Methods. Consecutive patients referred for cardiovascular assessment and prevention were prospectively included. Using applanation tonometry with SphygmoCor device, cSBP was estimated using an inbuilt generalized transfer function derived from radial pressure waveform, which was calibrated to oscillometric brachial SBP and DBP. The time-averaged MBP was calculated from the radial pulse waveform. The minimum acceptable error (DCBP-cSBP) was set at ≤5 (mean) and ≤8 mmHg (SD). Results. We included 160 patients . The cSBP was 123.1 ± 18.3 mmHg (range 86–181 mmHg). The (DCBP-cSBP) error was −1.4 ± 4.9 mmHg. There was a linear relationship between cSBP and DCBP (R2 = 0.93). Forty-seven patients (29%) had cSBP values ≥ 130 mmHg, and a DCBP value > 126 mmHg exhibited a sensitivity of 91.5% and specificity of 94.7% in discriminating this threshold . Conclusions. Using the DCBP formula, radial tonometry allows for the robust estimation of cSBP without the need for a generalized transfer function. This finding may have implications for risk stratification.Backround. Central systolic blood pressure (cSBP) provides valuable clinical and physiological information. A recent invasive study showed that cSBP can be reliably estimated from mean (MBP) and diastolic (DBP) blood pressure. In this non-invasive study, we compared cSBP calculated using a Direct Central Blood Pressure estimation (DCBP = MBP Hypertension, the leading cardiovascular risk factor, is diagnosed and managed based on brachial-cuff blood pressure (BP). There is an ongoing debate regarding whether there is a stronger association of cardiovascular clinical endpoints with the pressure in ascending aorta  compared to brachial artery , and whether this has any significant clinical implications ,2,3,4. FVarious waveform acquisition techniques were developed to non-invasively estimate central systolic BP (cSBP) ,8. AmongOur group recently conducted an invasive study that introduced a novel method for estimating cSBP called Direct Central Blood Pressure (DCBP) estimation. This method relies solely on peripheral MBP and diastolic blood pressure (DBP) values . The DCBThe rationale behind this approach is based on the following facts: (i) central MBP may be reliably calculated by taking the square root of the product of central SBP and DBP (geometric mean) ; and ii. While the high accuracy and precision of DCBP have been initially documented using invasively obtained BPs , it is uThe present study is a retrospective analysis of an observational prospective study that enrolled 186 consecutive patients who were recruited from the cardiovascular department at the Diagnostic and Therapeutic Center of Hotel-Dieu Hospital in Paris, France, between January and June 2010. These patients were referred for the evaluation of one or more cardio-vascular risk factors, including high-blood pressure, smoking, dyslipidemia, diabetes mellitus, and/or family history of premature cardio-vascular disease. Some patients had previously experienced clinical events. The inclusion criteria required the presence of an adequate quality of the pulse wave tonometric recording of radial arteries. Exclusion criteria encompassed patients under 18 years of age, those with acute medical conditions, atrial fibrillation, and severe heart failure (NYHA III-IV). Patients provided informed consent for additional non-invasive hemodynamic measurements and data collection during the day-hospital cardiovascular screening. The study complies with the Declaration of Helsinki. The study was registered with the French National Agency for Medicines and Health Products Safety (No. 2013-A00227-38) and was approved by the locally appointed ethics committee: the Advisory Committee for Protection of Persons in Biomedical Research.2 (m2). Additional information gathered from the form included family history of premature cardiovascular events among first-degree relatives, personal history of dyslipidemia, hypertension, smoking, previous diseases, and use of medications. Medication information, such as an antidiabetic drugs, lipid-lowering agents, and antihypertensive drugs, was obtained from patients’ files and self-reporting. Previous cardiovascular events, including coronary heart disease, cerebrovascular disease, and peripheral vascular disease, were retrospectively assessed both by scrutinizing the patients documents and by patients interviewing. Hypertension was defined as brachial SBP ≥ 140 mmHg and/or a DBP ≥ 90 mmHg, and/or use of antihypertensive drugs, following European recommendations [2. A calculated eGFR of less than 60 mL/min/1.73 m2 indicated kidney failure. The presence of albuminuria was assessed through a 24 h urine collection, and recorded as normo-albuminuria (less than 30 mg/24 h), microalbuminuria (30–300 mg/24 h), and proteinuria (more than 300 mg/24 h).Clinical and laboratory parameters were collected during the day-hospital visit using a form filled-out at inclusion. The form included age, sex, weight, and height, which were determined using a stadiometer affixed to a wall and a Tanita scale with a digital read-out. Body mass index (BMI) was calculated as weight (kg) divided by heightndations . Diabete2/DBP. Hemodynamic measurements were conducted in the supine position in the morning after an overnight fast. Brachial BP was determined using a validated oscillometric device , following at least 5 min of rest in the supine position. Brachial SBP and DBP were measured in both arms using cuffs of appropriate sizes (utilizing 3 different sizes) ,21. Aftep value < 0.05 was considered statistically significant. The power calculation analysis retrieved values close to 1 both for the correlation and the Chi-square analyses. Statistical analysis was conducted using MedCalc11.6.0 software . Agreement between DCBP and cSBP was evaluated by calculating the difference (error) between the two. The accuracy (mean error) and precision (standard deviation SD) of the DCBP estimate were computed. An acceptable mean error was defined as ≤5 mmHg and an acceptable SD of the error was set at ≤8 mmHg . To cateOut of the 186 patients initially enrolled for a cardiovascular checkup at the day hospital, 26 patients were excluded due to poor quality of the tonometric signal or missing blood pressure data. Therefore, the final analysis included 160 patients. Anamnestic and biological data were available for 133 of included patients. The key characteristics of the study population are presented in The average age of the participants was 58 years, with 54% of them being male. Two-thirds of the patients had a higher-than-normal BMI, and 79% were receiving treatments for hypertension. Additionally, 22% of the participants had diabetes mellitus. The hemodynamic characteristics are listed in 2 = 0.93; p < 0.001)  was 123.1 ± 18.3 mmHg and DCBP was 121.6 ± 18.1 mmHg. A strong linear relationship between cSBP and DCBP was observed (R< 0.001) .The DCBP-cSBP error was −1.4 ± 4.9 mmHg .p = 0.52). The absolute error fell within the 5 mmHg zone (0 to 5 mmHg) in 126 out of 160 patients (79%), within the 10 mmHg zone (0 to 10 mmHg) in 153 patients (96%), and within the 15 mmHg zone (0 to 15 mmHg) in 158 patients (99%). The error was −1.1 ± 3.9% cSBP , and it was ≤10% cSBP in 154 patients (96.3%). There was no influence of the mean on the error  and it was negatively correlated with age  and cfPWV . The error was not related to brachial blood pressure  or body height, body weight and BMI. The (DCBP-cSBP) error was −2.5 ± 4.6 mmHg in men (n = 87) and 0.2 ± 5.0 mmHg in women (n = 73) (p < 0.01). The (DCBP-cSBP) error was −0.2 ± 4.3 mmHg in patients aged <60 years (n = 81) and −2.8 ± 5.2 mmHg in patients aged ≥60 years (n = 79) (p < 0.01). The (DCBP-cSBP) error was −0.5 ± 5.2 mmHg in patients with PWV < 11.3 m/s (n = 80) and −2.4 ± 4.4 mmHg in patients with PWV ≥ 11.3 m/s (n = 80) (p < 0.01).The (DCBP-cSBP) error was positively correlated with heart rate (Rp < 0.0001)  and cSBP was ≥130 mmHg in 47 subjects (29%). The DCBP showed 93% concordance in discriminating a cSBP threshold of 130 mmHg (Chi-squaretest  0.0001) . A DCBP  0.0001) .The radial MBP estimated by the rule of thumb (DBP + 0.33 PP) was slightly higher than the MBP obtained through pulse waveform analysis, with a mean difference of 0.9 ± 3.6 mmHg between the two methods . When caThe primary finding of our non-invasive tonometric study was that DCBP demonstrated both accuracy and precision in estimating cSBP, eliminating the need for a generalized transfer function. This could present important implications for risk stratification, particularly if errors in the cuff-blood pressure measurement used to calibrate the tonometer are minimized, ensuring the reliability of DCBP. 2/DBP ratio that we called DCBP, which we referred to as Direct Central Blood Pressure estimation [In a recent proof of concept and validation study, we demonstrated the reliable estimation of invasive cSBP using the invasive MBPtimation . It is itimation ,24,25. Itimation . FurtherWhen MBP was calculated solely using oscillometric blood pressures and a fixed form factor (FF) of 0.33, DCBP remained a highly accurate estimate of cSBP with a mean error of 0.8 mmHg. However, it is important to note that the precision of the cSBP estimate was moderate, as indicated by the SD of the error, which was 9 mmHg. The FF represents the fraction of pulse pressure that needs adding to DBP in order to estimate MBP. It serves as a pulse shape indicator influenced by factors such as arterial location, demographic characteristics, hemodynamics, pathologies, and methodologic factors. Our finding supports the notion that using a single FF is valuable from the viewpoint of predicting MBP average values, but it fails to capture the variations in the pressure waveform among individuals. Therefore, whenever possible, it is recommended to prioritize pressure waveform analysis over relying solely on a fixed form factor to calculate MBP ,26. DCBP is likely to possess similar strengths and limitations as cSBP estimated through the transfer function of the Sphygmocor device ,11,15,27The limitations of our study should be acknowledged. Firstly, there are various techniques available for non-invasively estimating cSBP, including tonometry, oscillometry, and echo-tracking. Our comparative results specifically pertain to the SphygmoCor device, which is currently the most widely used non-invasive system. Therefore, the generalizability of our findings to other waveform acquisition techniques remains to be studied. Secondly, it is important to note that our results are specific to the population under investigation. The mean age of the participants was 58 years with 59% being men. The prevalence of treated hypertension was 79%, while diabetes mellitus was present in in 22%. Additionally, a substantial portion of the population (64%) has a higher-than-normal BMI. Therefore, our results may not be directly applicable to younger patients or patients with different demographic or clinical characteristics. Further research is needed to validate our findings in these specific populations. Lastly, while our study focused on the accuracy of the single DCBP calculation, it is important to highlight that most of transfer function-based methods enable the estimation of the entire pressure wave shape and provide valuable arterial indices, such as the augmentation index. These indices have clinical relevance and may provide insights into arterial function beyond cSBP estimation.The implications of our study warrant discussion. Previous research has suggested that cSBP may have incremental value above and beyond brachial BP in diagnosis and management of patients with hypertension ,31. Howe2/DBP) is a highly accurate and precise estimation of the cSBP, which was estimated through radial calibration using brachial-cuff SBP and DBP (a type-I tonometric device). Additionally, DCBP proves to be a useful tool for diagnosing central systolic hypertension. It is important to emphasize the need to minimize cuff-blood pressure measurement errors to ensure the reliability of DCBP. Our findings have implications for risk stratification and have the potential to simplify and standardize the evaluation of the incremental value of cSBP beyond brachial SBP. Further research is warranted to explore and validate these findings in larger cohorts and diverse populations.In conclusion, our non-invasive study provides compelling evidence that DCBP (MBP


Kidney transplantation (KT) is the optimal therapeutic strategy for patients with end-stage renal disease. The key to post-transplantation management is careful surveillance of allograft function. Kidney injury may occur from several different causes that require different patient management approaches. However, routine clinical monitoring has several limitations and detects alterations only at a later stage of graft damage. Accurate new noninvasive biomarker molecules are clearly needed for continuous monitoring after KT in the hope that early diagnosis of allograft dysfunction will lead to an improvement in the clinical outcome. The advent of “omics sciences”, and in particular of proteomic technologies, has revolutionized medical research. Proteomic technologies allow us to achieve the identification, quantification, and functional characterization of proteins/peptides in biological samples such as urine or blood through supervised or targeted analysis. Many studies have investigated proteomic techniques as potential molecular markers discriminating among or predicting allograft outcomes. Proteomic studies in KT have explored the whole transplant process: donor, organ procurement, preservation, and posttransplant surgery. The current article reviews the most recent findings on proteomic studies in the setting of renal transplantation in order to better understand the effective potential of this new diagnostic approach. Kidney transplantation (KT) is, at present, the best choice of therapy for end-stage kidney disease (ESKD) patients, offering better survival, quality of life, and cost-effectiveness than any modality of dialysis ,2.Careful surveillance of allograft function is the key to post-transplantation management. Kidney injury may be induced by several different causes that require different approaches for patient management and are indistinguishable by any available noninvasive test. Histologic examination of the kidney allograft through invasive biopsy is currently the gold standard for assessing post-transplant complications . HoweverIn clinical practice, serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin/protein excretion are the current biomarkers of kidney damage. These parameters are easy to measure, non-invasive, and interpretable, but apply to a later stage of the disease; they are neither sensitive nor specific, and are not connected with the molecular pathophysiology . Thus, tIn the field of biomarker discovery, proteins and peptides (the main units of cell function and structure) have great potential, since differences in the composition of proteome and peptidome are indicative of pathologic conditions. The entire set of proteins or peptides expressed in a biological sample  can be fully characterized using proteomics or peptidomics, respectively. The analysis of the peptidome and proteome will be treated identically during the remainder of this review.There are several different techniques that can be used for studies on proteomic and protein biomarkers . Mass spProteomic technologies allow us to achieve identification, quantification, and functional characterization of proteins/peptides in a sample through a supervised or targeted analysis. Proteomic techniques have been applied extensively these last several years as a tool for biomarker discovery in kidney disease and the management thereof ,10,11,12In KT, then, new biomarker molecules are clearly needed for early determination and post-transplant success ,14,15,16Proteomic studies in KT have explored the whole transplant process: donor, organ procurement, preservation, and posttransplant surgery. While molecular markers in the donor may be useful in anticipating short- and long-term outcomes, molecular markers after transplantation may improve our knowledge on adaptation and any shortcomings of the graft.The number of patients on waiting lists for a kidney transplant is increasing rapidly. Since organ availability is low, the gap between patients on the list and the number of available kidneys has forced marginal kidneys to be employed from expanded criteria donors (ECD). The outcome of these transplants is, however, generally less favorable than with other donor types, and includes an increased risk of delayed graft function (DGF) and incidences of primary nonfunction (PNF) ,19. InnoTo date, the traditional methods used to assess graft quality have included visual assessment, kidney risk score and the Kidney Donor profile index, histologic analysis after biopsy, microbiologic analysis of storage fluids, perfusion monitoring, and imaging .2) after 12 months. Interactions between transforming growth factor-beta and calpain-1 may drive the degradation of key cytoskeletal proteins [Proteomics aims to assess biomarkers able to predict graft function in the recipient. Vaughan et al.  used LC-proteins .2) and suboptimal outcome 1 year after transplantation. Good outcomes were associated with the upregulation of 22 proteins, including four proteins involved in complement activation of the classical pathway, which suggests that the scavenging by tissue-resident complement proteins detected in perfusate may improve the outcome. On the other hand, 26 proteins proved to be downregulated in good outcome kidneys, among them 14 cytoskeleton proteins [Efficient preservation of the kidney prior to transplant surgery is a crucial issue in renal transplantation. The functional preservation period is limited to hours, during which proteins, peptides, and other molecules are released into the preservation medium by the organ. Moser et al.  comparedproteins .In summary, the results of these studies indicate that proteomic analyses may provide insights into the pathophysiologic mechanisms underlying organ preservation and represent a useful tool to identify and predict graft outcome .Further studies are required to confirm these results. Improving perfusion technologies is also needed to improve organ resistance and the success of transplantation . A recenIschemia-reperfusion injury (IRI) refers to increased tissue damage after the reperfusion of previously ischemic tissue . In KT, Lindeman et al.  examinedRemote ischemic conditioning (RIC) has been proposed as a therapeutic strategy in the protection of ischemia-reperfusion injury ,33. A raIn summary, a better understanding of the factors potentially leading to IRI may lead to early diagnosis and optimization of treatment. Proteomic identification of biomarkers for IRI may help in this.n = 469 deceased donors and n = 902 corresponding kidney recipients), prospective, observational cohort study by Schroppel et al. [n = 35) or placebo (n = 35) intraoperatively and at 24 h. Though the primary end point  in the first week after transplantation) was not met, treatment with C1INH significantly reduced the need for HD sessions 2 to 4 weeks posttransplant and improved the long-term graft function [Delayed graft function (DGF) is one of the severe manifestations of IRI in the setting of kidney transplantation and can be defined as the need for dialysis in the first week or weeks after transplantation . The incl et al.  investigl et al. . In theil et al. . A recenl et al. . Patientfunction .Mansour et al.  evaluateOther proteomic analyses were applied to urine samples from transplant recipients to identify protein biomarkers of DGF. Williams et al. , using Tn = 11), Hu et al. [Studies investigating protein biomarkers of DGF have also been performed in blood. In 27 cases of deceased donor transplantation  . Immune Histologic examination of kidney allograft is the gold standard to detect rejection, though there are the previously mentioned concerns about kidney allograft biopsy. The use of proteomics provides a unique opportunity to gain insights into the mechanisms involved in KT rejection processes. Several molecules in blood and urine have been evaluated as reflecting the molecular process in the allograft and, hence, may be predictive biomarkers for early diagnosis and monitoring. Most recent results on proteomics studies are reported below.Various biomarkers have been noticed in the urine of KTRs with AR. These include cytokines, extracellular matrix proteins, and renal tubular cell constituents, such as NGAL, kidney injury molecule-1 (KIM-1), interleukin (IL)-1R, IL-20, IL-18, C-X-C motif chemokine 9 (CXCL9), and C-X-C motif chemokine 10 (CXCL10). Shahbaz et al.  found thn = 60) and without (n = 189) ABMR with the same accuracy as histological examination. The negative predictive value of the 10-protein marker set for the exclusion of ABMR was 0.99 and the diagnostic accuracy was independent of the reason for performing the biopsy, as well as the time after transplantation, and proved better than the accuracy of gross proteinuria (AUC = 0.76) [Using a MS technology based on iTRAQ, Zhang et al.  detected = 0.76) . In a su = 0.76) .The role of proteomics in differentiating ABMR, acute cellular rejection (ACR), or acute tubular necrosis (ATN) in kidney biopsies from transplant recipients is suggested by a recent study . A totalHowever, noninvasive evaluation of AR in renal transplant patients using urinary proteomics may fail to prevent premature graft failure. In a prospective, multicenter, phase III diagnostic study enrolling 624 KTRs from different European countries, the use of a 14-peptide panel  on urine samples taken immediately before graft biopsy diagnosing TCMR did not predict borderline TCMR and was poorly predictive of acute TCMR, as the sensitivity of the model was 0.66, specificity 0.47, and AUC 0.60 . These rA further aid to the early diagnosis of problems related to transplant organ dysfunctions has in recent years been ascribed to extracellular vesicles (EVs). Three types of EVs  have been discriminated on the basis of their biogenesis . EVs havn = 10) or better (n = 10) transplant outcomes according to eGFR and from age-matched healthy controls. A series of well-defined protein clusters were highlighted per category of subject evaluated, by which it was possible to differentiate KTRs from healthy subjects and distinguish between transplanted patients according to their eGFR. Proteomic analysis of blood microvesicles could thus help to discriminate between transplant recipients with different graft prognoses [Exosomes (EX) are cell-derived membrane vesicles surrounded by lipid bilayers present in fluid such as blood and urine, which have a significant role in cellular activities, intercellular communication, and waste management . Severalrognoses . It is irognoses , workingResults of recent proteomic studies on AR are summarized in The development of proteomic technologies has opened up new opportunities for non-invasive diagnosis of AR, whether cell-mediated or antibody-mediated, thereby hastening and improving the management of kidney transplantation. Current evidence indicates potential roles for proteomic biomarkers in predicting or diagnosing AR . These pChronic allograft dysfunction (CAD) encompasses different etiologies of chronic dysfunction including true CR, drugs, and viruses . Biomarkn = 25 per group): Group one, patients with chronic allograft nephropathy (CAN); Group two, stable allograft function; and Group three, healthy subjects. Five peaks differentiated CAN patients from the control group with a sensitivity and specific rate of 100%, and the five peaks also distinguished between transplant patients with normal kidney function and the control groups, with the sensitivity rate being little less than 97% and a specific rate of 95.5% [By proteomic profiling using MALDI-TOF MS and magnetic beads, Hussien et al.  studied of 95.5% . In a crof 95.5%  enrolledof 95.5% . The resThe most complete and exhaustive data about biomarkers found in CR have been obtained with the online informatics tool STRING . The STRAltogether, these studies mark a noteworthy effort, in a noninvasive way, to detect CAD in an early phase and to differentiate true CR from other forms of chronic graft nephropathies, which may have therapeutic implications.Infections in KT are common and represent an important cause of curtailed life ,74. They10 copies/mL) in urine and viremia (>4 log10 copies/mL) in plasma for concomitant BKVAN with a sensitivity of 100% and specificity of 78 and 92%, respectively. The Society of Transplantation Infectious Disease Community of Practice 2019 (AST-IDCOP) recommends this type of screening [BK virus (BKV) is one of the most common pathogens in post-transplant infections. BKV is a double-stranded DNA member of the Polyomaviridae family; it induces a common viral infection in children without residual complications and remains latent in the renal tubular epithelium of healthy subjects. After transplantation, BKV may reactivate, leading to viruria in 30–40% of patients and to BKV viremia in 10–20% . A propocreening .The results of recent proteomic studies on BKV infection in kidney transplant patients are highlighted in Caller et al.  used tanA recent study by Wang et al.  used proBruschi et al.  performeHistologically, BKV-induced kidney damage can easily be confused with T-cell-mediated rejection (TCMR), causing diagnostic delays and therapeutic errors. Although blood and urine proteomics techniques are revealing possible predictive biomarkers of BKV infection, one recent work performing proteomics analysis on paraffin-embedded samples of renal tissue from transplanted patients quantified 2798 proteins, of which 638 were associated with TCMR, and 740 were significantly altered in BKV compared with STA samples .In summary, BKV is a serious infection in kidney transplant recipients; we need early biomarkers predicting the infection as a warning to implement preventive therapy, primarily based on reducing immunosuppressive therapy. Recent research has shown that proteome profiling can identify novel biomarkers able to discriminate BKV infection/nephropathy and to provide new mechanistic insights into BK infection. Additional studies need to be conducted to confirm these results in larger cohorts of patients.Other proteomic studies in the setting of kidney transplantation are reported in Some proteomic studies addressed the potential toxic effects of immunosuppressive therapy in transplant patients. A study by Jacobs-Cachá et al.  suggesteAmong the general population, progressive chronic kidney disease (CKD) is perceived in its advanced stages by a continuous decline in glomerular filtration. A classifier based on 273 urinary peptides, termed CKD 273, is well suited to the early detection of CKD and prognosis of progression . RecentlFinally, in a recent study Jalal et al.  used an ClinicalTrials.gov identifier NCT04851145). The “Proteogenomic Monitoring and Assessment of Kidney Transplant Recipients (Mini-Kidney)” trial is a prospective, observational single-center monitoring study that seeks to validate proteogenomic panels for AR and CAN/interstitial fibrosis and tubular atrophy in blood, urine, and graft tissue of kidney transplant recipients scheduled for standard of care biopsies (NCT01531257). A further ongoing trial is the “Molecular Biological and Moleculargenetic Monitoring of Therapy After Kidney Transplantation (MoMoTxRes)” study (NCT01515605). This is a prospective observational cohort study which analyzes over a 10-year period whether noninvasive diagnostic molecular monitoring may improve the outcome of KT. Finally, proteomics is employed in the exploratory efficacy of the study “FIH (First in Human) Trial Evaluating Safety of TUM012 to Minimize Ischemic Reperfusion Injury in Kidney Transplantation” (NCT05246618). It is the first in-human, randomized, double-blind, placebo-controlled trial which examines the safety and tolerability of ex vivo infusion of TUM012 to reduce IRI.Prompt recognition and treatment of adverse events occurring during the lifetime of a transplanted kidney is of major importance to save the allograft and, often, the patient. In KT there is an unmet need for reliable early biomarkers distinguishing between the different forms of graft damage so that an appropriate and effective therapy may be prescribed. We have here outlined how results obtained from proteomic studies show the potential value of such omics methodology in improving transplant outcomes. Proteomic research in KT has revealed its potential for developing valuable tools that improve patient management by enabling the prediction, early diagnosis, prognostic assessment, and therapeutic monitoring of pathological events related to kidney allografts. These results may be further supported by larger current clinical trials, which are studying proteomic biomarkers in KT. The “Mass Spectrometry-based Proteomics in Microvascular Inflammation Diagnosis in Kidney Transplantation. (TranSpec)” study is a single group diagnostic trial aiming to assess the sensitivity and specificity of MS-based proteomics for diagnosing microvascular inflammation through biopsy and urine samples (However, to translate proteomic biomarkers from bench to bedside is a challenge. One major problem is that only a few transplant studies range beyond the discovery phase of biomarker development to include fully independent validation studies demonstrating the clinical utility of the biomarkers proposed . Most trThe results of all of such efforts will be to clarify the role of proteomics in the management of kidney transplant patients.


Patients with neutrophil-mediated asthma have poor response to glucocorticoids. The roles and mechanisms of group 3 innate lymphoid cells (ILC3s) in inducing neutrophilic airway inflammation and glucocorticoid resistance in asthma have not been fully clarified.ILC3s in peripheral blood were measured by flow cytometry in patients with eosinophilic asthma (EA) and non-eosinophilic asthma (NEA). ILC3s were sorted and cultured in vitro for RNA sequencing. Cytokines production and signaling pathways in ILC3s after IL-1β stimulation and dexamethasone treatment were determined by real-time PCR, flow cytometry, ELISA and western blot.The percentage and numbers of ILC3s in peripheral blood was higher in patients with NEA compared with EA, and negatively correlated with blood eosinophils. IL-1β stimulation significantly enhanced CXCL8 and CXCL1 production in ILC3s via activation of p65 NF-κB and p38/JNK MAPK signaling pathways. The expression of neutrophil chemoattractants from ILC3s was insensitive to dexamethasone treatment. Dexamethasone significantly increased phosphorylation of glucocorticoid receptor (GR) at Ser226 but only with a weak induction at Ser211 residues in ILC3s. Compared to human bronchial epithelial cell line (16HBE cells), the ratio of p-GR S226 to p-GR S211 (p-GR S226/S211) was significantly higher in ILC3s at baseline and after dexamethasone treatment. In addition, IL-1β could induce Ser226 phosphorylation and had a crosstalk effect to dexamethasone via NF-κB pathway.ILC3s were elevated in patients with NEA, and associated with neutrophil inflammation by release of neutrophil chemoattractants and were glucocorticoid (GC) resistant. This paper provides a novel cellular and molecular mechanisms of neutrophil inflammation and GC-resistance in asthma.Trial registration The study has been prospectively registered in the World Health Organization International Clinical Trials Registry Platform (ChiCTR1900027125)The online version contains supplementary material available at 10.1186/s12931-023-02395-5. MeanwhiC3 cells  and a sire asthma. HoweverAsthma is a heterogeneous disease with two broad inflammatory phenotypes: eosinophilic asthma (EA) and non-eosinophilic asthma (mainly mediated by neutrophil) (NEA) , 9. NeutAdult patients with asthma were consecutively recruited from Respiratory Outpatient Department of West China Hospital, Sichuan University from June 2019 to December 2020, according to diagnosis of Global Initiative for Asthma (GINA) recommendations . Patient−CD45+CD3−CD4−CD8−CD127+CRTH2+, Group 1 innate lymphoid cell (ILC1) was defined as Lin−CD45+CD3−CD4−CD8−CD127+CRTH2−CD117− and ILC3 was defined as Lin-CD45+CD3−CD4−CD8−CD127+CRTH2−CD117+ [Fresh blood was collected in EDTA-treated tubes and peripheral blood mononuclear cells (PBMCs) were isolated as previously reported , 19 and 2−CD117+ –22  were received as a gift from Tianfu Life Science Center of Sichuan University West China Hospital. Human IL-2, recombinant human IL-1β and IL-23 were purchased from Peprotech. NF-κB inhibitor (TPCA-1), p38 inhibitor (SB203580) and JNK inhibitor (SP600125) were purchased from Selleck. Dexamethasone was purchased from Sigma.PBMCs from healthy donors were labeled with the antibody panel 2  together with antibody panel 3  cocktail in the presence of Brefeldin A  for 6 h at 37 ℃. After staining with Zombie Aqua (Live/Dead) and fixation with IC Fixation Buffer (eBioscience), cells were permeabilized with a permeabilization buffer (Invitrogen) and labeled with corresponding antibodies  following the manufacturer’s instructions and reverse-transcribed to cDNA using HiScript III RT SuperMix for qPCR (+gDNA wiper) Kit . Primers sequences were presented in Additional file ILC3s were stimulated with or without IL-1β + IL-23 for 48 h before sending to Beijing Novogene Bioinformation Technology Co., Ltd.  in TRIzol Reagent for RNA-Seq. Standard methods were used to extract RNA, and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer. 1 μg RNA was used for cDNA library preparation after purification using Poly-T oligo-attached magnetic beads. Sequencing was performed on the Illumina Novaseq 6000  platform .Cell supernatants were analyzed by Human CXCL8 Uncoated ELISA kit (Thermo Fisher Scientific) and human CXCL1 ELISA kit  following manufacturer’s recommendations.Proteins of cells were extracted with radioimmunoprecipitation (RIPA) lysis buffer  with phenylmethanesulfonyl fluoride (PMSF) and protease inhibitor cocktail and quantified by BCA Protein Assay Kit . The process was performed as previously reported . Antibod*P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. Data was analyzed by Prism 6.0 (GraphPad) and flow cytometry data was analyzed by FlowJo 9.7.6 software (Tree Star).Continuous variables are expressed as mean ± standard deviation (SD) or median . Categorical variables are summarized as frequencies and proportions. Differences between groups were analyzed with Student’s t test or Mann–Whitney test. And for three or more groups, Analysis of Variance or the Kruskal–Wallis test were performed, Tukey HSD (Tukey Honest Significant Differences) were used for multiple comparisons as post hoc test. Categorical data were compared using Chi-square test or Fisher exact test. Test of Spearman was performed for evaluating correlations. Differences were considered statistically significant at P-value was adjusted using the Benjamini & Hochberg method for controlling the false discovery rate. Genes with adjusted P-value < 0.05 and absolute value of log2(FoldChange) ≥ 1 were assigned as differentially expressed genes. Gene Ontology (GO) enrichment analysis and KEGG pathways analysis of differentially expressed genes were conducted by the clusterProfiler R package, and terms with adjusted P-value < 0.05 were considered significantly enriched.For sequencing data, differential gene expression analysis was performed using the DESeq2 R package (1.20.0). The 6/L vs 570.00 ± 201.15 × 106/L, P < 0.001) and percentage , fractional exhaled nitric oxide (FeNO)  were lower compared to the EA group (Table P = 0.067) and positive correlation with ILC2  but not ILC1 and numbers  of ILC3s in NEA group was higher than EA group  and percentage  . In the NEA group, blood eosinophils count   (data not shown) were participated in ILC3s upon IL-1β + IL-23 stimulation. Further enrichment analysis also showed CXCL8 was involved in the NF-κB signaling pathway  are mediated by their binding and activation of GC receptor (GR) , and phoFinally, we explored the potential signaling pathways that regulate phosphorylation of GR in ILC3s. MAPK signaling pathway has been found to directly phosphorylate GR , and in The pro-inflammatory roles of ILC3 in asthma AHR and inflammation have been known for several years , but theOne of the clinical characteristics of patients with NEA is the poor response to corticosteroids, leading to a higher severity of disease and difficult-to-control asthma . In thisThe phosphorylation of GR has been reported to be controlled by the p38 MAPK and Erk/JNK MAPK signaling pathways, of which p38 MAPK promotes the Ser211 phosphorylation while Erk/JNK MAPK down-regulates Ser211 phosphorylation and increases Ser226 phosphorylation , 51, 52.However, there are several limitations that need to be addressed in this study. Firstly, due to the COVID-19 pandemic, we were unable to obtain induced sputum samples from asthma patients, thus unable to detect ILC3 in airways and distinguish neutrophilic asthma from NEA; therefore, EA and NEA were classified according to blood eosinophil counts 300/μL. Secondly, different subgroups of ILC3 have not been investigated, which could provide more information of ILC3 heterogeneity. Third, we did not directly compare the stimulation responses of ILC3 from healthy control and different asthma phenotypes/severities, which might imply the functional differences of ILC3 in the context of disease and asthma phenotypes. Fourth, we did not investigate other potential mechanisms that could be involved in the regulation of glucocorticosteroids such as GR isoforms (GRβ) and the lack of GRE in CXCL8 gene. Finally, we focused on patients with asthma, however, it would be interesting to investigate the roles of ILC3 in a broader spectrum of diseases such as patients with chronic obstructive pulmonary disease (COPD) as they are generally less sensitive to inhaled steroids and age dependent, and evidence showed ILC3s were notably increased in lung tissue from patients with COPD .In conclusion, ILC3s are elevated in patients with NEA compared to EA and are crucial contributors to the neutrophilic airway inflammation via production of neutrophil chemoattractants through p65 NF-κB and p38/JNK MAPK signaling pathways. Specifically, the expression of neutrophil chemoattractants induced by IL-1β is insensitive to dexamethasone, which is due to aberrant GR phosphorylation with increased phosphorylation at Ser226 and weak phosphorylation at Ser211. Our findings provide evidence of the roles of innate immunity in neutrophilic inflammation and steroids insensitivity, which help to discover novel treatment targets for future drug development and better management of neutrophilic phenotype of asthma.Additional file 1: Figure S1. Flow cytometric gating strategy for ILCs in PBMCs. ILC2 populations were gated as Lin−CD45+CD3−CD4−CD8− CD127+CRTH2+, ILC1 populations were gated as Lin−CD45+CD3−CD4−CD8− CD127+CRTH2−CD117−, and ILC3 populations were gated as Lin−CD45+CD3−CD4−CD8−CRTH2−CD127+CD117+ Lineage markers contained CD11b, CD11c, CD14, CD19, CD56, CD123, and FcεRI. ILCs, innate lymphoid cells; ILC1, group 1 innate lymphoid cell; ILC2, group 2 innate lymphoid cell; ILC3, group 3 innate lymphoid cell.Additional file 2: Figure S2. A, Blood neutrophils count and percentage among healthy control (HC) and patients with NEA and EA. B, Correlation between ILC subsets and blood neutrophils count and percentage. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05, ns p > 0.05.Additional file 3: Figure S3. A, Level of p-GR S226 and p-GR S211 in HBEs with or without dexamethasone treatment. B, Level of p-GR S226 and p-GR S211 in ILC3s with or without dexamethasone treatment.Additional file 4: Table S1. Antibodies used in flow cytometry and cell sorting. Table S2. Primer sequences used in RT-PCR. Table S3. Antibodies used for western blotting.


Physical activity (PA) is of benefit and particularly important for cardiovascular disease risk factors as being sedentary becomes a lifestyle habit. Research into Chinese complex association among physical activity, body-fat percentage (BF%), blood pressure, and serum lipids is limited. The present study is based on an observational study among adults (>18 years old) residing in fifteen provinces in China. Data of 10,148 adult participants in the 2015 China Health and Nutrition Survey (CHNS) were analyzed. The simple mediation effect models with covariates were utilized to assess the association among PA and blood pressure or serum lipids, and BF% was played as a mediator. The serial multiple-mediator models with covariates were constructed to the further analysis of the relationship between PA and blood pressure, and BF% was the mediator 1 and blood lipids were the mediator 2. Based on the above hypothesis, the moderated mediation models with covariates were used to analyze the association among PA, BF%, and blood pressure; in addition, BF% was used as the mediator and blood lipids played as the moderator. In the simple mediation models, the model with a dependent variable was high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C); BF% was played as the partly mediation effect and the proportion of contribution was 0.23 and 0.25, respectively. In the serial multiple-mediator models, blood lipids, as the second mediator, played the mediation effect; however, the effect was smaller than the BF%. In the moderated mediation model, blood lipids had the moderation effect as the moderator variable. HDL-C played a moderating role in the latter pathway of the “PA→BF%→SBP/DBP” mediation model, and LDL-C/TC played a moderating role in the direct effect of the “PA→BF%→DBP”. In conclusion, BF% played a mediating role in the relationship between PA and blood pressure. HDL-C, LDL-C, and TC were more likely to act as moderating variables in the mediation model “PA→BF%→SBP/DBP”. PA could directly and indirectly benefit to control the CVD risk factors simultaneously. More people die each year from cardiovascular diseases (CVD) than from any other cause. Hypertension, dyslipidemia, and obese are the independent risk factors of cardiovascular disease, and they often co-exist . HyperteObese is an independent chronic disease, with abnormal or excessive fat accumulation, which presents a risk to health. Its threats are CVD, diabetes, and even cancer . In ChinPhysical activity (PA) refers to a person’s all movement, which has been shown to contribute to the prevention and management of CVD . PreviouAccording to the previous studies and the results of our CHNS team, PA has beneficial effects on the chronic diseases above. However, the mechanism is not clear in relation to whether PA affects blood pressure and serum lipids through adipose, and the sequence of effects of PA on blood pressure and lipids has not been reported. Exploring the importance of body fat in the process of PA on CVD plays a vital role in lifestyle preventing CVD and improving health. Meanwhile, understanding the sequence of PA affects the risk factors of CVD has a positive effect on prevention and controlling the diseases at different course of diseases.Mediation analysis is widely used in psychology and sociology to investigate the mechanism of interventions, but epidemiologists are also using it to analyze mechanisms in the “black box” in epidemiology in recent years; for example, exploring the mechanism of hand and knee osteoarthritis, frailty, disability, and other diseases ,34,35,36To address these knowledge gaps, data from Chinese adults (age ≥ 18 years) obtained in 2015 CHNS were used to examine the complex association among PA, BF%, blood pressure, and serum lipids. We hypothesize that the influence of PA on blood pressure and lipids partly comes from direct effect, and partly goes through BF% to effect on blood pressure and/or serum lipids. Specifically, we constructed the simple path “PA→BF%→SBP/DBP/HDL-C/LDL-C/TC/TG”. Based on the strengthened association between blood lipids and blood pressure, we constructed the serial mediators path “PA→BF%→HDL-C/LDL-C/TC/TG→SBP/DBP”. In addition, drawing from the interaction effect between BF% and serum lipids, we also constructed the more complex path based on “PA→BF%→SBP/DBP” as HDL-C/LDL-C/TC/TG played the role of moderator in it. According to these hypotheses, we aimed to assess the most reasonable path mechanism among PA, BF%, blood pressure, and serum lipids.https://www.cpc.unc.edu/projects/china (accessed on 1 March 2023), and elsewhere [We leveraged the 2015 wave of the CHNS, an ongoing multipurpose large-scale longitudinal survey, with the aim of capturing sociological, economic, and demographic factors that influence health and nutritional status across the Chinese life span. Until now, China has seen a total of 11 CHNS surveys  being conducted. During the thirty years, the survey provinces are increasing from eight provinces in 1989 to sixteen provinces in 2018. In 2015, there were fifteen provinces, including Shandong, Liaoning, Heilongjiang, Jiangsu, Henan, Guizhou, Hunan, Hubei, Zhejiang, Yunnan, Shanxi, and Guangxi and three autonomous cities . A multistage, stratified, random cluster sampling design was used to ensure a probability sample. Specific individuals participated in the survey repeatedly at each round unless they were lost to follow-up. Further details regarding the CHNS are described and can be accessed at the following internet website: lsewhere .n = 1859) or PA (n = 1742) or BF% (n = 335) were deleted.The 2015 survey included 7319 households within 360 communities (approximately 20 households per community). The number of participants (age ≥ 18 years) recorded who were not disabled, not pregnant, and not lactating was 16,799. Participants that omitted data on blood lipid profiles (n = 2296); and those associated with either extreme daily energy intake  (n = 393), those with extreme BMI , and those with extreme PA , n = 17), a total of 10,148 participants were included in the final analysis , and the Institutional Review Committee of the University of North Carolina at Chapel Hill; and written informed consent was obtained from all the CHNS participants before data collection.A standard PA questionnaire  was relSedentary behaviors were calculated as the average hours per day (hour/day) spent in various non-occupational recreational activities, which was not included in the PA calculation. Lying down, sitting , and watching TV or movies/videos belong to sedentary behaviors. We used a summation of the time spent engaged in these activities to access the total time expenditure on sedentary behaviors.According to the standardized procedures, trained health workers or nurses measured the height, weight, body-fat percentage, waist circumference, and blood pressure of participants. Using a body composition analyzer , weight was measured to the nearest 0.1 kg, with the participant in lightweight clothing standing without shoes. Body-fat percentage was calculated using the bioimpedance method utilizing a proprietary algorithm that requires age, sex, height, and PA level inputs by technicians. This method is being used widely and has previously been validated in other studies ,43. We rBlood pressure was obtained on the right arm, and all of the workers followed the standardized procedure using regularly calibrated mercury sphygmomanometers with appropriate-sized cuffs. We measured SBP at the first appearance of a pulse sound (Korotkoff Phase 1) and DBP at the disappearance of the pulse sound (Korotkoff Phase 5). Three measurements of SBP and DBP were averaged to reduce the effect of measurement error and the average of the three measurements was used in analysis. g for 10 min at room temperature and separated into 9 aliquots) within two hours of collection in local hospitals. The aliquots were frozen and stored at −86 degrees centigrade for later laboratory analysis. All samples were analyzed in a national laboratory in Beijing with strict quality control. Serum TG, TC, HDL-C, and LDL-C were measured via the CHOD-PAP  method.Overnight fasting blood was collected from the antecubital vein of participants in the morning. Serum and plasma samples were processed , in combination with the household weighing of condiments over the same three-day period, according to the Chinese Food Composition Tables. X transmits its effect on a consequent variable Y. Mediator is the link connecting the relationship between X and Y, which theoretically means some internal mechanism. They represent a possible or proposed mechanism—the contents of the “black box”—by which message framing influences behavior. Once X exerts its effect on M, then M’s causal influence on Y produces a variation in Y. Based on ordinary least-square regression, the mediation effect model can explain the mechanism of path analysis and demonstrate how independent variables affect dependent variables when they are divided into direct effects and indirect effects.The mediation effect model  is a staX variable is proposed as influencing an outcome Y through a single intervening variable M. There are two pathways by which X can influence Y in this model. These pathways are found by tracing the ways one can go from X to Y while never tracing in a direction opposite to the direction an arrow points. One pathway leads from X to Y without passing through M and is called the direct effect of X on Y. The second pathway from X to Y is the indirect effect of X on Y through M. The indirect effect represent show Y is influenced by X through a causal sequence in which X influences M, which in turn influences Y.The most basic of mediation models—the simple mediation model—is represented in conceptual diagram form in a, b, and c′ are the regression coefficients given to the X in the model in the estimation of the consequents; and e is the error. In this diagram, c′ estimates the direct effect of X on Y. The indirect effect of X on Y through M is the product of a and b. The indirect effect tells us that two cases that differ by one unit on X are estimated to differ by ab units on Y as a result of the effect of X on M which, in turn, affects Y. The direct and indirect effects perfectly partition how differences in X map on to differences in Y; the total effect of X and denoted here as c. This relationship can be written as c = ab + c′. In ab), and this test process is also the core of the mediation effect. About the estimating coefficients, the most efficient power of test is Bootstrap in mediation effect analysis as mediation model developing. It uses the repeated sampling method to test the product of test coefficients .Compared with the simple mediation model, the serial multiple-mediator models could obtain the total mediation effect. Meanwhile, under the premise of controlling other mediation variables (such as M), the specific mediation effect of each mediation variable (such as W) is focused. It could reduce the parameter estimation deviation caused by ignoring other mediators in the simple mediation model. It also could compare the importance degree of mediation effects among several mediators. We can analyze the model from three aspects. Firstly, there are three specific pathways. Path 1 is “X on Y is influenced by or dependent on U, as reflected by the arrow pointing from U to the line from X to Y. Then, U affects the direction (positive and negative) and strength. Moderation is depicted in the form of a conceptual diagram in a1 is the effect of X on mediator M, a2 is the effect of moderator U on M, and a2 is the effect of the interaction effect between X and U on M. Coefficient b1 is the effect of M on Y controlling X, U, and covariate iC; and b2 is the effect of the interaction effect between U and M on Y. Coefficient c1′ is the effect of X on Y controlling M, U, and iC. Coefficient c2′ is the direct effect of U on Y controlling X and M. Coefficient c3′ is the effect of the interaction effect between U and X on Y. Coefficients if and ig are the effect of iC on M and Y, respectively.According to the theoretical assumption and model comparison, we conducted the moderated mediation model with covariates. U has a moderation effect on the mediator M and the dependent variable Y is determined by testing whether a3, b2, and c3′ are statistically significant. Coefficient a3 is statistically significant, which indicates that U has a moderation effect on the influence of X on M. Coefficient b2 is statistically significant, which indicates that U has a moderation effect on the influence of M on Y. Coefficient c3′ is statistically significant, which indicates that U has a moderation effect on the influence of X on Y.Whether the moderator a1 + a3U)(b1 + b2U), the report gives the results of 95%CI at the 16th, 50th, and 84th percentiles. If the 95%CI excludes zero, then the moderated mediation effect is statistically significant. Otherwise, the effect is not statistically significant.Through a Bootstrap test on the moderated mediation effect . p-value of <0.05 was considered to be statistically significant.All statistical descriptions were conducted using SAS software . The mediating and moderating model used IBM SPSS Statistics 25 and the plug-in PROCESS by Hayes’ team . We seleCI) of the simple mediation models between PA and blood pressure, serum lipids among Chinese adults from fifteen provinces in 2015; in addition, Based on the simple mediation models with concomitant variables, we constructed the path “PA-BF%-blood pressure/ serum lipids”. ab and c’ was 0.48.The total effect coefficient between PA and HDL-C was 0.0229, and the direct effect and the indirect effect were 0.0177 and 0.0052, respectively. They were all statistically significant. This meant the indirect effect occupied the total effect 0.23, and it was called the partly mediation effect. The situation of LDL-C was similar to HDL-C; the ratio of indirect effect and direct effect was 0.25. However, the association between PA and TC/TG/SBP belonged to the mediation effect because the coefficient of direct effect was not statistically significant. Due to the opposite signs about the coefficients of direct and indirect effect, BF% played the suppression effect, and the ratio between According to the results from a2b2 were positive, which were contrary to the common sense. Then, we drew mediation effect path diagrams to carefully analyze the reasons (b2 between HDL-C and SBP was 1.4097 (p < 0.05), and b2 between HDL-C and DBP was 0.1289 (p > 0.05). These meant SBP would increase as HDL-C was increasing. Therefore, the coefficients a2b2 in path 2 were positive in a2b2 in path 2 were not statistically significant in the Bootstrap tests when TC or TG played as the second mediator W.Path 1 “PA→BF%→SBP/DBP” was established, which was consistent with the results in  reasons . Figure a1a3b2 in other paths “PA→BF%→HDL-C/LDL-C/TC/TG→SBP/DBP” were statistically significant in the Bootstrap tests. We hold the opinion that BF% and blood lipids acted as the mediators simultaneously in the hypothesis models, and they were also showed sequential effect. The contrast mediation effect was reflected by the difference value of the regression coefficients of path 1, path 2, and path 3. In the paths “PA→BF%→HDL-C→SBP/DBP”, the coefficients in path 1 were negative, and the coefficients in path 2 and path 3 were positive. The opposite signs indicate that the effect in path 2 and path 3 had the opposite effect, called the inhibitory effect. In all the paths, |a1b1| > |a2b2| > |a1a3b2|, which meant that BF% played a greater mediation role than blood lipids among all the mediation effects.In path 3, except the path “PA→BF%→HDL-C→DBP”, the coefficients In view of the fact that the mediation effects of serum lipids were not as important as BF% in the serial multiple-mediator models, especially in the path 2 in In p < 0.05), and the value between HDL-C and SBP was 1.1820 (p < 0.05), which meant that BF% would decrease while SBP would increase when HDL-C was increasing. The coefficients between PA × HDL-C and BF%/SBP were not statistically significant (dashed line), which meant HDL-C had no moderation effect in the front path of the mediation effect and the direct effect. However, HDL-C showed the moderation effect in the back path of the mediation effect because of the statistically significant coefficient  between BF% × HDL-C and SBP (the red solid arrow). In In In b2= −0.1100, p < 0.05). It would not affect DBP directly , but moderated DBP together with BF% (the red solid arrow). The result was consistent with b2 = 0.1289, p > 0.05).In c3′ = 0.2356, p < 0.05, the red solid arrow). This moderated direct effect was statistically significant at the 84th percentile . TC as the moderator had a similar effect to LDL-C in the path “PA→BF%→DBP”, as c3′ = 0.2129, p < 0.05, the red solid arrow); and the moderated direct effect was statistically significant at the 84th percentile  after adjusting the age. However, only one confounder in the model was not reasonable, and it was necessary to control the factors that had an important influence on blood pressure, such as alcohol consumption and dietary sodium-potassium ratio. Hariri [BF% played the mediation effect of PA on blood pressure, and serum lipids tended to be the moderator in the hypothesized models. According to our previous  and the . Hariri  reportedThe epidemiology evidence showed that there was a positive relationship between obese and blood pressure. Blood pressure and the incidence of hypertension increased with the increase of obese . The preThe underlying mechanism of the relationship between obese and hypertension is not fully clear . It has ab and c’ had the opposite signs; then, it was not statistically significant. In the hypothesis model, the direct effect seemed the main effect due to the absolute value being nearly double than the indirect effect. With the establishment of the “PA→BF%→blood lipids” pathway, we assumed that blood lipids were also the mediator in the effect of PA on blood pressure. Then, we constructed a serial multiple-mediators effect model, “PA→BF%→blood lipids→blood pressure” pathway.We found that PA had no direct effect on SBP , while BThe path “PA→BF%→blood pressure” in path 1 of For TC and TG, path 2 was not established, but path 3 was conducted, indicating that the role of TC and TG in the pathway was affected by BF% rather than the direct effect of PA. HDL-C and LDL-C in the model of PA on SBP, and LDL-C in the model of PA on DBP, as the second mediators, were both established in pathway 2 and pathway 3, indicating that PA and BF% have simultaneous and sequential effects on them. It was also possible that there were other mediating variables that have a significant effect on HDL-C and LDL-C and ultimately, achieve the effect of lowering blood pressure. Yang’s results proposed that insulin-related indicators were the mediating variables in the association between BF% and blood pressure . It was a2 were all statistically significant. HDL-C played a moderating role in the latter pathway of the “PA→BF%→SBP/DBP” mediation model, and LDL-C/TC played a moderating role in the direct effect of the “PA→BF%→DBP” in the mediation model . HoweveAccording to the serial multiple-mediation model  and the Although LDL-C has no moderated effect in the “PA→BF%→DBP” mediation model, it acted together with PA on DBP, and LDL-C also played a mediating role as the second mediator in the serial multiple-mediation model, indicating that LDL-C could have acted both as a second mediator or a moderator in the model of PA on DBP. However, LDL-C had an effect on SBP as the second mediator in the mediation model of PA on SBP, but LDL-C did not have a moderated effect in the moderated mediation model. In the serial multiple-mediation model, TC, as the second mediator, did not play a mediating role in path 2, and had little effect in path 3. However, as a moderator, it played a moderating role in the direct effect of PA on DBP. Therefore, although TC played a small role in the model, it also had a certain influence on DBP with the change of PA. TG showed a small effect in the serial multiple-mediation model, and it did not played as a moderator in the “PA→BF%→SBP/DBP” mediation model.In the serial multiple-mediation model, blood lipids could be used as a mediator, whether it was the first or the second mediator, and they were all statistically significant in the models. We observed that the effect on BF% was statistically significant when blood lipids were used as the first mediator. However, in the moderated mediation model , blood lThe physiological mechanism could be inferred from our moderated mediation models. According to our previous results and references, we know that PA did not affect the blood pressure directly. The lack of PA increased the BF%; and adipose tissue would release the negative adipokines like leptin, resistin, and chemoattractant, which were the “bad adipokines” promoting atherosclerosis and endothelial dysfunction . When adOur study extends contemporary knowledge in several important ways. First, based on the mediation effect model, our study is the first study discussing the association between PA and CVD risk factors at the same time in China. This study only preliminarily explored the order relationship and influence mode of PA, BF%, and CVD risk factors from the epidemiological level, and found the connecting role of blood lipids in the pathway. This is an exploration of mechanisms of epidemiology to solve the “black box” problem. We also tried to analyze the “PA→BF%→blood pressure→blood lipids” pathway, because it has been reported that the effects of blood lipids and blood pressure were interactive. But, the sample data of this study showed that the “blood pressure→blood lipids” pathway was not established, and the results were not listed in this paper. This study was based on the hypothesis that the relationship between PA and CVD was statistically significant, the interaction between CVD was known from the references, and the results were verified by statistical analysis. However, the analysis of mediation variables might be insufficient. Also, in the models, we adjusted for as many confounders as possible, including social demographic characteristics, diet, and physical measurement; and the estimated values were reasonable and strictly controlled. When a large number of confounders were controlled, the stability of the effects of independent variables, dependent variables, and mediating variables depends on the sample size. CHNS data structure was based on hierarchical sampling scheme, which required the application of a multilevel mediation effect model. In order to satisfy both the sample size and the multilevel data structure, the further research is to use data from multiple survey rounds for analysis. Some limitations of this study should be considered. First, though CHNS was a long-term follow-up study, 2015 was the first year to collect body composition data. But, the public data of BF% were only in 2015. Second, the anthropometric methods we used to measure PA and BF% in our study were not the most precise available, but they were broadly acceptable and were the most economical and useful way for a large-scale survey covering fifteen provinces in China. PA questionnaire was effective and validated by professors from the University of North Carolina at Chapel Hill, as well as the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention. BF% was measured by bioelectrical impedance analysis because it was cost-efficient and easily carried. Third, a large proportion of the participants were excluded from our analysis for varying reasons, which meant the generalization of our results to the entire survey population should only be made with caution. Lastly, the mediation models in our study were based on the previous basic theory, and many mediators and confounders might not be taken into account. In a further study, we could consider more variables when we would collect more data, and construct more reasonable models like a multiple-level moderated mediation effect model to reveal more robust associations between PA and CVD risk factors and figure out the complex “black box” question.The findings from the present study indicate that BF% played a mediating role in the relationship between PA and blood pressure. HDL-C, LDL-C, and TC were more likely to act as moderators in the mediation model “PA→BF%→SBP/DBP”, while TG had little effect in the model. PA could directly and indirectly benefit to control the CVD risk factors simultaneously.


Mini-invasive surgery (MIS), ERAS, and preoperative nutritional screening are currently used to reduce complications and the length of hospital stay (LOS); however, inter-variable correlations have seldom been explored. This research aimed to define inter-variable correlations in a large series of patients with gastrointestinal cancer and their impact on outcomes.performance status using nutritional screening and comorbidity. Analyses were conducted using structural equation modeling (SEM).Patients with consecutive cancer who underwent radical gastrointestinal surgery between 2019 and 2020 were analyzed. Age, BMI, comorbidities, ERAS, nutritional screening, and MIS were evaluated to determine their impact on 30-day complications and LOS. Inter-variable correlations were measured, and a latent variable was computed to define the patients' p < 0·001); and (c) their impact on the outcomes was based on direct effects , indirect effects , and regression-based effects . Finally, LOS and complications were correlated (p < 0·001).Of the 1,968 eligible patients, 1,648 were analyzed. Univariable analyses documented the benefit of nutritional screening for LOS and MIS and ERAS (≥7 items) for LOS and complications; conversely, being male and comorbidities correlated with complications, while increased age and BMI correlated with worse outcomes. SEM analysis revealed that (a) the latent variable is explained by the use of nutritional screening (p0·004); (b) the variables were correlated , MIS, and nutritional screening are beneficial in surgical oncology; however, the inter-variable correlation is reliable, underlying the importance of the multidisciplinary approach. Enhanced recovery after surgery (ERAS) is a multidisciplinary pathway established to improve surgical patient care. The approach is based on the collaboration and synergy of different physicians, including surgeons, anesthesiologists, gastroenterologists, and clinical nutritionists, and aims to reduce the response to surgical stress, optimize patients' physiological function, and facilitate recovery.Since the initial experiences in colorectal surgery in the 90's , its appmodus operandi, rather than a collection of procedures that includes a preoperative phase , an intra-operative phase , and a postoperative period .Although it is composed of several procedures, the protocol is now perceived as a way of working, an evolving Indeed, the ERAS pathway is structured on several articulated measures linked to the common ground of improving patients' physiology before the surgical intervention (stress event); these measures could be divided into “general” and “procedure-specific” measures, which balance ERAS measures with the specific risks associated with the different gastrointestinal (GI) procedures .In particular, the pre-habilitation phase is common in all cancer subspecialties, and preoperative nutritional screening is one of its pillars. Malnutrition in patients with cancer undergoing surgery has been associated with a poor prognosis, higher costs, longer length of hospital stay (LOS), and increased risk of postoperative complications . As expeHowever, despite the several pieces of evidence available that support the use of preoperative nutritional screening tools , their aAlthough the application of ERAS, nutritional screening, and MIS was documented as cost-effective in CRC, particularly with respect to LOS , the relIndeed, weighing the impact of all these interrelated variables is challenging, and in this context, the use of structural equation modeling (SEM) could contribute to the analysis and interpretation of results. To date, clinical studies have focused on the relation of single items with clinical outcomes or adverse events using standard multivariable analyses but have not explored the relationship among variables (including the aforementioned). This interrelation and the “collinearity” of variables are usually understudied. However, the clinical variables used in medicine often present several degrees of collinearity , and an SEM approach has the potential to overcome this issue.Structural equation modeling (SEM) analysis is a set of statistical techniques that combines different regression models used to describe the relationship among observed variables and their linear causal relationships while simultaneously accounting for measurement error. It also allows the analysis of latent factors, not directly observed or measured but defined by other observed features. Latent variables are used to translate the fact that several observed and measurable variables are imperfect measurements of a single underlying concept. In other fields, examples of latent variables include quality of life, business confidence, morale, happiness, and conservatism. All these are variables that cannot be measured directly. SEM has been extensively used in economics, sociology, and behavioral science, but its current application in clinical medicine is scant, probably because of technical difficulties .Thus, this research aimed to explore the impact of MIS, ERAS, and preoperative nutritional screening on 30-day complications and LOS in a large multi-institutional dataset of patients with GI cancer undergoing surgery. The primary aim was to define the inter-variable correlations and the impact of direct (single variable) and indirect (multiple correlated variables) effects on the outcomes.Patients who underwent elective surgery with curative intent upfront and after neoadjuvant therapy for CRC, UGI, and hepatobiliary–pancreatic (HPB) malignancies, including metastatic and patients with PSM candidates for cytoreductive surgery with/without intraoperative chemotherapy or other malignancies requiring a surgical resection of the gut in 2019 and 2020 at five Italian research hospitals  were reviewed and analyzed. Patients were excluded if they were <18 years of age, had missing data, or had undergone palliative or urgent/emergency procedures. The hospital setting, nutritional evaluation, and use of nutritional screening tools in participating centers were portrayed using a quality analysis, as described before  and presFor data collection, a database was designed adhering to the STROBE statement for collection, interpretation, and divulgation of results . ClinicaDemographic data (age at the time of the procedure and sex), presence of comorbidities (defined as Charlson index >3), tumor site, nutritional data , surgical variables (year and type of procedure and use of MIS), and adherence to the ERAS protocol (defined as the application of at least seven items)  were colThe outcomes of interest were postoperative LOS (measured in days) and 30-day postoperative complications, regardless of their severity.Continuous variables were reported as means and standard deviations (SD) or median and interquartile ranges (IQRs), whereas categorical variables were reported as frequencies and percentages. Statistical analyses followed a three-step approach: first, univariable analyses were performed, and second, correlation and partial correlation were identified. These steps allowed the identification of possible direct effects (first step), the degrees of collinearity among variables, and those presenting the strongest uniqueness criteria (absence of collinearity) to define indirect effects and the latent variable (see below). Third, the final analysis was conducted.t-tests and non-parametric Mann–Whitney tests, according to the distribution of variables, whereas qualitative χ2 tests were performed for categorical variables. Two-tailed univariable analyses were performed for the two outcomes of interest, and a p-value of 0·05 was considered statistically significant. As a second step, correlations between the qualitative  and quantitative variables were tested. Qualitative variables were tested using polychoric and polyserial correlations, whereas Pearson's correlation was used to test quantitative variables . An exploratory factor analysis (EFA) was then performed, including the Kaiser-Meyer-Olkin test and Bartlett's test for sphericity, to measure the sampling adequacy and homogeneity of variance . Correlations were obtained and defined as strong if ranging between 1·00 and 0.80, medium if between 0.79 and 0.50, moderate if between 0.49 and 0.20, or weak if <0.19. Confirmatory factor analysis (CFA) was conducted, and partial correlations were evaluated with the exclusion of exogenous variables/outcomes: LOS and postoperative complications. These partial correlations and results of the factorial analysis were used to define a latent variable that could summarize the patient's performance status without any collinearity. Finally, as a third step, the latter and all the endogenous variables were computed in an SEM analysis including multivariable regression, inter-variable correlations, and the measure of direct (of a single variable) and indirect effects (of multiple correlated variables) on the outcomes. As specified earlier, the variables computed for SEM analysis were defined by the results of the univariable tests and the partial correlations, as shown in the lavaan package of the R software (https://cran.r-project.org/)  .Of the 1,968 patients registered between 2019 and 2020, 320 were excluded due to missing data; thus, 1,648 patients  were included in the data analysis .The clinical settings and practices of the participating institutions are presented in t-test: CRC vs. UGI, p 0.34; CRC vs. HPB, p 0.22; UGI vs. HPB, p 0.08), but there was a prevalence of male in UGI and HPB patients comparing CRC , and probably as expected, we documented a difference in BMI comparing CRC/HPB and UGI patients .Regarding the cohort analyzed, the mean age of the patients was 65.9 years, and the mean BMI was 25.8. Almost half of the patients were treated with MIS, and 71.8% of patients had at least one comorbidity. Of note, 85.5% of the patients underwent nutritional screening, and 23.9% were treated with perioperative nutritional support . The mean LOS was 8.5 days, and 26.7% of the patients presented with at least one postoperative complication . A breakStatistical analyses showed that the use of MIS, the practice of preoperative nutritional screening, and adherence to the ERAS protocol were inversely correlated with LOS; conversely, increased age and BMI were directly correlated with increased LOS. Being male, presence of comorbidity, increased age, and BMI correlated with postoperative complications; opposite findings were reported in univariable analyses with respect to the use of MIS and ERAS practices . The vasFirst, the variables were tested to evaluate possible correlations, and factor analyses (EFA and CFA) documented the consistency of the dataset, particularly with respect to sampling adequacy and variances . Then, tperformance status based on unrelated independent (non-collinear) variables was computed. As presented in Factor analysis was conducted to test complexity and uniqueness. On this basis, a latent variable that could mirror patients' performance status was best explained by the use of preoperative nutritional screening (p 0·004). Following, the correlations among clinical variables were shown, in particular: ERAS, tumor location; MIS, tumor location; co-morbidity, age; MIS, ERAS; nutritional screening, ERAS, p < 0·001.These findings were computed using SEM and path analyses to evaluate the direct and indirect effects on the outcomes of interest, as presented in p0·001), indirect effects , and regression-based effects . As expected, LOS and complications were correlated , and this model presented optimal performance (p < 0·001), as shown in Finally, the impact of the clinical variables on the outcomes was disclosed, and based on direct effects  and key elements of GI cancer care .It was demonstrated that these features are correlated, these correlations are consistent, and they produce direct and indirect effects on the outcomes: prolonged hospitalization and an increased rate of postoperative complications.The results reported here are in line with the literature documenting that ERAS , MIS 2323, and nperformance status was best explained by the practice of screening patients for malnutrition, even if the focus was more on the practice itself than on the tool utilized, given the few discrepancies among the participating institutions. However, it is important to remark that this latent variable is just a closer approximation of the construct and not a real measurable condition.In particular, the SEM analysis highlighted the decisive relationship between ERAS and MIS in reducing postoperative complications and the direct effect of several measures, including nutritional screening, in reducing LOS. Moreover, the latent factor we used to depict patients' It is also essential to stress the advantages of SEM over conventional multiple regression analyses for greater statistical power. Indeed, SEM is a similar but more powerful tool, since it evaluates linear causal relations among variables while simultaneously accounting for the measurement error .The SEM analysis was developed to estimate factor analysis coefficients by the Swedish statistician–psychometrician Karl Jöreskog in the 70's and based on the linear structural relationship (LISREL) approach to address the issue of causality among variables, a fundamental topic in the field of econometrics and associated with the path analysis. The path analysis was indeed developed to part the correlation of variables into different segments in order to interpret their effects, assuming that some variables are related by a causal effect, and to estimate the magnitude of this correlation. Through these estimates, it was possible to provide information on the underlying random process and the technique provided the basis for structural equation models, later implemented and adopted by the R packages –29.In the model herein presented, the total sum of effects (direct and indirect) was significant. One of the strengths of this study is indeed the methodology used. Although SEM was not conceived as causal modeling, these results revealed that the variables were significantly correlated and performed coherently with clinical assumptions. While largely used in psychology, SEM application in medical research has been highly advocated , but curHowever, this is surprising, as the contribution of SEM to clinical research is substantial and it is unique in this field. From a clinical point of view, it showed that the contribution of each item/variable is valuable and strong; however, given the inter-variable correlation, a multidisciplinary approach is the key when interpreting results. Indeed, the vast majority of multivariable analyses involuntarily ignore measurement error by not modeling it clearly, whereas SEM models estimate this variance for both independent and dependent variables .With this in mind, the results highlighted by the present research help clinicians in understanding the strong contribution of both nutritional screening and mini-invasive surgery in the setting of enhanced recovery protocols in protecting patients from adverse outcomes . Thus, the identification of the value of a single ERAS item over the others in several disciplines –36 may sOn the other hand, when discussing study limitations, it should be noticed that the dataset analyzed was large and robust after excluding patients with missing data but included patients with different GI cancers, regardless of their stage. In relation to this issue, it should be also noted that several ERAS guidelines were published over the years , includivs. 9·3%), consistent with the appropriateness of the screening and treatment.Another set of possible discordant data lies in the relatively low rate of patients who received perioperative nutritional support (before and/or after surgery), in total less than one quarter, lower compared with previous experiences . For thiIn addition, to counterbalance this issue, more than half of the entire cohort was treated in compliance with ERAS practice; thus, oral intake commenced within 24 h, following surgical resection.With respect to other possible limitations, additional features such as ASA or other risk factor scores  were not collected. Nevertheless, patients' age was analyzed and correlated with the presence of comorbidities. Given the population analyzed (patients with cancer), a cutoff for comorbidity was set with a Charlson index >3, since patients with this value have a 10-year survival rate of 77·5% , greaterIn this study, significant evidence for the benefits of MIS, ERAS, and nutritional screening in patients with cancer undergoing GI surgery was added, exploring the interrelation of variables and sustaining the multidisciplinary approach. Therefore, the exploration of this methodology in all branches of clinical research should be encouraged, particularly when evaluating outcomes in relation to patients' factors or practices that may have an impact on these features before an intervention.The original contributions presented in the study are included in the article/All procedures performed in studies involving human participants were in accordance with the ethical standards and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All patients provided written informed consent for the surgical procedures. This study was preliminary part of the Nutracare Project approved by the IRB at Fondazione Policlinico Universitario A. Gemelli, Rome. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LL, RC, VC, GS, PD, MT, and DD'U: conceptualization, formal analysis, methodology, project administration, resources, and software. FI, MMe, CP, PP, AP, PF, AM, EF, AAv, FB, MN, RP, AB, FT, AAg, RF, VQ, MMi, DR, MRi, PD'E, MRh, and CC: data curation. LL, RC, VC, GS, PD, FI, MT, MMe, CP, PP, AP, PF, AM, EF, AAv, FB, MN, RP, AB, FT, AAg, RF, VQ, MMi, DR, MRi, PD'E, MRh, CC, and DD'U: investigation, writing, reviewing, and editing. LL, RC, VC, PD, FI, MT, MMe, CP, PP, AP, PF, EF, AAv, FB, MN, and DD'U: funding. PP, PF, PD, and DD'U: supervision and validation. LL, RC, VC, GS, PD, FI, MT, MMe, CP, PP, AP, PF, EF, AAv, FB, MN, and DD'U: roles/writing the original draft. All authors contributed to the article and approved the submitted version.


Introduction: Chronic kidney disease (CKD) is a multifaceted non-communicable disease characterized by a progressive decline in kidney function ultimately requiring renal replacement therapy (RRT) in most patients. Due to the high cost and availability of a limited number of donors, the majority of patients depend on dialysis and conservative management. Thyroid hormones are indispensable for the growth, development, and homeostasis of our body. The kidney plays an important role in the metabolism, degradation, and excretion of thyroid hormones. Various studies have revealed significant dysfunction in thyroid hormone status in CKD patients, but the results are inconsistent.Aims: To evaluate and compare the thyroid hormone status in CKD patients with healthy controls along with a comparison of thyroid hormones in CKD patients on regular hemodialysis with those on conservative management.Materials and methods: The present cross-sectional study involved 100 subjects of both sexes between 40 and 70 years of age, out of which 50 were patients of stage 5 CKD with no previous history of any thyroid disorders, while 50 apparently healthy subjects served as control. Of the CKD patients, 52% were on regular hemodialysis while 48% were receiving conservative care. The participants were investigated for various biochemical parameters like blood urea, serum creatinine, total triiodothyronine (TT3), total thyroxine (TT4), and thyroid stimulating hormone (TSH). The estimated glomerular filtration rate (eGFR) was calculated using a modification of diet in renal disease (MDRD) 4 variable formula. The thyroid profiles were also compared between patients of CKD receiving conservative management and those on maintenance hemodialysis.Results: Of the total sample, 35 (70%) were male and 15 (30%) were female in each of the case and control groups. The mean age of CKD patients and the control group was 55.32 ± 9.62 years and 54.48 ± 9.63 years, respectively. TT3 was reduced in all 50 CKD patients. TT4 was normal in 31 (62%), reduced in 18 (36%), and high in one (2%) case. TSH was high in 38 (76%) cases, while reduced in one (2%) and normal in 11 (22%) cases. The mean blood level of TT3 and TT4 showed a statistically significant reduction (P < 0.0001 for each), while the TSH level showed a significant increase with a p-value of 0.0002 in CKD patients compared to controls. The mean blood urea and serum creatinine levels were statistically increased in cases than in controls (P < 0.0001). The thyroid hormone status revealed a significant difference between CKD patients on maintenance hemodialysis compared to those on conservative care with a p-value of 0.0005 for TT3, 0.0006 for TT4, and 0.0055 for TSH.Conclusion: Patients with CKD were at risk of thyroid hypofunction irrespective of their mode of treatment. This study highlights the clinically relevant interactions between renal and thyroid function, which may be helpful to clinicians for optimal diagnosis and management of CKD patients. India has experienced an epidemiological transition with a decline in communicable diseases and a growing burden of chronic diseases . Unhealt2 of body-surface area for more than three months irrespective of the underlying cause while glomerular filtration rate (GFR) <15 ml/min/1.73 m2 is considered as end-stage renal disease (ESRD) [CKD is characterized by a sustained estimated glomerular filtration rate (eGFR) of <60 ml per minute per 1.73 me (ESRD) . In devee (ESRD) .Thyroid hormones are important for the development of kidneys and the maintenance of the internal environment of the body . The thyLow triiodothyronine (T3) is the commonest laboratory finding in CKD patients. Factors like metabolic acidosis and protein loss in uremic patients affect the functioning of the iodothyronine deiodinase enzyme leading to reduced conversion of thyroxine (T4) to T3 . InflammIn view of the interrelationship between kidney function and thyroid hormone status and their variability, it is important for clinicians to understand their correlation. Despite a large number of available studies, there is a paucity of Indian data, especially in the northeastern region of India regarding their correlation. So, the present study was planned on CKD patients either on conservative management or hemodialysis to determine their thyroid profile status. To the best of our knowledge, no such study has been conducted in the Barak Valley region of the northeastern state of Assam. The outcome of our study may help to understand the comorbid conditions associated with CKD.The present hospital-based, cross-sectional, observational study was conducted in the Department of Biochemistry, Silchar Medical College & Hospital, Assam, a tertiary care teaching hospital in Northeast India from September 2016 to August 2017 after being approved by the Institutional Ethics Committee .Study participants and selection2, of which 24 (48%) cases were on conservative management, while 26 (52%) were on hemodialysis. The study subjects participated voluntarily after they were explained the nature and purpose of the study. The written informed consent was obtained from the participants and their confidentiality was maintained.The study comprised 50 patients of CKD between 40 and 70 years of age of both sexes with no previous history of any thyroid dysfunction, while 50 apparently healthy age and sex-matched individuals from the same ethnic population with normal renal function and no past history of thyroid disorders served as a control group. There were 35 males (70%) and 15 females (30%) in each group. Patients were recruited from both inpatient and outpatient departments of the medicine department and those attending the dialysis unit of the hospital. CKD patients with serum creatinine >5.5 mg/dl and urea level >55 mg/dl and a dipstick test positive for protein were included in the study based on their history, clinical signs, and symptoms. eGFR was calculated using a modification of diet in renal disease (MDRD) 4 variable formula . All theExclusion criteria included patients less than 40 years of age, family history of goiter or thyroid dysfunction, and those receiving concurrent treatment for thyroid disease and drugs known to affect thyroid hormone indices like glucocorticoids, salicylates, heparin, lithium, amiodarone, sulphonylurea, or phenobarbitone. All the participants included in the study underwent estimation for serum total triiodothyronine (TT3), total thyroxine (TT4), TSH, blood urea, and serum creatinine.Sample collection and techniquesTaking all aseptic and antiseptic precautions, 2 ml of venous blood from the median cubital vein was collected after overnight fasting in a clot activator vial for TT3, TT4, TSH, blood urea, and serum creatinine estimation. The sample was left for 30 to 45 minutes and allowed to clot completely. The vial was then centrifuged at 3000 rpm for five minutes in a centrifuge machine. Precautions were taken to prevent hemolysis of the sample. The supernatant serum was used for the investigations or transferred to a clean, dry vial for storage if the estimations were not done in the same sitting. All chemicals used in the study were of analytical grade, and deionized water (Millipore) was used for various purposes.The urease method and the Jaffe alkaline picrate method were used to calculate blood urea and serum creatinine on the Beckman Coulter Au 480 autoanalyzer . Estimation of TT3, TT4, and TSH was done in an autoanalyzer using the Access 2 Immunoassay System by Beckman Coulter using a chemiluminescent immunoassay. After biochemical estimations, the results obtained were statistically analyzed and compared between different groups of studies.Statistical analysisThe data collected were entered into Microsoft Excel  and analyzed using GraphPad InStat version 3.00 for Windows . The data were expressed as mean, standard deviation (SD), standard error of the mean (SEM), and confidence interval (CI). The significance between different variables was tested using Student’s t-test. The chi-square test was used to analyze discrete variables. A two-tailed p-value of less than 0.05 was considered statistically significant.2), out of which 36 (72%) had eGFR ≤ 10 ml/min/m2.The study subjects comprised 35 males (70%) and 15 females (30%) in each of the case and control groups. The mean age of the case and control groups was 55.32 ± 9.62 years and 54.48 ± 9.63 years, respectively. The maximum number of subjects in each of the two groups was in the age group of 54-60 years. All the subjects were investigated for blood urea, serum creatinine, TT3, TT4, and TSH. The eGFR was calculated using the 4-variable MDRD formula. All the cases were in stage 5 of CKD (eGFR < 15 ml/min/mThe mean blood urea level was 153.94 ± 72.50 (mg/dl) in cases and 22.91 ± 7.36 (mg/dl) in controls, while the mean serum creatinine level was 8.76 ± 3.48 (mg/dl) in cases and 0.75 ± 0.31 (mg/dl) in controls. In the unpaired t-test between the case and control groups, the two-tailed p-value was <0.0001 for both blood urea and serum creatinine , reduced in 18 (36%), and high in one (2%) case. TSH level was high in 38 (76%), reduced in one (2%), and normal in 11 (22%) cases , 6.61 ± 3.06 (µg/dl), and 16.92 ± 26.97 (µIU/ml), respectively, while in control, it was 109.27 ± 22.26 (ng/dl), 8.94 ± 1.91 (µg/dl), and 2.29 ± 1.24 (µIU/ml), respectively. The unpaired t-tests between the case and control groups in both TT3 and TT4 were extremely significant with a two-tailed p-value <0.0001, while for TSH, it was significant with a two-tailed p-value of 0.0002 , 8.10 ± 2.381 (µg/dl), and 6.19 ± 4.07 (µIU/ml), respectively, while in cases on hemodialysis, the mean levels were 34.48 ± 11.21 (ng/dl), 5.24 ± 3.019 (µg/dl), and 26.85 ± 34.59 (µIU/ml), respectively. In the unpaired t-test between cases on conservative treatment and cases on hemodialysis, the two-tailed p-value was 0.0005, 0.0006, and 0.0055, respectively . These changes may describe the increased frequency of hypothyroidism in CKD patients .The present study was conducted on 50 CKD patients and compared with healthy controls for their thyroid hormone status. The most common thyroid function abnormality in the current study was low TT3 found in all the CKD patients and their mean TT3 was significantly reduced compared to healthy control. The majority of CKD patients in the present study showed normal or reduced TT4 levels; however, the mean TT4 showed a statistically significant reduction compared to the healthy controls. The TSH level in the present study was found to be mostly elevated or normal while the mean TSH was significantly increased in CKD patients compared to control. Similar results were obtained by Falhi et al. who investigated 50 CKD patients aged 20 to 50 years and showed a highly significant decrease (P < 0.01) in T3 and T4 levels and an increase in TSH level as compared with controls . While tAnalysis of mean TT3, TT4, and TSH was also done between CKD patients receiving conservative management and those on hemodialysis. The CKD patients on hemodialysis showed a significant reduction in mean TT3 and TT4, while mean TSH showed a significant increase than those on conservative treatment. Most CKD patients on hemodialysis are euthyroid; however, hypothyroidism is not uncommon, which is usually associated with a reduction in serum total and free T3. This decline is associated with endothelial damage, systemic acidosis, and inflammation . Malik rLimitations of the studyThe CKD patients included in the study were not categorized based on their etiology, so the individual causal relationship between CKD and their thyroid dysfunction could not be assessed. The iodine status of the participants was not assessed, which may be a limitation, as excess iodine nutrition or iodine deficiency may contribute to thyroid disorders. The current study was not strict about the frequency and duration of hemodialysis while selecting the patients, which may affect the thyroid hormone status.The present cross-sectional, hospital-based observational study observed a significant reduction in mean TT3 and TT4 along with elevation of mean TSH in uremic patients irrespective of the mode of treatment and duration of disease as compared to healthy controls. In light of the results revealed from the present and most of the previous reports, there is a likelihood of dysfunction in the activities of thyroid hormones with a decline in renal function. Early identification of thyroid dysfunction and its intricate relationship with kidney function may be important in planning treatment strategies. Clinicians must take into account thyroid disorders along with treating renal abnormality in CKD patients.