Plasma lipoprotein particles number and size were determined by nuclear magnetic resonance spectroscopy. Associations of different lipoprotein subpopulations with lipoprotein insulin resistance (LPIR) score and glycoprotein acetylation (GlycA) were assessed using multi-regression analysis. In stepwise regression analysis, VLDL and HDL large particle number and size showed the strongest associations with LPIR , whereas the concentrations of the small VLDL and HDL particles were associated with GlycA . In adjusted multi-regression analysis, small and large VLDL particles and all sizes of lipoproteins independently predicted LPIR, whereas only the number of small LDL particles predicted GlycA. Conventional markers HbA1c and Hs-CRP did not exhibit any significant association with lipoprotein subpopulations. Our data suggest that monitoring insulin resistance-induced changes in lipoprotein subpopulations in T2DM might help to identify novel biomarkers that can be useful for effective clinical intervention.Plasma lipoproteins exist as several subpopulations with distinct particle number and size that are not fully reflected in the conventional lipid panel. In this study, we sought to quantify lipoprotein subpopulations in patients with type 2 diabetes mellitus (T2DM) to determine whether specific lipoprotein subpopulations are associated with insulin resistance and inflammation markers. The study included 57 patients with T2DM (age, 61.14 ± 9.99 years; HbA1c, 8.66 ± 1.60%; mean body mass index, 35.15 ± 6.65 kg/m Type 2 diabetes mellitus (T2DM) is associated with striking abnormalities of plasma lipid and lipoproteins that are associated with reduced high-density lipoprotein (HDL) cholesterol, elevated triglycerides, and a predominance of small dense low-density lipoprotein (LDL) particles . These aThere is a large body of information linking inflammatory status and dyslipidemia ,9,10. InWhen assessed by the conventional lipid panel, alterations of lipids and lipoproteins in diabetic dyslipidemia do not consistently provide the full information needed for comprehensive evaluation of the effects of insulin resistance and the accompanying risks of cardiovascular disease (CVD) in diabetic patients. A recent longitudinal study of 15,464 non-diabetic participants demonstrated the importance of unconventional lipid parameters for predicting the risk of diabetes underscoring the need for better characterization of a comprehensive lipid profile evaluation in diabetic patients . In the Lipoprotein insulin resistance score (LPIR) is a novel composite metabolomic biomarker that captures the multidimensional effects of insulin resistance using high throughput NMR . It is aGlycoprotein acetylation (GlycA), a robust biomarker for systemic inflammation, has been gaining increasing interest, particularly as a new surrogate marker for diabetes and CVD risk assessment ,26,27,28It is well-established that combination of hyperglycemia and chronic inflammation is associated with early subclinical carotid atherosclerosis progression and increased risk of new vascular events in diabetic as well as nondiabetic subjects ,34. The Our aim in this study was to examine the effects of diabetes on plasma lipoprotein subpopulations including conventional lipids in T2DM patients. Additionally, we sought to relate specific NMR-derived lipoprotein subpopulations to the newly emerging markers LPIR and GlycA. A total of 57 patients with T2DM were recruited from King Abdulaziz Hospital (KAH), Ministry of National Guard-Health Affairs (MNGHA) at Al-Ahsa, Kingdom of Saudi Arabia between January 2020 and April 2021. MNGHA is an internationally respected healthcare organization government-funded multispecialty accountable health system providing outstanding patient care. It integrates clinical care, training and academics with research and cutting-edge technology with well-developed information system infrastructure that reached HIMSS INFRaM-6 maturity level. The study protocol was approved by the Institutional Review Board of MNGHA (IRB protocol# IRBC/1972/18), and written informed consent was obtained from each participant. Patients were excluded from the study if, at baseline, patients met one or more of the following criteria: patients were receiving chronic renal replacement therapy , had a history of active malignancy  within the last five years (prostatic cancer within the last two years), systemic lupus erythematosus and other autoimmune diseases that may affect kidney function, history of type 1 diabetes, acute infection or fever, pregnancy, chronic viral hepatitis or HIV infection, and current unstable cardiac disease. The following standard methods and definitions were adopted: Diabetes: subjects with a history of T2DM on medication, or HbA1c ≥ 6.5%, or fasting glucose ≥ 126 mg/dL (≥7 mmol/L). The family history of T2DM was defined as any first-degree relative being diagnosed with T2DM. Dyslipidemia: subjects with history of dyslipidemia on medication, or fasting lipid profile with total cholesterol > 200 mg/dL, or LDL > 70 mg/dL. Hypertension: subjects with systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg and under antihypertensive medication use. CKD: subjects with eGFR < 90 mL/min, using modification of diet in renal disease (MDRD) equation or proteinuria (≥2+ on urine dipstick).® assay . We included a random sample of healthy controls (N = 13) to generate internal reference of normal values range for NMR-derived lipoproteins subpopulations testing including relevant lipid parameters and LPIR and GlycA markers.Fasting blood samples were collected in the morning, after a minimum of 12 h of fasting, into EDTA-containing tubes and centrifuged at 4 °C at 3000 rpm for 10 min to separate the plasma for biochemical tests. Samples from T2DM patients and healthy subjects were aliquoted and stored at −80 °C until further analysis. Patient medical history, demographic, and laboratory parameters were collected from the electronic medical records BESTCare database. VLDL, LDL, and HDL concentrations and size were quantified by NMR spectroscopy . LPIR and GlycA, as well as extensive lipid profiles, were assessed by NMR lipoProfilep-values < 0.05 were considered statistically significant.Statistical analyses were conducted using SPSS software version 24 . Normal distribution of the data was evaluated using Kolmogorov–Smirnov test. Continuous variables with normal distribution were presented as means ± SD, and non-normally distributed variables were reported as medians (IQR). Categorical variables were presented as frequencies and percentages. Independent student’s t-test was used to compare means. Associations between lipoprotein subpopulations, LPIR, GlycA, and other variables were assessed using the non-parametric Spearman’s correlation test. Stepwise analysis was performed to determine associations between PLIR, GlycA, HbA1c, and Hs-CRP as dependent variables and all lipoprotein subpopulations as independent variables. Multiple regression analysis was performed to evaluate the association between lipoprotein subpopulations and other dependent variables using adjusted-based models for covariates assessment, including factors such as age, sex, weight, height, BMI, hypertension, stroke, CAD, dyslipidemia, platelet count, creatinine, total cholesterol, LDLc, HDLc, triglycerides, and HbA1c. All adjusted β-coefficients were accompanied by approximate 95% confidence limits. Two-sided tests with 2). All subjects were Saudi citizens with 79% and 53% family history with diabetes and cholesterol, respectively.The patient characteristics of the study subjects with T2DM are detailed in p = 0.01). Total and large HDL particles were lower in T2DM patients compared with reference range values , and negatively correlated with large LDL and HDL particles . In contrast, there was no significant correlation between GlycA and lipoprotein particle concentrations. Based on the particle size of lipoproteins, LPIR positively correlated with VLDL particle size (p = 0.0001) and negatively correlated with LDL and HDL size (p = 0.0001). HbA1c levels positively correlated with LDL-c and large LDL particles (p = 0.02). Extended lipid panel (ELP) analysis demonstrated that LPIR positively correlated with VLDL-c, LDLc, and triglycerides  and negatively correlated with HDLc (p = 0.0001). Among conventional measurements for lipids, LPIR score positively correlated with triglyceride levels (p = 0.016); whereas GlycA positively correlated with total cholesterol and LDLc levels . There was no significant correlation between Hs-CRP and various lipoprotein subpopulations and conventional lipid parameters. Additionally, fasting glucose was positively correlated with small LDL particles concentration, VLDLc, triglycerides and non-HDLc as calculated by the NMR-extended plasma lipid panel  than other individual lipoprotein subpopulations. In contrast, small VLDL and HDL particles had the greatest associations with GlycA . Additionally, LDL particles number had the greatest association with HbA1c  while HDL particles number had the greatest association with Hs-CRP .A stepwise multiple regression was used for identification of optimal subset of predictors with estimated coefficients which best predict the value of lipoprotein subpopulation association with selected variables. LPIR, GlycA, HbA1c, and Hs-CRP were individually entered as dependent variables into stepwise regression models (Models A–D), and all lipoprotein subpopulations (number and size) were used as independent variables . The anap = 0.0001 and R2(model) = 0.75); whereas small VLDL particles were negatively associated with LPIR (p = 0.011 and R2(model) = 0.51) after adjustment of model with potential confounding variables. In addition, small LDL particles were independently and positively associated with LPIR score (p = 0.035 and R2(model) = 0.77). In contrast, VLDL size was independently and positively associated with LPIR (p = 0.0001 and R2(model) = 0.85), whereas HDL and LDL particle size were independently and negatively associated with LPIR  = 0.45 and β = −0.88, p = 0.04, Model R2 = 0.37, respectively).To further analyze the independent relationship of LPIR score, GlycA, and other parameters with different lipoprotein subpopulations, multi-regression analysis was performed for each dependent variable after adjusting models for independent variables such as age, sex, weight, height, systolic blood pressure, diastolic blood pressure, platelets count, creatinine, total cholesterol, LDLc, HDLc, triglycerides, hypertension, CAD, stroke, dyslipidemia, and BMI. Among all lipoprotein subpopulations, large VLDL particles were positively associated with LPIR score  , the Womp = 0.019 and r = 0.277, p = 0.037, respectively). Although, there is a modest association between GlycA and Hs-CRP, it is worth noting that these two markers reflect distinct inflammatory metabolic processes. GlycA is a composite marker that involves multiple circulating glycosylated proteins, whereas Hs-CRP reflects a single acute-phase protein [Inflammation and dyslipidemia are well-established cardiovascular risk factors and strongly associated with each other. The presence of dyslipidemia by itself can trigger the inflammatory process through alterations of lipoproteins such as oxidized LDL . Interes protein ,66. A st protein . However protein .Stepwise regression analysis demonstrated that the size and number of large VLDL and HDL particles were the strongest predictors for insulin resistance score LPIR followed by small VLDL particles , Model AOur findings are also consistent with results from the PREVEND study reporting close association of triglyceride-rich lipoprotein particles and LDL particles with incident T2DM . FurtherSeveral limitations of the current observational study must be considered. First, this is a retrospective study of a relatively small sample size of overweight/obese T2DM subjects recruited from a single center which may have limited the power to detect weak correlations among measured variables; however, the sample size of T2DM participants in this study was sufficient to demonstrate strong correlations between specific lipoprotein subpopulations and the index of insulin resistance LPIR. Second, no conclusion regarding causality can be drawn regarding reported associations in this observational study for which duration of T2DM was not recorded. Third, the majority of participants were on various medications which may have biased the value of associations between relevant variables. The current study did not assess the effects of lipid-lowering drugs such as statins which could affect lipoproteins distribution; however, it is unlikely that these drugs would directly impact LPIR association with atherogenic lipoprotein subpopulations. Lastly, the study population represents a community-based cohort of relatively obese T2DM participants  from the Eastern region of the Kingdom of Saudi Arabia; therefore, these results cannot be generalized to other ethnic groups. However, through a comparison with results from other ethnic groups this study may provide better understanding for the relationship between lipoprotein subpopulations and insulin resistance in T2DM patients.In summary, advanced NMR-derived lipoproteins showed that VLDL and HDL were the strongest predictors for insulin resistance score in T2DM patients. Large VLDL particles number and size were positively associated with LPIR, whereas size of LDL and HDL particles were inversely associated with LPIR, but not with HbA1c levels. In contrast, the systemic inflammation marker GlycA was positively associated with small LDL particles only. Interestingly, the number of particles and size of the atherogenic lipoprotein subpopulations seem to be more robust in predicting insulin resistance than systemic inflammation in T2DM patients. Large prospective longitudinal studies are warranted for further investigations to demonstrate clinical superiority of advanced lipoprotein profiling. This will provide the basis for the identification of potential biomarkers-based lipoprotein subpopulations that can be measured to predict and prevent T2DM.


The aim of this study was to investigate the associations of neck circumference (NC) and neck-to-height (NHR) with diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM).2 in the absence of other primary kidney diseases.A total of 2,615 patients with prevalent T2DM were enrolled. NHR was calculated through NC (cm) divided by height (cm), and prevalent DKD was defined as the urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g or the estimated glomerular filtration rate (eGFR) < 60 ml/min per 1.73 mThe levels of NC and NHR were higher in DKD patients compared with non-DKD patients . After full adjustments, individuals at the highest tertile of NHR had higher odds of DKD than those at the lowest tertile , but this association was not pronounced with NC . Individuals at the highest tertile of NHR had lower eGFR  and higher UACR levels  than those at the lowest tertile. The adverse association between NHR and prevalent DKD remained statistically significant among most of the subgroups analyzed and no interaction effects were observed.The increase in NHR was adversely and independently associated with DKD in this Chinese T2DM population. PatientObesity is proved to be an important risk factor for kidney damage. Adipose tissue releases a mass of signaling molecules, including inflammatory and hormonal factors, which are critical for inter-organ crosstalk. The communication between adipocytes and the kidney, known as the adipo-renal axis is critical for normal kidney function and the effective response of the kidney to injury , 5. MeanUpper-body subcutaneous fat, a unique fat depot independent of generalized and central adiposity, could present extra risk for metabolic disorders , 10. EviTherefore, the goal of our study was to explore the associations of NC and NHR with DKD in patients with type 2 diabetes mellitus (T2DM).22.1A total of 3267 adults were enrolled from National Metabolic Management Center (MMC) , 19 in tThe study was approved by the Ethics Committee in Clinical Research of the First Affiliated Hospital of Wenzhou Medical University (No: KY2021-173), and all participants have been given written informed consent.2.22). NHR was calculated through NC (cm) divided by height (cm).Including systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, height, WC and NC were measured by trained staff according to standard protocols. Body weight and standing height were measured accurate to the 0.1 kg and 0.1 cm without shoes or heavy clothes. WC was measured at the midpoint between the lowest rib and the iliac crest. NC was measured with the upper border of a flexible tape placed below the laryngeal prominence and circled vertically to the long axis of the neck . BMI wasBiochemical indicators, including fasting plasma glucose (FBG), glycosylated hemoglobin A1c (HbA1c), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid (UA) were assayed through venous blood samples obtained in the morning after an overnight fast (≥ 8h). Non-HDL-c was calculated through TC minus HDL-C. Serum creatinine (Cr), urinary albumin and urinary creatinine were measured with an automatic biochemical analyzer (Beckmann AU 5800). UACR was ratios of urinary albumin to urinary creatinine, the estimated glomerular filtration rate (eGFR) was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation .Diabetes duration, lifestyle factors including education attainment, current smoking and drinking and medication history were all obtained by standardized questionnaires.2.32, meanwhile in the absence of other primary kidney diseases as suggested by the ADA recommendations . Data were displayed as means ± standard deviation or as median (interquartile range) for continuous variables, numbers and percentage for categorical variables. Discrepancies between subjects with and without DKD were analyzed using Student’s t test for normally distributed continuous variables, Mann-Whitney U test for abnormally distributed continuous variables, and chi-square test for categorical variables. Multivariable logistic regression models were applied to investigate the relationship between DKD and the tertiles of NC and NHR, odds ratios (ORs) and 95% confidence intervals (CIs) were provided. In addition, multivariable linear regression models were used for eGFR and log-transformed UACR (LnUACR) in relation to the tertiles of NHR. Subgroup analyses were conducted to test the potential interactions between NHR and the other cardiometabolic factors on DKD. For regression models that mentioned above: age and sex were adjusted for in model 1; age, sex, diabetes duration, smoking and drinking status, SBP, TC, FBG were adjusted for in model 2; age, sex, diabetes duration, smoking and drinking status, SBP, TC, FBG, BMI, WC, antidiabetic agents currently and antihypertensive agents currently were adjusted for in model 3. All P values were two-sided and considered statistically significant when < 0.05.33.1P = 0.003; 0.232 vs. 0.227, P < 0.001). In contrast to patients without DKD, those with DKD were older, less educated, and had longer diabetes duration, higher proportions of women and non-smokers, as well as had higher levels of SBP, DBP, BMI, WC, FBG, TG, TC, Cr, UACR, UA, current antidiabetic agents, current antihypertensive agents usage and lower height, eGFR . There were no statistical differences in current drinking status, HbA1c, HDL-c or LDL-c between the two groups (There were 702 (26.85%) patients with DKD and 1913 (73.15%) patients without DKD enrolled in the present study. NC and NHR levels were significantly higher in patients with DKD compared to those without  and higher LnUACR level  after full adjustments Table 3.3.3P for interaction > 0.05)Interaction effects were analyzed in strata of sex, age, diabetes duration, overweight/general obesity, central obesity, hypertension, and dyslipidemia after total adjustments. As presented in 4The current study was the first population-based epidemiological study to investigate the associations of NC and NHR, as indicators of upper-body subcutaneous fat, with prevalent DKD in Chinese population with T2DM. Our major finding indicated that NHR, instead of NC, was positively associated with the presence of DKD in patients with T2DM, independent of cardiometabolic risk factors. The increase in NHR was also related to a decrease in eGFR and an increase in UACR levels. Such discoveries suggested that NHR might be a potential indicator for identifying patients at a higher risk of DKD.Studies about the relationship between NC and kidney dysfunction were limited and inconsistent. A research based on the general Chinese adults found the negative association between NC and eGFR . SimilarLikewise, several other studies have showed that in contrast to NC, NHR was more closely related to metabolic disorders, such as arterial stiffness, liver stiffness, obstructive sleep apnea syndrome (OSAS) and metabolic syndrome (MetS) , 28–30. via promoting oxidative and nitrosative stress  , 36, whie stress –50. At le stress , might ae stress . Shortere stress . Previoue stress –56. TeloThere were several limitations in our study. Firstly, it was a cross-sectional study, causality between NHR and DKD cannot be established. Secondly, our study participants were from a single center and the great majority of them were hospitalized for relative poor glycemic control, it’s generalizability should be verified by involving outpatients or community patients in the future. Thirdly, direct adipose tissue measurements, such as CT or MRI are required to verify the authenticity of NHR. Cohort studies with larger and multicentric samples should also be done.5The present study demonstrated that the higher levels of NHR was significantly associated with the higher presence of DKD in Chinese patients with T2DM, independent of cardiometabolic risk factors. NHR might be a potential indicator for screening renal dysfunction in patients with T2DM, which needs more prospective studies to be confirmed in the future.guxuejiang@wmu.edu.cn.The datasets are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to X-JG, The study involving human participants were reviewed and approved by the Ethics Committee in Clinical Research of the First Affiliated Hospital of Wenzhou Medical University. The patients/participants provided their written informed consent to participate in this study.Z-YH has made substantial contributions to the draft of the manuscript, and analysis of data. Z-YH, XG, L-JD, Y-QL, JL, and L-YP have made acquisition of data. XH, X-XZ, L-JY, BY, X-JG, and X-LL have been involved in revising the manuscript. X-JG and X-LL have given final approval of the version to be published. All authors contributed to the article and approved the submitted version.


To investigate the predictive value of preoperative neutrophil to lymphocyte ratio (NLR) on acute kidney injury (AKI) after on-pump coronary artery bypass (ONCAB).Patients who underwent elective ONCAB for coronary heart disease were included. NLR was calculated according to the results of preoperative routine blood test, patients were divided into non-AKI and AKI groups, and the differences in clinical baseline data between the two groups were compared.p = 0.002), higher serum creatinine  vs. 70.9 ± 16.8 umol/L, p < 0.001), longer cardiopulmonary bypass time, and longer aortic cross clamp time. After dividing patient into tertiles based on NLR, those with higher NLR had higher risk of postoperative AKI than those with lower NLR . Patients in Tertile2 and Tertile3 had higher NLR compared to those in Tertile1 (p < 0.05); multivariate logistic regression analysis showed patients with elevated preoperative NLR and blood creatinine had higher risk of postoperative AKI. ROC curve showed that patients’ preoperative NLR combined with blood creatinine had better predictive value for postoperative AKI.A total of 154 patients were included in this study, including 57 (37%) with postoperative AKI and 97 (63%) without AKI. Compared with the patients in non-AKI group, those in AKI group had higher NLR  vs. 2.06 , Elevated preoperative NLR is associated with AKI after ONCAB, and had prognostic utility independent of other recognized risk factors.The online version contains supplementary material available at 10.1007/s11748-022-01772-z. Globally, coronary heart disease is the most common cause of death . CoronarPatients undergoing ONCAB for coronary artery disease at Sinopharm Dongfeng General Hospital from October 2016 to June 2021 were retrospectively analyzed. All patients underwent CABG under mild hypothermia cardiopulmonary bypass. Myocardial protection was performed in all cases by intermittent antegrade perfusion of cold blood cardioplegic solution. Extracorporeal rewarming was started after completion of proximal anastomosis and stopped when nasopharyngeal temperature reached 36–37 °C, rectal temperature reached 35–36 °C, hemoglobin ≥ 80 g/L, and hematocrit ≥ 24%. A single aortic cross clamp (ACC) technique was used in all cases.Inclusion criteria (1) age ≥ 18 years; and (2) diagnosis confirmed by cardiac ultrasound and coronary angiography, with clear indications for CABG.Exclusion criteria (1) incomplete clinical data; (2) preoperative renal replacement therapy or maintenance hemodialysis; (3) simultaneous cardiac combined renal surgery; (4) preoperative infectious status or history of AKI within the past 3 months; (5) chronic autoimmune disease with hormone therapy; (6) severe heart failure; (7) renal transplantation; and (8) postoperative death.Baseline clinical data were collected from all eligible patients, including gender, age, underlying disease, comorbidities, surgical approach, intraoperative recording of cardiopulmonary bypass time, ACC time, changes in blood creatinine and urine volume during the 7 days after surgery, and preoperative laboratory tests .Relevant definitions: (1) the diagnosis of chronic kidney disease was referred to the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines . (2) AKIt test; continuous variables of non-normal distribution were expressed as median concentration and quartile distance. Mann–Whitney U test is used for the comparison between the two groups, and Kruskal–Wallis h test is used for the comparison between multiple groups. Categorical variables were expressed as percentage, and the chi-square test was used for the comparison between groups. The analysis of linear trends was used to assess the correlation between increasing the NLR levels and risk of AKI after the sample was divided into tertiles based on the distribution of controls. Multivariate logistic regression analysis was used to further analyze the correlation between NLR and AKI. The best cut-off value of NLR for predicting AKI was analyzed by receiver operating characteristic (ROC) curve. Differences with a p value of < 0.05 (two-tailed) were considered to be statistically significant. Analysis was performed using R version 3.6.0. GraphPad prism 8 and Photoshop version 6.0 software are used for graphic production.Continuous normal distribution variables were expressed as mean ± standard deviation in each subgroup. The two samples were compared by independent sample According to the inclusion and exclusion criteria, 154 patients were finally included in this study. Among them, 66.9% were male and 33.1% were female; 34.4% cases were under 60 years old and 65.6% were over 60 years old. 37% patients had previous diabetes mellitus, 74% had hypertension, 7.1% had atrial fibrillation, 20.1% had cerebrovascular disease, 5.8% had chronic kidney disease, 46.1% had myocardial infarction, and 30.5% had heart failure.n = 57, 37%) and non-AKI group  to determine whether there were differences in clinical baseline data between the two groups. Compared with the patients in non-AKI group, those in AKI group had higher proportion of chronic kidney disease , significantly lower lymphocyte count  vs. 1.7  × 109/L, p = 0.005), higher NLR  vs. 2.06 , p = 0.002), higher serum cystatin C  vs. 1.01  mg/L, p = 0.003), higher brain natriuretic peptide  vs. 118.2  pg/ml, p = 0.041), higher serum creatinine  vs. 70.9 ± 16.8 umol/L, p < 0.001), higher proportion of patients with decreased LVEF , longer cardiopulmonary bypass time  vs. 114.5  min, p = 0.004), and longer ACC time  vs. 63  min, p = 0.019). However, there was no significant difference between the two groups in terms of age, gender, or comorbidities, including diabetes, hypertension, atrial fibrillation, myocardial infarction, cerebrovascular disease, and heart failure (p > 0.05), while no difference was found between the two groups in terms of white blood cell count, neutrophil count, albumin, blood lipids, uric acid, and ultrasensitive troponin (p > 0.05), as shown in Table We divided patients into AKI group (p < 0.001), also was significantly higher in Tertile3 compared to those in Tertile1  vs. 1.585 , p < 0.001). Compared to patients in Tertile2, NLR was significantly higher in those in Tertile3  vs. 3.04 , p < 0.001). The distribution of NLR in Tertiles is shown in Fig. p < 0.05), and lymphocyte count was higher in Tertile1 and Tertile2 patients compared to Tertile3 (p < 0.05). We also found a progressively higher percentage (30% vs. 25% vs. 55.8%) and risk of AKI after ONCAB in the three groups .While there was no significant difference among the three groups in age, gender, or comorbidities, including diabetes, hypertension, atrial fibrillation, myocardial infarction, cerebrovascular disease, and heart failure (p > 0.05), and no significant differences (p > 0.05) were found among the three groups in hemoglobin, albumin, blood lipids, ultrasensitive troponin, serum Cystatin C, uric acid, BNP, cardiopulmonary bypass time, ACC time, and cardiac function, as shown in Table p = 0.0104], and patients with higher preoperative serum creatinine also had greater risk of AKI after surgery , as shown in Table To clarify the relationship between NLR and postoperative AKI after ONCAB, we further grouped the patients by the tertiles of NLR. The results showed that NLR was higher in patients in Tertile2 compared to those in Tertile1  vs. 1.585 , 2, as shown in Tables To clarify the predictive value of NLR for the occurrence of AKI after ONCAB, we performed the analysis using the receiver operator characteristic (ROC) curve, which showed that the area under the curve (AUC) of NLR to predict AKI after ONCAB was 0.6504 (95% CI 0.555–0.7457) with the high specificity of 96.91% but the sensitivity of nearly 36.84%, and the cut-off value was 3.09, this similar trend also exists for those who eGFR more than 60 ml/min/1.73 mThis study retrospectively analyzed the association between preoperative NLR and the incidence of AKI after ONCAB in 154 cases. The results showed that preoperative NLR > 3.09 was an independent predictor of postoperative AKI. We also found that patients with high preoperative serum creatinine levels (> 87.5 umol/L) indicated higher risk of postoperative AKI.CABG is one of the treatments for coronary atherosclerotic heart disease. In the United States in 2018, 181, 000 cases underwent CABG accounting for 63% of all cardiac surgeries, while in China in 2019 nearly 50, 000 cases CABG . AKI assPrevious studies have found that alterations in neutrophil and lymphocyte counts and postoperative NLR imbalance are strongly associated with severity of coronary artery disease, poor long-term prognosis, and postoperative complications , 17. SimThe present study also found that preoperative serum creatinine levels directly influenced the incidence of postoperative AKI. Our results showed that preoperative serum creatinine was significantly higher in patients in the AKI group compared with those in non-AKI group and was positively correlated with the highest blood creatinine value, suggesting that the earlier the onset and the greater the elevation of blood creatinine, the greater the likelihood of AKI. After adjusting other risk factors, high preoperative serum creatinine levels also significantly increased the risk of postoperative AKI, which was consistent with previous studies , 19, 20.As one of the important indicators of renal function, increasing serum creatinine before surgery to some extent suggests the concomitant renal insufficiency or reduced renal compensatory capacity, and is prone to induce postoperative hemodynamic flocculation, inflammatory factor release and ischemia–reperfusion injury due to the complexity of CABG, which can further deteriorate renal function. Therefore, it will be of great positive significance for improving prognosis to active monitor renal function after CABG, timely detect abnormal serum creatinine, take appropriate renal protection measures, stop nephrotoxic drugs as early as possible, and correct factors affecting renal perfusion.Previous studies showed that prolonged cardiopulmonary bypass time and ACC time were also independent risk factors for postoperative complications of AKI in patients undergoing ONCAB , 23. SinThere are many limitations in the present research: (i) this is a single-center retrospective study with a small sample size, there is a publication bias, so the results might not be directly extrapolated to other patient groups; (ii) data on C-reactive protein, a common inflammatory index were not collected; (iii) the history of chronic kidney disease were based on self review of patients, and may underestimate the actual incidence rate; (iv) the characteristics of vascular lesions, combined surgery and other factors that may affect the prognosis of surgery were not analyzed. Larger trials are warranted to further substantiate the findings of the present study.In conclusion, our study showed a high incidence of AKI after ONCAB. An elevated preoperative NLR (> 3.09) and serum creatinine (> 87.5 umol/L) are independent risk factors for postoperative AKI and can serve as an early warning. Early intervention is needed in high-risk patients to reduce the occurrence of AKI to improve long-term prognosis.Supplementary file1 (DOCX 39 KB)Below is the link to the electronic supplementary material.


However, to date, no studies have applied this metric to analyze its associations with poor blood pressure control in hypertensive patients. (2) Methods: A cross-sectional design was implemented in one tertiary hospital and one community hospital in China. Using Feihua Nutrition Software to analyze participants’ two-day dietary log, the quantity of carbohydrate and fiber was obtained and the carbohydrate to fiber ratio (CFR) was calculated. The participants were divided into Q1, Q2, Q3, and Q4 groups by quartile method, from low to high according to CFR. The poor systolic and diastolic blood pressure (SBP and DBP) controls were defined as ≥140 mmHg and ≥90 mmHg, respectively. (3) Results: A convenience sample of 459 participants was included and the mean CFR was 29.6. Taking Q1 as reference, after adjusting for covariates, the CFR in Q4 was associated with higher poor SBP-controlled rate . Taking Q2 as reference, after adjusting for covariates, the CFRs in Q3 and Q4 were associated with higher poor DBP-controlled rates . The CFR was the stronger protective determinant of SBP and DBP than DF or carbohydrate alone. (4) Conclusions: A higher CFR is a stronger risk factor for blood pressure (BP) control, and low CFR foods or a combination of corresponding food components, should be recommended in the dietary management of hypertensive patients. Lifestyle changes are the cornerstone of cardiovascular disease (CVD) prevention and diet is one of the most effective strategies for blood pressure (BP) control . In the Carbohydrate, as an important element of diet, is important source of energy and accounts for more than half of daily caloric intake ,5. CompaDietary fiber (DF) is the fraction of the edible part of plants that are resistant to digestion and absorption in the human small intestine  and can The hypothesis of this study is that, the poor BP-controlled rate increases in the higher CFR group, compared with that in the low CFR group, and this increase is greater than the individual contribution of carbohydrate or DF.This was a cross-sectional study to investigate the association between the dietary CFR and the BP control in patient with essential hypertension (EH). We enrolled patients by convenience sampling who met the inclusion criteria from Jinchang Community and No. 1 Affiliated Hospital of Soochow University in Suzhou, China, between September 2019 and August 2021. The study protocol was approved by the ethics committee of the First Affiliated Hospital of Soochow University on 27 September 2019 (ECSU-2019000148) and implemented in accordance with the Declaration of Helsinki.The inclusion criteria were adults over 18 years old with proven diagnosis of EH according to the latest guideline of hypertension  and obtaAfter resting for ten minutes, upper arm BP measurements were performed twice with a five-minute break between 8:30 am and 10:30 am, in a seated position with a calibrated electronic sphygmomanometer  in a community service room or hospital demonstration room before taking anti-hypertensive drugs. We took the average of the two values as the final BP value.In this study, SBP ≥ 140 mmHg was defined as poorly controlled SBP and DBP ≥ 90 mmHg was defined as poorly controlled DBP according to the Chinese definition of hypertension .Participants were instructed by three well-trained researchers to record their two-day dietary log , which iSociodemographic data  and clinical data  were assessed by general information questionnaire. Exercise ≤3 times a week, ≤20 min each time, and continuous time ≤3 months, was considered irregular exercise; otherwise, it was considered exercise . A 10-pop-values were 2-tailed, and a significance level of 0.05 was used.(1)25, P75) if they were skewed.Description of demographic, clinical data, and nutrients: The categorical variables were expressed as frequencies (percentages); the continuous variables were expressed as mean ± standard deviation (SD) if they conformed to normality, or quartiles M (P(2)Comparisons of demographic, clinical data, and nutrients: For continuous variables, the independent samples T test , the Mann–Whitney U test (skewed), or the analysis of variance (ANOVA) were applied. For categorical variables, Pearson’s chi-square test, Yates’ correction chi-square, or Fisher’s exact test was used.(3)Restricted cubic splines (RCS) were plotted by the ggplot2 and rms packages of R software 4.0.2, with nodes assigned at the 5th, 35th, 65th, and 95th percentiles to assess the shape of the relationship between the dietary CFR (continuous data) and BP control.(4)Binary logistic regression was used to investigate the associations between the dietary CFR, carbohydrate, or DF alone, and poorly controlled rates of SBP and DBP.Statistical analysis was performed using SPSS statistical software  and R programming language . All p < 0.1 in “Crude” represents the use of univariate binary logistic regression. The variables with A total of 459 EH participants were included in the analysis. The shorter duration of hypertension, not taking anti-hypertensive drugs, and having anxiety and depression were related with the poor control of both SBP and DBP. Participants with poor SBP control were more likely to be married and have constipation, lower calcium intake, and higher sodium intake, while those with poor DBP control were younger, more likely to be male, had a higher education level, worked, were more likely to smoke and drink, and had higher BMI and cholesterol intake .F = 15.438, p < 0.001), while the DF decreased   .In this study, 187 (40.7%) patients had poor SBP control and 179 (39.0%) had poor DBP control. The highest poorly controlled SBP and DBP rates were in Q4 (60.0% and 45.2%), while the lowest poor SBP-controlled rate and poor DBP-controlled rate were found in Q1 (31.8%) and Q2 (33.6%), respectively .Unadjusted multivariate RCS analysis showed a non-linear relationship between the continuous variable CFR and the poor SBP control . A CFR oFirst of all, the continuous variable CFR was positively correlated with poor control of SBP after adjusting for variables . Secondly, in univariate logistic regression, taking Q1 as the reference group, the dietary CFR in Q4 was significantly associated with the poorly controlled SBP rate. After adjusting for nutrient intake, demographics, and clinical indicators, the dietary CFR in Q4 was still significantly associated with the poorly controlled SBP rate . In addition, the association between DF or carbohydrate alone and the poor BP-controlled rate was analyzed separately. The results indicated that after adjusting for the same covariates, the higher the DF intake, the better the control of SBP ; carbohydrate intake was not associated with SBP control  .Unadjusted multivariate RCS analysis showed a non-linear relationship between continuous variable CFR and DBP control . A CFR oFirst, the continuous variable CFR was positively correlated with poor control of DBP after adjusting for variables . Secondly, in univariate logistic regression, taking Q2 as the reference group, the dietary CFRs in remaining groups were not significantly associated with the poorly controlled DBP rate. However, after adjusting for nutrient intake, demographics, and clinical indicators, the CFRs in Q3  and Q4  were significantly associated with the poorly controlled DBP rates. In addition, the association between DF or carbohydrate alone and the poor DBP-controlled rate was analyzed separately. The results indicated that after adjusting for the same covariates, the higher the DF intake, the better the control of DBP ; carbohydrate intake was not associated with SBP control  .The dietary CFR is an indicator used to define carbohydrate quality. Higher ratios, reflective of a poorer carbohydrate quality diet, have been associated with higher risk of T2DM  and coroThe average age of the participants in this study was 51.31 ± 12.62 years and the majority were male (62.7%), which is consistent with the epidemiological characteristics of hypertension in China ,38. CompIt has been found that the anxiety and depression in hypertensive patients can lead to poor adherence to anti-hypertensive medication and, consequently, poor BP control , which iStaple foods are mainly coarse grains in the traditional Chinese diet; therefore, DF intake increases as carbohydrate intake increases . HoweverThere has been an increased focus on carbohydrate quality over quantity in determining the risk of chronic disease . HoweverLower dietary CFR as a protective factor for T2DM and CHD has been proven by relevant studies. However, there is currently little evidence on the association between CFR and poor BP control. SBP is known to be an important independent risk factor for cardiovascular disease (CVD) in hypertensive patients over 50 years of age , and is Due to the concept that “SBP is the most important”, the awareness and treatment of DBP in hypertensive patients is very low ; for exaIn this study, we found that compared with Q2, CFRs in Q3  and Q4  had higher poor DBP-controlled rates, after adjusting for covariates. At the same time, we found that CFRs had a stronger association than carbohydrate  or DF  alone.Regarding the mechanism of CFR lowering blood pressure, one area of emerging interest is intestinal dysbiosis induced by a deficit in high-glycemic/high-carbohydrate food in DF , which cThis study showed a lower dietary carbohydrate to fiber ratio had a greater protection against poor blood pressure control in patients with essential hypertension. Thus, low CFR foods or a combination of corresponding food components can be recommended in the dietary management of hypertensive patients.To our knowledge, this is the first study to investigate the relationship between the dietary CFR and blood pressure control. We found that a low quality of carbohydrate was consumed by most participants with essential hypertension; a lower dietary CFR showed a greater protective effect against poor BP control in patients with essential hypertension. However, there are several limitations to this study that need to be considered. First, this study was a cross-sectional design and a causal relationship could not be established. Therefore, further studies are needed to assess the relationship between the CFR and BP control. Second, we used 24 h dietary recall for dietary intake assessment. Therefore, some participants may have misreported their dietary intake due to memory-related issues. Third, the effect of other micronutrients such as vitamin D and minerals on BP control was not considered, although the intakes of sodium and potassium were adjusted for in the multivariate model. Finally, this study did not consider participants’ adherence to anti-hypertensive medication, which may limit the interpretation of the effect of CFR on BP control.


External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes.We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event.When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients.It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur. Key MessagesCompeting events often occur when predicting time-to-event outcomes .In model development, using methods with inappropriate assumptions on competing events will lead to predicted risks that are too high. This bias will not be detected in external validation studies if the validation does not explicitly account for competing risks in the statistical methods.Statistical methods to adapt discrimination and calibration performance measures for externally validating prognostic models in settings with competing events are available.The 5-year Kidney Failure Risk Equation is not suitable for risk prediction in patients with advanced kidney disease due to the overestimation of kidney failure. This overestimation can be explained completely by the competing risk of death.Accounting for competing risks in the statistical methods of external validation studies will provide a more reliable assessment of the performance of the model in clinical settings in which competing risks occur.Prognostic models have rapidly become an integral part of medical practice. As clinical care moves towards individualized monitoring, decision-making and treatment, it is imperative to collect information on an individual’s risk profile and many prognostic models have been developed.Competing events prohibit patients from experiencing the prognostic outcome of interest and often occur when studying high-risk interventions, long prediction horizons or cause-specific mortality. For instance, the prediction of kidney failure in patients with advanced chronic kidney disease (CKD) is complicated due to patients dying from other causes before they can develop kidney failure. A conventional time-to-event regression model (such as a standard Cox model) that predicts an individual’s risk of kidney failure would censor all patients with incomplete follow-up in the same manner, including patients with competing events (death). Such a model would therefore overestimate the absolute risk of kidney failure.,,,The importance of using appropriate competing-risk modelling techniques (such as Fine & Gray subdistribution regression models or combined cause-specific Cox models) for prognostic model development is increasingly recognized.The aim of this paper is to draw attention to the importance of externally validating time-to-event prognostic models in a manner that appropriately accounts for competing events. First, we concisely discuss the technicalities of assessing performance measures in a competing-risk setting. Second, we provide a real-data example in which we externally validate an existing prognostic model of kidney failure in patients with advanced CKD. This example illustrates the effects of competing events on measures of prognostic performance and details how such analyses can shift clinical conclusions considerably.In this paper, we assume that the time-to-event model of interest has already been developed and may or may not have accounted for competing risks. Second, we assume that the aim is to validate this model in a setting in which competing events occur, and that clinicians and patients want individualized absolute risk predictions that reflect this. Finally, we assume a specified prediction horizon for which validation is of interest.IJE online) includes a more in-depth explanation on these various methods and we have provided a GitHub repository (in collaboration with authors from a STRATOS initiative guidance paper) with available R-code on how to validate a competing-risk model.External validation of a prognostic model assesses the accuracy of predictions made by the model in individuals who were not used to develop the model.The calibration of predicted and observed outcomes can be assessed through calibration-in-the-large  and visualized using calibration plots.,,Calibration-in-the-large can be assessed by comparing the average predicted risk for the outcome to the observed probability at the prediction horizon. Dividing the observed probability by the average predicted probability gives the O/E ratio. The average predicted risk is known, since we assume that all individual predicted risks according to the existing prediction model are given. In the case of censoring, the non-parametric Kaplan–Meier (KM) estimator is often used to calculate the observed probability. However, in the presence of competing events, the KM estimate will overestimate the absolute risk of the event of interest.et al.In calibration plots, the predicted and observed outcome probabilities are plotted against each other to visualize their agreement. Often the cohort is divided into subgroups based on quantiles of predicted risks. The average predicted and observed outcome probabilities for each subgroup can be computed (accounting for competing events as described above) and plotted. This approach has been criticized as the categorization is arbitrary and can lead to loss of precision and misleading results.Discrimination examines the ability of the model to distinguish between those who will experience the outcome of interest from those who will not and is based on the ranked order of predicted risks.,,et al. can be employed.,,,,R2D.In the presence of competing events, various methods to calculate a C-index are available, some of which are referenced.Predicting kidney failure in advanced CKD patients is of interest for timely preparation of dialysis and transplantation, adequate monitoring of patients, possible referral back to primary care and informing patients of their likely prognosis. As the rate of progression to kidney failure highly varies between individuals, prognostic models have been proposed for use in clinical practice. The Kidney Failure Risk Equation (KFRE) is a prognostic model that was developed to predict kidney failure in patients with CKD stage 3–5 who were referred to a nephrologist.,Cox proportional-hazards models were used in the KFRE model development and external validation studies, meaning that patients who died before experiencing kidney failure were censored.In this clinical illustration, the aim is to externally validate the KFRE in two ways, first using methods that are fitting for a Cox prediction model and treat patients with a competing event in the same way as any other censored patient (similar to the development study) and, second, using methods described previously to account for competing events in order to validate how well the KFRE predicts the real-world risk of kidney failure.IJE online).The KFRE includes the four following predictors: age, sex, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio. The outcome of kidney failure is defined by the initiation of dialysis or kidney transplantation within 2 or 5 years. The full prediction formulae are provided in the development studies and are also shown in the Supplementary Material  was inclusion in the SRR, which is generally the first referral to a nephrologist.Patients were included from the Swedish Renal Registry (SRR)—an ongoing registry of CKD patients capturing 98% of the nephrology clinics in Sweden.IJE online).In total, 13 489 patients were included in our analysis, of whom 1818 (13%) developed kidney failure (the outcome of interest) within 2 years and 2764 (20%) within 5 years. Slightly more patients died without experiencing kidney failure: 2158 (16%) within 2 years and 3357 (25%) within 5 years. No patients were lost to follow-up. All patients were administratively censored on 30 June 2018. The median follow-up was 1.7 years and the maximum was 6.7 years. In total, 3548 patients (26%) were administratively censored within 2 years and 6410 patients (48%) within 5 years. For each individual, the predicted 2- and 5-year risks were calculated using the KFRE formulae. Missing predictors were imputed using the R-package mice.The differences between the observed outcome probabilities of kidney failure, death and event-free survival, calculated using the KM and CIF methods, are shown in a stacked histogram  and in cTo assess the calibration-in-the-large, the observed kidney-failure outcome probabilities based on KM and CIF were compared with the average predicted risk of kidney failure of the model . The 2-yFor model discrimination, the differences are less pronounced between accounting for competing risks or not . When pan = 8654). These patients had a higher risk of death; 1064 patients (12%) experienced kidney failure within 5 years, whereas 2847 patients (33%) died without kidney failure. The median follow-up time was 1.7 years and the maximum follow-up time was 6.5 years. All analyses were repeated in this subgroup and these results are shown in As death without kidney failure is more frequent in older CKD patients, we also validated the KFRE in a subgroup of patients who were ≥70 years old , the overestimation could lead to patients being unnecessarily prepared for dialysis . As the four-variable 5-year KFRE substantially over-predicted kidney-failure risk when considering the competing risk of death, this model is not recommended for use in patients with advanced CKD. An alternative model that accounts for competing events, such as the 4-year Grams model, is recommended instead.R2D) that have been adapted to a competing-risk setting.In this paper, we highlighted the importance and implications of appropriately managing competing events during external validation. We provided explanation and tools on existing measures of calibration  and discrimination  is larger than on relative measures (discrimination). Researchers should carefully consider and select the risk that they wish to predict; if a model censors patients who experience a competing event, the predicted risk is the hypothetical risk in a setting in which the competing event does not exist.The predicted risk of prognostic models is crucial in regard to medical decision-making. For instance, the KFRE is proposed for use in timely preparation for dialysis and kidney transplantation. Predicted risks that are too high may negatively influence clinical treatment decisions. External validation without accounting for competing risks may lead to the implementation of prognostic models that surreptitiously over-predict real-world outcomes and consequently result in overtreatment of patients.The current study has a number of limitations. We have not developed any novel statistical approaches and do not provide information on how to adapt all available performance measures to a competing-risk setting. Particularly measures of net benefit and decision-curve analysis were outside the scope of the current paper. Additionally, further research may focus on adapted measures of the calibration slope and integrated calibration index to a setting with competing events.In conclusion, depending on the underlying clinical question, competing events may be crucial to consider when externally validating time-to-event prognostic models. If an existing prediction model has targeted the incorrect estimand, we can expect a poorer performance when validating this model while accounting for competing events (and thereby adjusting the estimand). Such external validation studies can help to determine whether such models are transportable to a real-life setting in which competing events occur.According to Swedish law, healthcare-quality registries can be used for research. Patients have the right to opt out, but no additional individual consent is required for specific research projects. This study was approved by the Regional Ethical Review Board in Stockholm, Sweden (Dnr 2018/1591–31/2).C.L.R. and M.v.D. conceived of the study. M.E. contributed to data collection. C.L.R. wrote the initial draft of the manuscript. The analysis were performed by C.L.R. with aid from L.T. and N.v.G. All authors provided a substantial contribution to the design and writing of the study and approved the final version of the manuscript. C.L.R. is the guarantor for the study.https://github.com/survival-lumc/ValidationCompRisks. This repository uses different data than the current article due to data-sharing restrictions. The exact R script from our data illustration will be shared on reasonable request to the corresponding author.The data underlying this article cannot be shared publicly due to the privacy of individuals who participated in the study and according to Swedish law. The data can be obtained through the Swedish Renal Registry upon official request and proposal. R-code is available via a GitHub repository at IJE online.The work on this study by M.v.D. was supported by a grant from the Dutch Kidney Foundation [grant number 16OKG12]. K.I.E.S. is funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR Launching Fellowship). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. M.E. was funded by a grant from the Center for Innovative Medicine (CIMED) and ALF Medicin. L.T. is supported by the UK National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM).dyab256_Supplementary_DataClick here for additional data file.


Smartphones may provide a highly available access to simplified hypertension screening in environments with limited health care resources. Most studies involving smartphone blood pressure (BP) apps have focused on validation in static conditions without taking into account intraindividual BP variations. We report here the first experimental evidence of smartphone-derived BP estimation compared to an arterial catheter in a highly dynamic context such as induction of general anesthesia. We tested a smartphone app (OptiBP) on 121 patients requiring general anesthesia and invasive BP monitoring. For each patient, ten 1-min segments aligned in time with ten smartphone recordings were extracted from the continuous invasive BP. A total of 1152 recordings from 119 patients were analyzed. After exclusion of 2 subjects and rejection of 565 recordings due to BP estimation not generated by the app, we retained 565 recordings from 109 patients (acceptance rate 51.1%). Concordance rate (CR) and angular CR demonstrated values of more than 90% for systolic (SBP), diastolic (DBP) and mean (MBP) BP. Error grid analysis showed that 98% of measurement pairs were in no- or low-risk zones for SBP and MBP, of which more than 89% in the no-risk zone. Evaluation of accuracy and precision [bias ± standard deviation (95% limits of agreement)] between the app and the invasive BP was 0.0 ± 7.5 mmHg , 0.1 ± 2.9 mmHg , and 0.1 ± 4.2 mmHg  for SBP, DBP and MBP respectively. To the best of our knowledge, this is the first time a smartphone app was compared to an invasive BP reference. Its trending ability was investigated in highly dynamic conditions, demonstrating high concordance and accuracy. Our study could lead the way for mobile devices to leverage the measurement of BP and management of hypertension. Worldwide, hypertension is one of the largest contributor to heart disease and stroke and the leading risk factor responsible for ten million premature deaths , 2. WithMobile revolution applied to healthcare is a promising candidate to leverage out-of-office blood pressure monitoring as numerous systematic reviews and meta-analyses have enforced the effectiveness of mobile health to improved medical adherence by lowering both mortality and morbidity . Hence, In the last decade, the development of techniques based on the indirect measurement of pulse wave velocity using non-invasive techniques such as tonometry, photoplethysmography (PPG), and electrocardiography have opened new horizons for the cuff-less monitoring of blood pressure –11. ThesIn this context, a smartphone application based on PWA  has been validated on a general population compared to a dual-head stethoscope and sphygmomanometer method as described in ISO 81060-2 standards, and in various other settings –24. AlthThe aim of our current study was to evaluate the blood pressure trending ability of the OptiBP app using a Pulse Wave Analysis compared to invasively acquired blood pressure measurements in a context of high BP variability such as induction of general anesthesia. The second and third part of our analysis aimed at assessing clinical concordance using error grid analysis as well as accuracy and precision based on the ISO 81060-2 standards for non-invasive sphygmomanometers.For this prospective multicentric study, we obtained informed consent from 121 patients aged 18 years or more and scheduled for an elective surgery requiring general anesthesia and invasive BP monitoring at CHUV  and HUG  between April 2019 and November 2019. Exclusion criteria were patient refusal, age below eighteen, inability to give informed consent, ASA risk > 3, dysrhythmia , contraindication to the placement of an arterial catheter , or known contact dermatitis to nickel/chromium .Patients were prepared for anaesthesia according to the existing safety and standard procedures of the Department of Anesthesiology of CHUV Lausanne and HUG Geneva, tailored individually to the patient, depending on his concomitant disease, treatments, and procedures. A dedicated catheter  was inserted predominantly into the right or left radial artery under local anesthesia, allowing beat-to-beat continuous blood pressure monitoring. Patients were monitored with Philips® IntelliVue MP50 monitor conjointly with M3001A module for IAP measurements . General anesthesia was induced with an infusion of propofol (2–3 mg/kg) while intravenous analgesia was provided by boluses of fentanyl (1–2 μg/kg) or continuous remifentanil infusion (0.1–0.5 μg/kg/min) depending on the surgical intervention. Rocuronium (0.6 mg/kg) was administered before intubation. The management of the anesthesia was left at the discretion of the anesthesiologist in charge and maintenance provided with propofol (6–12 mg/kg/h). Boluses of ephedrine (5–10 μg) or phenylephrine (50–100 μg) depending on the clinical context were used in case of necessity for hemodynamic support.www.clinicaltrials.gov. The clinical investigation was conducted in compliance with the European Directive 93/42/EEC on medical devices  of 0.0 ± 7.5 mmHg , 0.1 ± 2.9 mmHg , and 0.1 ± 4.2 mmHg  for SBP, DBP and MBP respectively. Expressed as percentage errors, these figures became 0.3 ± 6.6 , 0.3 ± 4.9 , and 0.3 ± 5.4 , respectively.When evaluating the accuracy and precision between BPIn this study we were able to evaluate the trending ability of a Pulse Wave Analysis method included in a smartphone application measuring blood pressure compared to an invasively acquired values in highly dynamic conditions. With an angular bias < 5° for SBP, DBP and MBP, our app demonstrates no bias on estimating the amplitude of BP changes and a good trending ability in term of direction of change. Considering magnitude of change, most of the measurements (90–95%) are in the same radial limit of agreements range as those defined by Critchley et al. for the cardiac output (± 30°).PPG changes was compared to that of the ∆BPinv changes in terms of accuracy (bias) and precision of agreements (SD of the differences) based on the ISO 81060-2:18 norm [Studies comparing accuracy and precision of continuous non-invasive arterial pressure monitoring systems with invasive arterial pressure measurements in the operating room and critical care settings have led to poor accuracy and precision as reported by Kim et al. . Surpris:18 norm . Based o:18 norm .We choose to transpose Saugel et al.  error grPPG by the average of all BPinv values. This implies a perfect calibration which is not feasible in practice. Finally, we observed a poor—but expected—acceptance rate due to induction context that was not found in the final use-case of the investigated smartphone app [Our study has several limitations. First, we used polar plots as defined by Critchley et al. which was validated against concordance and the opinion by authors of limited comparatives studies. As suggested by Peyton and Chong  guidelinhone app , 22. AddThe most frequently used home blood patient monitoring system is the oscillometric cuff. Although it brings many advantages, its usage is limited in restrained human resources and infrastructure conditions, leading to less controlled BP values. With six billion mobiles phones users worldwide, our study could lead the way for mobile devices to leverage the monitoring of BP in the near future and impact health assessment capabilities.To the best of our knowledge, this is the first time a smartphone app was compared to an invasive BP reference. Its trending ability was investigated in highly dynamic conditions, demonstrating high concordance and accuracy. Further studies are needed to assess implementation in clinical practice.


To summarise evidence on both appropriate and inappropriate antihypertensive drug withdrawal.Deprescribing should be attempted in the following steps: (1) identify patients with several comorbidities and significant functional decline, i.e. people at higher risk for negative outcomes related to polypharmacy and lower blood pressure; (2) check blood pressure; (3) identify candidate drugs for deprescribing; (4) withdraw medications at 4-week intervals; (5) monitor blood pressure and check for adverse events. Although evidence is accumulating regarding short-term outcomes of antihypertensive deprescribing, long-term effects remain unclear.The limited evidence for antihypertensive deprescribing means that it should not be routinely attempted, unless in response to specific adverse events or following discussions between physicians and patients about the uncertain benefits and harms of the treatment.Clinical controlled trials are needed to examine the long-term effects of deprescribing in older subjects, especially in those with comorbidities, and significant functional decline. High blood pressure (hypertension) is the leading modifiable risk factor for cardiovascular disease and the most common condition in older people with multiple long-term conditions . HyperteHowever, antihypertensive treatment is not without harm. A recent meta-analysis of 58 randomised controlled trials showed that treatment is associated with an increased risk of hypotension, syncope, acute kidney injury and hyperkalaemia . They arOne proposed approach to achieve these patient-centred goals is through reducing the number of medications an individual is prescribed, known as deprescribing . BecauseDeprescribing is described as the process of withdrawing an inappropriate medication, supervised by a healthcare professional with the goal of managing polypharmacy and improving outcomes . ImportaAn antihypertensive medication might also be inappropriate if continued prescription does not align with the goals of care, for example in patients at the end of life, where the likelihood of accruing any further benefit (in terms of cardiovascular disease prevention) from treatment is very small . Here, dIn both situations, physicians must have an understanding of the benefits and harms of antihypertensive treatment, the timescales of such effects, and their impact in the specific population of interest.The best evidence for the benefits and harms of antihypertensive treatment comes from randomised controlled trials, which, combined, include hundreds of thousands of people , 5. ThesThe most common indication that an antihypertensive medication may be inappropriate is following an adverse event thought to be caused by that medication. This is typically a reason for deprescribing antihypertensives in nursing homes  and may Therefore, a priority of deprescribing is to identify inappropriate medications to withdraw in high risk patients before they lead to an adverse event. This is not straightforward, since it is difficult to know who is at high risk and such an approach is not without harm, since withdrawing an antihypertensive to prevent a fall could precipitate a “worse” cardiovascular event such as a stroke. These issues were considered in a recent review by Scott et al. , which sAnother situation in which to consider antihypertensive deprescribing is in older people with low systolic blood pressure. While individuals with long-term lower blood pressure, not due to a co-morbidity, are likely to be at lower risk of cardiovascular disease , others Furthermore, a recent systematic review of observational studies found that normal blood pressure < 140/90 mm Hg) is associated with a reduced risk of mortality in the general older population without frailty, but not in those with frailty . For the0 mm Hg iIdentify eligible patientsAs described in the previous section, patients are likely to be eligible for antihypertensive deprescribing if they are older (e.g. aged 75 + years) and at high risk of adverse events. Patients may be at high risk due to a previous adverse event in the preceding 12 months, or because they have developed moderate to severe frailty, functional decline and loss of autonomy. Such frailty may be identified using frailty measures such as the electronic frailty index or the clinical frailty scale , 30. HeaMeasure blood pressureIn patients potentially eligible for deprescribing, blood pressure should be measured to check that it is controlled below guideline recommended levels before withdrawing treatment. Typically, this should be a clinic systolic blood pressure below 150 mm Hg (80 + years) or 140 mm Hg 75–79 years) [ years 9, 29. DepIdentify candidate drugs for deprescribingTo identify candidate drugs for deprescribing, an individual’s currently prescribed medications should be reviewed to identify antihypertensives which may have become contraindicated due to concomitant prescriptions or newly developed conditions. Such contraindications can be identified using published tools such as the STOPP/START  and STOPIn several cases, it may be inappropriate to deprescribe antihypertensive medications, particularly if they have been prescribed for indications other than to lower blood pressure. Examples could include beta-blockers prescribed in patients with atrial fibrillation, diuretics  in patients with symptomatic heart failure and ACE inhibitors/angiotensin II receptor blockers/aldosterone antagonists or beta-blockers in patients with heart failure with low left ventricular ejection fraction.reverse of guideline recommended treatment [nd line therapy for hypertension in older people) and finally calcium channel blockers (1st line therapy for hypertension in older people) [In the absence of any potentially contraindicated prescriptions, healthcare professionals should identify all prescribed antihypertensive medications and withdraw them one by one in reatment . Typical people) . Such an people) , 36, whiWithdraw medicationAntihypertensives should be withdrawn in order of preference for deprescribing, one at a time at 4-week intervals. If withdrawing beta-blockers, healthcare professionals should consider first reducing the dose, before removing the drug altogether to avoid rebound adrenergic hypersensitivity. A similar progressive strategy could be also applied when withdrawing diuretics, especially in patients prescribed high doses of loop diuretics (e.g. > 40 mg of Furosemide) in order to avoid harms related to salt/water retention, and any other antihypertensive medications prescribed at high doses . It is important to check the patient’s systolic blood pressure 4 weeks after withdrawing therapy, to ensure it remains below target , 10, 29.Monitor outcomesIn addition to monitoring blood pressure 4 weeks after deprescribing, healthcare professionals should check for adverse events associated with drug withdrawal. These might include signs of accelerated hypertension (defined as a blood pressure > 180/110 mm Hg), palpitations , prostatism  and peripheral oedema . Once again, if such signs and symptoms are present, healthcare professionals should consider re-introducing the medication previously withdrawn at a lower dose. If symptoms persist, it is important to seek expert advice.Ideally deprescribing should be undertaken by a qualified pharmacist or physician with experience managing hypertension in older people. The process should involve 5 steps, focusing on the characteristics of the individual and careful monitoring of blood pressure and adverse events. These steps are summarised below and in Fig. The evidence supporting antihypertensive deprescribing was recently examined in a Cochrane systematic review of randomised controlled trials . This idAnother limitation of recent trials is that many fail to achieve antihypertensive deprescribing in the study intervention groups. For example, the recent Evaluating Cessation of STatins and Antihypertensive Treatment In primary Care (ECSTATIC) trial  examinedThe COmmunication, Systematic assessment and treatment of pain, Medication review, Occupational therapy, Safety (COSMOS) study  was not The most recent trial of antihypertensive deprescribing was the OPtimising Treatment for MIld Systolic hypertension in the Elderly (OPTIMISE) trial , which eThese trials, like those included in the preceding Cochrane review , had folThese issues are illustrated in the recent study by Aubert et al. , which sp = 0.049) in 48 patients, compared to 132 patients who did not deprescribe [One small observational study , which drescribe . Most rerescribe  attempterescribe . HoweverThe current state of knowledge clearly shows the need for more evidence of the long-term benefits and harms of deprescribing. It is important to assess these effects both in robust elderly subjects but also in individuals with multiple comorbidities, cognitive disorders, functional decline and loss of autonomy, that is, patients who have hitherto always been excluded from long-term interventional trials , 28, 45.The OPTIMISE2 trial begins in the summer of 2022 and will examine a similar intervention to that being studied in RETREAT-FRAIL, but in patients aged 75 + years, living in the community who are at high risk of adverse events. Participants will have a baseline systolic blood pressure of less than 150 mm Hg and be taking at least two antihypertensive medications. The primary outcome will be all-cause emergency hospitalisation and participants will be followed-up for at least 1 year to examine whether antihypertensive deprescribing is non-inferior to usual care (i.e. continued treatment).Given the lack of evidence for antihypertensive deprescribing from trials and observational studies, one alternative approach to understanding the long-term effects is to model them as part of a cost-effectiveness analysis. Such analyses are uncommon in deprescribing research, and until recently were limited to studies examining the cost-effectiveness of nonsteroidal anti-inflammatory drugs and sedatives , 48. TheThe first cost-effectiveness analysis of antihypertensive deprescribing was undertaken using data from the ECSTATIC trial . In patiThe concept of deprescribing is still relatively new  and as sThe present review has summarised the current evidence on the benefits and harms of antihypertensive deprescribing. While many of these remain unknown, studies have now shown that deprescribing is possible in both community and nursing home settings, and appears to be safe in the short term. These studies provide a framework for deprescribing, including how to identify eligible patients and potentially inappropriate medications, as well as how to withdraw treatment and monitor outcomes. However, the current lack of evidence on the long-term effects of antihypertensive deprescribing means that such an approach should not be routinely attempted, unless in response to specific adverse events or following informed discussions between physicians and patients about the uncertain benefits and harms of such an approach .


We established a model that predicts the possibility of chronic schistosomiasis (CS) patients developing into advanced schistosomiasis (AS) patients using special biomarkers that were detected in human peripheral blood. Blood biomarkers from two cohorts (132 CS cases and 139 AS cases) were examined and data were collected and analyzed by univariate and multivariate logistic regression analysis. Fisher discriminant analysis (FDA) for advanced schistosomiasis was established based on specific predictive diagnostic indicators and its accuracy was assessed using data of 109 CS. The results showed that seven indicators including HGB, MON, GLB, GGT, APTT, VIII, and Fbg match the model. The accuracy of the FDA was assessed by cross-validation, and 86.7% of the participants were correctly classified into AS and CS groups. Blood biomarker data from 109 CS patients were converted into the discriminant function to determine the possibility of occurrence of AS. The results demonstrated that the possibility of occurrence of AS and CS was 62.1% and 89.0%, respectively, and the accuracy of the established model was 81.4%. Evidence displayed that Fisher discriminant analysis is a reliable predictive model in the clinical field. It’s an important guide to effectively control the occurrence of AS and lay a solid foundation for achieving the goal of schistosomiasis elimination. Schistosoma spp., mainly endemic in 78 countries and territories of Asia, Africa, and South America [Schistosomiasis japonica is widely distributed in 12 provinces (cities and autonomous regions) in China [Schistosomiasis is a zoonotic parasitic disease that is caused by the trematode flukes of  America , which i America . Schistoin China , posing in China ,5. Althoin China , new chain China , while sin China .Clinical presentation varies in chronic schistosomiasis cases, but many cases present mild symptoms or an absence of symptoms thus leading to misdiagnosis or a lack of treatment. Advanced schistosomiasis is mainly characterized by liver and spleen lesions, such as periportal hepatic fibrosis, portal hypertension, spleen enlargement, and congestion . Tissue Currently, the main diagnostic methods for schistosomiasis liver fibrosis are: (1) histological examination, which is a reliable method for diagnosing liver fibrosis, but has great trauma to the tissue, and the sampling site cannot reflect the overall condition of the patient’s liver; (2) imaging methods, ultrasonography is the most widely used method for diagnosing schistosomiasis liver disease or assessing curative effectiveness based on grading of liver fibrosis; (3) detecting biomarkers of serum or plasma, including extracellular matrix components, degradation products, and metabolism of enzymes and cytokines, etc., which is readily assayed and non-invasive ,20, but Schistosomiasis japonica, to achieve early detection and early intervention, and effectively reduce the new cases of advanced schistosomiasis japonica.This study was designed to analyze and compare peripheral blood-related metabolic indicators in patients with chronic schistosomiasis and advanced schistosomiasis to select specific predictive diagnostic indicators and establish a Fisher discriminant analysis for advanced χ2 = 1.567, p = 0.211). The average age of male AS and CS patients was 64.1 years and 61.8 years, and females with AS and CS aged 57.8 and 59.9 years old, respectively, with no statistically significant difference that was detected between the two groups with the same gender . The AS and CS groups were matched for gender and age , MON (X2), GLB (X3), GGT (X4), APTT (X5), VIII(X6), and Fbg (X7), thus the following discriminant function was obtained :The AS group and the CS group were distinguished according to the blood biomarkers, and the FDA was constructed based on the nine biomarkers that were screened by multivariate analysis. According to the results of the statistical analysis, seven variables with statistical significance were screened out: HGB  markers and related clinical data of 271 schistosomiasis patients  and found that HGB, MON, GLB, GGT, APTT, VIII, and Fbg were related to fibrosis. The new non-invasive diagnostic function model that we established can predict the possibility of chronic schistosomiasis cases developing into advanced schistosomiasis cases. The model is sufficiently reliable in diagnosing fibrosis and predicting the progression of liver fibrosis.Previously, it was found that hepatitis B virus infection and schistosomiasis interacted in liver damage . Recent S. japonicum in the venous system, and the deposition of eggs in liver tissue causes specific pathological reactions in its definitive hosts [There are three markers reflecting coagulation function in the FDA model that was constructed from seven markers in this study. The parasitism and migration of ve hosts . Theoretve hosts ,30,31. Hve hosts . Previouve hosts ,30,31,32ve hosts ,34,35, tIn summary, we found that the discriminant function that was established by the use of body fluid (serum/plasma) markers can effectively warn the occurrence of AS. FDA is a reliable prediction model with strong practicability. It plays an important guiding role in effectively controlling the occurrence of AS and lays a solid foundation for achieving the goal of schistosomiasis elimination. We will further carry out early treatment intervention for CS patients who may develop into AS cases, and establish the best treatment strategy for patients with chronic schistosomiasis to effectively prevent the development of AS.Being an exploratory study with a small sample size, the participants in our study were selected from different schistosomiasis endemic counties  of Jiangxi province which might present a certain representativeness. In addition, a series of independent comparisons were conducted in our study which might increase the statistical error. Thus, further study that is based on an increased sample size or random sampling strategy should be conducted to verify the results that were explored in our study.The case-control study included two groups of cases from eight counties  in the schistosomiasis severely endemic area of Poyang Lake District, Jiangxi Province, from February to March 2013. A total of 271 patients were recruited, including 139 AS patients and 132 CS patients who were diagnosed according to the “Diagnostic Criteria for Schistosomiasis” (WS261-2006) that was issued by the Ministry of Health of the People’s Republic of China  File S2File S2. For all study subjects, elbow venous blood was collected and sent to the First Affiliated Hospital of Nanchang University within 2 h. Blood routine examinations and biomarkers that were related to liver function, fibrin degradation product D-dimer, coagulation index, HBV, alpha-fetoprotein, and 36 tests of liver fibrosis were conducted . Biomarkp > 0.05 were excluded.Among the 36 blood biomarkers, 30 blood biomarkers were continuous variables followed by normality testing. Although the logarithm was taken for correction, there were still some data that were not normally distributed. Therefore, in order to facilitate the unified analysis of the data, we intend to convert these continuous variables into categorical variables. The receiver operating characteristics (ROC) curve was applied to evaluate the various biomarkers in AS and CS patients to find the optimal clinical diagnosis critical value and complete the qualitative classification. The biomarkers were assigned a value as ″0″ when it is less than the critical value, otherwise is ″1″. Biomarkers with area under the ROC curve (AUC) ≤ 0.5, or Statistical analysis was performed using the SPSS Statistics 22.0 software  with a test level of α = 0.05. Data analysis methods include the following parts:First, we generally characterized the participants according to gender and age to ensure consistency between the samples. Then, univariate analysis was performed on the differences between the biomarker classification variables of the AS group and the CS group by Chi-square test to select the variables that could be used for analysis in the next step.p < 0.2 were included in the multivariate analysis [p < 0.10 remained in the multivariate model after the stepwise backward selection process. The final report is presented as odds ratios (ORs) with 95% confidence intervals (95% CI) and significance levels .We also consider that there may be a certain correlation between variables with no significant difference in univariate analysis and other confounding variables. To avoid the true effect of this variable being masked by the effect of other confounding indicators, all variables with analysis , and theFisher discriminant analysis (FDA) is a commonly used multivariate statistical method , which u


This pilot study suggested the RST as reliable tool for assessing the RFR by ultrasound. Specifically, RST could help in clinical practice suggesting the patient’s management and iron chelation therapy.Beta-thalassemia syndromes are the most common inherited monogenic disorders worldwide. The most common pathophysiologic and clinical renal disease manifestations of in β-TM patients is the tubular dysfunctions related to iron overload, chronic anemia, and the need for chronic iron chelation therapy. The aim of this pilot study is to apply an innovative ultrasound and Doppler technique to assess the Renal Functional Reserve (RFR) in β-TM patients, and to evaluate its reliability in iron overload tubulopathy. Ultrasound assessment of intra-parenchymal renal resistive index variation (IRRIV) has recently been proposed as a safe and reproducible technique to identify RFR presence. We define the preserved RFR when the Delta Renal Resistive Index (RRI) is >0.05  in the Renal Stress Test (RST). Nineteen β-TM patients were enrolled for this study. In our series, we found a strong negative correlation between mean ferritin values and Delta RRI (R = −0.51,  Hemoglobinopathies are the most common inherited monogenic disorders worldwide. ß-thalassemias are heterogeneous disorders caused by reduced or absent beta-globin synthesis, leading to an imbalance of the globin chains .β-thalassemia is associated with oxidative stress, ineffective erythropoiesis and chronic hemolytic anemia with compensatory hemopoietic expansion ,3,4; in In this clinical condition, iron overload is a consequence and, accordingly, iron chelation is needed to prevent or reverse iron overload. Currently, three iron chelators are available: oral agents deferasirox (DFX) and deferiprone (DFP), and the parenteral deferoxamine mesylate (DFO) . This irIn β-thalassemia, chronic anemia reduces the systemic vascular resistance that leads to hyperdynamic circulation and higher plasma flow in the kidneys with increased glomerular filtration rate (GFR) . Over tiRecently, an ultrasound RFR analysis test was validated using an ultrasound and Doppler technique in a population of healthy volunteers  and subsThe aim of this pilot study is to apply the RST in a β-TM population to evaluate the RFR presence and correlate it to the iron overload and to provide a guide parameter in assessing patients’ renal injury and their susceptibility to AKI.2. Exclusion criteria were: (a) chronic therapy that may modify renal blood flow and/or GFR  and/or (b) nonsteroidal anti-inflammatory drugs (NSAIDs) in the 2 days before the tests, (c) ultrasound evidence of morphological kidney abnormalities and/or renal artery stenosis, (d) atrial fibrillation, (e) hypertension, (f) history of primitive glomerular disease and (g) ascertained urinary infection, (h) fever in the past 7 days, (i) reduced water intake with possible dehydration.From 1 October 2021 to 1 July 2022 the data of all patients undergoing the RST for the internal good clinical practice at the Nephrology Unit of the University Hospital of Padua were collected retrospectively in order to identify the population for this pilot study. All subjects have given informed consent to the use of their data upon acceptance of the ultrasound execution. Patients affected by β-TM were considered eligible for the study. Inclusion criteria were: (a) age more than 12 years old, (b) diagnosis of β-TM, (c) baseline estimated GFR, calculated using the CKD-EPI equation, higher than 90 mL/min/1.73 mThe RRI measurement was performed by one trained sonographer using a multi-frequency convex probe and the same ultrasound machine in controlled environmental conditions (HI Vision Ascendus—Hitachi with probe C715 model—constant ambient temperature of 21 °C). RST was performed in the patient in supine position after a rest period of at least 15 min. RRIs were manually measured for all patients excluding the automatic software. The RRI were calculated by the built-in software as the following formula: RRI = [(peak systolic velocity − end diastolic velocity)/peak systolic velocity], in which peak systolic velocity and end diastolic velocity were measured in the same wave . The basSC) describe the RST as quantitative parameters of RFR. In details, as previously described by the literature [RST was performed in all subjects as a standard ultrasound for the best clinical practice. After a rest of at least 15 min in supine position, the patient was studied for the baseline RRIs. Then, a saline bag of 10% of the patient’s body weight was placed on the anterior abdomen wall to induce the kidney stress reducing the renal perfusion, stimulating the kidney self-regulation mechanism to maintain the GFR by afferent artery vasodilation. The sonographer recorded the RRIs in a middle interlobular artery each minute for 10 min during this stress in order to assess the change in RRIs related to the self-regulation mechanism . The Intterature , the Delterature .Data are reported as means and standard deviations (SD). Categorical variables are reported as absolute and relative frequencies. The correlations between data were tested using Pearson correlation analysis.t-test for paired variables.ANOVA was used to compare quantitative variables between groups and Student’s p < 0.05) were considered significant.Values of 5% or less (Nineteen β-TM patients were enrolled in this study. The characteristics of subjects at baseline and during the RST are shown in 2 (CKD-EPI equation). The average ferritin value of the last year was 1282 ± 723 ng/mL. Fourteen patients had tubulopathy during their medical history defined as the presence of proteinuria, acute tubular necrosis or Fanconi syndrome. All patients were under iron chelation therapy: five patients in deferiprone (DFP), five in deferoxamine (DFO) and nine patients in deferasirox (DFX). The mean baseline creatinine (sCr) was 50 ± 11 umol/L, corresponding to an estimated baseline GFR (eGFR) of 130 ± 19 mL/min/1.73 mp = 0.03 and p = 0.05), as shown in p = 0.03) as shown in p = 0.01) as shown in SC  as shown in In males, the baseline RRI and Delta RRI were significantly higher than female patients (respectively, We did not identify any correlation between the RST results, and the type of iron chelation therapy. There is also no correlation between the RST and a positive history for tubulopathy.2. In the literature, RRIs’ normal values are between 0.60–0.70 [SC. This data led us to hypothesize that in patients with iron overload, the RFR is present but the lower Delta RRI, IRRIV and IRRIVSC values indicate its reduced amplitude, probably due to the renal damage induced by the β-TM [Our results suggested that patients with β-TM have a substantially higher baseline RRI than expected values in relation to low creatinine levels of 50 ± 11 umol/L and high eGFR of 130 ± 19 mL/min/1.73 m.60–0.70 , with 0..60–0.70 . Therefo.60–0.70 . Furtherthe β-TM .SC values. This RFR reduction can first be subclinical with no alterations in the basal creatinine levels and eGFR, and then become clinically evident with an increase in creatinine levels, determining the loss of more than 50% of the renal parenchyma. Interestingly, β-TM patients have a condition of hyperfiltration linked to anemia and, at the same time, a tubular damage due to the iron overload and iron chelators. These two factors can determine a balance in which hyperfiltration compensates for kidney damage until there is a loss of at least 50% of nephron mass. In this situation, even with normal creatinine levels, there is a progressive reduction in RFR that currently cannot be identified using only the plasma creatinine level and the eGFR as occurs in common clinical practice.β-TM patients also present changes in glomerular filtration due to anemia that is thought to reduce systemic vascular resistance, causing an increase in renal plasma flow and GFR . In factAccording to our preliminary data in β-TM patients, the positive RST indicates the presence of RFR, but the progressive mesangial and tubular injury can be responsible for hemodynamic and functional kidney rearrangement, thus determining the higher baseline RRI. Finally, the RFR could be affected by iron overload as demonstrated by the reverse relation between ferritin higher levels and the lower Delta RRI, IRRIV and IRRIVsc.A total of 1/19 patients did not present RFR in an history of severe tubulopathy. In this situation, the RST could not detect RFR, and the presence of anemia-induced hyperfiltration kept creatinine and eGFR levels within the normal range despite the presence of kidney damage, masking this kidney injury under an apparent renal normal function.The results of this pilot study suggested that the RST can be used to assess the RFR during the β-TM patient’s follow-up to identify clinical conditions that can reduce the RFR such as the β-TM renal complications and the toxic effects of the iron chelation therapy.In fact, it is known from the literature that RFR measurement may reveal subclinical loss of renal function, early phases of CKD and a patient’s high susceptibility to toxic exposures with AKI .Our pilot study showed an inverse correlation between ferritin values and RST in a population affected by β-TM by a reproducible, inexpensive and safe ultrasound and Doppler test. In conclusion, in β-TM the RST can provide useful clinical information to manage and modify the therapeutic approach to detect, early on, subclinical or clinical reduction in the RFR with higher predisposition to AKI with evolution in CKD over time.A limitation of this study is the limited number of patients and the monocentric population. Further studies in larger and multicentric populations are necessary to better understand the role of the different iron chelators and the tubulopathy history in the kidney damage.The RST is a safe, reproducible, and inexpensive ultrasound tool to identify the RFR in those individuals who have normal creatinine and eGFR values that can present a kidney injury due to an underlying disease such as the β-TM. The RST seem to allow for an early identification of those patients who may be predisposed to a subclinical or a clinical AKI in order to modify the medical therapy and provide clinical management tailored to the patient.