Conclusions: Among predictors of suboptimal TIMI flow grade after PCI, we noted lower TIMI grade flow pre-PCI, greater serum troponin concentrations in the periprocedural period and lower mean minimal systolic blood pressure.Background and aims: Primary percutaneous coronary intervention (PCI) is regarded as the most preferred strategy in ST-segment elevation myocardial infarction (STEMI). Thrombolysis in Myocardial Infarction (TIMI) flow grade has been an important and cohesive predictor of outcomes in STEMI patients. We sought to evaluate potential variables associated with the risk of suboptimal TIMI flow after PCI in patients with anterior wall STEMI. Methods: We evaluated 107 patients admitted to our hospital between 1 January 2019 and 31 December 2021 with a diagnosis of anterior wall STEMI treated with primary PCI. Results: Suboptimal TIMI flow grade (≤2) after PCI occurred in 14 (13%) patients while grade 3 was found in 93 (87%) of them presenting with anterior wall STEMI. Failure to achieve optimal TIMI 3 flow grade after PCI was associated with lower TIMI grade prior to PCI (OR 0.5477, 95% CI 0.2589–0.9324,  Percutaneous coronary intervention (PCI) has become accepted as the preferred reperfusion strategy in acute ST-segment elevation myocardial infarction (STEMI) as it results in lower rates of death, reinfarction and stroke compared to fibrinolysis ,2. DespiIn the current study, we aimed to assess the potential variables associated with the risk of TIMI ≤ 2 in patients with anterior wall myocardial infarction.This retrospective observational study was performed in the Second Department of Cardiology, University Hospital in Cracow, and included patients presenting with anterior wall STEMI between 1 January 2019 and 31 December 2021 who underwent primary PCI. We excluded patients with MI that were qualified for conservative treatment. Overall, 107 patients were included in this study. Anterior wall STEMI was diagnosed when there were, in at least 2 contiguous leads, ST-segment elevation ≥ 2.5 mm in men < 40 years, ≥2 mm in men ≥ 40 years, or  ≥1.5 mm in women in leads V2–V3 and/or ≥1 mm in the remaining anterior wall leads, according to the guidelines proposed by the European Society of Cardiology . PatientMedical history, physical examination, risk factors, post-procedural ECG, echocardiographic parameters and laboratory test results were obtained retrospectively with the PCI data. Information was gained on the clinical outcomes from hospital discharge summary records and emergency records. Left ventricular function was assessed in B-plane echocardiography according to the Simpson’s rule and current guidelines, and was performed one day after procedure and at the day of discharge . The deg2 or markers of kidney damage [STEMI and non-ST-segment elevation myocardial infarction (NSTEMI) diagnoses made according to the International Classification of Diseases 10th Revision (ICD-10) codes were verified by analysis of angiographic images and consulted with operators of individual PCI procedures. Patients with an admission diagnosis of NSTEMI that was verified as anterior wall STEMI during following days of hospitalisation were also enrolled in this study. Category “not classified’’ included patients with negative troponin at admission. While clinically characterising the patients in our study and considering their blood pressure, we considered the minimum and maximum systolic and diastolic blood pressure recorded during the entire hospitalisation period . Moreovey damage .The principal outcome of interest for the current study was suboptimal coronary blood flow. Suboptimal coronary flow was defined as final TIMI ≤ 2 flow and optimal flow as TIMI 3 flow in the infarct-related artery.Strategies adopted for abnormal TIMI flow grade at the time of PCI included intracoronary administration of drugs in regimen, and doses according to operator’s preferences and depending on clinical situation and patient’s condition. Sodium nitroprusside and adenosine were applied in the dose of 200 µg–1 mg. Eptifibatide was administered in a bolus 180 µg/kg bwt and subsequent continuous infusion 2 µg/kg bwt/min. Abcyximab was used in a bolus of 0.25 mg/bwt and afterward in continuous infusion 0.125 µg/kg bwt/min. The dose of nitroglycerin was adjusted to the patient’s clinical state with particular concern for blood pressure values.t-tests, depending on the equality of variance for normally-distributed variables. The Mann-Whitney U test was applied in the case of continuous variables without normal distribution. Categorical variables were compared via Pearson’s chi-squared or Fisher’s exact test if 20% of cells had a count less than 5. Ordinal variables were compared with the Cochran-Armitage trend test. All baseline/demographic characteristics were used as potential predictors of suboptimal blood flow assessed by TIMI flow grade after PCI in univariable logistic regression models. Multivariable model was not constructed due to the small number of patients with TIMI grade after PCI other than 3. All statistical analyses were carried out using JMP®, Version 13.1.0 .Categorical variables are presented as numbers and percentages. Continuous variables are expressed as mean ± standard deviation, and additionally, as median and interquartile range in the case of non-normal data distribution. Normality of distribution was assessed using the Shapiro-Wilk test. Equality of variance was evaluated using Levene’s test. Differences between groups were compared via the Student’s or Welch’s A total of 107 anterior STEMI patients were enrolled in this study; 14 (13%) had final TIMI flow ≤ 2  after PCI and 93 (87%) demonstrated a post-PCI TIMI flow grade of 3 . In the group of patients with suboptimal blood flow after PCI, there were six patients with TIMI flow grade 2, six patients with 2/3, one patient with 0 and one with a 1/2 TIMI flow grade after PCI. The clinical characteristics, laboratory and echocardiographic data for the entire study population are shown in As shown in p-value 0.04), lower TIMI grade before the procedure , higher troponin 0 level  and troponin 2 level  as predictors of suboptimal flow after primary PCI in the patient with anterior wall STEMI  the incidence of TIMI ≤ 2 was uncommon in patients enrolled in the present study, occurring in 13% of patients with anterior wall myocardial infarction undergoing primary PCI; (2) among predictors of the TIMI flow grade ≤ 2 after PCI was lower minimal systolic blood pressure, greater troponin concentration at baseline and after PCI as well as lower TIMI grade before the procedure and significant predictors of suboptimal flow after primary PCI in the patient with anterior wall STEMI.Prompt restoration of normal TIMI flow grade 3 in the infarct-related artery is associated with the advantages of reperfusion therapy for ST elevation myocardial infarction . SuboptiOur study has several limitations. Firstly, it is a single-centre retrospective study. Therefore, the results may have been affected by some confounding factors and sample size biases. Secondly, comparisons between patients from the optimal and suboptimal groups were not randomised. The number of patients with TIMI flow ≤ 2 was too small to perform multivariable analysis; hence, no independent predictors could be identified. Finally, we did not perform any follow-up examination.In the present study, the incidence of suboptimal blood flow in the target artery among patients treated with primary PCI due to anterior wall STEMI assessed as TIMI flow grade ≤ 2 was low and occurred only in 13% of individuals. Suboptimal TIMI flow grade was associated with lower initial TIMI grade, higher troponin levels and lower minimal systolic blood pressure. The results of this small study remain in line with previously published research, and we trust that outcomes of our study will be useful for physicians in treating patients with myocardial infarction undergoing primary PCI and its influence will be mirrored in better treatment results.


Background and objective: In patients with asthma, fractional exhaled nitric oxide (FeNO) is a biomarker for eosinophilic airway inflammation. However, the correlation with the blood eosinophil count, immunoglobulin E (IgE), and spirometric values is not well established. We aimed to investigate the correlation between FeNO, blood eosinophils, IgE, and spirometric values in asthmatic patients.Methods: Data were extracted from the electronic medical records of adult asthmatic patients, and included the FeNO, blood eosinophil count, IgE, and spirometric values. The correlation between the markers was investigated using receiver operating characteristics (ROC) and the area under the curve (AUC).3, and 42% had a blood eosinophil count > 300 cells/mm3. There was a significant positive correlation between FeNO and the blood eosinophil count  as well as with the IgE . There was significant negative correlation between FeNO and FEV1% .Results: A total of 135 adult patients  were analyzed. The mean FeNO was 36 ppb and almost half (48%) had a FeNO > 25 ppb. The mean blood eosinophil count was 377 cells/mmConclusion: The blood eosinophil count, IgE, and spirometric values were correlated with the severity of the eosinophilic airway inflammation (high FeNO). Asthmatic patients with a higher FeNO level had a higher eosinophil count, higher IgE levels, and lower FEV1 values. Asthma is a heterogeneous disease characterized by chronic airway inflammation . It is dBecause asthma is a heterogeneous and complex disease, an assessment based on symptoms and spirometry alone does not accurately represent the underlying airway inflammation, the patient characteristics or profile (phenotype), or the underlying mechanism of airway inflammation (endotype) . We aimeThe FeNO measurement is an easy and non-invasive test. A FeNO concentration greater than 25 parts per billion (ppb) in adults is a marker of eosinophilic airway inflammation and predicts the likelihood of corticosteroid responsiveness ,4. An elSeveral studies attempted to establish a correlation between the blood eosinophil count and FeNO, but with contrary findings. A study reported no relationship between FeNO and the blood eosinophil count in uncontrolled asthma ; howeverAs most of the studies investigating the correlation between the biomarkers in asthma reported conflicting results with variable conclusions, we aimed to investigate the correlation between FeNO, the blood eosinophil count, serum IgE level, and spirometric values in patients with asthma.This was a retrospective analytical study, conducted at the Pulmonary clinic at King Abdulaziz Medical City in Riyadh, Saudi Arabia. Both genders, 15 years and older, with a diagnosis of asthma, and FeNO, spirometry tests, and the blood eosinophil count results available in the electronic medical records system, from June 2019 to June 2022, were included in the study. The sampling technique was non-probability purposive sampling. Smokers and patients with chronic pulmonary disease or significant co-morbidity which can affect the value of FeNO  were excluded.3) and low blood eosinophil count (< 300 cell/mm3). Asthma control was assessed using the Asthma Control Test (ACT > 20 was considered as controlled asthma and < 20 uncontrolled). The patients were subdivided into impaired or normal lung function values . Bronchodilator reversibility was defined as >200 mL and > 12% post-bronchodilator. The study protocol was approved by the Institutional Review Board of King Abdullah International Medical Research Center.The data collected consisted of the baseline characteristics, including age, gender, body mass index (BMI), and duration of asthma diagnosis as well as an asthma control test (ACT). The other variables were the FeNO, eosinophil count, IgE, forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and the ratio of FEV1/FVC as well as bronchodilator reversibility. In this study, the level of FeNO was categorized into two groups: high FeNO (FeNO ≥ 25 ppb) and low FeNO (FeNO < 25 ppb). The level of the blood eosinophil count was categorized as a high blood eosinophil count  and the area under the curve (AUC). A p-value ≤ 0.05 was considered statistically significant.3 and the mean percentage was 4.6%. Less than half (42%) had an absolute eosinophil count > 300 cells/mm3. The mean IgE level was 311 KU/L, and the majority (68%) had an IgE > 100 KU/L. The mean FEV1%, FVC%, and FEV1/FVC ratio were 85%, 91%, and 78% respectively. Of the group with reduced spirometry values (ratio less than 75%), the mean FEV1%, FVC%, and FEV1/FVC ratio were 56%, 63%, and 64% respectively. The mean bronchodilator reversibility value was 192 mL, and 48% had a bronchodilator reversibility response (> 200 mL).A total of 135 patients with asthma met the inclusion criteria. The baseline characteristics of the patients are displayed in Table There was no significant difference between the high and low FeNO groups in terms of age, gender, BMI, ACT and the duration of the disease. There were significant differences between the two groups and the eosinophil count and IgE. The patients in the high FeNO group also had a higher eosinophil count and IgE in comparison to the patients in a low FeNO group . Regarding the spirometric values, there was a significant difference between the two groups as the patients in the high FeNO group had a lower FEV1 (mean 78%), FEV1/FVC ratio (mean 74%) and bronchodilator reversibility compared to the patients in the low FeNO group  , and between the FeNO levels and the IgE levels  , and a weakly positive significant correlation between the FeNO level and bronchodilator reversibility  . In addition, a positive correlation was found between FeNO and serum IgE . However, a significant but negative correlation existed between FeNO and FEV1 , and a significant correlation between FeNO and bronchodilator reversibility . There was no significant correlation between FeNO and asthma control (p = 0.12). Patients in the high FeNO group had a higher eosinophil count, higher IgE levels, and lower spirometric values.As inflammation is the main pathognomonic feature of asthma, measurement of its biomarkers is important to establish the diagnosis of asthma, assess the severity of the disease and monitor the response to therapy. FENO is a non-invasive, inexpensive, and easy-to-perform test used with the blood eosinophil count as a biomarker for eosinophilic airway inflammation, and the level correlates well with type 2 asthma and the severity of inflammation. The current findings confirm the correlation between FeNO and the blood eosinophil count as an indication of the severity of eosinophilic airway inflammation, also supported in the literature. FeNO was significantly correlated with the blood eosinophil count in uncontrolled childhood asthma 8 as well as in uncontrolled adult asthmatic patients . A studyThe current findings demonstrated a significant and positive correlation between FeNO and the serum IgE level. Patients in the higher FeNO group had higher IgE levels. Strunk et al.  reportedHowever, we found a significant negative correlation between FeNO and FEV1% predicted. The spirometry measurements are lower with increasing inflammation (increasing FeNO level). A study indicating no correlation between FENO and the spirometric values considered them as different physiological parameters for diagnosing asthma . FeNO anInterestingly, the current findings found no significant correlation between FeNO and asthma control . A patient may have a high FeNO (airway inflammation) but controlled asthma (ACT>20) and vice versa. This can be explained by the definition of severe eosinophilic asthma, which is not the same definition as uncontrolled asthma  reportedThe current study had several advantages. These included a large sample size, exclusion of several confounding factors such as smoking and chronic lung diseases other than asthma, using optimal cut-off points of 25 ppb for FeNO as recommended by the American Thoracic Society, and finally, an optimal cut-off point of 300 cell/mm3 for the eosinophil count, as recommended by GINA .A limitation of the study is the retrospective analysis of data from a single pulmonary function laboratory. In addition, some of the variables have been measured on separate occasions which were minimized by establishing a three-month data collection period between all the tests. Another limitation is not investigating the correlation between the FeNO and the risk of exacerbation. Choosing an analytic retrospective medical record study did not allow for studying such a correlation. Another limitation is that the sputum eosinophil count was not measured, but such a measurement is not routinely available in most centers and laboratories as it is time-consuming and needs special preparation.In conclusion, the FeNO, blood eosinophil count, IgE, and spirometric values correlated well with the severity of the eosinophilic airway inflammation in adult asthmatic patients. Additional prospective multi-center studies with multiple measurements of the biomarkers with a larger sample size are required to explore and verify the current results and ultimately support appropriate monitoring of asthma management.


Acute and chronic kidney diseases are an evolving continuum for which reliable biomarkers of early disease are lacking. The potential use of glycosidases, enzymes involved in carbohydrate metabolism, in kidney disease detection has been under investigation since the 1960s. N-acetyl-beta-D-glucosaminidase (NAG) is a glycosidase commonly found in proximal tubule epithelial cells (PTECs). Due to its large molecular weight, plasma-soluble NAG cannot pass the glomerular filtration barrier; thus, increased urinary concentration of NAG (uNAG) may suggest injury to the proximal tubule. As the PTECs are the workhorses of the kidney that perform much of the filtration and reabsorption, they are a common starting point in acute and chronic kidney disease. NAG has previously been researched, and it is widely used as a valuable biomarker in both acute and chronic kidney disease, as well as in patients suffering from diabetes mellitus, heart failure, and other chronic diseases leading to kidney failure. Here, we present an overview of the research pertaining to uNAG’s biomarker potential across the spectrum of kidney disease, with an additional emphasis on environmental nephrotoxic substance exposure. In spite of a large body of evidence strongly suggesting connections between uNAG levels and multiple kidney pathologies, focused clinical validation tests and knowledge on underlining molecular mechanisms are largely lacking. The kidney is a vital organ with critical functions in maintaining body fluid balance, waste product secretion, hormone production, and acid-base and electrolyte homeostasis . It makeDiagnosis of kidney disease currently relies on the estimation of functional parameters, such as glomerular filtration rate (GFR), kidney biopsies, or the more sensitive indicators such as detection of proteinuria or assessment of urinary enzymes . The divIn contrast to other tubular cell enzymes, the PTEC lysosomal enzyme NAG has been extensively studied and has been shown to be a sensitive, persistent, and robust indicator of kidney injury. The amount of NAG in urine can be related to tubular damage and can be rapidly quantified using reproducible and well-defined enzymatic assays . The firN-acetylglucosamine (GlcNAc), a monosaccharide that is reversibly added to proteins at the oxygen atom of serine and threonine side chains (NAG is a large protein of 916 amino acids (the median protein size in humans is 375) with a predicted molecular mass of 140 kDa . It is ae chains . In contNAG presents in high concentrations in the PTECs as a lysosomal brush-border enzyme and the most active glycosidase. Thus far, three isoforms of NAG have been recognized in humans, differing in the N-terminal length of the (presumably) catalytic domain of the protein. Although all isoforms retain catalytic activity, it is significantly diminished in the truncated protein . Two of Due to its large molecular weight, plasma-soluble NAG cannot pass the glomerular filtration barrier ,37,38. TUrinary NAG primarily originates from PTECs that, under physiological conditions, excrete small and stable quantities of NAG by exocytosis . When PTAKI is a common clinical entity whose main outcome is a rapid decline of renal function . The cliAKI can originate from multiple causes such as decreased renal perfusion, renal tubule injury from toxins or obstruction, tubulointerstitial inflammation, and primary reduction in the glomerulus’ filtering capacity. The majority of AKI cases are due to ischemia and toxins, which cause loss of integrity and polarity in the kidney epithelium, leading to necrosis and apoptosis. This, in turn, leads to inadequate filtration and filtrate leak, i.e., proteinuria and the possibility of subsequent identification of specific proteins in urine . Even thAmong candidate molecules, NAG, more precisely its urine concentration, has been studied extensively . ImportaMultiple recent studies have evaluated NAG as a marker of AKI in patients with cardiovascular diseases. In a study group of patients with chest pain, NAG was compared with other markers, such as creatinine and cystatin C, and was shown to be the only marker with a promising potential for AKI prediction. Its additional clinical value was also shown in its possibility to predict the necessity of renal replacement therapy . In addiIts applicability as a marker of AKI has been studied in various age groups, which is illustrated by several studies, including the study of Bíró et al., which showed serial uNAG tests (at least 5 samples/patient) in a group of pediatric patients with neoplastic disorders to identify 1.5× more clinical and subclinical AKI episodes, with a relatively high sensitivity and specificity. They also highlighted that serial uNAG measurements decrease the number of false positives, which they mostly attributed to overhydration . IncreasIn order to establish uNAG’s predictive value for AKI in clinical use, the dependence of its levels on other parameters has to be evaluated, as well as its performance in acutely or critically ill patients. Changes in levels of thyroid hormones and blood glucose have been shown to influence uNAG levels ,60. HoweThe existing body of research shows promise for uNAG’s role as an easily accessible marker for early detection, and even prediction, of AKI. These are crucial for treatment initiation, thereby facilitating better outcomes and, in some cases, prevention of CKD. However, the use of uNAG in AKI also has some limitations. Among others, as in all urinary sampling, obtaining adequate samples from oliguric or anuric patients presents a substantial challenge; conversely, hydration levels also seem to cause variations in uNAG levels, thus blurring the line of clinical significance. Additionally, a possible limitation of NAG is the fact that urinary excretion of the enzyme is elevated not only in acute but also in chronic glomerular diseases .2 [CKD is a heterogeneous disease that may occur due to multiple underlying disorders and is defined by the KDIGO initiative as “abnormalities of kidney structure and/or function, present for over three months, with implications for health” ,64,65. C2 ,74. Ther2 ,76,77.uNAG is a potential biomarker of early CKD. Several cross-sectional studies observed higher levels of uNAG in patients with CKD in comparison to healthy controls. Furthermore, in a nested case-control study performed by Kern et al., urinary NAG concentrations measured at the study baseline were successful predictors of micro- and macroalbuminuria in patients with type I diabetes mellitus. Additionally, Vaidya et al. found that lower uNAG levels were associated with regression of microalbuminuria in patients with type I diabetes mellitus ,79. HoweBesides the common intrinsic acute/chronic kidney disease risk factors, several significant environmental influences have been described. The high blood flow rate makes the kidneys susceptible to nephrotoxic substances, which are widely distributed in the environment. Tobacco smoking, environmental pollutants, and occupational-linked toxins such as metals, aristolochic acid, mycotoxins, and solvents alike, are major contributors to this public health issue .Cadmium (Cd) is one of the most toxic heavy metals used in several industrial fields and it is mainly derived from pigments, nickel-cadmium batteries, metal coatings, etc. People are usually exposed to Cd via food, water, children’s plastic toys, air, and tobacco smoking . CadmiumThe innate mechanism to compensate for the toxic effects of cadmium accumulation  is mediated by highly conserved, cystein-rich metal binding proteins called metallothioneins, that form cadmium-metallothionein complex . In highOther sources of nephrotoxic compounds are microfungi that naturally produce over 500 mycotoxins that can be present in one’s diet and have an impact on human health. Over years, oral exposure to mycotoxins can induce renal damage, which was confirmed in a number of studies that correlated Ochratoxin A (OTA), Citrinin (CIT), Zearalenone, Fumonisin B1, Sterigmatocystin, and Alfatoxin B with nephron segment injury, predominantly proximal tubule damage ,98. The Kidney injury can often be drug-induced and is common (up to 37.5%) in patients in intensive care units . The druMultiple environmental and intrinsic factors can significantly induce kidney injury, which usually results from damage induced to PTECs. Among other biomarkers in use, uNAG helps assess the mentioned damage, specifically, without going into any details about the molecular aspects of its occurrence in urine. Therefore, more studies should be carried out in order to identify the turning point in a series of events leading to damage of the kidney segments and later connect it to the most reliable biomarker.β-catenin signaling pathway [The injury of PTECs triggers multiple pathways that contribute to kidney damage. The activated oxidative stress pathway causes an imbalance between the reactive oxygen species production and the body’s antioxidant defenses, in turn damaging proteins, lipids, and DNA and causing cell death mediated by caspases and/or endonucleases . Governe pathway . Althoug pathway . The pot pathway .In order to enhance the validity of the above findings, advanced technologies can be applied to provide additional insight. Computational drug target identification and drug discovery approaches can accelerate drug development and improve patient outcomes. Network-based analysis and structural biology can help identify key molecular pathways and, within them, specific nodes in the protein interaction networks that are likely to be critical in kidney disease pathogenesis. Aided by machine learning algorithms that analyze large datasets and identify complex patterns, the study of the three-dimensional structure of key proteins can uncover potential drug-binding sites and further the design of customized drugs . FinallyAs the renal tissue is stressed, the underlying inflammatory and profibrotic molecular pathways mediate acute and chronic kidney damage. Currently, there is no “golden standard” for the diagnosis of early, subclinical kidney disease, and it is therefore difficult to conduct a validation test of uNAG (or other biomarkers) in the setting of early disease onset. In this review, we present an overview of research pertaining to uNAG’s potential to fill this niche. Even though a large body of evidence exists suggesting a clear connection between uNAG and kidney injury, further cohort studies are needed focusing on the kinetics of uNAG in different etiologies of kidney disease. Its possible role as a diagnostic molecule is supported by the fact that in physiologic conditions its urinary levels are insignificant; however, as the renal tubule epithelium becomes permeable under stress, its urinary levels increase sharply . AlthougFinally, whether NAG could be used as a potential drug target in kidney disease remains to be seen. Machine learning methods could be employed to predict drug–target interactions and a novel computational model could be developed to identify drug–target interactions based on the protein sequence, structure, and drug chemicals . Moreove


Subclinical hyperthyroidism (SCH) is found to be associated with renal dysfunction. Hyperthyroidism is a well-known cause of secondary systolic hypertension. However, the effect of SCH on the kidney and its vasculature is still unknown.To assess the presence of renal function changes and renal vasodysfunction in SCH patients and their relation to hypertension.The study included 321 patients with SCH and 80 healthy matched controls. Laboratory investigations included thyroid function tests, anti-TSH receptor antibody (TRAb), creatinine, estimated glomerular filtration rate (eGFR), serum osmolarity (S. Osmol), urine osmolarity (U. Osmol), Fractional Excretion of Sodium (FeNa), Fractional Excretion of Potassium (FeK), copeptin (CPP), and aldosterone/renin ratio (ARR). Ultrasound for the thyroid gland, echocardiography, total peripheral resistance (TPR), flow-mediated dilatation (FMD), and Renal Arterial distensibility (RAD) was also done.p = 0.001 and mean 128.28 ± 14.69 ml/min/1.73m2 Vs mean 100.49 ± 14.9 ml/min/1.73m2, p = 0.013, respectively). The TPR and FMD showed a significant decrease in SCH group compared to controls . RAD was significantly higher in hypertensive SCH patients compared to normotensive SCH patients .Serum creatinine was significantly lower while eGFR was significantly higher in SCH patients compared to euthyroid subjects (mean 0.59 ± 0.11 mg/dl Vs mean 0.8 ± 0.1 mg/dl, SCH patients showed vascular resistance reduction. Alterations in thyroid hormones and blood pressure could be the driving mechanisms for the change in renal functions in patients with SCH. Thyroid disorders are very common with a high prevalence among the general population. Thyroid diseases are associated with many detrimental effects that have a serious impact on various body systems .Hyperthyroidism is a form of thyroid disease in which excess thyroid hormones are synthesized and secreted by the thyroid gland. This is distinguished from thyrotoxicosis which refers to the clinical hypermetabolic syndrome resulting from serum elevation in thyroid hormone levels that could be either due to hyperthyroidism or extra thyroidal sources of thyroid hormone secretion. High thyroid radioactive iodine uptake is pathognomonic to hyperthyroidism .4), tri-iodothyronine (T3), or both. Whereas subclinical hyperthyroidism (SCH) is defined as low serum TSH level with normal serum levels of T3 and T4. The prevalence of SCH ranges from 0.6 to 1.8% in adults  × 1.159 [if black]eGFR = 141 × min (Scr/k)FENa (%)  = 100 × FeK (%)  = 100 × Serum osmolarity = (2 × (Na+K)) + (glucose/18) + (BUN/2.8)Urine osmolarity was detected by osmometer.Other laboratory tests included blood urea nitrogen (BUN), creatinine, estimated glomerular filtration rate (eGFR), serum osmolarity (S. Osmol), urine osmolarity (U. Osmol), Fractional Excretion of Sodium (FeNa), Fractional Excretion of Potassium (FeK), Copeptin (CPP), and Aldosterone/Renin ratio (ARR).Blood pressure (BP) was assessed twice with 2 weeks apart. Systolic hypertension (HTN) was diagnosed according to the criteria of the JNC 8: defined as systolic blood pressure (SBP) ≥ 140 mmHg and diastolic blood pressure (DBP) < 90 mmHg.Echocardiography , Ejection fraction (EF), Fractional shortening (FS), Early diastolic velocity (E),Late diastolic velocity (A), and E/ATotal peripheral resistance (TPR) (mmHg.min/L)  . Participants were kept sitting in a quiet room for 15 min before measuring arterial stiffness. RAD was determined by acquiring waveforms at the right and left renal arteries. Three measurements were acquired from each kidney, and the mean values for right and left kidneys were recorded for further analysis [analysis .t-test & Mann Whitney test. Categorical variables were expressed as a number (percentage) and compared using a χ2 test.Statistical analyses were performed using SPSS for Windows , version 25. The continuous variables were expressed as the mean ± standard deviation and compared using an unpaired Multivariable models for several factors affecting GFR were adjusted for age, sex, BMI and smoking index to avoid possible confounding factors. The multivariable models were additionally adjusted for SBP, diastolic blood pressure, mean arterial pressure (MAP), smoking status, fasting blood glucose and BMI. We performed an analysis of covariance where mean values of decline in eGFR, FeNa, and FeK were compared across categories of endothelial function and blood pressure.p < 0.05) in univariate analysis between the patients with SCH and euthyroid controls after adjustments for the potential confounding factors. The adjusted mean (±standard error) was provided by the estimated marginal means in the ANCOVA analysis. A statistically significant interaction, we reported the simple main effects between blood pressure and endothelial dysfunction (at p ≤ 0.025). The linear relationship between each variable and the levels of GFR were examined. Univariate and multivariate linear regression analyses were used. The multivariate linear regression model for GFR was applied to find the independent determinants among the candidate variables  after adjustments for the potential confounding factors and quantified by the standardized regression coefficient (β).Analysis of covariance (ANCOVA) was performed to compare the significant parameters  and 60 patients had toxic multinodular goitre . Patients and euthyroid controls were well matched. The mean age in the SCH group was 43 ± 11.46 years while in the euthyroid group was 39.63 ± 12.83 years. Most of the studied subjects were females; accounting for 57.14% in the SCH group and 62.5% in the euthyroid group. In the SCH group, the mean BMI was 22.5 ± 2.1 Kg/mp = 0.001). Mean eGFR was significantly higher in SCH patients than in euthyroid controls . Mean BUN was significantly higher in SCH group than in the control group . Yet, there was no significant difference between SCH group and euthyroid controls regarding; S. Osmol, U. Osmol, serum Na + , Urine Na + , FENa %, serum K + , urine K + , FEK %, Renin/Aldosterone ratio, S/U(Na/K), and CPP.Table  shows thp = 0.002), while there was no significant difference in DBP and mean BP. The mean pulse was significantly higher in SCH patients compared to control group . The mean CO was significantly higher in SCH group compared to control group . The mean EF and FS were significantly higher in SCH group in comparison to control group .The mean TPR showed a significant decrease in SCH group than in control group . The results of FMD of the brachial artery revealed that there was a significant decrease in mean FMD in patients with SCH compared to euthyroid controls . (Table Ninety-three patients 28.97%) with SCH were hypertensive, while none of the euthyroid group was hypertensive. The mean SBP was significantly higher in SCH group compared to control group . After adjustment for age, TSH, FT3, FT4, CO and presence of HTN. It was found that HTN is the only significant factor that affected eGFR and FeNa in SCH patients  without effect on FMD . However, the present study has two limitations. First, given the case-control and observational design, it is not possible to infer causality from the associations described. Secondly, the effects of thyroid autoantibodies (TRAb) and the duration of SCH were not taken into consideration. Our study can be the basis for further prospective studies in this context.In patients with SCH, the main driving mechanisms behind changes in renal glomerular and tubular functions could be directly through vascular resistance reduction as a direct effect of thyroid hormones and indirectly through hypertension. Our study may add a novel postulated mechanism for kidney function changes in patients with SCH. Hence, the impact of SCH on the kidney should be reconsidered which may influence the interpretation of renal function status and may affect treatment decisions in the setting of SCH.


The aim of this study is to investigate the clinical characteristics of acute asthma exacerbations (AEs) with community‐acquired pneumonia (CAP) in adults and establish a CAP prediction model for hospitalized patients with AEs.We retrospectively collected clinical data from 308 patients admitted to Beijing Luhe Hospital, Capital Medical University, for AEs from December 2017 to August 2021. The patients were divided into CAP and non‐CAP groups based on whether they had CAP. We used the Lasso regression technique and multivariate logistic regression analysis to select optimal predictors. We then developed a predictive nomogram based on the optimal predictors. The bootstrap method was used for internal validation. We used the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) to assess the nomogram's discrimination, accuracy, and clinical practicability.P < 0.05) were C‐reactive protein > 10 mg/L, fibrinogen > 4 g/L, leukocytes > 10 × 109/L, fever, use of systemic corticosteroids before admission, and early‐onset asthma. The AUC of the nomogram was 0.813 (95% CI: 0.753–0.872). The concordance index of internal validation was 0.794. The calibration curve was satisfactorily consistent with the diagonal line. The DCA indicated that the nomogram provided a higher clinical net benefit when the threshold probability of patients was 3% to 89%.The prevalence of CAP was 21% (65/308) among 308 patients hospitalized for AEs. Independent predictors of CAP in patients hospitalized with an AE  is a common comorbidity in patients with asthma exacerbations (AEs). Our findings confirm that patients with AEs and CAP have distinct clinical features and a high incidence. Early‐onset asthma, fever, use systemic corticosteroids before admission, and some markers  were independent predictors of CAP in patients hospitalized for AEs. We identified some of the clinical characteristics of adult AEs patients with CAP. We developed a prediction model to evaluate the CAP risk in adults with AEs.22.1We retrospectively analyzed medical records of patients admitted to the Department of Respiratory and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, from December 2017 to August 2021. All patients had a discharge diagnosis of AEs. A bronchial asthma diagnosis was made according to the Global Initiative for Asthma (GINA) diagnostic criteria.The inclusion criteria of the study included the following: age ≥ 18 years and hospitalization for AEs. Exclusion criteria included the following: age < 18 years, a discharge within 24 h of admission or incomplete information, pregnancy, pulmonary vasculitis, human immunodeficiency virus infection, and receiving immunosuppressive therapy.This study was performed in accordance with the principles of the Declaration of Helsinki. The research was approved by the Ethics Committee of Beijing Luhe Hospital, Capital Medical University (No: 2021‐LHKY‐055‐02). As it was a noninterventional observational study using anonymous data, the ethics committee issued a waiver for informed consent.2.2Clinical data on these patients were collected anonymously within 24 h of admission from electronic medical records. The data consist of the following six categories: (I) demographic characteristics: age, gender, body mass index (BMI), and smoking; (II) clinical outcomes: length of hospital stay and total costs of hospitalization; (III) clinical characteristics: asthma duration, early‐onset asthma, family history of asthma, hospitalization due to AE in the past year, AEs severity, prehospital maintenance medications, inhaled corticosteroid (ICS) types, and use systemic corticosteroids before admission; (IV) major comorbidities: hypertension, congestive heart failure, coronary heart disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), allergic rhinitis, stroke, chronic renal disease, gastroesophageal reflux disease, and the calculated Charlson Comorbidity Index score; (V) laboratory examinations: white blood cell, neutrophils, eosinophils, lymphocytes, platelet, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), C‐reactive protein (CRP), procalcitonin (PCT), fibrinogen, albumin, and blood urea nitrogen; (VI) primary symptoms and vital signs on admission: fever, respiratory rate, heart rate, and oxygen saturation.Fever is described as body temperatures >37.3°C. Patients are classified as early‐onset or late‐onset asthma depending on their age at the first presentation of asthma symptoms: ≤40 or >40 years old, respectively. This age cutoff value is based on prior literature.2.3The outcome indicator was CAP. The patients were divided into CAP and non‐CAP groups based on the Infectious Diseases Society of America/American Thoracic Society (IDSA/ATS) guidelines for CAP in adults.2.4Multiple imputation was used to handle the missing values in some variables. We used all the predictors and the outcome indicator in the imputation model. Considering the sample size and statistical efficiency, we performed five imputations. We integrated the results of the imputed datasets according to Rubin's rules.P < 0.05 indicating that the difference was statistically significant. We described continuous variables conforming to a normal distribution as mean ± standard deviation and those meeting a nonnormal distribution as the median and interquartile range (IQR). Categorical data were presented as the frequency and the percentage. We used the Mann–Whitney U‐test, Fisher's exact, and chi‐square test to compare the differences between the two groups, as appropriate.We used R4.1.3 software for statistical analysis. All tests were performed using two‐sided tests, with We transformed continuous variables related to laboratory examinations and vital signs on admission into categorical variables. The transformation was based on clinically relevant reference values to facilitate the clinical application of the prediction model. We used CAP as the outcome variable in the database, and the Lasso regression screened the best predictors of the risk of CAP in patients with AEs. The CAP risk prediction model was further developed using multivariate logistic regression. Subsequently, a predictive nomogram was created based on the CAP risk prediction model. The predictive accuracy of the nomogram was assessed by the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation of the calibration of the nomogram was performed by resampling 500 times using the Bootstrap method. The corrected concordance index and calibration curve were applied to determine the consistency between the actual results and the predicted outcome. We used the decision curve analysis (DCA) to evaluate clinical benefits.3We screened 366 patients with a discharge diagnosis of AEs between December 2017 and August 2021. Fifty‐eight patients were excluded according to exclusion criteria, and a total of 308 patients were included in the final analysis  and longer asthma duration  as compared with the non‐CAP group. Fever, early‐onset asthma, and severe to life‐threatening AEs were more common in the CAP group . In addition, we found that the CAP group was less likely to use systemic corticosteroid prehospital than the non‐CAP group . There were no differences observed between the two groups in age, gender, BMI, smoking, family history of asthma, prehospital maintenance medications, history of hospitalization due to AE in the past 12 months, major comorbidities, and the Charlson Comorbidity Index score (P > 0.05). In terms of laboratory examinations, patients in the CAP group had higher levels of white blood cells , neutrophils , neutrophil‐to‐lymphocyte ratio (NLR) , platelet‐to‐lymphocyte ratio (PLR) , C‐reactive protein (CRP) , and fibrinogen  as compared with patients in the non‐CAP group. We found no significant difference between the two groups in the peripheral blood eosinophil count and procalcitonin (P < 0.05).A comparison of the baseline characteristics of the CAP and non‐CAP groups is shown in Table 3.19/L, fever, use systemic corticosteroids before admission, and early‐onset asthma. These six predictors were included in a multivariate logistic regression analysis, which showed that CRP > 10 mg/L , fibrinogen > 4 g/L , white blood cell > 10 × 109/L , fever , use systemic corticosteroids before admission , and early‐onset asthma  were all independent predictors of CAP in patients hospitalized with an AE . In addition, a family history of asthma (P = 0.008) and severe acute asthma attacks (P = 0.027) were more common in the early‐onset asthma group. The age  and comorbidity index  were lower in the early‐onset asthma group compared with the late‐onset asthma group. Moreover, the proportion of comorbid hypertension (P = 0.002), diabetes mellitus (P = 0.022), coronary heart disease (P = 0.02), and inhaled corticosteroids (P = 0.047) was lower.We performed a subgroup analysis of early‐onset asthma and late‐onset asthma Table . The res4Respiratory infections commonly cause AEs.In our study, the CAP incidence in patients hospitalized with AEs was similar to a Korean (20.2%) study,9/L and fibrinogen > 4 g/L were independent predictors of CAP in patients with AEs. These results suggest that the possibility of CAP should be considered for those patients with AEs in the presence of fever and elevated leukocytes and fibrinogen.In our study, fever was an independent predictor of CAP in adult patients with AEs. This result was consistent with the findings of studies of pediatric patients with AEs combined with CAP.CRP is an acute‐phase reactive protein. Proinflammatory cytokines can trigger the synthesis of CRP in the liver. CRP is often used as a biomarker to reflect the degree of inflammation and tissue damage and to evaluate the effectiveness of treatment. Adding CRP to CAP diagnostic models based on the clinical presentation of patients has improved the model's accuracy.When an infection occurs in the lungs, alveolar macrophages release cytokines and other inflammatory mediators to eliminate pathogens.Asthma is a heterogeneous disease with various clinical phenotypes. Patients can be classified into early‐onset asthma and late‐onset asthma according to their age of asthma onset.This study has several strengths. First, in almost all previous clinical asthma studies, patients with a history of heavy smoking or comorbid COPD were usually excluded. However, in clinical practice, many inpatients with asthma have a history of heavy smoking and COPD. Our study included patients with a history of smoking and comorbid COPD, a more realistic clinical scenario. Second, in constructing the prediction model, we did not select predictors based on the results of univariate analysis, as this usually leads to model instability.5Adult patients with AEs and CAP had distinct clinical features and occurred with a high incidence. Early‐onset asthma, fever, use of systemic corticosteroids before admission, and some markers  were independent predictors of CAP in patients hospitalized for AEs. We developed a simple and reliable nomogram to predict the risk of CAP in patients with AEs. The nomogram had good accuracy, discrimination, and clinical practicability. Using the nomogram, clinicians can determine the risk of CAP in patients hospitalized with an AE. With this prediction model, we can accurately predict the risk of CAP in patients with acute AE and play a role in early screening and diagnosis of CAP. This has an important role in guiding the use of antimicrobial drugs in patients with acute AEs. In summary, the nomogram is a practical and accurate tool for diagnosing and managing CAP in patients with AEs.Y. D., J. S., and J. W. designed the study. D. N. and J. Y. contributed to data collection. Y. D., M. L., and Z. H. contributed to the statistical analyses of the study. Y. D. drafted the manuscript. Y. D. and J. W. revised the manuscript. All authors read and approved the final manuscript.None.This study was performed in accordance with the principles of the Declaration of Helsinki. The research was approved by the Ethics Committee of Beijing Luhe Hospital, Capital Medical University (no: 2021‐LHKY‐055‐02). The study was a noninterventional observational study, and the data used were anonymous. Therefore, the ethics committee of Beijing Luhe Hospital, Capital Medical University, issued an informed consent waiver.Data S1. Supporting InformationClick here for additional data file.Data S2. Supporting InformationClick here for additional data file.


A Cox proportional hazard model was used to assess the cardiorenal protective effects between the GLP-1RA and DDP-4i groups.In this cohort study, patients with type 2 diabetes mellitus and eGFR < 30 mL/min per 1.73 mA total of 8922 participants  were eligible for this study. During a mean follow-up of 2.1 years, 78 (13%) and 204 (13.8%) patients developed composite cardiovascular events in the GLP-1RA and DPP-4i groups, respectively . Composite kidney events were reported in 134 (38.2%) and 393 (44.2%) patients in the GLP-1RA and DPP-4i groups, respectively .GLP-1RAs had a neutral effect on the composite cardiovascular outcomes but reduced composite kidney events in the patients with advanced DKD compared with DPP-4is.The online version contains supplementary material available at 10.1186/s12933-023-01793-9. Diabar death . TherefoAlthough the first-line medication for type 2 diabetes is metformin, glucagon-like peptide-1 receptor agonists (GLP-1RAs) along with sodium–glucose cotransporter 2 inhibitors (SGLT2is) are recommended for patients with established atherosclerotic cardiovascular disease (ASCVD) or multiple ASCVD risk factors , 7. CardResearch on the effect of GLP-1RAs on cardiovascular outcomes in patients with advanced DKD (eGFR < 30) is limited. Previous GLP-1RA trials have mainly excluded advanced DKD patients, and completely excluded those with ESKD. Results from the LEADER study showed favorable cardiovascular outcomes in terms of MACEs in patients receiving liraglutide with an eGFR 30–60 compared to a placebo cohort . LiragluThis study enrolled type 2 diabetes patients with a first prescription for GLP-1RAs and an eGFR < 30, and compared their effect to DPP-4is. The primary outcomes were composite cardiovascular outcomes including cardiovascular death, myocardial infarction (MI) and ischemic stroke, and composite renal outcome including a decline in eGFR > 50%, progression to ESKD with dialysis, and cardiovascular death. The aim of the study was to investigate the potential cardiovascular and renal protective effects of GLP-1RAs in DKD patients with moderate to severe kidney function Additional file Data were acquired from Chang Gung Research Database (CGRD). The CGRD is the largest multi-institutional electronic medical record (EMR) database in Taiwan , includiThe study cohort included patients with a first prescription for GLP-1RAs or DPP-4is from January 1, 2012 to December 31, 2021. The date of first prescription was defined as the index date. GLP-1RAs included liraglutide and dulaglutide, and DPP-4is included sitagliptin, vildagliptin, saxagliptin, and linagliptin. Patients with missing demographic data (age or sex), type 1 DM, < 40 years old, eGFR > 30, missing baseline eGFR data, and those with a follow-up period < 3 months were excluded. Patients with prescriptions for GLP-1RAs not of interest in this study, including exenatide and lixisenatide, were also excluded. The Modification of Diet in Renal Disease (MDRD) equation was used to calculate the eGFR. Patients were followed until the occurrence of an outcome , death, drug switch, or adding on another drug  or December 31, 2021, whichever occurred first. Due to the retrospective nature of this study, no formal sample size calculation based on estimated effect size was performed.The baseline characteristics included demographics, severity of DM, kidney function and stages, comorbidities, vital signs, laboratory data, and concomitant medications. Demographic data including age, sex, body mass index (BMI) and smoking were recorded. The duration of DM, baseline glycated hemoglobin (HbA1c) level, DM retinopathy and DM neuropathy were used as a proxy for the severity of DM. Kidney function and stages were categorized as an eGFR between 15 and 30, < 15, and dialysis. The baseline comorbidities included hypertension, hyperlipidemia and seven others. Charlson’s Comorbidity Index (CCI) score was also recorded. Vital signs included systolic and diastolic blood pressure and heart rate. The laboratory data included triglycerides, total cholesterol and three others. Concomitant medications were classified into glucose-lowering therapies  and cardiovascular agents .Outcome measurements included clinical events and continuous outcomes. The primary cardiovascular outcome was a composition of cardiovascular death, MI, and ischemic stroke. Cardiovascular death was defined according to the standard definitions for cardiovascular and stroke endpoint events in clinical trials by the US Food and Drug Administration. The definitions of MI and ischemic stroke were acute episodes requiring hospitalization. The renal outcomes included a decline in eGFR > 50%, and progression to ESKD with dialysis. ESKD with dialysis was defined as the need for permanent dialysis regardless of hemodialysis or peritoneal dialysis. The composite renal outcome was defined as any one of a decline in eGFR > 50%, ESKD with dialysis, and cardiovascular death. The secondary outcomes were all-cause death, heart failure admission, admission due to any cause, composite major adverse limb events including newly diagnosed peripheral arterial disease, claudication, clinical limb ischemia, limb revascularization or amputation, hypoglycemia, diabetic ketoacidosis (DKA) or hyperosmolar hyperglycemic state (HHS), and infection death. The date, place and causes of death were extracted using data linked to the Taiwan Death Registry.Continuous outcomes included systolic and diastolic blood pressure, body weight, HbA1c, eGFR, and heart rate. The continuous outcomes were extracted at baseline, and then 6, 12, 18 and 24 months of follow-up. Since the data of the patients were substantially impacted by dialysis, the continuous outcomes after dialysis at baseline or during follow-up were not analyzed.A propensity score matched cohort was created to compare outcomes. The propensity score was the predicted probability to be in the GLP-1RA group derived from a multivariable logistic regression model. All of the variables listed in Table P value < 0.05 was considered significant.The risk of a fatal outcome  between groups was compared using a Cox proportional hazard model. The incidence of nonfatal clinical events  between groups was compared using the Fine and Gray subdistribution hazard model which considered all-cause death during follow-up as a competing risk. Post hoc subgroup analysis of composite cardiovascular outcome and new-onset dialysis was further conducted. The selected subgroup variables were age (< 65 vs. ≥ 65 years), sex, duration of DM (< 10 vs. ≥ 10 years) and ten others. Changes in the continuous outcomes from baseline to follow-up measurements between groups were compared using a linear mixed model, with the random intercept and slope. The duration from baseline to dialysis during follow-up was compared between the two groups using the Mann–Whitney U-test. The cause of death between groups was compared using the chi-square test. Statistical analyses were performed using SAS version 9.4 . All statistical tests were 2-sided, and a This study enrolled 125,392 patients with a first prescription for GLP-1RAs or DPP-4is between January 1, 2012 and December 31, 2021 . According to the exclusion criteria, a total of 759 GLP-1RA users and 8,163 DPP-4i users were eligible for analysis  were male (Table The mean follow-up in the matched cohort was 2.1 years (standard deviation = 2.1 years). The results showed that the risk of composite cardiovascular outcome was not significantly different between the GLP-1RA and DPP-4i groups    , 10, 26, , 10, 26In contrast, a significant renal protective effect was found in the GLP-1RA group compared to the DPP-4is group with regards to a decline in eGFR > 50% and ESKD progression to dialysis. The time to dialysis initiation was 6 months later in the GLP-1RA group than in the DPP-4is group. There are multiple hypotheses for the kidney protective effect of GLP-1RAs, however the mechanism remains unclear. Possible indirect factors include appropriate body weight maintenance and glycemic control, while direct factors target the kidneys. GLP-1RAs have several extra-pancreatic functions, including reducing oxidative stress-induced autophagy and endothelial dysfunction . GLP-1RAWe also found a significant reduction in all-cause death and admission due to any cause in the GLP-1RA cohort, which is compatible with a previous study on patients with ESKD . PreviouAlthough this study is based on real-world data on outcomes of patients with advanced DKD receiving GLP-1RAs, there are several limitations. First, we cannot infer causal associations between GLP-1RAs and cardiovascular or kidney outcomes due to the retrospective observational design of this study. Nevertheless, we enrolled patients who received GLP-1RAs and DPP-4is and evaluated the same parameters and outcomes in both groups. Therefore, the causal relationship should be relatively valid in this study. Second, background heterogeneity existed in the GLP-1RA and DPP-4i cohorts. The GLP-1RA users usually had a longer DM duration, more complications, and a refractory tendency to antiglycemic agents. These differences may have interfered with the outcomes; however, we mitigated sampling bias using propensity score matching to balance covariates including DM duration, DM complications, drug categories, and laboratory data. Therefore, we believe that the study outcomes should not be influenced by heterogeneity. Third, it is difficult to avoid coding errors in database research. We diminished possible miscoding by pairing diagnostic code and drug registration data. For instance, hypertension was defined as patients receiving antihypertensive agents and a diagnosis of hypertension, and similar definitions were also applied to other diseases. We also defined kidney function using direct eGFR data rather than CKD stage diagnosis code, which may have been coded inappropriately. In addition, the outcome measurements including ischemic stroke and MI required admission records. Therefore, disease miscoding in this study should be limited. Fourth, the GLP-1RAs in this study only included the human GLP-1-like analogues liraglutide and dulaglutide. Semaglutide was not included because few patients used this drug as it was relatively new in Taiwan during the enrollment period. We excluded exendin-4-like analogues such as exenatide and lixisenatide because they are different drug subcategories. Although the outcomes were limited to liraglutide and dulaglutide, the results should be robust and homogenous. Finally, we cannot ensure medication compliance in each patient, which is a common limitation in real-word observational studies. However, the National Health Insurance Administration in Taiwan created the Diabetic Shared Care Program (DSCP) to ensure that diabetic patients receive standard care in Taiwan. The DSCP team includes physicians, nurses, nutritionists and pharmacists who receive standard care courses to provide integrated care. This approach should increase the medication adherence of diabetic patients in Taiwan.GLP-1RAs had no influence on the composite cardiovascular outcomes but reduced composite kidney events including a decline in eGFR > 50% and progression to ESKD with dialysis, all-cause mortality, and admission in patients with advanced DKD (eGFR < 30) compared with DPP-4is.Additional file 1: Figure S1. Time to Dialysis Distribution and Causes of Death. Table S1. Number of patients with advanced chronic kidney disease with prescription of GLP1RA and DPP4i. Table S2. Number of patients receiving dialysis with prescription of GLP1RA and DPP4i.


Resistant hypertension (RH) defined as uncontrolled blood pressure despite the use of a combination of a renin-angiotensin system blocker, a calcium channel blocker, and a diuretic at maximally tolerated doses is associated with a substantially increased risk of cardiovascular and renal events. Despite targeting relevant pathophysiological pathways contributing to elevated blood pressure, approximately 10–15% of hypertensive patients remain above recommended blood pressure targets. Further optimization of blood pressure control is particularly challenging in patient populations who frequently present with RH such as elderly and patients with chronic kidney disease, due to the unfavorable safety profile of the recommended fourth-line therapy with mineralocorticoid receptor antagonists. This review explores the potential role of endothelin antagonists as an alternative fourth-line therapy.Despite the well-described role of the endothelin pathway in the pathogenesis of hypertension, it is currently not targeted therapeutically. Recently however, main outcome data from the PRECISION study, a randomized placebo-controlled phase 3 trial, in patients with RH on guideline-recommended standardized single-pill background therapy convincingly demonstrated the safety and blood pressure-lowering efficacy of the dual endothelin antagonist Aprocitentan.Findings from the phase 3 PRECISION study could signify a turning point in the utilization of endothelin receptor antagonists as a standard treatment for patients with RH. Hypertension is a prevalent leading cause of disability and mortality worldwide . ResistaWith up to 15% of hypertensive patients above recommended BP targets despite the use of three guideline-recommended antihypertensive drugs . SpironoCurrently licensed antihypertensive drug classes are mainly targeting various aspects of the renin–angiotensin–aldosterone system , calcium channels , fluid retention and sodium homeostasis (various classes of diuretics), adrenergic receptors (α- and β-blockers), centrally acting sympatholytic agents, and vasodilatory pathways. Targeting the endothelin pathway therapeutically appears as an obvious neglect in the context of RH, given its pivotal role in various aspects of BP control, as summarized below ••.A receptors and ETB (ETB1 + ETB2) receptors which are located on the vascular smooth muscle cells and endothelial cells regulating BP by inducing vasoconstriction or vasodilation. ET-1 is an extremely potent vasoconstrictor with long half-life, whose primary mechanism of inducing vasoconstriction is via interaction with ETA receptor. In the context of pathophysiological conditions, such as hypertension or pulmonary hypertension, the interaction between ET-1 and ETB2 in the smooth muscle cells also leads to vasoconstriction. Vasodilation by ET-1 is mediated by ETB1 on endothelial cells via production of NO and PGI2  is the predominant isoform of the endothelin (ET) family in human cardiovascular system. It is most abundantly produced by vascular endothelial cells both constitutively to maintain vascular tone and in response to different stimuli, while also being generated in a variety of other cells including vascular smooth muscle cells, cardiomyocytes, fibroblasts, macrophages, neurons, and epithelial cells in lungs and kidneys , 11•. InB-mediated paracrine release of NO and other vasodilators is crucial in acting as a counterregulatory pathway to limit ETA-mediated vasoconstriction. In pathophysiological conditions where there is endothelial dysfunction with a loss of vasodilators, the vasoconstrictor and other pathophysiological effects of ET-1, such as cell proliferation, inflammation and fibrosis, will be potentiated  15 to < 60 mL/min per 1·73 m2). The clinical benefit is particularly significant given the higher cardiovascular risk profile of the indicated study groups. No difference in treatment effect was detected between patients with or without β-blocker treatment at screening.The results from ABPM provided additional verification of the office readings Fig. . A signiAs anticipated, Aprocitentan was associated with mild-to-moderate fluid retention compared to placebo in a dose-dependent manner . Edema was managed with addition or uptitration of diuretic therapy. Seven patients taking Aprocitentan 25mg discontinued the study during parts 1–3 due to edema, and 10 out of 11 patients who required hospitalization for nonfatal heart failure were taking Aprocitentan during parts 1–3. However, all patients had a high cardiovascular risk and relevant medical history including pre-existing heart failure, chronic kidney disease, and diabetes. A slight increase in N-terminal pro-brain natriuretic peptide (NT-proBNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) was observed in part 1 with Aprocitentan, followed by stabilization during part 2 and reversal during part 3. There was no evidence of hepatotoxicity.The unopposed endothelin pathway might represent an important contributor to persistent RH in patients on current pharmacological therapies targeting the three well-established pathophysiologic pathways. As demonstrated by the PATHWAY-2 study, the mineralocorticoid receptor antagonist spironolactone is currently recommended as the preferred fourth-line therapy option for RH where suitable, showing a decrease in home systolic BP by 8.7 mmHg after 12 weeks of treatment. Common adverse effects of spironolactone include hyperkalemia and reduction in eGFR which limits the use of this medication in high-risk patients frequently presenting with RH such as those with stage 3 and 4 chronic kidney disease. Higher doses of spironolactone can lead to gynecomastia and erectile dysfunction, particularly in higher doses of 50 mg or more per day for primary resistant hypertension. The PRECISION study demonstrated that Aprocitentan could be a particularly useful treatment option for patients who are unsuitable for spironolactone, such as the elderly and those with chronic kidney disease stage 3–4. Indeed, a greater reduction in SBP was observed during the fourth week of study among these specific patient groups, i.e., older patients aged ≥ 75 years, as well as those with macro-albuminuria  and chronic kidney disease stage 3–4 ••.Furthermore, Aprocitentan may exhibit a synergistic effect on BP reduction when co-administered with conventional medications by neutralizing the effect of endothelin pressor system, which is distinct from the renin-angiotensin aldosterone pathway and its impacts on blood vessels and kidneys .A receptor antagonist, reduced urine albumin-creatinine ratio by 51.8% in patients with type 2 diabetes and CKD (eGFR of 25–75 mL/min per 1·73 m2 of body surface area) after a 6-week treatment. Moreover, long-term use of endothelin receptor antagonists substantially reduced the risk of end-stage kidney disease in high-risk patients with type 2 diabetes [A receptor antagonist, compared to dapagliflozin alone, in patients with chronic kidney disease and albuminuria will explore the potential of such an approach .Proteinuria is an independent risk factor for chronic kidney disease progression and cardiovascular events. Aprocitentan has demonstrated substantial antiproteinuric properties, especially in patients with chronic kidney disease stage 3–4 with RH. The Study of Diabetic Nephropathy with Atrasentan (SONAR), showed that atrasentan, a selective ETdiabetes . These fThe most common adverse effect related to ET receptor antagonists is mild-to-moderate fluid retention occurring in a dose-dependent manner predominantly in the first 4–6 weeks of therapy. To enhance patient compliance and prevent exacerbation of pre-existing heart failure, it seems essential to maintain a euvolemic state by prescribing appropriate diuretics prior to commencing therapy. Patient’s volume status should be closely monitored particularly during the first 4–6 weeks of treatment. Reassuringly, ET receptor antagonist associated fluid retention is very responsive to additional diuretic therapy, particularly loop diuretics and therefore clinically manageable.The PRECISION study has the potential to revolutionize the common application of ET receptor antagonists. PRECISION provided substantial evidence for a new treatment option for patients with high cardiovascular risk and RH, who have not responded to current therapies, by effectively targeting the unopposed endothelin pathway with a favorable safety profile. The pronounced impact on nocturnal blood pressure, known to be the most accurate predictor of cardiovascular death, and the favorable effects in elderly and in patients with chronic kidney disease, patient cohorts in whom BP control is known to be particularly difficult to achieve, render Aprocitentan a very useful addition to the therapeutic armamentarium for resistant hypertension.


Posttransplantation diabetes mellitus (PTDM) is a serious complication of solid organ transplantation. It is associated with major adverse cardiovascular events, which are a leading cause of morbidity and mortality in transplant patients. This study aimed to develop and validate a score to predict the risk of PTDM in kidney transplant recipients.A single-center retrospective cohort study was conducted in a tertiary care hospital in Medellín, Colombia, between 2005 and 2019. Data from 727 kidney transplant recipients were used to develop a risk prediction model. Significant predictors with competing risks were identified using time-dependent Cox proportional hazard regression models. To build the prediction model, the score for each variable was weighted using calculated regression coefficients. External validation was performed using independent data, including 198 kidney transplant recipients from Tübingen, Germany.Among the 727 kidney transplant recipients, 122 developed PTDM. The predictive model was based on 5 predictors  and exhibited good predictive performance . The risk score, which included 33 patients with PTDM, was used as a validation data set. The results showed good discrimination . The Brier score and calibration plot demonstrated an acceptable fit capability in external validation.We proposed and validated a prognostic model to predict the risk of PTDM, which performed well in discrimination and calibration, and is a simple score for use and implementation by means of a nomogram for routine clinical application.  The pathophysiology of PTDM is multifactorial,8 Several studies have shown that PTDM increases risk of severe infections and early cardiovascular complications after transplantation.11 PTDM also increases the risk of graft rejection, graft failure, and mortality because of premature cardiovascular diseases.13 A retrospective analysis of the United States Renal Data System, which included >11 000 kidney transplant recipients, showed that PTDM is an independent predictor of mortality and graft failure.14 PTDM is also a major cause of chronic kidney disease in liver transplants.15The clinical relevance of PTDM lies in its impact on posttransplant outcomes.16 Therefore, the identification of patients at risk for PTDM might help to guide the implementation of strategies to prevent PTDM,8 including lifestyle modifications, early pharmacological treatment,1 and, potentially, the use of less diabetogenic therapy as an immunosuppressive regimen in low immunologic risk patients.2 Currently, 2 risk prediction models for PTDM have been developed.18 The first model developed by Chakkera et al included variables of the pretransplant period; however, this score did not consider specific risk factors after transplantation, in particular, transient posttransplantation hyperglycemia and the use of tacrolimus,17 which has been identified as a factor that increases the risk of PTDM19; and the second scale included the use of tacrolimus but only during the first 3 mo after kidney transplantation.1 Our study aimed to explore the clinical risk factors related to PTDM, including not only transient posttransplantation hyperglycemia but also tacrolimus use throughout follow-up, to build a predictive risk model for early detection of kidney transplant recipients at high risk for PTDM.To date, no study has shown that treatment of PTDM can reduce the morbidity and mortality in these patients. In fact, the presence of PTDM itself is associated with cardiovascular outcomes independent of treatment.This was a retrospective cohort study of kidney transplant recipients. The derivation cohort was a single-center cohort of patients aged older than 18 y who underwent their first kidney transplantation between January 2005 and December 2019 at Pablo Tobón Uribe Hospital, Medellín, Colombia. We excluded patients with a history of diabetes mellitus before kidney transplantation and those with <1 y of follow-up. Patients were followed up until the onset of PTDM or otherwise censored at a loss to follow-up or at the end of the study. The final follow-up period was December 2020. The external validation cohort included kidney transplant recipients from Tübingen University Hospital, Germany, who underwent kidney transplantation between January 2007 and December 2013 without a history of diabetes mellitus before transplantation. Patients aged younger than 18 y and recipients of combined organ transplantations were excluded.Clinical information, including demographic data, comorbidities, and laboratory tests, was collected from the electronic medical record system and from the Renal Transplant Scientific Registry of the Hospital Pablo Tobón Uribe. Multiple sources were used to identify the onset of PTDM, such as laboratory test data  42 d after transplantation and clinical diagnosis in electronic medical charts, in which data on fasting plasma glucose and glycated hemoglobin (HbA1c) levels were collected during the follow-up as well as clinical outcomes such as graft and patient survival. Data on prescribed medications, including immunosuppressive therapy, were extracted from the electronic database of the hospital from 2005 to 2020, thus allowing immunosuppressive therapy to vary over time at “posttransplantation intervals.”In the validation cohort of Tübingen, a longitudinal assessment of glucose metabolism (fasting plasma glucose and HbA1c levels) was performed. Additionally, the patient and transplant characteristics (such as immunosuppressive therapy) were assessed.20 PTDM status was evaluated 42 d after transplantation (derivation cohort). According to the diagnostic criteria for PTDM, recipients who met the inclusion criteria were divided into 2 groups: PTDM and non-PTDM groups. Posttransplant transient hyperglycemia was defined as fasting blood glucose values ≥126 mg/dL or glycosylated hemoglobin >6.5% in the first wks after kidney transplantation.The primary endpoint of this study was to evaluate the independent prognostic factors associated with PTDM. Patients were defined as having new-onset diabetes if they met at least 1 of the following criteria : (1) fasting blood glucose  ≥ 126 mg/dL, (2) random plasma glucose >200 mg/dL, (3) 2-h plasma glucose ≥200 mg/dL during an oral glucose tolerance test, or (4) HbA1c ≥ 6.5%.21 For a fixed sample of 727 patients, an exponential maximum likelihood test of equality of survival curves with a 0.05 two-sided significance level (α) will have 80% power to detect the difference between the exponential parameter λ1 of 0.105  and an exponential parameter λ2 of 0.062 (1-y PTDM event rate 5.5%), and thus a constant hazard ratio (HR) of 1.70. Furthermore, the sample size estimation assumes an average observation time of 2 y.A post hoc power calculation was performed by assuming exponential survival times.For the derivation cohort, the sample size had a power of 99%, whereas for the external validation cohort, it was 86%.Summary statistics were used to describe the characteristic data of the PTDM and non-PTDM groups. Categorical variables were expressed as absolute and relative frequencies. Continuous variables were described as mean and SD or median and interquartile range according to the distribution of the data, which was assessed using skewness and kurtosis, as well as visual inspection using QQ plots, box plots, and histograms.22 Graft loss and all cause-death were considered competing risk events. The Fine and Gray subdistribution HR (sdHR) was used to identify independent factors associated with the risk of PTDM.23 A time-dependent Cox regression model was also performed to evaluate the differences in the hazard risk of immunosuppressive therapy on PTDM.25 Immunosuppressive therapy  was defined in a time-varying manner, whereby the periods were divided into time intervals after each switching treatment discontinuation.The primary endpoint of PTDM was analyzed using cumulative incidence, adjusting for competing risks.P < 0.1) and backward selection using all eligible baseline variables.26 Based on the covariates selected, multivariable time-dependent Cox regression models were used to assess HRs and 95% confidence intervals (CIs) for PTDM. To address the missing potential censoring information from the competing risk failure time, we used the model described by Ruan et al,27 this method uses a nonparametric multiple imputation method that recovers the missing potential censoring information in the competing risk failure times, so the analysis of time-varying covariates can be applied.27 Interactions between the independent covariates were tested using the final model. The linearity of the relationship between the variables and the outcome was evaluated by plotting martingale residuals. The collinearity of the independent variables was evaluated using a correlation matrix. The proportional hazard assumption was tested by plotting the Schoenfeld residuals. The fit of each model was evaluated using the Akaike information criterion and the chi-square goodness of fit statistic.Prespecified predictors were included on the basis of the published literature related to the onset of PTDM. Variable selection included screening univariate associations (C/D),28 as well as the concordance probability estimated statistic , which is the best choice in time-to-event data studies.29 Calibration was evaluated using Brier scores and calibration plots.30 A Brier score of <0.25 indicates that the model can correctly predict the occurrence of the target event, and the closer the score is to 0, the better the model performance.31 The bootstrapping sampling method (1000 resamples) was used for internal validation to evaluate the accuracy of the model and reduce overfitting.33 A prognostic nomogram was constructed on the basis of the multivariate analysis of the validation cohort and was used to predict the 6-, 12-, 18-, and 24-mo risk of PTDM in the presence of competing risks.34 Based on the calculated score, the patients were divided into quartiles, and each risk represented a different prognosis. Kaplan-Meier survival analysis was used to generate survival curves, and log-rank tests were used for comparisons between groups.Discrimination of the prognostic model was evaluated by calculating the concordance Harrell’s C-index (C-index), time-dependent receiver operator characteristics (tROC), and area under these curves  and R (version 4.1) software were used.For all analyses, a 2-tailed 37This study was approved by the Ethics Committee of the Pablo Tobón Uribe Hospital  and the University of Tübingen and was conducted in accordance with the principles of the Declaration of Helsinki. Finally, our article complies with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement.A total of 964 patients underwent kidney transplantation between 2005 and 2019 in Medellín, Colombia. Among them, 237 were excluded; the reasons for exclusion are shown in Figure Figure S1, SDC,http://links.lww.com/TXD/A592).Patient characteristics are shown in Table Figure S2, SDC,http://links.lww.com/TXD/A592). The 4 predictive models were compared using the partial likelihood test and the Akaike information criterion criteria, Harrell’s C-index, and CPE, which performed very similarly in all models ; the Brier score indicated a prediction error close to zero , and the calibration curve showed a good performance for both model 1 and model 2  and a CPE of 0.687 (standard error 0.016). Furthermore, the discriminative ability was similar over time, as shown by the AUC of the time-dependent area under the receiver operating  + (0.7 × BMI) +  + (4 × transient hyperglycemia) + (6 × tacrolimus)sdHR de 1.09; 95% CI, 1.07-1.012; P < 0.0001). Internal validation using the bootstrap method (1000 bootstrap resampling) was used to evaluate the accuracy of model 2. Calibration was obtained in the form of the shrinkage coefficient to quantify the lack of fit of the model. The shrinkage coefficient was 99.7%, indicating a –0.98% lack of fit in the model, whereas Harrell’s C-index corrected was 0.7348, indicating an adequate correlation between the log hazard and the observed survival time. The detailed results of the model validation indices are provided in Table S2 . We then calculated the 1-, 2-, and 5-y nomogram predictions. The total score of the patients was obtained by summing the scores corresponding to each subvariable to obtain the predicted probability of PTDM at 1, 2, 3, and 5 y after transplantation ]. The tROC-AUC at 6, 12, 18, and 24 mo were 0.71, 0.72, 0.73, and 0.73, respectively. The calibration curve showed a good fit . The sensitivity and specificity of the predictive model at different time points are presented in Table P < 0.001; data not shown).The performance of the proposed model was tested using a cohort of 251 adult renal transplant patients from Tübingen, Germany, of whom 19 were excluded for failure to measure the PTDM result and 34 for lack of measurement of the predictor transient posttransplantation hyperglycemia. A total of 198 patients with complete cases were included in the analysis, of whom 33 had PTDM during follow-up (16.6%). The baseline characteristics of the patients are shown in Table In this retrospective real-world study, a predictive model of PTDM risk was developed using a survival analysis methodology with time-dependent variables and competing risk analysis. The model included the following predictors: age, gender, BMI, transient posttransplantation hyperglycemia, and tacrolimus therapy. This model showed good predictive performance. The tROC curve showed a stable AUC during the follow-up and indicated that the best discrimination ability occurred during the first y posttransplantation. Finally, external validation in a cohort from a different country with different ethnicities was performed adequately, indicating that this risk prediction model is a sensitive and specific prognostic indicator of PTDM, transferable to other regions.2, family history of type 2 diabetes mellitus, fasting blood glucose level ≥100 mg/dL pretransplant, triglycerides ≥200 mg/dL, use of gout medication, and pretransplant assignment to corticosteroid maintenance protocol, with good performance with an AUC-ROC of 0.70 in both the derivation and validation set18; however, this model did not include transient posttransplantation hyperglycemia, which is a newly recognized strong biomarker for PTDM.A predictive model for application in the pretransplant period was published in 2011. The incidence of PTDM was 26.7% (85/318) at the 1-y follow-up and included 7 dichotomous variables such as age 50 y or older, BMI ≥30 kg/m2, tacrolimus blood levels >10 ng/mL during the first 3 mo, transient posttransplantation hyperglycemia, delayed graft function, and acute rejection. The performance was very good, with an AUC-ROC of 0.916 , but they did not report whether a calibration test had been performed or how to use this model in clinical practice. Furthermore, this model has not been externally validated.1Another predictive model for PTDM was developed in 2022 in 495 renal transplant recipients; the incidence of PTDM was 11.1% (55/495) at 6 y of follow-up, which was lower than previously reported. They also found that the cumulative incidence of PTDM increased over time to 15.3% after 3 y, which is comparable with our findings. The prediction model included 6 dichotomous variables: age 45 y or more, body weight >25 kg/m19 our study showed that transient posttransplantation hyperglycemia was a strong predictor of PTDM. Posttransplantation hyperglycemia is a common early adverse event after renal transplantation, affecting up to 91% of kidney transplant recipients within the first 48 postoperative h.38 Notably, despite being thought to be transient in most patients, it reveals that in patients at risk for beta-cell failure in stressful situations, such as the early posttransplant period, high doses of glucocorticoids and calcineurin inhibitors are administered, and the surgery and in-hospital stay may be diabetogenic. Notably, by reducing beta-cell stress via reduction in glucotoxicity through the early application of  insulin, the risk of PTDM can be substantially reduced.40In line with previous publications,42 Similar to previous studies, we found that BMI was an independent risk factor for PTDM.44 BMI is a modifiable risk factor that can be addressed by lifestyle intervention, which is powerful in weight reduction and has proven efficacy in improving glucose metabolism,45 even after kidney transplantation.46 Bariatric surgery before kidney transplantation may also be considered for high-risk patients.47 Furthermore, the use of glucagon-like peptide 1 receptor analogs or dual glucagon-like peptide 1/ gastric inhibitory polypeptide analogs for weight reduction may also be an interesting option for future research.BMI is a well-established risk factor for PTDM because of the increase in insulin resistance and an alteration in insulin secretion, triggered or not by factors associated with renal transplantation.43 Tacrolimus is still recommended as the first-line immunosuppressive drug because of its efficacy in preventing rejection and graft loss48 because preservation of graft function is one of the major determinants of cardiovascular and overall mortality in kidney transplant recipients.49 Current recommendations on PTDM also argue against modification of immunosuppression based on diabetes risk,20 although the option of steroid-free maintenance immunosuppression is discussed controversially51 and may be an option for immunologically low-risk patients.Tacrolimus was also an important predictor of PTDM in the present study, increasing the risk of PTDM by 1.8-fold, whereas cyclosporine did not show a diabetogenic effect, consistent with previously reported data.In summary, our study has several strengths. To our knowledge, this is the first prognostic prediction model for PTDM in renal transplantation using time-dependent survival analysis and competing risks. Therefore, this model can be applied regularly after transplantation, which may improve patient monitoring and provide a better basis for decision-making regarding changes in therapy and close monitoring of glucose levels. Secondly, our model has also been validated in an independent population from another country. Also, our model uses clinical variables that are easy to measure and available during kidney transplant follow-up; therefore, it can be easily implemented in any kidney transplant center. Furthermore, a user-friendly nomogram that facilitates its daily application was provided. Thirdly, this model includes the effect of immunosuppressive therapy and its changes over time on the risk of PTDM. Renal transplant recipients may receive different immunosuppressive therapies or undergo change in immunosuppressive treatment during follow-up, which could influence the risk of PTDM. Therefore, we used adequate statistical methods to avoid bias resulting from the difference in the duration of follow-up and time of treatment exposure between patients.43 However, in our study, the incidence of PTDM in the derivation and external validation cohorts was comparable, 16.78% versus 16.66%, respectively. Furthermore, although we aimed to evaluate all immunosuppressive therapies used, there were insufficient data to associate mammalian target of rapamycin (mTOR) inhibitors with the outcome; however, the combination of tacrolimus and mTOR inhibitors with the development of PTDM remains controversial in the literature and could be an issue for future research.41Our study also has some limitations. Firstly, because of the retrospective data collection, we could not completely rule out patients with subclinical pretransplant diabetes mellitus. Additionally, no information was collected on a family history of type 2 diabetes mellitus. Secondly, the potential of misclassification bias, given that some patients were diagnosed with glycosylated hemoglobin and others with fasting blood glucose levels, could underestimate or overestimate the incidence of the outcome.This study has 2 important implications for clinical practice. First, this predictive model, shown as a nomogram, is a simple tool that can be implemented by kidney transplant teams to estimate the probability of developing PTDM and can be applied not only at the time of transplantation but also during follow-up. The model helps identify patients at risk and individualize monitoring and preventive strategies, including lifestyle intervention or adaptation of immunosuppressive therapy in some cases, weighing the risk of PTDM against the risk of renal graft rejection in each patient. It also helps physicians to provide more reliable information when advising on the risk of future outcomes.By identifying the patients most at risk of developing PTDM, specific, personalized pharmacological and nonpharmacological measures could be adopted to address modifiable risk factors that, in the long term, could positively impact cardiovascular risk and renal graft survival in these patients.The future research issues are the predictive role of tacrolimus and mTOR inhibitor combination therapy in the development of PTDM and lipid profile components as predictors of PTDM and prospective exploration of ≥1 accurate definitions of transient posttransplantation hyperglycemia in the prediction of PTDM.The authors thank the Hospital Pablo Tobón Uribe for allowing us to conduct this study and for their help in extracting >100 000 laboratory test data points from all available sources and reliable access to information from medical records.