibition, PV was significantly lower with RAAS-inhibition (P = 0.037) while Hbmass, RCV and BV were indifferent.Figure p = 0.038). The details of the anemic HTX patients are presented in Table When defined by the WHO criteria, anemia was more common in HTX (7 patients (19%)) than in CON (2 subjects (4%), p = 0.139), RCV (p = 0.182), PV (p = 0.249) and BV (p = 0.161). Except for the least common combination MMF / Tacrolimus (4 patients), anemia was observed in the three other regimens  and there was a trend towards an expanded PV in the male subset (+ 6% BSA-corrected PV). Accordingly, the concentration-dependent variables venous [Hb] and Hct were significantly lower in HTX compared to CON but with presumably low clinical relevance judged upon the magnitude of the difference. Thus, our results support the previous findings of a PV expansion after transplantation also in so far as PV was lower in the subgroup analysis of patients treated with RAAS-inhibitors opposed to an increased PV in the non-RAAS-inhibition group. Although we present a cross-sectional approach, this is in line with previous work demonstrating a PV reduction of 12% upon administration of high-dose captopril . Braith In the past, immunosuppressant agents such as cyclosporine were also discussed to contribute to an expanded PV . In contThe rate of diuretic therapy was 53% in the HTX group, which might have obscured PV expansion in some patients. However, if tested against HTX patients without diuretic therapy, there was no difference in PV, RCV or Hbmass. In a previous study of CHF patients, recompensation in response to diuretic therapy leads to a relatively small change in quantitative BV, most likely as a result of transcapillary shifting from the interstitial to the intravascular compartment .2 was found compared to 1919 ± 208 ml/m2 in this study demonstrating an about 8% lower PV in HTX compared to CHF. It could be hypothesized that over time, the pathophysiologic BV regulations known in CHF are no longer active after heart transplantation. Thus, based on our data, heterogeneity of PV distribution in heart transplant recipients becomes evident and PV expansion is perhaps less common than previously described. Interestingly, our HTX group yielded an elevated proBNP in line with previous observations  and Hct do not adequately reflect intravascular volume status . HoweverInterpretation of the impact of immunosuppression seems limited by the small numbers and the most common immunosuppression regimen in this patient cohort (everolimus/cyclosporine) which might be considered not reflective of the greater transplant community. Furthermore, the number of female and anemic subjects is low, which limits the significance of the analyses. Another possible limitation to the direct comparison with the few previous studies after heart transplantation , 4 is thFinally, we studied compensated heart transplant recipients. It would have been interesting to focus on patients that re-developed heart failure or require hospitalization. This seems important, as data exists even in nonedematous CHF patients linking unrecognized hypervolemia to increased cardiac filling pressures and worse patient outcomes . In the There was no difference in blood volumes and Hbmass between HTX patients and control subjects in the long-term after heart transplantation. This is the first study applying CO rebreathing in a larger group of patients after heart transplantation. Based on our data, we hypothesize that the pathophysiologic BV regulation in CHF does not seem to be longer active in long-term HTX recipients. While PV was not significantly different from CON it may show a more heterogeneous distribution after HTX without a clear expansion. Anemia occurs in a considerable number of patients as true anemia that is suggestive of anemia in chronic disease and functional iron deficiency , 39. Var


A number of differential metabolites were shared by male and female RCC patients. These RCC specific changes included lower lactate, threonine, histidine, and choline levels together with increased levels of pyruvate, N-acetylated glycoproteins, beta-hydroxybutyrate, acetoacetate, and lysine. Additionally, serum lactate/pyruvate ratio was a strong predictor of RCC status regardless of gender. Although only moderate changes in metabolic profiles were observed between control males and females there were substantial gender related differences among RCC patients. Gender specific metabolic features associated with RCC status were identified suggesting that different metabolic panels could be leveraged for a more precise diagnostic.Renal cell carcinoma (RCC) is the most common form of kidney malignancy. RCC is more common among men with a 2/1 male/female incidence ratio worldwide. Given the underlying epidemiological differences in the RCC incidence between males and females, we explored the gender specific  The renal cell carcinoma (RCC) is the most common form of kidney malignancy constituting more than 90% of all kidney cancer cases . The mosKidney metabolism may differ between men and women, as many gender related differences in renal function and resulting blood pressure have been observed in animals and in humans ,9. FurthThe current standard for RCC diagnosis is based on abdominal ultrasound scanning, MRI, CT PET, and intravenous pyelogram , while tA number of excellent metabolomics studies on RCC have already been conducted. These investigations were performed with the use of various analytical platforms such as NMR and MS using blood ,20,21,221H NMR, 44 metabolite resonances were consistently detected and showed sufficient signal to noise and resolution to be quantified  resulted in a substantial separation between groups (p < 0.0005)  C. Yet, s 0.0005) B.p value < 0.01 from logistic regression are reported in p-value, and percentage change. No lipids were found in this list and neither creatinine nor creatine were statistically different between the RCC and Control groups, which was somewhat unexpected given the fact that creatinine is a marker of renal function. Among the most significantly changed metabolites we found lactate, N-acetylated compounds (NAC2 δ = 2.05 ppm), threonine, and histidine, followed by ketones and choline  scores. Metabolites characterized by high VIP values were additionally subjected to single logistic regression (SLR) analysis, in order to evaluate their individual predictive power regarding RCC status. All metabolites with VIP score > 1 from PLS-DA and  choline . The top three most important metabolites except threonine are presented in detail in Interestingly, metabolite signals associated with ketone body metabolism were also detected as significantly increased in serum of RCC patients comparing to Controls . This wa1H NMR provide sufficient amount of information for identification of RCC patient either based on multivariate PLS-DA or multiple logistic regression model.Taken together, this data indicates, that quantification of serum metabolic profiles using p = 0.0245) and showed moderate performance in the permutation test , indicating that indeed these three metabolites were only associated with RCC status in male subjects (p < 0.0001) for RCC diagnosis among females , which was consistent with pyruvate being selected by female specific PLS-DA model  trend in relation to the T stage , exhibited strong T stage related characteristics. For example, glycine, which had  staging D. Althou staging D. Tyrosi staging E. Perhap staging F.1H NMR; however, the degree with which these changes occur could be dependent on gender, as some of these metabolites were significantly different between males and females.Taken together these data indicate that metabolic signatures of RCC progression are present in serum and can be detected by In the current study, we evaluated serum metabolic profiles of RCC patients in joint and gender-specific manner. The goal of this investigation was to select differential metabolites for RCC status and to assess whether gender-specific metabolic signatures of RCC can be leveraged for a more precise diagnostic.Our analysis revealed existence of moderate metabolic differences between male and female controls. Among identified differential metabolites some has previously been associated with gender and showed same trends in our study. In particular, serum creatinine is commonly reported to be higher among men based on differences in muscle mass ,32,33,34Although we detected only moderate changes in metabolic profiles of Control males and females, there were substantial gender related differences among RCC patients. Similarly, to the Control group, male RCC patients had higher creatinine levels comparing to female RCC patients, indicating, that some of gender related differences are maintained despite RCC status. However, the strongest changes were associated with increased lipids among RCC females, which is consistent with typically observed trend of higher fatty acids and lipid among healthy women ,36,37. AImportantly, although a number of differential metabolites was common for male and female RCC patients, there were some gender specific metabolic features associated with RCC status. A proof-of-concept MLR models based on three gender specific metabolites  provided good predictive power for their respective genders, and had limited utility when applied for other patients. Therefore, gender specific metabolic models could be advantageous for RCC diagnosis. 13C NMR signals Keshari et al. showed that RCC cells had a significantly higher conversion rate of pyruvate to lactate compared to normal renal tubular cells [13C glucose in ccRCC patients followed by the isotopomer analysis of metabolites extracted from tumor tissues, indicated increased flux from glucose to lactate with impairment in oxidation of glucose derived carbon in the TCA cycle, demonstrating that ccRCC is the first among human tumors that exhibit convincing shift toward glycolytic metabolism [Lactate, pyruvate, and their ratio as well as glucose were strongly associated with RCC status in our study. In kidney cancer, mutations in the enzymes of the TCA cycle such as succinate dehydrogenase (SDH) and fumarate hydratase (FH) lead to impairment of oxidative metabolism . This suar cells ,41. Simitabolism . Based otabolism ,21, yet tabolism . Converstabolism ,22. Constabolism ,25,28, wtabolism , unchangtabolism . Our stuBesides the liver, the kidney is the only organ capable of meaningful gluconeogenesis. Based on increased levels of circulating glucose in combination with decreased concentration of lactate it is tempting to suggest upregulation of gluconeogenesis in RCC patient group. However, it is unlikely that a renal tumor would produce glucose by itself. First, the genes controlling renal gluconeogenesis  were found to be the most under expressed metabolic gene set based on analysis of RNA-sequencing data of ccRCC  and secoProduction of lactate by kidney tumor could lower the local pH  and thusKetones, BHB, and AcAc as well as acetone were all detected in greater concentration among RCC patients than in controls. Ketogenesis is generally thought to occur solely in the liver ,53 and tFiltration is one of the most important roles performed by kidneys to maintain whole body homeostasis. Changes in filtration rate could affect concentration of metabolites in the blood. However, metabolites identified to be differential for RCC showed both increasing and decreasing changes, which suggest that they were not caused by only impaired renal filtration. A common although limited indicator of kidney function is serum creatinine level . CreatinThe study group consisted of 50 RCC patients aged 39–87 (mean age: 64.4 years), including 20 women and 30 men. All participants agreed to participate in the study by signing an appropriate form approved by the Bioethics Committee of the Wroclaw Medical University (Opinion No. KB-102/2012 of 10 April 2012). The patients diagnosed with kidney tumor were eligible for surgical treatment based on imaging examinations such as abdominal CT and MRI scans. The clinical stage was determined according to the 2012 IUAC TNM classification. The RCC patients’ characteristics are shown in g for 10 min. The obtained serum was frozen at −70 °C. The frozen samples obtained from the study group and the control group were used for the metabolomic analysis using 1H NMR spectroscopy.The control group consisted of 50 patients with no evidence of kidney tumor whom volunteered to participate in the study conducted by the Department of Ophthalmology. Blood serum was the material used for metabolomic analysis. It was collected on the day before surgery during standard laboratory tests. Blood was collected into 10 mL BD Vacutainer tubes with Becton Dickinson clot activator. After the blood clot was obtained, the serum was centrifuged at 3000× 2O; 3 mM TSP). After centrifugation , a 550 μL of each sample supernatant was subsequently transferred to a 5-mm NMR tube. Samples were maintained at 4 °C prior to measurement.Prior to the metabolomics experiments, serum samples were thawed and vortexed. Subsequently, 200 μL aliquot was taken from each sample and mixed with 400 μL of saline solution  operating at proton frequency of 600.58 MHz. A one-dimensional Carr–Purcell–Meiboom–Gill (CPMG) spin echo pulse sequence with water suppression was utilized (Burker library: cpmgpr1d). This allowed removal of broad spectral resonances originating from macromolecules and improved the visibility of low molecular weight metabolites. For each sample, 128 consecutive scans were collected with a 400 μs spin-echo delay, 80 loops, a 3.5 s relaxation delay, 64 K TD, and 20.01 ppm SW. The spectra were Fourier transformed with 0.3 Hz line broadening, manually phased and baseline corrected using Topspin 1.3 software , and referenced to an α-glucose signal (δ = 5.225 ppm). Metabolites were identified based on previously reported assignments in serum [All in serum ,60, ChenAll spectra were exported to Matlab (Matlab v. 8.3.0.532) for pre-processing. Regions affected by solvent suppression were excluded (4.32–5.15 ppm). Signals alignment was performed by the correlation of optimized warping (COW) and interval correlation shifting (icoshift) algorithms . The speThe multivariate and statistical data analysis was performed on a set of the 42 (+2 unknown) assigned metabolites. The relative concentration of metabolite measured by NMR was obtained as the sum of data points in data matrix for the no overlapping resonances (or a part of partly overlapping resonances) range. Principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA) were conducted in Metaboanalyst v5.0 using log transformed and autoscaled data. The PLS-DA models statistical significance was tested based on the permutation test with respect to separation distance (2000 permutations).Student’s t-test, analysis of variance (ANOVA), as well as simple and multiple logistic regression analysis were conducted in GraphPad Prism version 9.1.1. For univariate Student’s t-test equal variance was assumed. For logistic regression, the RCC status was set as a categorical variable Y encoding outcome, while either single or multiple metabolite abundances were set as continuous variables with main effects. One-way analysis of variance (ANOVA) was used to determine statistical significance of metabolites with respect to T stage, with α = 0.05 for statistical significance. Two-way ANOVA with interaction was used to determine statistical significance of metabolites with respect to gender and RCC status.


Given the importance of high blood pressure, it is important to control and maintain a constant blood pressure level in the normal state. The main aim of this article is to design a model predictive controller with a genetic algorithm (GA) for the regulation of arterial blood pressure. The present study is an applied cross‐sectional study. In order to do this research, studies related to designing mathematical models for blood pressure regulation and mechanical models for heart muscle and pressure sensors are investigated. Then, a model predictive controller with GA is designed for blood pressure control. All control and design operations are performed in the MATLAB software. According to the viscoelasticity of blood, transducer, and injection set, we can assume the mechanical model as Mass, Spring, and Damper. Initially, the patient's blood pressure is lower than normal, and after controlling, the patient's blood pressure returned to normal. By using a GA‐based model predictive control (MPC), mathematical validation, and mechanical model, the patient's blood pressure can be adjusted and maintained. The simulation result shows that the GA‐based MPC offers acceptable response and speed of operation and the proposed controller can achieve good tracking and disturbance rejection. Normally, blood pressure increases and decreases continuously throughout the day, but if left elevated for a long time, it can damage the heart increasing the risk of heart attack , 2.Hypertension is a serious medical condition that raises the risk of heart attack, stroke, and sightlessness leading to premature death. Of the approximately 1.2 billion people with high blood pressure, only 1%–5% of the population keeps it under control. An unhealthy diet, inactivity, alcohol, and red meat are the main reasons for high blood pressure , 4. The In fact, the controller is aware of the input and output restrictions and will in no way produce an input signal that violates the restrictions. Extensive research has been done on the combination of predictive model control and intelligent optimisation methods, which will be discussed further below. In Ref. , an MPC Generally, the goal of controllers is to control the amount of drug being injected into the body, which in turn controls blood pressure and reduces complications during surgery and post‐operative treatment. In Ref. , a propoThe drug injection control system is an electromechanical device that allows intravenous drug injection into the human body and increases its effectiveness by monitoring the rate and timing of drug release. Controlling moderate MAP and cardiac output during clinical practice is highly needed. The type‐2‐fuzzy logic controller based on PID is considered for MAP control and it is observed that the designed controller is robust under conditions of uncertainty, external disturbances, and noise .Hence, the purpose of this study is to control blood pressure based on a combination of MPC with the metaheuristic optimisation method. The proposed approach improves the sensitivity of the blood pressure control system. Indeed, the MPC is an effective strategy for controlling non‐linear systems, and delay systems can optimise responses of the system when the system is under the states and control constraints. Due to using the GA method is expected to improve the responses.The rest of this article is divided as follows. The blood pressure model and model prediction controller based on a genetic algorithm (GA) will be described in Sections 2Blood pressure is actually the average blood pressure over a heart period and is determined by the cardiac output, vascular resistance, and central venous blood pressure. Sustained control of MAP is important in the prevention of acute life‐threatening conditions such as stroke and the reduction of hypertensive diseases. Previous studies have shown that MAP is more accurate than the predicted metabolic syndrome among elderly people with hypertension determined by systolic, diastolic, and pulse pressure . Today, Hypotensive anaesthesia (anaesthesia by lowering blood pressure) is widely used in general surgeries, which results in decreased intraoperative bleeding and requires postoperative blood transfusion. On the other hand, this anaesthesia requires multiple injections of drugs to regulate key physiological variables such as alertness, heart rate, MAP, and respiratory rate . The purIn Figure In this form, the electrical signal is sent to a mechanical environment and is excited by the mechanical elements and eventually converted into an electrical signal. We considered a mechanical source as a source that is excited using an electrical signal. The mechanical source then generates a mechanical signal in a mechanical environment, and this signal is eventually converted to an electrical form and then used to control.ΔMAP is blood pressure changes, SNP is drug injection rate, K patient sensitivity to drug, T1, T2, and T3 drug effect time constants, θ system delay, and α The constant is the circulation of the drug.The MAP model is considered as follows :(1)ΔM3MPC is a control technique, that is used to control a process which includes a set of constraints. MPC is an advanced controller for controlling industrial processes in which the physical constraints on the system can be considered during design. MPC operates based on iterative optimisation of a cost function with a finite horizon at each sampling time, and the control action generates based on the past, present, and future information.Figure The prediction horizon is continuously moved forward, and for this reason, MPC is also called the receding horizon control.At every instant, the behaviour indicated by the plant as well as future plant outputs are calculated in the first place by using a dynamic process model based on the most recent observation from system inputs and outputs.Control signal inputs are calculated by minimising the signal error tracking the difference between the predicted output and the signal of the desired path to follow the trend as much as possible by considering the objective function and constraints.While other control signals are removed as a result of the following sampling instant, merely the first control signal is applied within the plant.The updated value went along with Step 1.MPC has been used as a very practical and successful control method with a simple configuration and it also has been known as a practical controller, that is it able to consider multivariate systems and control the online optimization process. The main structure of the MPC strategy with horizon control is shown in Figure P is the prediction horizon, M is the control horizon, μi and λi are weighting coefficients. y^(t+i|t), w(t + i), u(t + i) are predicted output, desired output, and future control signal, respectively, and the constraints considered as below:u¯, u¯ are lower bound and upper bound of signal control, x¯, x¯ are lower bound and upper bound of state of the system, respectively.The cost function and would be demonstrated as follow:The main goal is to minimise the cost function Equation  by consi4On the basis of natural selection theory, GA is a means of resolving constrained and unconditioned (unrestricted) optimisation problems (issues), which constantly alter the population of single answers. People from the most recent generation at GA such as parents, are chosen which are later utilised to make children. It is important to note that these children themselves are members of the upcoming generation. Among generations that appear consecutively, the number of responses is seen to have developed to an optimal (optimum) response. Considering other features GA, it can be said that several optimisation problems for which standard optimisation algorithms were not compatible to solve, could be resolved .Selection rules, select individual answers called parents.Displacement laws, combine parental characteristics to form their next‐generation child.Mutation laws, randomly apply changes to one (or both) parents to form the offspring of the next generation.At each stage, GA uses three basic rules to create the next generation of the current population:The differences between the GA and the classical derivation‐based optimisation algorithm are summarised in Table One of the key benefits of using GA‐based MPC is its ability to manage a variety of objective functions and processing models without changing the structure of the controller.5GA‐based control uses the process model to search for control movements, which meet process constraints and optimise a cost function. On the other hand, due to the high computational load in MPC and its slowness, the GA‐MPC method can increase the speed of the system and significantly reduce the computational volume. MPC structure based on GA is shown in Figure While making use of the process model, examine process outputs.By doing a GA search in order to find optimal control motion through which the cost function is increased to an optimum level and the limitations (constraints) of the process are satisfied.The optimal control movements created in Step 2 are implemented to the process.Step 1 to three applies to time.Adjusting the input weight matrix can greatly fluctuate the closed‐loop control system. Therefore, using the MPC‐GA approach can reduce these fluctuations to some extent and improve the system response. The following steps describe the operation of the GA‐MPC algorithm:The flowchart of the algorithm of the GA based on MPC is shown in Figure 6The cardiovascular system is actually a mechanism of blood transfusion that delivers nutrients to the tissues and organs of the body and eliminates toxic waste.This cycle involves the heart, and most importantly the heart muscle that circulates blood and blood vessels, which transfers blood to various organs as well as the lung, thereby activating the pulmonary arteries to exchange oxygen and carbon dioxide in the lungs.Blood is transmitted from the aorta to the arteries, arteries, and capillaries. The blood flow to the cardiovascular system is under the laws of mass, motion, and interaction with the arterial wall. Choosing the right model size depends on the goals and accuracy you are looking for. Parametric models of distribution operate mainly on uniform distribution and take into account fundamental variables such as pressure, flow, and volume of the muscle to each muscle or organ at any given moment.This approach provides conventional differential equation heart muscle modelling to evaluate the pervasive distribution of pressure, flow, and blood volume across a range of physiological conditions. Mathematical models of blood flow, with their non‐destructive properties, facilitate the study of pathological and physiological forms, and the calculation of pressure and flow profiles can be part of potential future diagnostic tools.t=0, the pressure suddenly increases and the heart muscle begins to act quickly. Mx Indicates a momentary change in heart muscle function, which is considered here to be an increase in pressure. M represents an effort made in response to increased pressure by the heart muscle. Given these conditions, we will rewrite Newton's Second Law as follows [J is the point where the heart resides.On a patient‐specific basis, profiles can be compared with physiological cases showing normal or pathological blood flow. Investigating the dynamics of neuromuscular reflex movements can provide valuable insights into the status of patients with hypertensive disorders. Consider a patient who sits comfortably with his or her shoulder and elbows with adjustable support and does not perform any activity that increases or decreases heart rate and is in a constant position. Then, over time  follows :(7)MxMx is simplified to a mechanical model of the heart muscle as follows  and the viscosity change (B) parameter are considered as constant units. The equations of motion for the muscle model are:It should be noted that in this mechanical system, muscle contraction, transducer and its set can be summed as mas, spring, and damper, which can show the vascularity of the cardiac muscle, transducer and its set. B)and considering (M0 is a torque that is produced by the heart muscle at the same conditions and M0(t) is actually a temporal operator. By combining the two equations above, a kinetic equation is obtained that determines the dynamics of the system as well as θ changes in the torque applied by hypertension. The equation will be as follows:As stated in the mechanical model of the heart, muscle tissue is modelled by mechanical elements. In this model, the spring is used as muscle tissue tension, the damper as the spring‐resistance, and the spring stabiliser.We draw the concern to this point that damper can mention the resistance of the injection, muscle and injection liquid, spring and mass are noted as viscoelacity of these three items. Assume mass, spring, and damper in one block box. In this block box, mass and spring can be named for the added viscoelacity and damper also be added to the resistance system. Block box  is drawn, fixed, and simulated in Simulink toolbox of MATLAB. Simmechanic is one part of SIMULINK which can place the sensor and monitor the measurement online. Sensors in SimMechanic can produce mechanical signals. We transfer the mechanical signals to electrical signals to m‐file and run the mechanical model in Simmechanic, and transfer mechanical data to m‐file that can be noted as innovation; additionally we also run one mechanical black box as the system's resistance and viscoelacity has merits and demerits that will be explained.7Regular and multi‐parameter implementations since the control system performance simulation time aremainly related to the execution of the control algorithm, and it is sufficient to compare the execution time of the control algorithms to compare the total run time of the simulation for different controllers.7.1For the first simulation, it is assumed that the model of the system operates as follows :(11)GSimulation is performed in MATLAB and Simulink environments It can be assumed that the runtime in other software environments is proportional to the time taken in MATLAB software. Figure Ts=1). Therefore, the discrete system will be as follows:The first design step is changing the continuous model to discrete model. First, we discretise the continuous system using zero‐order hold with a sampling time of 1 , we consider The limitation of the drug infusion rate is the constraint of the proposed controller :(15)uGiven that the GA is involved in optimisation, the following figure shows the convergence performance of this algorithm in each iteration. Due to the long simulation time, these quantities gradually converge to zero. This demonstrates the high capability of the algorithm to find the optimal point without the need for sophisticated computation. The parameters of the GA adjustment are summarised in Table In Figure We then plot the complete system response presented in Figure The initial visit blood pressure for the patient is considered 40 mmHg. As shown in Figure 7.2Investigating the effect of parameters is a reliable way to check how a control approach works, and by applying the changes to each one, we can find out how the control system works and use it in designing or improving previous designs.P) on the system response is shown. As the horizon increases, the computational volume increases, but the signal becomes softer and has a lower jump than lower values. On the other hand, this increase makes the system slower. As the prediction horizon decreases, the system speed increases, but on the contrary, the amount of jump increases.Overall, this review provides a comprehensive overview of the performance of the controller under different conditions. In Figure P in Table Additionally, Integral Absolute Error (IAE), Integral Square Error (ISE), and Mean Square Error (MSE) error criteria are presented for different values of 7.3The following is a review of the effect of the perturbation on the patient, which is the effect of diuretics that is mainly used in patients with renal impairment, and its major side effect is the reduction of the patient's blood pressure. The disturbance is considered to be sinusoidal and also can be noted as the heartbeat, injection device, percentage of injection material and nervous system. The amplitude of this perturbation is 10. Figure Also, Table In this section, we consider the unit step as the reference value. The output (MAP) and the control signal (Drug infusion rate) of the proposed GA‐based MPC control system are shown in Figures Figure 7.4The comparison in Figure It should also be noted that the system always responds faster than other controllers. This is one of the main benefits of MPC. With significant advances in system response, this control approach is applicable to a wide range of patients.Additionally, the comparison between the performances of the controllers is shown in Table 8Among the advanced control methods, MPC has been in place for decades, as the most successful advanced process control method has found a good place in various industries.MPC can be described, firstly, by foretelling the future output of the system, and secondly, by calculating future output signal online which is carried out by means of bringing the target function to the lowest level under state and input constraints. On the final stage, they apply to real systems and then the stages covered so far are again duplicated by measuring state, output, and input variables.Therefore, the presence of a proper model of the real system is a prerequisite for the design of interpolation control. The approaches that are nowadays more favoured are the combination of this controller with meta‐heuristic methods and optimisation algorithms. Therefore, in this study, hypertension control strategy using a predictive control technique and its combination with GA are presented. The main focus of this article is to evaluate the performance of the GA based on MPC strategy that has been used to achieve optimal SNP drug infusion rate to regulate MAP and also to apply this method to the cardiac muscle mechanical model and using this mechanical model is the main difference compared with Ref. . The GA As can be seen from the results of parameter changes, the large forecast horizon will provide more stability for the closed‐loop system. Therefore, the idea of the forecast horizon has a significant impact on the design of a stable predictor controller. Of course, choosing large quantities for the forecast horizon increases the optimisation calculations. Changes to the control horizon also allow the control of the large horizon control to be activated, making the system slower and less mobile for smaller values of the control horizon. In general, this study focuses on the control of MAP. Other haemodynamic variables were not considered. In order to bring the results of this research to real and practical blood pressure control systems, the heart muscle mechanical model has been used and mainly mechanical systems have many limitations in their performance, as well as its configuration and relationship with the controller which actually has an electrical nature put a lot of restrictions. The simulation results achieve the main objectives and the algorithms provide good performance.Applying and testing the control approach presented in clinical trials using animals.Implement interactive MIMO control instead of using SISO control loop.Provide model patient models to further manage physiological parameters such as mean pulmonary artery pressure as output and adjust it by injecting phenylephrine as input and also considering patient stress and age as impactful parameters on blood pressure.However, the following suggestions for future research are available:Obtain real patient information and use a neural network to obtain a non‐linear model that best describes the patient's response to various drugs.