Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein–protein interaction network of the genes containing the identified SNPs  reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures. Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease worldwide4. These conditions are strongly associated with high rates of cardiovascular disease and mortality6, as well as very high medical expenditure7. DKD risk prediction via reliable biomarkers is currently an unmet clinical need. Urinary albumin excretion and serum creatinine are the common clinical biomarkers used for diagnosis and staging of renal impairment among chronic kidney disease (CKD) patients. However, abnormalities in these two parameters often indicate existing kidney damage rather than a predisposition of renal impairment in the future8. Their usefulness when estimating glomerular filtration rate (eGFR) is subject to several limitations due to various potential confounders, such as age, sex, muscle mass, and changes in glomerular hemodynamics due to hyperfiltration that is secondary to the patient’s hyperglycaemia status9. Other biomarkers of kidney injury, including cystatin C, kidney injury molecule 1, neutrophil gelatinase-associated lipocalin, and liver fatty acid-binding protein, are not specific to DKD and also have disadvantages. Furthermore, liver or thyroid dysfunction, an alteration in urine volume, a change in creatinine concentration, and treatment with various medications can also affect the levels of these biomarkers13. An approach that accurately predisposes high risk DM patients to DKD remains urgently needed.Diabetes mellitus (DM) remains a major medical challenge and affects 463 million adults globally14. Given the various hemodynamic and metabolic disarrangements exerted by hyperglycaemia on kidney tissue, metabolomic analysis represents a very useful way of addressing this clinical issue15. In addition, genome-wide association studies (GWAS) have been able to identify a number of potential genes, loci, and single-nucleotide polymorphisms (SNPs) that are associated with DKD, which implies that genetic susceptibility is part of the pathogenesis of DKD18. However, up to the present, the development of biomarkers focusing on the genomic-metabolomic signatures specific to DKD has not taken place.High-throughput omics approaches have revolutionized biomarker research and have helped to advance our understanding of renal progression in DM patientsTo fill this knowledge gap, we conduct a trans-omics study that integrated high dimensional data collected from an extensive clinical information dataset, an untargeted metabolomics dataset and a lipidomics dataset, as well as genome-wide SNP genotyping. We adopt a machine learning (ML) methodology to delineate the complex biological processes associated with four health conditions: control subjects, subjects with DM, subjects with CKD and subjects with DKD. Furthermore, we performed protein connectivity mapping of the genes containing the relevant SNPs in order to determine their potential roles in the molecular pathogenesis of CKD and DKD, specifically their functional connectivity and their protein–protein interaction network.2. The DKD patients were more like to be older, to have hypertension, to have a higher serum triglyceride level, a higher level of calcium, and a higher level of insulin . The analysis consisted of three parts: 1) an analysis of the performance of metabolomics, SNPs and the clinical information data associated with the four groups of subjects; (2) feature selection; and (3) model derivation and validation , six features were selected by AI from all of the groups, namely three metabolites and three annotated SNPs Fig. . The thrp < 0.001; Wald test), the PCDH9 genotype , the LY6D genotype , and the ALDH1L1 genotype . Conversely, a higher level of serine  appeared to be associated with a lower risk of the occurrence of DKD  was present for the occurrence of DKD in relation to resolvin D1 . Conversely, a significant decrease in the frequency of the AG genotype of ADCYAP1 gene was found among non-diabetics CKD patients compared to the other groups , abundance of mannose/inositolI , the FAM53A genotype , the CDS1 genotype , and the CCDC182 genotype . On the other hand, there was a significant decrease in the risk association of non-diabetic CKD with the B3GNTL1 genotype , and the abundance of PC C30:2  , and Model 3 (identification of CKD). Eight of the protein-coding genes identified in Model 1 (RAPTOR and CLPTM1L), Model 2 (PCDH9 and ALDH1L1), and Model 3  are able to be connected to each other and they form an obvious protein–protein interaction network , CKD and non-CKD in DM patients , and CKD and non-CKD in non-DM patients  with good prediction performance. Additionally, we identified a biomarker signature made up of several phospholipids (Phosphatidylcholine and Lysophosphatidylcholine), a lipid mediator (resolvin D1) and a purine/pyrimidine metabolism mediator (pseudouridine) that are associated with DKD. Furthermore, the findings related to specific genotypes of the annotated SNPs associated with CKD or DKD highlight that there seems to be genetic susceptibility in predisposed subjects toward renal impairment. Intriguingly, analysis of the protein–protein interaction network of the genes containing the SNPs identified by the three models revealed that, at the molecular level, there seems to be an overlap between the pathogenesis of DM and the pathogenesis of CKD; it also identified various interconnected factors that may affect the progression of renal impairment among DM patients, namely the occurrence of DKD.21. These methodologies have been able to identify altered lipidomic profiles associated with the risk of renal progression among CKD patients22 and to uncover proteomics and metabolomics biomarkers associated with early vs. advanced stage DKD23. Here, we have utilized AI-based techniques combined with traditional approaches to select subsets of genomic and metabolomic features that give the best performances and avoided overfitting the training data24. Importantly, the performances of the three models built in this study were verified using an independent validation cohort and this cohort gave similar results that are comparable to those obtained from the training cohort. Our findings indicate that an AI-assisted approach is able to considerably improve the precision diagnosis of CKD and DKD and this represents an important tool for providing insights into the potential molecular mechanism(s) that underlies various renal disease states.The ability to generate trans-omics big data makes machine learning approaches suitable for obtaining both biological and translational perspectives related to questions in kidney biology23. Our findings are consistent with previous reports whereby there are perturbations affecting the metabolites associated with glycerol-lipid metabolsm and oxidative stress25, such as glycerol-3-galactoside23 and asymmetric dimethylarginine26, as well as purine and pyrimidine metabolism, namely pseudouridine27, in patients with DKD28. Diacylglycerol can activate protein kinase C, which, in turn, affects various inflammatory and growth-promoting pathways9. Conversely, serine, glutamine and lysophosphatidylcholine C18:2 are associated with a decreased risk of DM29. We also found an increase of serum kynurenine levels in CKD patients. Kynurenine is derived from tryptophan metabolism; it appears to play a role in several biological processes, including energy metabolism, the pro-inflammatory response, atherosclerosis and oxidative stress31. Increased concentrations of kynurenine have been found to be associated with acute kidney injury, CKD, DKD and cardiovascular diseases33. Intriguingly, we also found an increase in the level of resolvin D1 in DKD patients; this molecule is a bioactive lipid mediator derived from omega-3 fatty acid. Previous studies have shown that resolvin D1 seems to be able to promote the resolution of the inflammatory response and has anti-oxidant effects35. Accordingly, it is possible that the increase in resolvin D1 is a compensatory effect in response to the elevated inflammation and increased oxidative stress in DKD patients. Additionally, some of the biomarkers discovered in the present study may serve as mediators of the post-transcriptional and/or post-translational modifications involved in progression of injury-repair processes in the kidney under hyperglycemia.The use of serum metabolites increases accuracy when estimating kidney function during DKD compared to serum proteins36. Previous publications have reported a variety of SNPs that are associated with DKD, including CNDP1, FRMD3, RGMA-MCTP2 (rs12437854), AFF3 (rs7583877), ERBB4 (rs7588550), COL4A3 (rs55703767), and MUC738. However, combination of SNPs does not improve discrimination of DKD or CKD beyond traditional risk factors in a range of ethnic populations41. In this study, several protein-coding genes containing SNPs were selected by AI and used as important features when distinguishing the various groups; this highlights the importance of genetic susceptibility to renal dysfunction among predisposed subjects. Among the SNPs associated with DKD, ALDH1L1 encodes a protein that belongs to the aldehyde dehydrogenase family and is associated with the NADPH pathway. Furthermore, LY6D acts as a specification marker during the earliest stage specification of lymphocytes during B-cell and T-cell development. In addition, PCDH9 is responsible for encoding a potential calcium-dependent cell-adhesion protein. On the other hand, B3GNTL1, CDS1, ADCYAP1 and FAM53A SNPs are associated with CKD among non-DM patients. Among these, B3GNTL1 is a putative glycosyltransferase, CDS1 catalyzes the conversion of phosphatidic acid to diacylglycerol and ADCYAP1 encodes a secreted protein that is involved in the control of glucose homeostasis and the regulation of insulin secretion in pancreatic beta cells42. Intercommunication between these genetic factors, which was revealed by the connectivity map, deserves further functional investigation in order to decipher their roles in the pathogenesis of DM and CKD.Genomics studies have identified several genetic variants associated with kidney disease; however, their functional effects remain poorly understoodThere are several limitations to the present study in spite of the meticulous application by us of a range of ML approaches to leverage the big datasets associated with our multi-omics data in order to gain insights into disease phenotype association. Firstly, we used the levels of circulating biomarkers to identify disease association. Analysis of multiple tissues, for example, kidney, liver, and muscle, needs to be performed to clarify the sources of these biomarkers. Secondly, renal biopsy was not conducted to ascertain the histopathological definition of DKD in a community setting due to its intrusiveness. Nevertheless, various clinical clues that are indicative of superimposed glomerulonephritis, such as hematuria, red cell cast or nephrotic range proteinuria, were minimal among our participants. Thirdly, this is a cross-sectional study derived from a single ethnic group. Although an external validation cohort was used, our findings need to be verified on a larger scale and using a number of ethnically diverse cohorts. Fourthly, questions relating to the cause-and-effect relationship between the genes identified in the present study need to be investigated using transcriptional profiling, a cell platform system and/or an animal model approach. Finally, longitudinal studies are needed to validate the usefulness of our models when distinguishing renal progression; this is essential so that the trans-omics signature can become a biomarker profile that can be used for personalized medicine on DKD patients.In conclusion, our findings reveal the potential of employing ML models to augment traditional methods and to help to identify molecular mechanism(s) underlying the complex interaction between DM and CKD; this was done via protein–protein interaction network analysis. Knowledge of how various SNPs and the interaction of metabolites are associated with the CKD and DKD phenotype provides researchers with insights into possible genetic predispositions for these diseases. Moreover, the development of AI-assisted models in this study will advance the ability to carry out precision diagnosis and the molecular classification of DM, CKD, and DKD; this in turn will help the prevention of these diseases and thus will benefit clinical practice in general.Between August 2013 and November 2019, 618 prospectively recruited participants within the Northeastern Taiwan Community Medicine Research Cohort  were enrolled in this study. Community members who were aged over 30 years old were included in the study after obtaining individual informed consent. Subjects who were pregnant, who were undergoing dialysis therapy, or who had undergone renal transplantation, were excluded. At recruitment, all participants provided a detailed personal history and received a clinical examination. An independent cohort (178 subjects) that was used for external validation was collected from individuals who attended outpatient clinics at Chang Gung Memorial Hospital in 2019 and 2020; the same inclusion and exclusion criteria mentioned above were used for these individuals. Demographic information was collected by questionnaire. Fasting blood and spot urine samples were collected for biochemistry analysis. This study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Chang Gung Medical Foundation . Written informed consent was obtained from all subjects involved in the study.2 or more, which was calculated as weight divided by height2, was defined as obesity43. CKD was defined using the National Kidney Foundation: Kidney Disease Outcomes Quality Initiative classification with a persistent proteinuria or an eGFR of less than 60 mL/min/1.73 m2, as determined by the abbreviated Modification of Diet in Renal Disease equation44. Proteinuria was defined if the individual’s urine protein to creatinine ratio was ≥150 mg/g or the individual’s urine albumin to creatinine ratio was ≥ 30 mg/g. DKD was diagnosed if subjects fulfilled both DM criteria and CKD criteria at the same time. Current smoking status was defined as having smoked more than 100 cigarettes in their lifetime and having smoked in the one month before enrollment.Type 2 DM was defined as a fasting glucose of ≥126 mg/dL, a glycosylated hemoglobin ≥6.5, or the use of hypoglycemic medications. Blood pressure was measured using the average of two seated measurements. Hypertension was defined if the patient was receiving medical therapy for such a condition or if their blood pressure was >140/90 mmHg. A body mass index (BMI) of 30 kg/mPeripheral venous blood was obtained after an overnight fast. After centrifugation in 1000 g, the plasma component of the blood was used for either immediate biochemistry analysis or for storage at −80 °C for the subsequent measurements. Genomic DNA was then isolated from peripheral white blood cells using the phenol/chloroform  DNA extraction method after lysis of red blood cells. Finally, the DNA from each subject was precipitated and washed using 95% isopropanol , followed by 80% alcohol ; the resulting DNA was used as total genomic DNA of each individual. Various clinical parameters were determined, including complete blood cell count, liver biochemical marker levels, renal biochemical marker levels, lipid profile, fasting sugar level intact parathyroid hormone level and total 25 (OH) vitamin D level. Serum creatinine was assessed by spectrophotometric analysis using a modified kinetic Jaffe reaction with standardization of the creatinine calibration by an isotope dilution mass spectrometry reference measurement procedure. Electrolyte levels  and carbon dioxide level were assessed using ion-selective electrode methods. Serum calcium and phosphate were measured by spectrophotometric methods . Serum albumin and uric acid levels were assessed by colorimetric methods . Blood urea nitrogen, was measured conductometry . Serum intact parathyroid hormone and vitamin D were measured by electrochemiluminescence immunoassay . Lipid profiles were obtained by enzymatic methods . Hemoglobin concentrations were obtained by the cyanide-free sodium lauryl sulphate-Hb spectrophotometric method . Urine protein and albumin levels were quantified by colorimetric methods .The plasma samples were collected and then they were extracted using methanol before there use in both the untargeted metabolomics analysis and an analysis by commercially available kit .For the untargeted metabolomics analysis using ultra-high performance liquid chromatography-time-of-fly mass spectrometry (UPLC-TOF/MS), in total, 50 μL plasma and 200 μL of cooled methanol were mixed to precipitate any protein present. After centrifugation at 12,000 g for 15 min, the supernatant after transfer was dried using nitrogen gas. The residue was then dissolved in 200 μL 50% acetonitrile for LC-MS analysis. Liquid chromatographic separation was achieved on an ACQUITY UPLC BEH Amide column  using an ACQUITY TM Ultra Performance Liquid Chromatography (UPLC) system (Waters Corp.). The column was maintained at 45 °C, and the flow rate was set at 0.4 mL/min. The mobile phase A was 0.1% formic acid in water and mobile phase B was acetonitrile containing 0.1% formic acid. Mass spectrometry was performed on a Waters Q Tof-MS  operated in ESI positive and negative ion modes. The scan range was from 50 to 1000 m/z. The desolvation gas flow was 800 L/hr at 500 °C. The source cone voltage was 25 V. The capillary voltage was 2.5 kV in the positive mode and 2 kV in the negative mode. The lock mass was leucine encephalin (m/z 120.0813 and 556.2771 for positive and m/z 236.1035 and 554.2615 for negative).For the targeted lipidomic analysis using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), the plasma samples were analyzed using a commercially available kit . The targeted 184 metabolites included amino acids, biogenic amines, glycerophospholipids, sphingolipids, acyl carnitines, and hexose and all of these molecules were quantified. The samples were processed as previously described (3). The biogenic amines and amino acids were determined by LC-MS/MS, and the other lipid species were quantified by flow injection analysis coupled with tandem mass spectrometry (FIA-MS/MS). The analysis was performed in positive electrospray ionization mode using a Waters tandem mass spectrometer . Chromatographic separation was performed on an Acquity BEH C8 column  at 50 °C using a linear gradient that ranged from 0.2% formic acid in water to 0.2% formic acid in acetonitrile at a flow rate of 0.9 mL/min. The capillary was set at 3.2 kV. The desolvation gas flow was 1200 L/h at 650 °C. The source temperature was 150 °C and the cone voltage was 10 V. For FIA analysis, 0.03 mL/min was used with a commercial solvent, and the capillary was set at 3.9 kV; the desolvation gas flow was 650 L/h and 350 °C, while the source temperature was 150 °C and the cone voltage was 20 V. All data was processed and analyzed using MetIQ software . During further analysis, metabolites with >10% missing values, as well as values below the limit of detection (LOD), were excluded. Data on a total of 147 metabolites from five compound classes were collected and these consisted of: 15 acylcarnitines, 21 amino acids, 9 biogenic amines, 88 glycerophospholipids and 14 sphingolipids. All values were processed by median normalization and log2 transformation as appropriate.The genomic DNA was collected from peripheral venous blood and each subject was genotyped using AxiomTM Genome-Wide TWB 2.0 array plates . After excluding those with a minor allele frequency rate of 0 or SNPs with a missing rate of more than 10%, a total of 392,885 SNPs were available for further analysis.For the model training and testing, all machine learning analyses were performed with R Version 4.2.1  and Python 3.7.11 (scikit-learn and xgboost packages). First, we defined three models  for differentiating DM and CKD using two-stage model building. Model 1 was used to identify DM based on metabolomes, SNPs and clinical tests. Model 2 was used to identify CKD in DM patients based on metabolomes and SNPs. Model 3 was used to identify CKD in non-DM patients based on metabolomes and SNPs. We carried out the same steps for each model using the following feature selection methods.45 a shrinkage coefficient > 0 using the Least Absolute Shrinkage and Selection Operator (LASSO) approach. Subsequently, the results obtained from these three algorithms were integrated to produce feature importance ranking lists for the three models, namely Model 1, Model 2 and Model 3  or χ2 test  were used to extract the minimum features required for highest performance in terms of AUC and accuracy rate .The ANOVA was used to compare differences between the continuous variables that were derived from more than two groups. The results are presented as medians  or means (standard deviations). The χFurther information on research design is available in the SUPPLEMENTAL MATERIALReporting Summary


Rates of clinically significant annual asthma exacerbation were also decreased by 71.22% after 52-week therapy with mepolizumab (p < 0.001) associated with a reduction in the mean daily dose of oral corticosteroids. Two patients (3.27%) had to discontinue mepolizumab due to musculoskeletal disorders with no severe safety issues reported. The use of mepolizumab as an add-on therapy in routine clinical practice was safely associated with significant clinical and functional in the overlapping eosinophilic-and-allergic SA phenotype. The current data should support clinical and therapeutic decision-making in this T2-high SA endotype.Severe asthma (SA) is categorized into multiple overlapping phenotypes and clinical characteristics driven by complex mechanistic inflammatory pathways. Mepolizumab is a human monoclonal antibody effectively targeting interleukin-5 in severe eosinophilic asthma. However, the eligibility of biologics in coincident SA phenotypes is still unclear. We assessed the efficacy and safety of mepolizumab in real-life patients with the overlapping T2-high SA endotype. This was a phase IV, single-centre observational cohort study including patients with severe refractory T2-high asthma in treatment with mepolizumab. After 12 months of treatment with mepolizumab, significant improvements ( Severe asthma (SA) is a complex heterogeneous condition characterized by chronic inflammation of the pulmonary airways that affects 3–10% of the asthma population worldwide, comprising several pheno-endotypes that are distinguished by different clinical characteristics, pathobiological mechanisms, and biomarker expressions . ConsideHouse dust mites (HDM) and storage mites (SM) allergens are nowadays considered key players in the pathogenesis and severity of type 2-high inflammatory responses in asthma ,9. HDM aAs monoclonal antibodies (mABs) have become available, the inflammatory endotype characterization of SA turned out to be crucial to provide a more personalized workup and management when considering biologic therapy to treat patients with uncontrolled SA more efficiently . AccordiMepolizumab is a subcutaneously (SC), monthly-administered humanized IL-5 antagonist mAB, approved in Spain in 2016 for the treatment of patients with severe refractory asthma inadequately controlled with standard high-dose inhaled corticosteroids (ICS) and other long-term controller agents ,18. AlthThe present phase IV, single-centre observational cohort study included 61 subjects with severe uncontrolled asthma in treatment with mepolizumab 100 mg every 4 weeks (100 mg-q4w) for 52 consecutive weeks in the Severe Asthma Unit and Outpatient Allergy Clinic at Hospital Universitario de Canarias . Key inclusion criteria to start treatment with mepolizumab were a clinician-confirmed diagnosis of severe refractory asthma with the T2 signature—i.e., eosinophilic; high total IgE and sIgE to airborne allergens—according to the 2020 Global Initiative for Asthma (GINA) Guidelines  in subje®, Sibel S.A.U., Barcelona, Spain- and fractional exhaled nitric oxide (FENO), Fenom Pro®, Caire Diagnostics, Ball Ground, GA, USA—were performed according to standardized guidelines [The following data were retrospectively collected from patients’ clinical records from January 2021 to July 2022, over an 18-month period: Sociodemographic data, clinical profile—age of asthma onset, atopy, nasal polyps, or any relevant comorbidity to asthma—, number of asthma exacerbations, and concomitant use of systemic corticosteroid therapy. The validated self-administered Asthma Control Test (ACT) and Sino-Nasal Outcome Test-22 (SNOT-22) questionnaires were assessed and pulmonary function, including pre- and post-bronchodilator spirometric tests—Datospir 600idelines ,27.Clinically significant AE were considered as those demanding systemic steroids for at least 3 days or doubling the dose in those subjects on maintenance OCS, or if the patient had visited an emergency department or was admitted to hospital. Corticosteroid dependence was defined as the daily use of OCS for at least 6 months. Asthma exacerbation rates, ACT, SNOT-22, use of systemic steroids, pulmonary respiratory function, and FENO were compared from baseline to 52-weeks values after subsequent treatment with mepolizumab 100 mg-q4w. Pregnant and breast-feeding women were excluded. The study was approved by the local Ethical Committee of our Institution and informed consent was signed by all subjects and parents/guardians for those participants <18 y.o.Dermatophagoides pteronyssinus (D. pteronyssinus), Dermatophagoides farinae (D. farinae), and Blomia tropicalis  mite extracts [The skin prick test (SPT) was performed according to European Guidelines, enclosing a diagnostic panel  with standardized extracts . HistamiD. pteronyssinus, D. farinae, and B. tropicalis (raw extracts), and sIgE to Der p 1, Der p 2, Der p 5, Der p 7, Der p 10, Der p 11, Der p 20, Der p 21, Der p 23, Blo t 5, Blo t 10 and Blo t 21  according to the manufacturer’s instructions. In brief, ALEX is a multiplex array containing 282 reagents . The different allergens and components are coupled onto polystyrene nano-beads, and then the allergen beads are deposited on a nitrocellulose membrane, as published elsewhere [Serum blood samples including blood eosinophil measurement were obtained from all participating subjects, identified with a code label, stored at −40 °C, and thawed immediately before the in vitro analysis. Total IgE levels, specific IgE (sIgE) to lsewhere .A/L. Values ≥ 0.35 kUA/L were considered positive. Blood eosinophils, total IgE levels, specific sIgE to D. pteronyssinus, D. farinae, and B. tropicalis were compared after 52 weeks of treatment with mepolizumab 100 mg-q4w to baseline values.Total IgE levels were expressed in international units per unit volume (IU/mL), sIgE levels were expressed in kUDemographic features were summarized by medians and standard deviations for continuous variables and percentages for categorical variables. A paired two-tailed t-test was used for comparison of before/after periods for continuous variables and the McNemar test for categorical variables. To compare more than two groups, ANOVA was used. A P value of less than 0.05 was considered statistically significant. All statistical data were analysed using GraphPad Prism version 8.0.0 for Windows, GraphPad Software, La Jolla, CA, USA.A total of 111 patients were screened, with 61 of them—37 females, 24 males, median age 49.0 years—finally confirming their eligibility for the study .Dermatophagoides spp. and/or B. tropicalis and had their asthma onset during childhood or adolescence −42 out of 61 patients, 68.85%—with a high (80.32%) known family history of atopy reported. Regarding main comorbidities, 52 patients (85.24%) suffered from allergic rhinitis, 22 subjects (36.06%) had nasal polyposis, NSAID sensitivity was present in 11 individuals (18.03%), 17 of them (27.86%) suffered from chronic rhinosinusitis (CRS), gastroesophageal reflux disease (GERD) affected 15 out of 61 subjects (24.59%), and atopic dermatitis was confirmed in 14.75% of the studied population. The mean body mass index (BMI) was 29.10 ± 4.04 kg/m, with 25 (40.98%) and 24 (39.34%) patients being obese or overweight, respectively  at baseline with a mean value of 13.08 ± 4.61. The mean number of annual clinically relevant asthma exacerbations was 3.37 ± 1.34, and daily OCS were needed in 10 (16.39%) patients with a mean maintenance dose of 11.3 ± 9.6 mg/day. Regarding previous biologic treatments, a total of 11 (18.3%) out of 61 subjects were switched from omalizumab to mepolizumab due to inadequate symptom control or limited clinical response.Blood eosinophils showed a mean value of 541 ± 283.04 eosinophils/μL at baseline. A total of 19 out of 61 patients (31.14%) showed an overall mean baseline blood eosinophil count of 876.1 ± 242.98 cells/µL. In addition, a quantitative analysis of serum total IgE was performed to evaluate the atopic status in the study population with a mean value of 614.53 ± 1196 IU/mL at baseline.A/L) for either the raw extract of D. pteronyssinus, D. farinae or B. tropicalis, showing a corresponding mean value of 48 ± 78.68, 50 ± 81.96 and 28 ± 45.9 at baseline.Fifty patients (81.96%) were independently sIgE positive , followed by Der p 1 (75.0%), Der p 23 (72.22%), Der p 5, Der p 21 (58.33%), Der p 7 (47.22%), Blo t 5 (41.23%), and Blo t 21 (35.33%). Minor allergens—i.e., Der p 10, Blo t 10, Der p 11, and Der p 20—were represented in less than 12% of the present sample. The quantification (mean ± SD) of single molecular allergens was led by Der p2 (17.69 ± 16.51), Dep p 23 (12.62 ± 14.59), and Der p1 (10.47 ± 5.98).p < 0.0001) improved from 13.8 ± 4.61 to 18.91 ± 4.79 after 52-weeks of treatment with mepolizumab 100 mg-q4w (p < 0.001) were also observed in the mean number of annual AE rates from 3.37 ± 1.34 at baseline to 0.97 ± 1.19 after mepolizumab , from 72.75 ± 17.85 at baseline to 80.95 ± 18.17 (p = 0.0134) after treatment with mepolizumab (p = 0.341).Pulmonary function—FEV1, measured both in mL and as a percentage—was significantly increased up to 318 ± 150 mL (olizumab . In contp < 0.0001)  after 52-week treatment with mepolizumab  , with noIn addition, improvements in asthma control, pulmonary function, and rate of annual AE were confirmed after 52 weeks of mepolizumab, irrespective of the blood eosinophil threshold (<600 cells/µL vs. ≥600 cells/µL) at baseline .p = 0.008) and number of clinically relevant asthma exacerbations (p = 0.002), but not in FEV1 (p = 0.104), were observed in the subgroup of subjects (n = 11) switched from omalizumab to mepolizumab despite the fact that a significant reduction in blood eosinophils (p = 0.002) was confirmed after the 52-week treatment with mepolizumab compared to baseline values.Improvements in asthma control (p = 0.2215), serum total IgE , or sIgE to D. pteronyssinus, D. farinae or B. tropicalis.A total of 22 (36.06%) out of 61 subjects—54% males, median age 48.5 years—had a concomitant diagnosis of nasal polyposis (NP), with 9 (14.75%) of them associating NSAID-Exacerbated Respiratory Disease (NERD). No significant differences were found in this subset compared to patients without nasal polyps in terms of mean levels of basal blood eosinophils , FEV1 , but similar in the rate of AE  was observed in the subset of patients with NP in relation to those without after 52 weeks of mepolizumab. The mean baseline SNOT-22 value showed a marked improvement after 52 weeks of mepolizumab in the subgroup of NP  (p = 0.0233). In addition, a significant decrease in the mean number of endoscopic sino-nasal surgeries for NP prior and after treatment with 52 weeks of mepolizumab was found    in contr 0.0001) .p = 0.0574) but a higher baseline FEV1  and fewer AE  at baseline compared to partial or no-responders. Regarding biological markers of T2-inflammation, no significant differences were found in the mean levels of basal blood eosinophils , serum total IgE , sIgE to D. pteronyssinus , D. farinae , or B. tropicalis , in the subset of super-responders in respect to those regarded as partially or no-responder subjects.A full response after the 52-week treatment with mepolizumab was considered when no AEs were recorded, ACT score ≥ 20, FEV1 ≥ 80% and OCS could be withdrawn . AccordiOverall, 3 out of the 61 (4.91%) patients reported five adverse events related to mepolizumab during the 52-week study period. Two patients (3.27%) had to discontinue mepolizumab after 24 and 40 weeks of treatment with mepolizumab due to musculoskeletal and/or connective tissue—arthromyalgias—disorders. Both patients remained asymptomatic from their musculoskeletal disorders after discontinuation of mepolizumab and the subsequent 52-week follow-up. No severe adverse effects related to mepolizumab were recorded during the follow-up of the study .As only a minority of patients with SA would be eligible for inclusion within a RCT, it has been proposed that those data could be complemented with RWE studies to achieve a broader understanding of the efficacy and safety of biological therapy under real-life conditions ,32. As tAs expected, recruited subjects associated a marked T2-inflammation co-morbidity spectrum, with a higher proportion of atopy (>83%) compared to MUSCA, mepolizumab, as adjunctive therapy in patients with severe asthma (46%), and REDES, Real world effectiveness and safety of mepolizumab (41.5%) studies, including allergic rhinitis (>85%), NP 36.06%) or atopic dermatitis (14.75%) ,35. Foll.06% or aNotably, a widely heterogeneous molecular response to relevant mite allergens was identified in this cohort (>80%), including Der p1, a papain-like cysteine protease, which has a pivotal role in the maturation of HDM serine protease allergens that stimulates innate immune pathways, and Der p 2, belonging to the NPC intracellular cholesterol transporter 2, Niemann-Pick proteins type C2 family—which tends to be targeted by adaptive immune responses because of its auto-adjuvant characteristics ,38,39.Significant benefits were observed in the current population after the 52-week treatment with mepolizumab in several clinically relevant outcomes, including asthma control, lung function, annual rate of AE and use of OCS. In line with former post hoc meta-analysis of 2 RCTs, mepolizumab significantly improved ACT and FEV1, and reduced the rate of AE, regardless of blood eosinophil count at baseline ,41, showConsidering a minimally important difference (MCD) previously established at 3 points , the meaA higher reduction in the rates of clinically significant AEs (71.22%) was also confirmed in the selected cohort after 52 weeks with mepolizumab, compared to the pivotal MENSA—Mepolizumab treatment in patients with severe eosinophilic asthma—and MUSCA trials (up to 58%) and in subsequent RWE studies ,45. FurtConsidering pulmonary function, and 200 mL as the MCD for FEV1, our data confirmed a significantly higher improvement (>300 mL) compared to previous large clinical trials as MENSA (98 mL), SIRIUS—Steroid reduction with mepolizumab study—(128 mL), REDES (210 mL), and even in a post-hoc research considering therapy with mepolizumab according to omalizumab eligibility (114 mL) ,44,45,46Although the current SA cohort showed higher (541) mean blood eosinophils at baseline (cells/µL) compared to MENSA (290)  and SchlThe clinical response to mepolizumab was also assessed in the present cohort, and despite different eligibility criteria being applied, a similar rate (>27%) of SR was obtained in comparison to a large Australian post-marketing surveillance registry of patients treated with mepolizumab for SA (31.34%) . In lineInterestingly, despite it having been shown that NP increases the risk of mortality and impairs the quality of life in patients with SA and CRS , asthma Besides the size of the studied population, the present real-life investigation has some limitations, as sputum induction was not assessed, and the lack of a control group must be considered when interpreting the overall data. Moreover, a longer follow-up period (>52-weeks) would be required to further establish the efficacy and safety of mepolizumab in this SA cohort.Our results confirm that the use of mepolizumab as an add-on therapy in daily clinical practice for 52 weeks was associated with significant improvements in asthma control and lung function, and reductions in clinically relevant exacerbations in patients with the overlapping eosinophilic-and-allergic SA phenotype. In addition, treatment with mepolizumab was well tolerated, showing a safety profile coincident with former clinical investigations, providing clinically valuable insights to improve SA management in the studied population.


Heart failure (HF) is one of the greatest problems in healthcare and it often coexists with declining renal function. The pathophysiology between the heart and the kidneys is bidirectional. Common mechanisms leading to the dysfunction of these organs result in a vicious cycle of cardiorenal deterioration. It is also associated with difficulties in the treatment of aggravating HF and chronic kidney disease (CKD) and, as a consequence, recurrent hospitalizations and death. As the worsening of renal function has an undeniably negative impact on the outcomes in patients with HF, searching for new treatment strategies and identification of biomarkers is necessary. This review is focused on the pathomechanisms in chronic kidney disease in patients with HF and therapeutic strategies for co-existing CKD and HF. Heart failure (HF) is a heterogeneous clinical syndrome resulting from injury and cardiac overload that in consequence leads to the elevation of intracardiac pressure and inadequate cardiac output. Due to left ventricular ejection fraction (LVEF), HF is divided into three main categories: HF with preserved , mildly reduced , and reduced ejection fraction  [Patients suffering from HF are at high risk of comorbidities which are strictly connected with higher mortality risk, increased burden of healthcare costs, and adverse outcomes ,3. AdditThe heart and kidney are closely related. Their role is to maintain salt–water homeostasis and normal blood pressure. Renal impairment and disturbance of salt and water excretion results in an increase in cardiac preload as well as afterload. Furthermore, low cardiac output can decrease kidney perfusion and lead to kidney failure. Therefore, renal impairment is one of the most serious consequences of HF . Primary2 was 18/1000 person-years. CKD appears more common in HFpEF, nevertheless, worse outcomes are mostly related to HFmrEF and HFrEF [A decrease in glomerular filtration rate (GFR) seems to be the most significant determinant of the overall progression of HF . Damman The heart and kidneys play a major role in maintaining fluid homeostasis and normal blood pressure in the body. In physiological conditions, the cooperation between the heart and the kidneys enables a response to changes in renal perfusion, such as volume reduction or overload, which may cause ischemia or hyperperfusion injury . On the The coexistence of abnormalities in the function or structure of both the heart and the kidneys, in which impairment of one organ leads to damage of the other, is usually termed cardiorenal syndrome (CRS) . CRS is Approximately half (49%) of patients with heart failure suffer from CKD . Even a Hypotension and hypoperfusion of the organs in the course of chronic heart failure lead to the activation of compensatory mechanisms, especially the stimulation of the renin–angiotensin–aldosterone system and the sympathetic system . As a reStudies have shown a significant relationship between increased N-terminal pro-brain natriuretic peptide (NT-proBNP) and decreased GFR in patients with CHF ,23. NatrHowever, the role of NT-pro BNP in CHF with CKD is still not studied enough. To elucidate the benefits of this marker additional research is crucial to better understand the cause-and-effect relationship.Induced by CHF, hypotension and organ hypoperfusion result in increased production of AVP and endothelin (EI) stimulated by ATII ,19. EI iIn a study by Freise et al., inflammatory processes mediated by tumor necrosis factor (TNF) and interleukin 10 (IL-10) have an impact on pathobiological responses in the arteries of children with CKD and thus are associated with tissue remodeling and cardiovascular disease . ResearcPersistent hypoperfusion and hypoxia in CHF exacerbate oxidative stress in the kidney. Moreover, caused by AII activation of the oxidases, reduced nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) lead to the production of reactive oxygen species (ROS), which together with the impaired antioxidant barrier also cause oxidative stress ,30. The Oxidative stress also enhances the inflammatory response by activating phagocytes  and incrUremic toxins, retained through loss of renal excretion, have been described as contributing factors to the development of cardiac remodeling and kidney damage, but their exact role is less understood. It is suggested that uremic toxins start accumulating from the early stage of CKD . Some woHowever, the most crucial toxins are asymmetric dimethylarginine (ADMA), advanced glycation endproducts (AGE), and trimethyl amine N-oxide (TMAO) . ADMA toDue to the fact that the cardio and nephrotoxicity of uremic toxins is very high, finding an appropriate strategy that will prevent its accumulation in the body is necessary. Uremic toxins should be considered in the treatment of cardio-renal syndrome.In recent years, researchers have dwelt on anemia as a crucial factor in the pathophysiology and progression of heart failure and chronic kidney disease. The incidence of anemia increases with CKD and HF deterioration . The etiDrüeke et al.  reportedThe European guidelines recommend four well-known drugs as a gold standard in the treatment of chronic heart failure . They inThe RAAS plays a principal role in the development and progression of heart failure. Its main functions are the regulation of blood pressure and the maintenance of water and electrolyte balance in the body. Angiotensin-converting enzyme inhibitors (ACEI) significantly reduce mortality and hospitalizations as well as slow disease progression . They arBowling et al.  revealedCKD significantly impedes pharmacologic therapy in heart failure. Sacubitril/valsartan, an angiotensin receptor–neprilysin inhibitor (ARNI), might be the solution for HFrEF patients. Sacubitril inhibits neprilysin, increasing diuresis, natriuresis, and vasodilatation and suppresses adverse cardiac remodeling . NeprilyBeta-blockers have the ability to improve hemodynamics in chronic heart failure. Their role in treatment is equally important as ACEI . InitiatThe sympathetic nervous system has a huge impact on renal function. Its overactivity is specific to CKD. Modulation of beta1 receptors is associated with cardiac output and renin release; in turn, beta2 receptors are responsible for renovascular dilation. Besides all the benefits of beta-blockers, their advantages in the dialysis population can be uncertain due to their heterogeneity. It is worth noting that greater use of this kind of therapy could contribute to a decline in the risk of heart failure, which is the most common cause of death in the first year of starting dialysis. Zhou et al.  demonstrAnother noteworthy therapeutic option are MRAs. They work by blocking receptors that bind aldosterone. Activation of the mineralocorticoid receptors has been proven to result in negative consequences among people with cardiovascular disease. For example, it contributes to cardiac fibrosis , and as Spironolactone or eplerenone is recommended in all patients with HFrEF in addition to standard therapy . They arSGLT2 inhibitors are one of the most recent unique groups of drugs. They are commonly used in patients with type 2 diabetes as a glucose-lowering therapy. Their mechanism is based on the inhibition of renal glucose reuptake. Its additional benefits include weight loss, improvement of insulin tissue sensitivity, or blood pressure reduction. According to 2021 ESC Guidelines, SGLT2 inhibitors such as canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, and sotagliflozin are recommended in patients with HF regardless of diabetes status [Zannad et al. concluded that both empagliflozin and dapagliflozin have a wide range of positive effects in HFrEF therapy. They reduce cardiovascular and all-cause death, and decrease the number of hospitalizations . FurtherSGLT2 inhibitors are also characterized by renoprotective effects. For example, canagliflozin has been proven to reduce albuminuria as well as the risk of sustained loss of kidney function. As stated in the research by Heerspink et al., the risk of a composite of a sustained decline in the GFR of at least 50% was remarkably lower in the dapagliflozin group compared to the placebo group . In the Besides all these benefits, it is worth remembering that SGLT2 inhibitors, like every drug, have adverse effects. Recurrent genital fungal infections and volume depletion-related issues are some of them. Fortunately, they are generally mild and easy to treat . After iDevices such as cardiac resynchronization therapy (CRT) and implantable cardioverter-defibrillator (ICD) have become novel approaches for HF patients. CRT is currently a common nonpharmacologic therapy in patients with HF. ICD is nowadays the gold standard in the prevention of arrhythmic sudden cardiac death (SCD) episodes, and as it is widely known, ventricular arrhythmias are one of the major causes of death in HF patients. Factors that determine the validity of devices in the treatment of HF co-occurring with CKD are patients’ age, bloodstream infection, or rate of CKD progression. Even though these new therapeutic strategies have been widely investigated in major randomized controlled studies, there is still insufficient evidence for the appropriateness of CRT/ICD implementation in patients with HFmrEF .2. However, the ICD-CRT combination was associated with more adverse events than the ICD group. Another study revealed that mortality rates were high in a population undergoing dialysis, despite the use of implantable cardioverter-defibrillators [Tang et al.  demonstrillators . Additioillators . 2 is an appropriate moment for a decision on whether KRT should be implemented. Co-occurring conditions, patient expectations, and life quality should be taken into account.Careful consideration of all advantages and disadvantages is essential when starting kidney replacement therapy (KRT). eGFR <20 mL/min per 1.73 mBanerjee et al.  showed tIn another study, continuous ambulatory peritoneal dialysis (CAPD) was assessed . The resApproximately half of HF patients have a preserved ejection fraction, and the prevalence is still growing. As mentioned before, CKD is more frequent among patients with HFpEF. Nevertheless, there is currently no reliable research that has indicated effective therapy for HFpEF. Drugs such as RAAS-I, MRAs, or SGLT2 inhibitors are well tolerated and can decrease the risk of hospitalization . HoweverTreatment in HFpEF includes symptom management with diuretics and optimization of comorbidities. The 2021 ESC Guidelines highlight the role of diuretics in congested patients . AdditioIt is worth remembering that nonsteroidal anti-inflammatory drugs can lead to fluid retention in CKD patients, therefore they should not be used frequently.2 [Recent clinical trials about SGLT2 inhibitors have shown that flozin therapy, especially with dapagliflozin, in patients with heart failure reduces mortality and hospitalizations ,75, rega2 . Moreove2 .The novel nonsteroidal MRA finerenone has a beneficial effect on cardiovascular and kidney organ protection, particularly in patients with albuminuria and type 2 diabetes. The comparative analysis of the effect of SGLT2 inhibitor canagliflozin versus finerenone on cardiorenal outcomes demonstrated cardiorenal benefits of a similar magnitude . Research on finerenone demonstrates the benefits of therapy in patients with HF across CKD stages . FinerenDespite many benefits of finerenone in patients suffering from HF and CKD, this treatment may often lead to hyperkalemia complications, which are critical in patients with, e.g., diabetic nephropathy or demanding hemodialysis.Randomized controlled trial have shown that canakinumab therapy may be associated with a reduction in cardiovascular events, also in post-myocardial infarction patients with CKD . NeverthConcomitant diseases are a common and unfavorable indicator for people with HF. They are closely related to higher mortality and increased hospitalization. In this review, we focused on the important therapeutic strategies for co-existing CKD and HF. We paid attention to both pharmacological and non-pharmacological treatment. We drew attention to the main groups of drugs mentioned in the 2021 ESC Guidelines and considered the advantages and disadvantages of devices such as ICDs and CRTs. Moreover, we broached the subject of kidney transplantation. These findings may shed novel insight into the underlying mechanisms and identify potential therapeutic targets for the management of HF patients and multimorbidity in the future.


Background: Kidney function impairment in pregnancy is challenging, with incidence and prognosis only partially known. Studies concerning maternal and fetal outcomes in pregnancies occurring in patients with renal injury and the therapeutic strategies for improving the prognosis of these patients are scarce due to the limited number of cases reported. Objectives: We aimed to establish correlations between the main maternal and fetal outcomes in patients with severe CKD or AKI in pregnancy to improve the prognosis, referring to a control group of patients with mild kidney impairment. Methods: For this purpose, we conducted a retrospective study, at University Emergency Hospital in Bucharest, Romania, from January 2019 until December 2021, selecting 38 patients with AKI and 12 patients diagnosed with advanced CKD, compared to 42 patients displaying borderline values of serum creatinine (0.8–1 mg/dL), reflecting the presence of milder kidney impairment. Results: The probability of having a child that is premature and small for gestational age, with a lower Apgar score and more frequent neonatal intensive care unit admissions, delivered by cesarean section, is higher in patients with highly reduced kidney function. Conclusion: Severe kidney function impairment is associated with a grim fetal prognosis and obstetrical complications. During pregnancy, anatomical and physiological changes occur in all maternal organs and systems to support the metabolic requirements necessary for the fetus’s survival and development. Thus, the kidneys increase in size by 1–1.5 cm, and the renal volume increases by 30%. As a result of systemic vasodilatation and volume expansion, the glomerular filtration rate increases by 40–50%, reaching 180 mL/minute in the perinatal period. Consequently, creatinine clearance increases by 40–50% with reduced serum creatinine. In addition, there is increased urinary excretion of uric acid, glucose, proteins, and albumin. In contrast, there is reduced reabsorption of amino acids and beta-microglobulin. All these adaptive changes may make it difficult to diagnose renal injury promptly [Renal injury associated with pregnancy, including structural and functional renal alterations, is challenging ,3,4. ItsWith a reported incidence of 4.5 per 10,000 deliveries, acute kidney injury (AKI) is a heterogeneous situation that can develop at any perinatal time ,8,9. ConThe American College of Obstetricians and Gynecologists defines pregnancy-related AKI as an increase in serum creatinine level over 1.1 mg/dL ,11.Currently, a classification  that defines the degrees of severity of AKI in pregnancy, as well as the maternal outcomes dependent on the loss of renal function, has not been well confirmed, most likely due to the added physiological changes .The following criteria are used to diagnose virtually all reported AKI cases: a creatinine level ≥1 mg/dL, a sudden increase of 0.5 mg/dL from baseline in less than 48 h, oliguria or anuria, or the need to institute dialysis ,13.Preeclampsia is the hallmark of hypertensive disorder in pregnancy, with an increased risk of developing chronic kidney disease (CKD) .CKD is reported in up to 3–5% of pregnancies ,16,17. AAKI and CKD increase the risk of maternal and fetal complications, including preeclampsia, stillbirth, small for gestational age (SGA), fetal growth restriction (FGR), preterm birth, and cesarean section, and they may lead to a further decline in renal function ,21. The This study aims to evaluate the effect of maternal CKD and AKI on maternal–fetal outcomes compared to a control group of patients with mild renal impairment.We performed a retrospective study to evaluate the maternal and fetal outcomes in women with acute and chronic renal disease, and we compared their outcomes with those observed in women with milder kidney impairment  over 3 years. All parameters were recorded at hospital admission, and reference values were those of the central laboratory of the University Emergency Hospital in Bucharest, Romania. All CKD patients included were diagnosed with CKD stages 3–5 before hospital admission or pregnancy. None of the study pregnancies were obtained through assisted reproduction techniques.We performed a retrospective study on women with acute and chronic renal diseases in pregnancy observed over 3 years (from January 2019 to December 2021). The study was conducted at the University Emergency Hospital in Bucharest, Romania, an obstetric tertiary care unit treating high-risk pregnancies. Throughout the period of study, there were 6707 deliveries. We collected and analyzed data from 50 pregnant women with AKI or advanced CKD and 42 cases of pregnant women with milder kidney function impairment.We included all the cases with AKI, regardless of the trigger factor: sepsis, hypovolemia, and hypertension with its complications. For CKD, we included all the pregnant women with this diagnosis in the study, regardless of the underlying disease: lupus erythematous systemic, glomerulonephritis, chronic pyelonephritis, and polycystic renal disease. Only the patients with glomerulonephritis had a renal biopsy performed; the other diagnoses were established in the nephrology departments based on laboratory findings and imaging.The exclusion criteria were as follows: the absence of informed consent, pregnant women under 16 years, AKI caused by septic abortions in the first trimester, or hemorrhage from extrauterine pregnancies.The cutoff of 1.19 mg/dL of serum creatinine used in this study was established following ROC curve analysis.The patient distribution is shown in The baseline data were obtained from hospital observation sheets and the patient’s medical files. The patient’s informed consent was a mandatory inclusion criterion. The identification data were not included in the database we developed, which is structured as follows:-Personal data: age and area of residence.-Obstetric/gynecological history: abortions/pregnancies and ante/perinatal complications.-Details related to the current pregnancy: pregnancy follow-up or not, gestational age (GA) at the time of assessment, pregnancy-related disorders , GA at which renal failure was diagnosed, and the mode of delivery .-Data regarding fetal parameters: complications during pregnancy (SGA/FGR), fetal birth weight, 1 min Apgar score, and neonatal intensive care admission.-Data regarding the underlying disease that caused the CKD and the current treatment  as the trigger factors for AKI.-Data on the evolution of biochemical parameters .-The need for dialysis during hospitalization.p < 0.05 were statistically significant.Data analysis was performed using SPSS 26.0 Software. The Kolmogorov–Smirnov analysis was performed to assess the normal data distribution through the abovementioned variables. The Pearson correlation test was performed to establish the impact of risk parameters  on different variables . The chi-square test was applied to establish the interdependence between nominal variables. Results with During the 3 years of the study, out of 6707 registered births, 92 cases were eligible for our study: 50 cases of acute and chronic severe kidney impairment and 42 cases with borderline values of serum creatinine, indicative of milder kidney function impairment. Thus, over this period, the incidence was 0.56% of pregnancy-related AKI, and advanced CKD was present in 0.17% of the cases, while 0.62% had a mild increase in serum creatinine. The average maternal age was 30.87 ± 6.9 (18–42 years) for the AKI group and 29 ± 6.49 (21–42 years) for the CKD group. In the patients with milder serum creatinine values, the mean age was 31.23 ± 5.88 (18–42 years) .(1)n = 38;AKI group (2)n = 12 with late-stage disease (stages 3–5);CKD group (3)n = 42  at delivery revealed a risk of preterm birth in all three groups. In the AKI group, the average GA at delivery was 33.79 ± 4.73 weeks, with an increase of almost 2 weeks in the borderline serum creatinine values group (35.52 ± 4.03 weeks). In contrast, there was not a significant difference between the GA at delivery in the groups with severe acute and chronic kidney impairment (33.79 vs. 34.42 weeks).In the control group, we included seven pregnancies with no record of prenatal care who had a mean value of serum creatinine at the admission of 0.88 ± 0.07 mg/dL (95% CI 0.82 to 0.93), similar to that observed in the patients who benefited from prenatal care (0.86 ± 0.05 mg/dL) (95% CI 0.84 to 0.87).In the AKI group, we enrolled nine pregnancies with no record of prenatal care, with a mean serum creatinine value at the admission of 1.97 ± 1.13 mg/dL (95% CI 1.29 to 2.66). The mean value of serum creatinine in the AKI pregnancies that benefited from prenatal care was 1.55 ± 1,24 mg/dL (95% CI 1.06 to 2.04), with no significant difference compared with the previous group.In the advanced CKD group, three out of the 12 patients included were pregnancies without prenatal care, despite their known history of kidney injury, with serum creatinine values at the admission of 0.65 mg/dL, 1.95 mg/dL, and 8.98 mg/dL.During pregnancy, the considered normal range of creatinine concentration varies between 0.40–0.80 mg/dL, with values higher than 0.8 mg/dL raising the suspicion of possible kidney damage . As a reThe prognosis was unfavorable for the patient with 8.98 mg/dL serum creatinine at admission, who needed to start continuous renal replacement therapy in the ICU. The patient with 1.95 mg/dL serum creatinine died due to macrophage activation syndrome associated with lupus nephritis.Anemia was diagnosed in 63.15% of AKI cases and 58.33% of patients in the CKD group. Furthermore, 50% of CKD patients and 36.84% of AKI patients underwent dialysis, with most AKI patients requiring only short-term dialysis in the ICU.The underlying causes of CKD and the trigger factor of AKI are reported in The maternal prognosis of the dialysis patients was favorable, with complete restoration of renal function in only four cases (28.57%) in the AKI group and one case (16.66%) in the CKD group .Considering the higher percentage (16%) of patients who died in the study groups, in p = 0.01). The value of serum creatinine at admission was not a predictive factor for the fetal outcome or admission to NICU.The relationship between hospitalization days and serum creatinine at admission was significant only in the AKI group (p = 0.03). A favorable maternal result was recorded in almost all cases with creatinine <1.12 mg/dL (93.33% in the AKI group and 100% in the CKD group), while, in the CKD patients with serum creatinine levels >1.12 mg/dL, 37.5% needed dialysis .Considering the limit of 35 mg/dL of serum urea, we identified a difference among the three subgroups regarding fetal outcomes. In the borderline group, in patients with serum urea >35 mg/dL, an increased rate of low-birth-weight newborns was observed compared to those from patients with values <35 mg/dL  was caused by obstetrical complications.A study performed by Luders et al. emphasized that blood urea nitrogen <35 mg/dL was associated with an improved fetal prognosis in patients on hemodialysis . When anIn the AKI group, the maternal outcome in patients with serum creatinine >1.12 mg/dL was favorable in 73.91% of cases; meanwhile, in patients with serum creatinine values <1.12 mg/dL was favorable in 93.33% of all patients.A study performed by Mir et al. on 28 patients with postpartum AKI who required dialysis showed a higher percentage (53.5%) of favorable maternal outcomes, with complete restoration of renal function . The matp = 0.01). The maternal outcome was favorable in 94.73% (18/19) of all cases with serum creatinine <1.12 mg/dL, while, above this threshold, 10 patients (43.47%) from the AKI group and five patients (62.5%) from the CKD group needed dialysis, and the hospitalization period was prolonged, with a mean of 13.5 days. The longer hospitalization period was associated with worsening renal function following a study published by Shah et al. [Regarding maternal outcome, we found a relationship between the serum creatinine value at admission and the duration of hospitalization  and NICU admission (67%) .All patients with CKD in an advanced stage needed a cesarean section, in keeping with the study by Lim et al., which reported a cesarean section rate of 42.9% in early-stage patients (stages 1–2) and 100% in late-stage patients (stages 3–5) .Changes related to glomerular rate filtration can be underestimated during pregnancy; therefore, it is suggested that a serum creatinine level above 0.87 mg/dL should be considered abnormal and require further investigation ,19,37. TOur study had several limitations, mainly the reduced number of cases enrolled. Furthermore, we did not have access to kidney function parameters during each trimester of pregnancy and after discharge.CKD and AKI in pregnancy are confirmed as associated with a poor maternal and fetal prognosis. AKI during pregnancy continues to represent a major public health problem. Early identification of risk factors  may improve early diagnosis and therapeutic management. Monitoring renal function in pregnant women with CKD is essential because there is a major risk of worsening renal function and of serious pregnancy complications .


This study aims to investigate the potential association of peripheral inflammatory blood cell parameters with the incidence and progression of chronic kidney disease (CKD) in patients with diabetes.The cross‐sectional study included 1192 subjects with diabetes derived from one center. The cohort study included 2060 subjects with diabetes derived from another two centers followed up for 4 years. Logistic regression and Cox proportional hazards models were used to evaluate the association of peripheral inflammatory blood cell with CKD.9/L. In the cohort study, subjects were grouped based on tertile of neutrophil count and neutrophil‐to‐lymphocyte ratio. Cox regression analysis results showed that only neutrophil count was independently associated with CKD progression  after fully adjusting for potential confounders. The cumulative incidence of CKD progression in patients with diabetes gradually increased with increasing neutrophil count (53 (7.7%) subjects in the lowest group vs. 60 (8.2%) in the middle group vs. 78 (12.2%) in the highest group).In the cross‐sectional study, neutrophil count performed best as an independent risk factor for CKD  even after 1:1 case–control matching for age, gender, history of high blood pressure and duration of diabetes. Spline regression revealed a significant linear association of CKD incidence with continuous neutrophil count in excess of 3.6 × 10This study suggested that neutrophil count is an independent risk factor for progression of CKD in patients with diabetes. Highlights9/L, and increased neutrophil count could predict progression of CKD in patients with diabetes.Neutrophil count was an independent risk factor for CKD in patients with diabetes, especially when it exceeded 3.6 × 10 Approximately half of all affected patients will develop chronic kidney disease (CKD).An increasing number of studies has indicated that the pathogenesis of CKD in diabetes is multifactorial, and low‐grade chronic inflammation plays a crucial role.This study aimed to investigate the potential association of peripheral inflammatory blood cell parameters with the incidence and progression of CKD in patients with diabetes by cross‐sectional study of expanded sample size and cohort study. First, in the cross‐sectional study, WBC count, neutrophil count and NLR were all increased and lymphocyte count was decreased in patients with CKD. All were also associated with parameters of nephropathy including creatinine (Cr), estimated glomerular filtration rate (eGFR) and urine albumin/creatinine ratio (UACR). Nonetheless neutrophil count was the best independent risk factor for and predictor of CKD. In addition, significant linear associations between continuous neutrophil count, NLR and the incidence of CKD were analyzed by spline regression. Finally, our cohort study further confirmed that an increased neutrophil count, not NLR, predicted onset of CKD in diabetes. Moreover, there was a linear relationship between increasing neutrophil count and cumulative incidence of CKD.22.1A total of 3582 patients with diabetes were screened, of whom 3252 entered the study. A total of 1192 patients with diabetes (291 with CKD and 901 without CKD [NCKD]) who attended the Metabolic Disease Care Center of Shanghai Fifth People's Hospital Fudan University from May 2017 to July 2019 served as the cross‐sectional study. Another 2060 patients with diabetes were recruited into the cohort study, who were followed up annually for 4 years from January 2016 to December 2019 at Maqiao and Gumei Community Hospitals in Minhang District, Shanghai. This analysis involving human participants was approved by the Independent Ethics Committee of the Fifth People's Hospital of Shanghai, Fudan University (2010–2024). Consent has been obtained from each patient or subject after full explanation of the purpose and nature of all procedures used. The study procedure is described in Figure 2.1.1Diagnosis of diabetes was based on the 2019 Chinese Diabetes Society criteria: (1) typical symptoms of diabetes  plus random plasma glucose ≥11.1 mmol/L, (2) fasting plasma glucose ≥7.0 mmol/L, or (3) oral glucose tolerance test 2hPG ≥ 11.1 mmol/L.2.1.22) or UACR ≥ 30 mg/g (two of three specimens of UACR collected within a 3‐ to 6‐ month period).Equations to estimate GFR use serum creatinine and a combination of age, gender, ethnicity, and body size as surrogates for the non‐GFR determinants of serum creatinine. We used the modified Modification of Diet in Renal Disease equations for use in individuals from China and Japan that have been reported previously. In cross‐sectional study, definition of CKD is based on the presence of decreased kidney function  presence of any infectious disease excluding by medical history collection and routine examination such as chest X‐ray, electrocardiogram, and biochemistry test; (2) presence of viral infection or positive carrier status ; (3) previous diagnosis of urinary tract infection, urolithiasis, or liver cirrhosis; and (4) other known renal disease: IgA nephropathy, nephrotic syndrome. All study subjects were of Han Chinese origin and lived in the same region at the time of the study.2.2All subjects underwent blood and urine sample analyses. After an overnight fast for 12 h, blood samples were collected for blood cell counts  and measurement of biochemical parameters . A urine sample was collected for assessment of UACR. Anthropometric parameters were used to calculate body mass index (BMI), and blood pressure was measured with a digital automatic blood pressure monitor . A patient questionnaire was completed and included information about history of high blood pressure (HBP) and duration of diabetes.2.3T‐test was used for between‐group comparisons of continuous variables and the χ2 test for categorical variables. Skewed variables are expressed as median (interquartile range), and rank sum test was used to compare the difference between two groups. To avoid potential bias due to uneven distribution of covariates between type 2 patients with or without CKD, a case–control matching method was employed to match variables that included gender, age, history of HBP and duration of diabetes. Matching tolerance was 0, 2, 0, and 0 respectively. Logistic regression analysis was performed to detect the predictability of WBC, neutrophil and lymphocyte count, and NLR for CKD. The covariates were prespecified for inclusion in multivariable models based on clinical and biologic importance. A continuous association of neutrophil count and NLR with CKD incidence was determined by spline regression analysis.Data are shown as mean ± SD, unless otherwise noted. 9/L), middle group (3.19–4.10 × 109/L), and highest group (>4.10 × 109/L); and NLR: lowest group (<1.58), middle group (1.58–2.16), and highest group (>2.16). Analysis of variance (ANOVA) and Student's t‐test were used to identify difference in mean between groups. Variables with a skewed distribution were analyzed by Wilcoxon tests. The cumulative incidence of CKD in relation to neutrophil tertile was assessed by Kaplan–Meier plots.In the retrospective cohort study, patients were divided into three groups based on tertile of neutrophil count: lowest group . All 33.1p < 0.001). Compared with those without CKD, patients with CKD were more likely to have hypertension, higher systolic blood pressure (SBP), diastolic blood pressure (DBP), uric acid (UA), and UACR . There was no significant difference in BMI, fasting glucose, glycosylated hemoglobin (HbA1c), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐c), and low‐density lipoprotein cholesterol (LDL‐c) between the two groups (p > 0.05). The WBC count , neutrophil count , and NLR  were much higher in patients with CKD than those without, whereas the lymphocyte count was much lower in patients with CKD .Clinical characteristics of the patients were stratified according to their nephropathy status Table . A total9/L, p = 0.139), neutrophil count , and NLR  but lower lymphocyte count  in patients with CKD compared with those without  was performed to avoid the potential bias of covariates that were not evenly distributed between patients with and without CKD. After matching, patients with higher triglycerides (TG) were still more likely to develop nephropathy. Furthermore, there remained higher WBC count (6.59 ± 0.20 vs. 6.20 ± 0.16 × 103.2p < 0.05) and negatively associated with HDL (p < 0.05). There was also a strong association between UACR and age, SBP, UA, TC, TG, and LDL , whereas eGFR was negatively associated with age, SBP, DBP, BMI, and UA  (Table p < 0.001), but a negative association with eGFR . Additionally, lymphocyte count was negatively associated with Cr and positively associated with eGFR   Table . There w5) Table .3.3p = 0.030), neutrophil count  and NLR  were independently associated with CKD development but not lymphocyte count (Table p = 0.027) . We found that the mean value of neutrophils and NLR was relatively stable during the 4 years, and the CV were all less than 25% and 30% respectively Table .p = 0.024) and patients with HBP , as well as the level of TG, Cr, fasting glucose, and HbA1c  and a stepwise decrease in the eGFR and HDL level (both p < 0.05). Across the lowest, middle and highest groups of NLR, there was also a stepwise increase in the proportion of male patients , as well as age, level of fasting blood glucose, and Cr , and a stepwise decrease in eGFR (p = 0.046) and TC, LDL, and HDL level (p < 0.05) . After adjusting for age, gender, and BMI, HR of neutrophil count for eGFR decrease >30% in the highest group remained significantly different compared with the lowest group 1.651 . Finally, after fully adjusting for potential confounders, HR of neutrophil count for eGFR decrease >30% still showed a significant difference   subjects in the lowest group, 60 (8.2%) subjects in the middle group, and 78 (12.2%) subjects in the highest group over the 4‐year follow‐up period. The same applied to NLR: eGFR decreased by more than 30% in 55 (7.8%) subjects in the lowest group, 65 (9.4%) in the middle group, and 73 (10.6%) in the highest group. In the crude model of neutrophils, hazard ratio  for eGFR decrease >30% when comparing the highest group vs. the lowest group was 1.547  produced by neutrophils is a potential predictor of CKD and its progression,Our study also found that UA, advanced age, duration of diabetes, and history of hypertension were risk factors for CKD in patients with diabetes, consistent with previous studies.There were some limitations of this study. Although we have proved that among all white blood cells, neutrophils were the most predictive for CKD in patients with diabetes, the mechanism of the potentially pathophysiological role of neutrophils in the development of CKD has not been studied clinically. Further research in humans and in animal studies should be performed to clarify the function of neutrophil‐secreted proteins, especially NE and NGAL.9/L and increased neutrophil count could predict a faster decline of eGFR in patients with diabetes.In conclusion, our study is the first to demonstrate the close association of neutrophil count with CKD in patients with diabetes. Neutrophil count was an independent risk factor for CKD especially when it exceeded 3.6 × 10Natural Science Research Funds of Minhang District, Shanghai (2019MHZ066). Health Profession Clinical Funds of Shanghai Municipal Health Commission (201940295). Scientific Research Project funded by Shanghai Fifth People's Hospital, Fudan University (2018WYZD04). Medical Key Faculty Foundation of Shanghai (ZK2019B15). Minhang District Health Committee Project (2020MW38).No potential conflicts of interest relevant to this article were reported.FIGURE S1 Changes of neutrophils during 4 years among three groups categorized by tertile of neutrophil count in baseline. A for neutrophil count; B for neutrophil‐to‐lymphocyte ratio (NLR).Click here for additional data file.TABLE S1 Changes of neutrophils and neutrophil‐to‐lymphocyte ratio (NLR) during 4 years among three groups categorized by tertile of neutrophil count in baselineClick here for additional data file.


Post-cardiac surgery acute kidney injury (AKI) is associated with increased mortality. A high-protein meal enhances the renal blood flow and glomerular filtration rate (GFR) and might protect the kidneys from acute ischemic insults. Hence, we assessed the effect of a preoperative high-oral protein load on post-cardiac surgery renal function and used experimental models to elucidate mechanisms by which protein might stimulate kidney-protective effects.The prospective “Preoperative Renal Functional Reserve Predicts Risk of AKI after Cardiac Operation” study follow-up was extended to postoperative 12 months for 109 patients. A 1:2 ratio propensity score matching method was used to identify a control group (n = 214) to comparatively evaluate the effects of a preoperative protein load and standard care. The primary endpoints were AKI development and postoperative estimated GFR (eGFR) loss at 3 and 12 months. We also assessed the secretion of tissue inhibitor of metalloproteases-2 (TIMP-2) and insulin-like growth factor–binding protein 7 (IGFBP7), biomarkers implicated in mediating kidney-protective mechanisms in human kidney tubular cells that we exposed to varying protein concentrations.2) and 12 months . On stratification based on AKI development, the eGFR loss after 12 months was also found to be lower in the former . A dose–response analysis of the protein treatment of the primary human proximal and distal tubule epithelial cells in culture showed significantly increased IGFBP7 and TIMP-2 expression.The AKI rate did not differ between the protein loading and control groups . However, the mean eGFR loss was lower in the former after 3 months  and ClinicalTrials.gov: NCT03092947 .The online version contains supplementary material available at 10.1186/s12967-022-03410-x. Acute kidney injury (AKI) is the most common major complication of cardiac surgery and independently associated with increased short- and long-term morbidity and mortality , 2. AlthIn healthy humans and adults scheduled to undergo cardiac surgery, a high protein load reportedly enhances the glomerular filtration rate (GFR) through a global increase in renal blood flow due to afferent arteriolar dilation and a decrease in renal vascular resistance resulting in glomerular hyperfiltration . This prHowever, interventions that increase the renal blood flow  have been uniformly ineffective in preventing or treating AKI. A possible reason is that the protein load may stress the kidneys in the same way that direct or remote ischemic preconditioning (RIPC) may , 13. Thiitself may affect the development of AKI and long-term renal function after cardiac surgery in comparison with that in matched controls with similar demographic, risk, and surgical profiles who received standard preoperative care. Furthermore, we used in vitro cell culture models involving primary human kidney proximal and distal tubule cells to elucidate the potential mechanism by which protein might stimulate kidney-protective effects directly at the tubular level.We previously reported the value of the renal functional reserve (RFR), measured based on a preoperative high-oral protein load, for the prediction of AKI in patients undergoing elective cardiac surgery . In the 2 scheduled to undergo elective cardiac surgery  between November 2014 and October 2015 at the Department of Cardiac Surgery, San Bortolo Hospital, Vicenza, Italy. Detailed methods including surgical care are described in the Additional file This single-center, non-blinded, cohort study included patients enrolled in the prospective “Preoperative RFR Predicts Risk of AKI after Cardiac Operation” study . The stuThe study protocol has been described in detail elsewhere . BrieflyAKI was defined according to the KDIGO workgroup criteria as an increase in SCr by ≥ 0.3 mg/dl within 48 h or ≥ 1.5-times the baseline value within 7 days or urine output < 0.5 ml/kg/h for 6 h . BaselinAKI reversal was defined as the absence of any stage of AKI at hospital discharge . All casClinical outcomes were evaluated for 1 year after discharge. Changes in medication for clinical reasons were permitted. At 3 and 12 months of follow-up, analyses were conducted at the Outpatient Nephrology Department, and patients were contacted via the telephone 1 week before the follow-up analyses and advised to maintain their usual dietary habits and avoid high dietary protein intake (> 0.8 g/kg body weight per day). A high-oral protein diet during the follow-period was not intended as per study protocol. Patients were again advised to suspend renin–angiotensin-system blockers and non-steroidal inflammatory drugs a minimum of 48 h prior to outpatient presentation. The primary endpoint was AKI within 7 days post-cardiac surgery. Additional measures included the difference in eGFR at 3 and 12 months post-surgery compared to the preoperative values . Analyses were adjusted for age, sex, body mass index, hypertension, and diabetes. Conditional distributions of the response variables showed a sufficiently good approximation to the normal distribution, as assessed using normal quantile–quantile plots. Data were analyzed using R 3.6.0  [t-test function of GraphPad Prism 8.0 .Descriptive variables were expressed as median (interquartile range) or means [95% confidence interval] for continuous variables, and n (%) for categorical variables. Differences in the distributions between groups were tested using Mann–Whitney Austria) . Two-taiAustria)  and on t2 [95% CI 88–92], respectively; p = 0.914).Of the 540 patients screened for the study, 324 were enrolled and included in the analysis. 110 patients received preoperative high-oral protein loading as part of the RFR study, whereas 214 patients were matched to the protein-loading group . The eGFR values at 3 and 12 months after surgery in both groups are shown in Figs. 2 [95% CI 84–88], p = 0.028 at 3 months; and 87 [95% CI 84–90] vs. 79 mL/min/1.73 m2 [95% CI 77–81], p < 0.001 at 12 months). This effect was also visible among patients who did not develop AKI . The data for patients who developed AKI was insufficient to interpret the difference in eGFR levels between the groups. However, the mean decrease in eGFR levels per patient after 12 months was less pronounced in the protein-loading group for patients who developed AKI . Patients without AKI reversal exhibited the highest risk for long-term renal function loss, with a mean eGFR loss of –35.0 mL/min/1.73 m2 [95% CI − 60.9 to − 9.1]. Results remained significant after adjustment for potential confounders.At hospital discharge, almost all patients (98.5%) had a normal renal function, as defined by the eGFR  and at 3 months after surgery . However, patients in the protein loading group as compared to the control group had lower levels of albuminuria at 12 months after surgery . The result remained significant after adjustment for potential confounders.We next assessed whether a high protein load could affect kidney tubule cells directly using an in vitro cell culture model system to determine whether protein administration mediates the release of TIMP-2 and IGFBP7. We had previously identified that IGFBP7 secretion is primarily a proximal tubule phenomenon, and TIMP2 expression and secretion are distal tubule phenomena ; therefo2 undergoing cardiac surgery, a preoperative high-oral protein load did not reduce the occurrence of perioperative AKI, but was associated with an improved eGFR postoperatively and more preserved eGFR at 3 and 12 months after surgery, even after stratification based on the occurrence of AKI. Additionally, we validated an experimental proof of concept in vitro that direct interaction of dietary protein with kidney tubule cells can increase the amounts of biomarkers implicated in cell cycle arrest and tubule cell protection in apical media.In patients with presurgical eGFR values ≥ 60 mL/min/1.73 mProtein intake reportedly increases renal blood flow and GFR in healthy humans, and at any given level of protein intake, GFR is greater in younger than in older individuals and greater in women and men . AlthougProteins, once filtered at the glomerular level, are normally almost entirely reabsorbed at the proximal tubular level . ExperimHowever, the protein loads administered in this study were well within normal dietary variations and were not likely to induce damage to kidney cells as described above. Nevertheless, we speculate that these levels may have “stressed” the kidney in a manner analogous to preconditioning with ischemia . ZarbockMechanistically, TIMP-2 and IGFBP7 are two independent proteins that are released in response to proximal and distal tubular insults and are thought to be engaged in self-protective mechanisms such as AKI-induced transient cell cycle arrest , 39, 40.In line with a recent pilot study , our resOur findings may have important implications for clinical practice. First, recent evidence has shown that the AKI risk after cardiac surgery is potentially modifiable . HoweverOur study had limitations, including its non-blinded, non-randomized, and single-center design as well as the relatively low AKI rate. However, the measures of renal function were based on objective assays conducted by a central laboratory unaware of the group allocation. Moreover, the low occurrence of AKI was expected since we excluded patients with reduced eGFR, and selected patients with low comorbidities, elective surgical procedures, and a high rate of minimally invasive surgery; all factors associated with low AKI rates . BesidesIn summary, among patients undergoing cardiac surgery, a preoperative high-oral protein load did not reduce the occurrence of AKI (in comparison with standard care) but was associated with an improved eGFR postoperatively and more preserved eGFR at 12 months, even after stratification based on the occurrence of AKI. The potential mechanisms of action by which short-term protein loading induces a kidney-protective response may include the maintenance of renal blood flow and cell cycle inhibition of renal tubular epithelial cells. The observed preservation of renal function in the protein-loading group warrants further investigation.Additional file 1: Figure S1. Study flow chart. The diagram describes the protocol used for the enrolment of patients in the present study. ACEis, angiotensin-converting-enzyme inhibitors; ARB, angiotensin II receptor blockers; IV, intravenous; NSAIDs, non-steroidal anti-inflammatory drugs. Figure S2. Changes in eGFR among patients receiving high oral protein loading compared to those receiving standard care prior to cardiac surgery. The figure illustrates the changes in eGFR among patients receiving preoperative high oral protein loading (dark-grey circles), as compared to those in patients receiving standard care (light-grey circles) at different time points (a) and compared to the preoperative values (b). Horizontal lines indicate median values, boxes indicate the inter-quartile range, and whiskers indicate the minimum and maximum values. Data beyond the whiskers are plotted as outliers (circles). The number of patients in each group at different time points for each group are presented in parentheses. eGFR, estimated glomerular filtration rate. Figure S3. Serum creatinine levels among patients receiving high oral protein loading compared to those receiving standard preoperative care prior to cardiac surgery. (a) Serum creatinine levels at the time of admission as well at 3 and 12 months after surgery. (b) Change in serum creatinine levels relative to the time of admission. Light grey: patients with standard preoperative care; dark grey: patients receiving preoperative high oral protein loading. Points are mean values with error bars indicating the 95% confidence intervals. The number of patients in each group at different time points for each group are presented in parentheses. Figure S4. Serum creatinine levels categorized by the occurrence of AKI and AKI reversal at hospital discharge among patients receiving preoperative high oral protein loading compared to those receiving standard preoperative care. (a) Serum creatinine levels at the time of admission as well at 3 and 12 months after surgery. (b) Change in serum creatinine levels, relative to the time of admission. Light grey: patients with standard preoperative care; dark grey: patients receiving preoperative high oral protein loading. Number of patients at different time points for each group are presented in parentheses. AKI, acute kidney injury. Table S1. Bioimpedance vector analysis of the protein loading group categorized by occurrence of postoperative AKI. Table S2. Baseline, operative, ICU and follow-up characteristics categorized by occurrence of AKI. Table S3. Serum creatinine levels among patients receiving high oral protein loading, as compared to those in patients receiving standard care prior to cardiac surgery.


Kidney transplant recipients are a unique subgroup of chronic kidney disease patients due to their single functioning kidney, immunosuppressive agent usage, and long-term complications related to transplantation. Post-transplant diabetes mellitus (PTDM) has a significant adverse effect on renal outcomes in particular. As transplantations enable people to live longer, cardiovascular morbidity and mortality become more prevalent, and PTDM is a key risk factor for these complications. Although PTDM results from similar risk factors to those of type 2 diabetes, the conditions differ in their pathophysiology and clinical features. Transplantation itself is a risk factor for diabetes due to chronic exposure to immunosuppressive agents. Considering current evidence, this article describes the risk factors, pathogenesis, diagnostic criteria, prevention strategies, and management of PTDM. The therapeutic options are discussed regarding their safety and potential drug–drug interactions with immunosuppressive agents. The transplantation of kidneys is a treatment option for end-stage kidney disease (ESKD). As a result of the transplantation, the patient received a new kidney, but this also triggered the process of chronic kidney disease (CKD). As a subset of CKD patients, kidney transplant recipients (KTR) continue to have a high mortality rate and risk of dialysis progression. CKD progression in KTR involves a complex network of allogeneic and non-allogeneic risk factors, including hypertension, diabetes, proteinuria, dyslipidaemia, and bone mineral disorder .Comorbidities after kidney transplantation may affect short-term and long-term transplant outcomes. In recent years, short-term grafts and patient survival of kidney transplants have improved dramatically due to improvements in surgical techniques and the development of new immunosuppressive agents ,3. Kidnep < 0.0001), death-censored transplant failure , and mortality  during a median 3-year follow-up period [The term PTDM was adopted in 2014 after a consensus meeting at which it was decided that the time of diagnosis rather than the time of occurrence would be adopted . The purThis review was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . In briePTDM is a clinical entity distinct from type 1 and type 2 DM. The pathogenesis of PTDM is multifactorial, which is based on β-cell dysfunction in the presence of insulin resistance. Diabetogenic immunosuppressive medication may determine the occurrence of PTDM in a high-risk patient . Major rViral infections may also increase the risk of PTDM. An association of hepatitis C virus (HCV) infection with DM has been shown in the general population and among organ transplant recipients ,16. The The incidence of PTDM was higher in patients with cytomegalovirus (CMV) infection than in those without CMV infection . In the The development of PTDM is strongly related to immunosuppression and acute rejection . Acute rp = 0.046) at 6 months after kidney transplantation [p < 0.0001) [p = 0.002) and pulse steroid therapy for acute rejection  were independent predictors for PTDM development [The impact of major immunosuppressants on PTDM development has been compared by prospective trials, meta-analysis, and analysis of registered database. Evidence shows that tacrolimus is associated with an increased risk of PTDM compared to cyclosporine. In a randomized prospective trial by Vincenti F et al., PTDM or impaired fasting glucose occurred in 73 of 336 cyclosporine patients (26.0%) and 96 of 346 tacrolimus patients  . In a reelopment . In a reelopment .PTDM may be a consequence of intensive immunosuppression used to prevent or treat acute rejection. Conversely, acute rejection may occur as a result of decreases in the dosage of immunosuppressive agents in order to avoid or treat PTDM. The balance between immunosuppression minimization, acute rejection, and PTDM needs to be carefully considered individually.Diabetic kidney disease (DKD), also known as diabetic nephropathy (DN), is defined as an increase in urine albumin excretion or a decrease in the glomerular filtration rate (GFR) or both . WorldwiA change that is observable by electronic microscopy at the early stage of diabetes is thickening of the GBM, which can be detected as early as 2 years after the onset of type I DM . A lineaA number of extraglomerular lesions are closely related to renal function loss during the progression of ESKD, including tubular atrophy, interstitial inflammation, and tubulointerstitial fibrosis . As partAccording to reports, PTDM is most likely to develop in the first year after kidney transplantation and has been associated with poor graft survival and poor graft quality ,45,46. APTDM had been diagnosed on the basis of the level of insulin required, type of oral hypoglycemic agents administered, and level of fasting plasma glucose posttransplant; these diagnostic criteria were used to distinguish between PTDM and pre-existing DM, and PTDM was significantly underdiagnosed . The disPreventing PTDM starts with identifying the risk factors before transplantation. Kidney transplant candidates must undergo a thorough evaluation of their diabetes risk prior to enlistment. Preventive strategies such as weight reduction, lifestyle modification, caloric control, and physical exercise should be recommended to high-risk patients. For patients at greater risk of PTDM, such as those aged >45 years  or thoseImmunosuppressive agents such as glucocorticoids and CNI are well known to cause PTDM . Immunosp = 0.048) [Vitamin D is well known for its role in regulating bone health and calcium metabolism. Recent studies have shown that vitamin D is involved in a wide range of pleiotropic functions mediated through vitamin D receptors , such as= 0.048) . In a mu= 0.048) . At presp = 0.123) [Lifestyle modifications that promote fat and energy expenditure, moderate-intensity physical activity, and moderate weight loss are strategies that can be followed to reduce the risk of type 2 diabetes mellitus ,81. In a= 0.123) . FurtherIn the first few months following transplantation, stress hyperglycemia and immunosuppressive drugs frequently cause high blood sugar levels ,88. One 2 and BMI ≥ 25 kg/m2 in kidney transplant recipients [Recently, the Association of British Clinical Diabetologists and the British Renal Association released guidelines for the detection and management of diabetes following solid organ transplantation. The guidelines suggested the stratification of pharmacological therapies and recommended metformin as the first-line oral therapy if eGFR ≥ 30 mL/min/1.73 mcipients . In paticipients . Althougcipients . In a sucipients . Thus, tcipients . In a picipients . Howevercipients . In summp = 0.04) [Dipeptidyl peptidase 4 inhibitors (DPP4i) are selective inhibitors of DPP4 that have been linked to the degradation of two major incretins, glucagon-like peptide 1 and glucose inhibitory peptide 2 . Weight  = 0.04) .Early DPP4i administration may reduce insulin resistance in patients with PTDM. In a single-center study, Thiruvengadam S et al. discovered that the introduction of linagliptin at a median of 90 days post-transplant improved the calculated homeostatic model assessment of insulin resistance (HOMA-IR) score compared with the conventional treatment with insulin . BecauseGlucagon-like peptide-1 receptor agonists (GLP-1 RA) are beneficial in the treatment of PTDM in terms of improving insulin secretion and normalizing glucagon production . GLP-1 R2) treated with SGLT2 inhibitors, SGLT2 inhibitors significantly lowered HbA1c  and body weight  relative to baseline levels [Sodium–glucose cotransporter 2 (SGLT2) inhibitors inhibit glucose resorption in the renal proximal tubule and lead to glycosuria . In addie levels . Howevere levels . The effThere are potential complications of SGLT2 inhibitor in KTR. The glycosuric effect of SGLT2 inhibitors contributes to the side effect for urinary tract infection in non-transplant population. The altered genitourinary anatomy and immunosuppressive status in KTR may further increase the risk of severe urinary tract infection. In addition, SGLT2 inhibitors may carry risks for hypotension and acute kidney injury. In the immediate and early post-transplant period, KTR are predisposed to ischemic-reperfusion injury and the high intensity of the immunosuppressant used. The risk and benefit of SGLT2 inhibitor usage in the early post-transplant period should be carefully evaluated. Data in non-transplant patients also suggested that SGLT2 inhibitors may carry risks for euglycemic ketoacidosis . To avoiKidney transplant recipients are a subset of patients with CKD that remain at higher risk of CKD complications. In addition to long-term complications, PTDM can result in graft loss and mortality. The early identification of modifiable risk factors and timely implementation of prevention strategies such as lifestyle modifications and immunosuppressive agent adjustments are crucial parts of an overall post-transplant care program. While strategies involving the lowering of immunosuppressive agents reduce the PTDM risk, rejection rates may be higher as a result. To achieve optimal long-term outcomes, a care plan with a patient-specific regimen design and multidisciplinary care involving diabetes specialists and members of the transplant teams should be applied following kidney transplant.