GINA 2012-defined controlled asthma levels increased in all groups, rising from 6.1% at baseline to 43.0%, 53.8% and 67.8% at Weeks 4, 8 and 12, respectively. Most patients presented with severe persistent asthma. Newly diagnosed patients with asthma could benefit from at least 3 months of regular treatment followed by long-term pharmacological management.In China, there are an estimated 30 million people with asthma, a condition that remains poorly controlled in many patients. The INITIAL study (NCT02143739) was a 12-week, multicentre, prospective, observational study comprising 45 centres across Northern and Southern China that aimed to assess asthma severity among newly diagnosed patients as well as their prescribed medications and response to treatment. The primary objective was to evaluate asthma severity using Global Initiative for Asthma (GINA) 2006 research criteria. Secondary objectives included the distribution of asthma medication by GINA severity category and evaluation of GINA 2012-defined control levels. Medications were prescribed as per usual clinical practice. At baseline, among 4491 patients, 3.9%, 12.0%, 22.6% and 61.6% had intermittent, mild persistent, moderate persistent and severe persistent asthma, respectively. Inhaled corticosteroid/long-acting β A survey of 4125 outpatients with asthma (aged ≥17 years) conducted in mainland China using the Asthma Control Test (ACT)5 demonstrated that asthma was uncontrolled in 55.1% of patients (ACT score ≤19)2. A cross-sectional survey of 889 adult patients (aged ≥18 years) with moderate or severe asthma conducted in Jilin Province, China, demonstrated that asthma was partly controlled (ACT score 16–20) in 40.3% and controlled (ACT score 21–25) in 28.1% of patients at 1-year follow-up; asthma was unsatisfactorily controlled (ACT score ≤20) in 71.9% patients overall3. In Su et al.’s study of 2928 outpatients with asthma (aged ≥14 years) drawn from 10 major Chinese cities, using the Global Initiative for Asthma (GINA) 20066 definition of symptom control (as judged by a physician), 26.2% had uncontrolled asthma over the preceding 12 months4. Of the remaining patients, 28.7% and 45.2% were judged to have controlled and partly controlled asthma, respectively4. Among the 402 Chinese patients with asthma surveyed during a wider Asia-Pacific study using the GINA definition of symptom control, 56% had partly controlled asthma and only 2% were judged to have controlled asthma7.In China, there are an estimated 30 million people with asthma8. Treatment with regular low-dose inhaled corticosteroids (ICS) is highly effective in reducing asthma symptoms and reducing the risk of asthma-related exacerbations, hospitalisation and death8. For patients at GINA Step 2 or above, ICS (with or without long-acting β2 agonists [LABA] depending on Step) is the recommended initial controller treatment8. GINA recommends that asthma severity be assessed retrospectively after a patient has been on controller treatment, with adjustment as necessary, over several months8. However, classification of asthma by severity may be useful for management decisions during initial assessments and to characterise patients not receiving ICS when initiating a study6. Furthermore, assessment of disease severity  is recommended by the Chinese Guideline for Prevention and Management of Bronchial Asthma when selecting initial treatment10.According to GINA, the long-term goal of asthma management is to achieve good symptom control and maintain normal activity levels while minimising the future risk of exacerbations, fixed airflow limitation and side effects of treatment6 and the Chinese Thoracic Society Guidelines 20089. The main secondary objectives were to assess the distribution of asthma medication by GINA severity category and evaluate GINA 2012-defined control levels during 12 weeks of treatment11.To date, few studies in China have addressed disease severity in newly diagnosed patients and their initial response to treatment. Therefore, this non-interventional study aimed to assess asthma severity among newly diagnosed Chinese patients who had not received ICS , their prescribed medications and response to treatment. In the context of this study, a new diagnosis of asthma was defined as one occurring no more than 3 months prior to enrolment. The primary objective was to evaluate asthma severity using GINA 2006 research criteriaOf the 4907 patients screened, 4817 were subsequently enrolled and 4492 were included in the full analysis set (FAS). Overall, 79.9% (3587) of the FAS completed the study  patients had intermittent asthma, 538 12.0%) patients had mild persistent asthma, 1013 (22.6%) patients had moderate persistent asthma and 2767 (61.6%) patients had severe persistent asthma  and anti-cholinergic drugs .The most commonly prescribed baseline therapy was ICS/LABA , followed by leukotriene receptor antagonist , theophylline , short-acting βFor ICS/LABA, the most commonly prescribed initial therapy, the majority of patients were defined as moderate persistent  and severe persistent  Table .Table 3IICS/LABA was prescribed to the majority of severe persistent patients at baseline, often in combination with LTRA. Patients with intermittent asthma represented 9.3% of those prescribed LTRA without ICS/LABA at baseline, greater than the proportion of those prescribed ICS/LABA alone or ICS/LABA in combination with LTRA At Week 12, the majority (82.2%) of patients reported taking medication as prescribed by their physician at the previous visit. Missed doses were the most common form of non-compliance .Baseline severity, compliance, age, sex, occupation, education level, area of residence, allergy history, smoking status, standard of care at last visit and body mass index (BMI) reached significance p < 0.15) in a univariate analysis of risk factors  had ≥1 exacerbation. Among 78 patients who required an emergency room (ER) visit, 55 were classified as severe persistent, 13 moderate persistent, seven mild persistent and three intermittent at baseline, respectively. The majority of patients requiring hospitalisation (12) were classified as severe persistent, and the remaining patients were moderate and mild persistent (one patient each). Patients requiring systemic corticosteroids for ≥3 days were distributed between severe persistent (eight) and moderate persistent (six) asthma. Of the severe persistent patients initially prescribed ICS/LABA, 2.5% experienced an exacerbation compared with 3.1% of those who were not.14. While the severity measure used in that study was derived from medication use and markers of exacerbations15 rather than GINA criteria, it indicates that the course of severe asthma is potentially modifiable in line with the results presented here, albeit over a much shorter timescale.In this non-interventional study of 4492 newly diagnosed patients with asthma who had not received prior therapy with ICS , 61.6% presented with severe persistent asthma; 3.9%, 12.0% and 22.6% presented with intermittent, mild and moderate persistent asthma at baseline, respectively. A retrospective study of asthma trajectory over 10 years demonstrated that most patients classified as severe in the first year transitioned to less severe states in subsequent yearset al. and Thompson et al., in which 73.8% and 58% of patients overall achieved GINA-defined control (28.7% and 2%) and partial control (45.2% and 56%), respectively7. Possible explanations for this discrepancy include differences in study design , patient population (asthma diagnosis ≥1 month vs. newly diagnosed) and treatment. Despite being the most commonly used regimen, less than half (45.6%) of patients surveyed by Su et al. were using ICS/LABA regularly (see below)4. A clinically significant improvement in ACQ-5 test score from baseline was seen in 72.8% patients at Week 4, rising to 82.9% patients at Week 12. This suggests that even 4 weeks of regular treatment can improve patient-reported asthma control. This improvement in patient-reported asthma control follows the trajectory of the observed increase in the proportion of patients with GINA-defined controlled and partly controlled asthma.At baseline, all but the intermittent group had low levels of asthma control. The proportion of patients with GINA 2012-defined controlled asthma increased in all severity groups, rising from 6.1% at baseline to 67.8% at Week 12. There was a corresponding decrease in the proportion of patients with partly controlled and uncontrolled asthma from baseline to Week 12 of 50.4% to 29.3% and 43.5% to 2.9%, respectively. Taken together, 97.1% of patients achieved GINA 2012-defined control or partial control at Week 12. These figures represent a marked improvement over those seen in the studies of Su et al.4. Possible explanations for this discrepancy include the fact that patients in that study were not newly diagnosed, having a mean duration of disease of 153 months4. Furthermore, it is unclear if they were visiting healthcare professionals (HCPs) as regularly as the patients in the current study. In the study by Yan et al., regular follow-up appointments were associated with a higher likelihood of continuing ICS/LABA or ICS/long-acting muscarinic antagonist (LAMA) for longer than 3 months3. At baseline, during the patients’ hospital stay, 92.2% were using ICS/LABA or ICS/LAMA daily; upon discharge, only 38.5% continued to do so for >3 months3. Results from the present study also showed no discernible difference in GINA 2012-derived control level at Week 12, irrespective of whether an LTRA was prescribed with an ICS/LABA at baseline.Overall, 90.2% of patients were initially prescribed ICS/LABA. This figure is considerably higher than the proportion of daily ICS/LABA use (45.6%) reported by Su et al.2. Baseline severity, sex, occupation, education level and standard of care (ICS or ICS combination) at Week 12 were associated with good asthma control and should be subject to further investigation in the future.In the present study, the level of GINA 2012-defined asthma control increased at each visit and was greater at Week 12 than Week 8, which may be related to treatment compliance. Multivariate analysis provided further evidence that treatment non-compliance is a risk factor for suboptimal asthma control, in line with the findings of the study by Zhong 9, the collection of lung function data after the initial visit was at the investigator’s discretion. This is a limitation of our study, as GINA 2012 control level could not be determined in 2942, 2947 and 964 patients at Weeks 4, 8 and 12, respectively. The GINA 2014 major revision altered the determination of symptom control by removing lung function testing from the assessment criteria16. Since all other criteria were unchanged in the revision and were collected during this study, a post hoc analysis of this data set using the latest GINA criteria (2018)8 is planned to assess how the level of asthma control compares with GINA 201211. Control levels improved during the study, and the involvement in the trial itself combined with regular visits and contact with HCPs may have positively influenced the outcome. However, monthly visits do represent the standard of care in China.As lung function testing is not mandatory as per the Chinese Thoracic Society Guidelines 2008In conclusion, over 60% of newly diagnosed patients in this large, observational study presented with severe persistent asthma and only 0.5% of these patients had controlled asthma at baseline. ICS/LABA was the most commonly prescribed initial therapy type, with most patients receiving budesonide/formoterol. Asthma control levels improved considerably following the introduction of therapy, and after 12 weeks of regular treatment two-thirds of patients achieved GINA 2012-defined controlled asthma. Greater baseline severity, medication non-compliance, male sex, educational level, occupation and non-standard care at Week 12 were identified as risk factors for poor asthma control. Asthma is a chronic disease; these results suggest that newly diagnosed patients with asthma could benefit from at least 3 months of regular treatment followed by long-term maintenance therapy. Further work is required, but this study provides evidence that special attention should be paid to those presenting with severe persistent asthma at baseline. Furthermore, patient education may be required to encourage patients to contact HCPs sooner and to maintain long-term therapy once symptoms improve.The INITIAL study (NCT02143739) was a 12-week, multicentre, prospective, observational study comprising 45 tier 3 hospitals in major cities across Northern and Southern China. The study was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice guidelines. Ethical approval was obtained from the ethics committee of the China-Japan Friendship Hospital, Beijing, China, the principal site, and from the local ethics committee at each site. Written consent was obtained from all patients. The first patient was enrolled on 7 June 2014 and the last patients completed the study on 13 September 2016. Patients visited the clinic four times over the 12-week (±7 days) study period.In this study, a new diagnosis of asthma was defined as one occurring no more than 3 months prior to enrolment. Patients newly diagnosed with asthma aged ≥18 years were eligible for study enrolment providing they were stable (i.e. no asthma exacerbation in the previous 2 weeks) and had not used ICS in the 3 months prior to enrolment. An exacerbation was defined as an asthma deterioration that required the use of systemic corticosteroids for ≥3 days, an ER visit or hospitalisation. Exacerbations that occurred within 14 days of each other were defined as one event. Patients who had participated in other clinical studies in the 3 months prior to enrolment or who had a diagnosis of/suspected chronic obstructive pulmonary disease were ineligible for enrolment.6 and control were assessed11 as is standard clinical practice in China when initiating therapy10. Patient-reported outcomes were assessed using the ACQ-512. Patients then visited the clinic every 4 weeks as per the usual clinical practice in China. GINA asthma control status, ACQ-5 and exacerbations were assessed at Weeks 4, 8 and 12. Treatment decisions were not part of the present study; medications, if any, were prescribed as per usual clinical practice at baseline, Week 4 and Week 8 with no additional monitoring or diagnostic procedures.At baseline, informed consent and medical history were obtained. Patients were screened and GINA-defined asthma severityThe statistical analysis was primarily descriptive in nature. Quantitative variables were described by frequency, mean, standard deviation, median, minimum and maximum, and number of missing data. Qualitative variables were described using the absolute and relative (%) frequencies of each modality and number of missing data. Statistical tests were two-tailed and performed at the 0.05 significance level; 95% confidence intervals were calculated if applicable.Changes in ACQ-5 test scores from baseline were analysed by paired t-test. Factors influencing asthma control status at Week 12 were investigated using regression analysis. Variables with a p-value ≤ 0.15 in univariate analysis were subsequently included in multivariate analysis.According to unpublished market research conducted in 2010, among 800 newly diagnosed asthma patients in China, the proportion of patients with moderate and severe asthma was approximately 89%. Based on this assumption, with a ~30% drop-out rate, a sample size of approximately 5000 patients will provide a 95% confidence level with a 0.9% margin of error.Supplementary Information


ME/CFS). Studies on repeated cardiopulmonary exercise testing (CPET) show that previous exercise negatively affects oxygen uptake (VO2) and power output (PO) in ME/CFS. Whether this affects arterial lactate concentrations ([Laa]) is unknown. We studied 18 female patients (18–50 years) fulfilling the Canadian Consensus Criteria for ME/CFS and 15 healthy females (18–50 years) who underwent repeated CPETs 24 h apart (CPET1 and CPET2) with [Laa] measured every 30th second. VO2 at peak exercise (VO2peak) was lower in patients than in controls on CPET1 (P < 0.001) and decreased in patients on CPET2 (P < 0.001). However, the difference in VO2peak between CPETs did not differ significantly between groups. [Laa] per PO was higher in patients during both CPETs (Pinteraction < 0.001), but increased in patients and decreased in controls from CPET1 to CPET2 (Pinteraction < 0.001). Patients had lower VO2 (P = 0.02) and PO (P = 0.002) at the gas exchange threshold , but relative intensity (%VO2peak) and [Laa] at GET did not differ significantly from controls on CPET1. Patients had a reduction in VO2 (P = 0.02) and PO (P = 0.01) at GET on CPET2, but no significant differences in %VO2peak and [Laa] at GET between CPETs. Controls had no significant differences in VO2, PO or %VO2peak at GET between CPETs, but [Laa] at GET was reduced on CPET2 (P = 0.008). In conclusion, previous exercise deteriorates physical performance and increases [Laa] during exercise in patients with ME/CFS while it lowers [Laa] in healthy subjects.Post‐exertional malaise and delayed recovery are hallmark symptoms of myalgic encephalomyelitis/chronic fatigue syndrome ( Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multisystem and often debilitating disorder of unknown etiology (Institute of Medicine 2peak) and/or at the gas exchange threshold defined by the V‐slope method, when tested on two consecutive days   provides an accurate and objective assessment of functional capacity , and is registered with ClinicalTrials.org (ID NCT02970240).We used social network and media coverage to invite potential participants, and we received a large number of requests to participate in this study. All requests were pre‐screened with regard to age (18–50 years), gender , place of living (in the study area), health status, medication, and current level of physical activity. Eligible candidates were interviewed consecutively on telephone or in person. All included patients fulfilled the CCC for ME/CFS  and height (to the nearest 1.0 cm) were recorded and a spirometry was performed at baseline the first day . Baseline electrocardiogram (ECG) was monitored on both days . Prior to each test we inserted a catheter  into the radial artery for blood sampling.All tests were performed between 8 and 11 1 and CPET2), 24 h apart, on a cycle ergometer . Gas exchange and ventilation were measured breath‐by‐breath (Oxycon Pro). The flow sensor was calibrated with a 3 L syringe prior to each test , and the gas analyzer for O2 and CO2 was calibrated against commercial standards. The increment rate was set individually, with the aim of reaching VO2peak within 8 to 12 min of exercise. The rate was based on previous and current level of activity, physical examination, age, height and weight, and ranged from 10 to 24 W/min for the ME/CFS patients, and 15–30 W/min for the controls. The protocol included a 2‐min resting phase and 2 min of unloaded pedaling at a rate of 60–75 rpm, followed by a linear increase in power output until volitional exhaustion or until the participant was unable to maintain a cycling frequency above 45 rpm. They were given strong vocal encouragement during the tests. ECG was monitored continuously. Criteria to terminate the exercise test were any signs of distress, such as pallor or dizziness, chest pain, significant arrhythmias, or signs of ischemia on ECG. After reaching VO2peak, the participants continued to breathe through the facemask for a 3‐min recovery period.The participants performed two maximal incremental ramp tests . The samples were immediately placed in iced water and centrifuged at 1500g for 15 min at 21°C within 30 min . Plasma was separated and put on dry ice before storing at −80°C until analyzed. Arterial lactate concentration ([Laa]) was determined by spectrophotometer . We added 4‐chlorophenol, lactate oxidase, peroxidase and 4‐aminophenazone  to plasma and the tubes were mixed at 37°C for 10 min. The absorbance of samples and standards was read at 505 nm against the blank, with a detection limit of 0.044 mmol/L and a linearity limit of 16.85 mmol/L. Control sera  were used to monitor the performance of assay procedures, as recommended by the manufacturer. Arterial blood samples for analyses of hemoglobin concentration were collected at rest before each CPET and within 90 sec after peak exercise had been achieved, and analyzed within 1 minute after sampling .Arterial blood samples were collected at rest before each CPET and every 30th second during exercise. Test tubes contained an antiglycolytic agent to prevent continued glycolysis in the blood samples . The point where [Laa] started to accumulate was defined as the lactate turnpoint (LT) and was determined by log‐log transformation of [Laa] versus power output, that is, at the intersection of their regression lines, as described by Beaver et al.  .Variables measured breath‐by‐breath were averaged over a 30‐sec sampling time. The gas exchange threshold was visually identified by the V‐slope method  % from CPET1 to CPET2, corresponding to −1.33 (1.68) mL/kg/min . Mixed model analysis for repeated measurements with random intercept and random effect of power output was applied for [Laa] during the tests. Both power output and the square of power output were included in the model. Group and day were modeled as fixed effects. We tested for interaction between group and test, and between group and power output and the square of power output (i.e. whether there were group differences in [Laa] accumulation with increasing power output). To detect whether the difference in [Laa] curves from CPET1 to CPET2 was significantly different between the groups, we calculated the change in [Laa] for each power output per kilo body weight (POkg) in every participant. We performed mixed model analyses on the calculated change in [Laa], with fixed intercept, random effect of POkg, and with group modeled as fixed effect, to test for interaction between group and POkg. P‐values less than 0.05 were considered statistically significant. Statistical analyses were performed with SPSS .Data are presented as mean (SD). Two‐sample and paired 1 and CPET2.The inclusion process is illustrated in Figure 2peak was significantly lower in patients than controls on both CPETs  was significantly lower in patients than controls on both CPETs   Fig. B. Both gups Fig. C.a] did not differ between patients and controls before CPET1, but were significantly different before CPET2  and to the right in controls (P < 0.001), compared to the [Laa] curves for CPET1  was significantly lower in patients than controls on both CPETs  did not differ significantly between patients and controls on either CPET  occurred at a significantly lower power output in patients on both CPETs, but neither group had any significant difference in power output between CPETs Fig A and B. ET2 Fig. C. The diups Fig. D. The onET2 Fig. E, and thups Fig. F.2peak and VO2 at GET decreased significantly in patients, but not in controls, from CPET1 to CPET2, but the differences in VO2peak and VO2 at GET between the two CPETs did not differ significantly between the two study groups. However, the patients had elevated [Laa] for any absolute power output compared with the healthy controls, and both GET and LT occurred at a significantly lower VO2 and absolute power output in patients. Both study groups had similar relative exercise intensity (%VO2peak) at GET on both CPETs. When retested after 24 h, controls had no significant reduction in the power output at GET, but the [Laa] at GET was significantly reduced. In contrast, patients had a significant reduction in the power output at GET on CPET2, while no significant reduction in [Laa] at GET. Power output at OBLA demonstrated the same pattern, with a decrease in patients and an increase in controls from CPET1 to CPET2. Pre‐exercise [Laa] was also significantly different between the two study groups before CPET2. Whereas previous exercise (CPET1) led to lower [Laa] per absolute power output (right curve shift) on CPET2 in controls, [Laa] per absolute power output increased and the curve was shifted to the left on CPET2 in patients.In this study of repeated CPET in ME/CFS patients and healthy controls, VO1 amounted to approximately 150–200 mL blood which could explain the reduction in resting hemoglobin concentrations between the two CPETs , and that most patients stayed at the test facility in order to eliminate additional exertion between the tests. It should be noted that the patients experienced considerable symptom exacerbation after the tests, often lasting for weeks.Exercise intolerance, PEM and delayed recovery are prominent symptoms in ME/CFS. Intriguingly, this study indicates that previous exercise increases lactate accumulation in ME/CFS as opposed to the reduction seen in healthy controls, although the mechanism for this has yet to be established. We do not know whether this finding points to a central pathophysiological mechanism in ME/CFS, or if it is a secondary phenomenon. It might even be an attempt to alleviate the negative impact that exercise seems to have in these patients. Further research is needed in order to elucidate the causes of this abnormal response to exertion.None declared.


No association was observed between the knowledge about CKD and age, gender, hypertension stages, CKD stages, duration of diabetes as well as hypertension and co-morbidities. Based on the results of this study, we found poor knowledge of CKD among Indian T2DM patients. The government should start a CKD awareness programme to deal with this rising co-morbid condition.Diabetes and hypertension are the two major causes of chronic kidney disease (CKD). Epidemiological studies have found poor knowledge about the CKD among the general population. Hence, this study aimed to assess the awareness of CKD among type 2 diabetes mellitus (T2DM) patients in India. Patients with confirmed T2DM were included in the study. Patients receiving dialysis or with a history of a kidney transplant were excluded. A validated questionnaire was used to assess knowledge about CKD. Demographic characteristics were presented using descriptive statistics and trends in groups were calculated using the chi-square test. Statistical analysis was performed using SAS v9.4. A total of 323 patients completed the study. The mean age of the patients was 56 ± 11.25 years, and 51.7% were female. Only 33.43% of the patients correctly identified diabetes and hypertension as risk factors for CKD, while 44.27% were aware of the kidney’s function. Statistically significant associations were observed between kidney disease knowledge and education status ( Diabetic kidney disease (DKD), also known as diabetic nephropathy, is a common microvascular complication of type 2 diabetes mellitus (T2DM). DKD is considered a major public health problem for both the patient and the healthcare system. Diabetes and hypertension are two major risk factors of chronic kidney disease (CKD) . Even prReal world studies have also suggested a significant and incremental increase in the economic burden for DKD patients ,15. A reEarly identification and treatments for DKD have been shown to slow, stop, or even reverse the progress of the disease and the decline of kidney function ,22. Yet This cross-sectional study was conducted at the out-patient department of endocrinology, at Hakeem Abdul Hameed Centenary (HAHC) Hospital, Jamia Hamdard, New Delhi, India. The study period was from April 2017–May 2018. Before the initiation of the study, the study protocol was approved by the Jamia Hamdard Institutional Ethics Committee (JHIEC-2017-04/17), India. The study was conducted in full compliance with the Declaration of Helsinki guidelines  and writPatient selection was carried out on the basis of pre-defined inclusion and exclusion criteria. Only participants that fulfilled all the following criteria were included in the study: (a) willingness to participate in the study by providing signed informed consent form; (b) patients aged 18 years or above of either sex; (c) patients who had confirmed T2DM as per their medical records. Participants were excluded from the study if they were receiving dialysis or had a history of a kidney transplant. Patients with incomplete interview information were also excluded.Patients’ demographic characteristics like age, sex, marital status, substance use , and family history were recorded in the case record form. We used modified Kuppuswamy’s socioeconomic scale to assess the economic status of the patients. According to this scale, socioeconomic status was divided into five subscales  on the basis of occupation, education, and family income. The majority of the patients fell into two categories, lower or lower middle class, so the patients were categorized into two classes .The questionnaire used to assess the CKD awareness was adopted from Chow et al. . The que2) [Anthropometric parameters and blood pressure were recorded by the trained nurse personnel. Blood pressure was measured using Richter auscultatory sphygmomanometers at two times: one at 5 min of rest and another at sitting position. Mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded. Patients’ blood pressure records were also reviewed for an accurate reflection of hypertension stage. Patients were classified as hypertensive if the mean SBP was ≥140 mmHg and DBP was ≥90 mmHg or if they had been previously prescribed antihypertensive medication. Patients were further categorized as prehypertensive (SBP 120–139 mmHg or DBP 80–89 mmHg), stage-I hypertensive (SBP 140–159 mmHg or DBP 90–99 mmHg), and stage II hypertensive (SBP ≥ 160 mmHg or DBP ≥ 100 mmHg) . Similar2) . The Nat2) . Due to Blood samples (5 mL) were collected for the routine test. The HbA1c test was performed by using a fully automated HPLC using a BIORAD testing system. Serum creatinine was determined by a modified Jaffe colorimetric method using a fully automated Siemens adiva-1800 chemistry analyzer . Blood glucose levels were determined by using a fully automated Roche Cobas 6000 analyzer . The entire tests were performed in the central pathology lab of Hamdard Institute of Medical Sciences and Research.p value < 0.05 was considered statistically significant. All the statistical analyses were performed using SAS v9.4 .Demographic characteristics were presented using descriptive statistics. Categorical variables were presented by frequency and percentages. Differences and association were computed using the chi-square test and multiple logistic regression. We considered good knowledge (≥4 scores) and poor knowledge (<4 scores) as the dependent dichotomous variable and associations were computed using the chi-square test. Similarly, we considered correct and incorrect responses of every question (domains) as the dependent dichotomous variable and associations with the demographic characteristics and clinical parameters were calculated using the chi-square test. A 2) of patients, while 23.5% and 17.3% of patients had hypertension stage I and stage II, respectively. Demographic characteristics are presented in A total of 365 patients participated in the study, of which 42 were excluded, as they did not fulfill the inclusion criteria. The mean ± SD age of patients was 56 ± 11.25 years, and 51.7% were female. The majority (96.3%) was married and not covered by health schemes or health insurance and bound to pay out-of-pocket for the treatment. More than three-fourth of patients (77.7%) was of lower socioeconomic status and 63.5% were educated. Mean ± SD duration of diabetes was 10 ± 4.39 years and approximately 43.3% of the patients had a family history of diabetes. CKD stage III was prevalent in 34.4%  correctly answered the anatomy part of the questionnaire that only one kidney is required to live a normal life. Less than half of the study population (44.27%) was aware that the filtration of waste products in the blood is the function of the kidney. More than three-fourth of the respondents (77.08%) correctly identified the risk factors of CKD in which only one-third (33.43%) of the respondents were aware of diabetes and hypertension as risk factors. Only 6.5% correctly identified hypertension, diabetes, and inherited condition as risk factors for the development of CKD. While 29.72% of the patients correctly identified symptoms of early kidney disease that might progress to kidney failure, only 4.95% of patients were aware that kidney disease could present without any symptoms. Likewise, a similar percentage of patients correctly identified that medications cannot cure CKD. Only 13.93% of patients were aware that dialysis treatment can be carried out both in a dialysis center and at home. p < 0.05). No significant difference in knowledge score was observed among respondents stratified according to CKD stages, hypertension stages, BMI, gender, co-morbidities, family history, and duration of diabetes and hypertension .Only 21.36% of respondents were found to have good knowledge (≥4 scores) of CKD, and the remaining had poor knowledge (<4 scores) of CKD . RespondBeing older (≥50 years), female gender, substance use , and occupation were non-significantly associated with CKD knowledge in all of the seven domains of the questionnaire. Patients who had a family history of diabetes were more likely to have a higher knowledge of CKD in all the domains except the treatment domain.p < 0.05). Similarly, patients who had a monthly family income more than 20,000 INR were found to have a significantly higher knowledge of CKD (p < 0.05). Poor glycemic control patients were found to have a higher knowledge of the anatomy-related part of the questionnaire, with an odds ratio of 1.71 (95% CI: 1.04 to 2.80) as compared to good glycemic control patients (p < 0.05), in comparison to the stage I CKD patients. A detailed description of the results is presented in p = 0.02) and had a higher family income (AOR 2.26 (95% CI: 1.66–3.14), p = 0.04) was found to have significantly good knowledge of CKD.Patients belonging to the middle class were found to have a significantly higher knowledge of CKD in almost every domain of the CKD questionnaire  of the disease. Patients belonging to the middle class or having higher family income were also found to have a higher knowledge of CKD. Family income is considered to be an important factor affecting the quality of life among non-dialysis CKD patients [In this study, only one-third (33%) of the patients identified hypertension and diabetes as risk factors of CKD. A cross-sectional survey conducted among the non-CKD Malaysian population found half of the population (51.2%) to be aware of diabetes and hypertension as risk factors of CKD . Variatipatients . Previoupatients .No significant difference in knowledge score was observed among respondents stratified according to CKD stages, hypertension stages, BMI, age, gender, and co-morbidities. The findings of Finkelstein et al.  also fouThe study should be interpreted in light of certain limitations. Firstly, the findings of the study cannot be generalized to the entire CKD population, as the study was comprised only of patients from a single center. Secondly, the study was limited in regard to the selection of participants (selection bias); to overcome this, we consecutively selected the participants. There were several notable strengths of this study. Firstly, the study included T2DM and hypertensive patients and performed stratification based on CKD and hypertension stages. Secondly, the knowledge level was correlated with diabetes and hypertension status. The present study also highlighted the needs of CKD education among T2DM and hypertensive patients, since a lack of knowledge about these risk factors was reflected in the study that may have resulted in late referral to the nephrologist and poor participation in the decision-making process. Future studies are warranted to assess the CKD knowledge in a large population-based sample and to frame a CKD awareness model for high-risk patients as well as the general population in order to promote earlier diagnosis, better treatment, and innovative care.We found poor knowledge of kidney disease among T2DM and hypertensive Indian patients. The government should start a CKD awareness programme to deal with this devastating co-morbid condition, which would help in achieving cost-effective prevention.


Kidney Injury Molecule-1 (KIM-1) is a peptide whose release into circulation is specific to tubular injury. This study aimed to estimate levels of kidney injury molecule-1 in diabetic patients with and without kidney disease. And evaluate the role of KIM-1 as an early screening marker of progressive kidney injury.This follow-up study included n=85 subjects from the diabetic clinic of Jinnah Post Graduate Medical Center (JPMC) in collaboration with Aga Khan University from November 2016 till September 2017. They were divided as: i) Group A1 (n=30) participants with diabetes for <5 years without microalbuminuria ii) Group A2 (n= 30) subjects with diabetes for 6-10 years with microalbuminuria; iii) Group B (n=25) subjects as healthy control group. All study participants were followed for 6 months and their blood glucose, urea, creatinine, electrolytes, albuminuria and serum KIM-1 were assayed.High KIM-1 at baseline was present in group A2 patients as compared to controls and group A1 (p<0.001). Higher levels were seen after six months in group A1 along with the presence of micro albuminuria (p<0.001) suggesting kidney damage. Moderate positive association were seen for KIM1 with creatinine levels , and HbA1c  in all patients. While a strong positive association was seen for blood urea nitrogen as a marker for kidney function both at baseline  and follow up . Multiple logistic regression controlling for age showed that KIM1 was independently associated with BUN , creatinine  and HbA1c  in all groups.Rising KIM-1 levels with progressive kidney damage with or without derangement of kidney function is reported in this study. This finding may pave a way towards identifying KIM1 as a prognostic marker for kidney injury. Diabetic nephropathy (DN) is a disease of multifactorial pathogenesis involving several pathways which ultimately progress to reduction of renal functions.4Diagnosis and prognosis of DN largely depends on traditional markers such as estimated glomerular filtration rate (e-GFR) and albuminuria however, the gold standard for diagnosing DN is performing a renal biopsy which is less commonly used due to its invasive nature. Contrarily, micro albuminuria has been used as a diagnostic marker for >30 years for renal injury. But recent data suggests the inability of micro albuminuria changes to foresee the nephropathy progression because in at least 33% of patients renal decline has already ensued.6Kidney injury molecule-1 (KIM-1) is a type I trans membrane glycoprotein (having a six-cysteine immunoglobulin- like domain two N -glycosylation sites)This follow up study included 112 participants between the age of 35-65 year from the diabetic clinic of Jinnah Post Graduate Medical Center (JPMC) in collaboration with Aga Khan University from November 2016 till September 2017. Out of the n=112 patients, we were able to follow n=85 patients with a drop out of 27 patients. Taking the prevalence of diabetic kidney disease as 16.9% margin of error at 5% with confidence interval of 95%, and the power of 80% a minimum sample size of 83 subjects was required to conduct this study.The diagnosis of diabetes was made as per recommendation of American diabetic association13. Midstream Urine was collected for dipstick urine albumin test and spot urine albumin at baseline and 6 month follow up.While patients with chronic systemic disease , history of DM for >10 years, patients on nephrotoxic drugs, anti-inflammatory drugs and on dialysis were excluded from this study. All procedures were in accordance with the ethical standards of the institutional review committee of Basic Medical Sciences Institute, JPMC Karachi (Ref NO.F.2-81-IRB/2017/GENL/419/JPMC), Pakistan. All participants were informed about the study and gave a written and informed consent to participate. These study participants were divided into the following groups: Group A1 (n=30) diabetes for <5 year without microalbuminuria; Group A2 (n=30) diabetes for 5-10 year with microalbuminuria and Group B (n=25) healthy control subjects. All study participants were followed for 6 months. Blood samples from each participants were obtained in the morning after an overnight fast of 8 hours at baseline and at 6 months follow up. Serum was separated and stored at -80°C and was used to measure complete blood count, blood glucose, HbA1c, serum electrolytes, urea/creatinine levels by automation and KIM-1 levels by sandwich ELISA Statistical software SPSS version 21.0 was used for data feeding and analysis. A descriptive statistical analysis of continuous variables was performed. Data on continuous variables i.e. biophysical  and biochemical  parameters were presented as Mean + standard deviation (SD). One way analysis of variance (ANONA) with post hoc Tukeys test was used to compare groups. Pearson coefficient of correlation (r) was used to relate the levels of serum KIM-1. A p-value ≤0.05 was considered significant.Mean age of groups A1 and A2 were comparable (p>0.05) while group B was on the lower age bracket (p=0.01). HbA1c, FBG and RBG levels were higher in diabetic patients as compared to controls (p< 0.005). BUN, Urea creatinine levels were significantly higher in groups A1 and A2 (p<0.01) as compared to controls; whereas no difference was seen for serum electrolytes among groups (p>0.05). The comparison of serum KIM1 levels in study subjects is shown in This present study aimed at finding out the role of KIM -1, a relatively new marker for diabetic nephropathy. KIM-1 levels progressively rose with the duration of diabetes. This manifestation of elevation of detectable level of serum KIM-1 clearly shows that with the progression of diabetes and increasing duration, there is disruption of renal function and increase in severity of renal inflammation (nephropathy).Detection of KIM-1 in patients with a shorter duration of diabetes (Group A-1) in this study and elevated levels at six months follow up shows the progression of kidney injury with time. Diabetes is related to the deranged kidney functions that can remain undiagnosed many times. By measuring the levels at two different point in time and with different degree of disease severity in study participants; we were able to show that KIM-1 levels rises with the progression of kidney disease even when all other parameters such as BUN or creatinine level remain within the normal range. This finding is suggestive that as the kidney damage progressed, more KIM-1 is secreted; therefore KIM-1 levels can be used for early detection of DN in diabetic patients.This is also true in conditions when the micro albuminuria has not been detected in the urine as was the case in this study. Various distinct studies have reported that early renal tubular damage biomarker levels (including urinary KIM-1 levels) are elevated in patients with diabetes, even in those with normal albuminuria.We were however limited to perform renal biopsies to confirm the degree of damage and also were unable to recruit subjects with more than 10 years of disease as most of them suffered from either end stage kidney damage or were on dialysis and were non-compliant to the study follow up.Rising KIM-1 levels with progressive kidney damage with or without derangement of kidney function is reported in this study. This finding may pave a way towards identifying KIM1 as a prognostic marker for kidney injury.SSF & FAK: Designed the study, analyzed the data, wrote the manuscript and approved the final manuscript for submission.GMK & SS: Wrote the manuscript draft and all authors approved the final manuscript for submission.


The diagnosis and prognosis of chronic kidney disease (CKD) currently relies on very few circulating small molecules, which can vary by factors unrelated to kidney function. In end-stage renal disease (ESRD), these same small molecules are used to determine dialysis dose and dialytic clearance. Therefore, we aimed to identify novel plasma biomarkers to estimate kidney function in CKD and dialytic clearance in ESRD. Untargeted metabolomics was performed on plasma samples from patients with a single kidney, non-dialysis CKD, ESRD and healthy controls. For ESRD patients, pre- and post-dialysis plasma samples were obtained from several dialysis modalities. Metabolomics analysis revealed over 400 significantly different features in non-dialysis CKD and ESRD plasma compared to controls while less than 35 features were significantly altered in patients with a single kidney. N,N,N-trimethyl-L-alanyl-L-proline betaine  and pyrocatechol sulfate (AUROC = 0.888) outperformed creatinine (AUROC = 0.745) in accurately identifying patients with a single kidney. Several metabolites accurately predicted ESRD; however, when comparing pre-and post-hemodialysis, TMAP was the most robust biomarker of dialytic clearance for all modalities (AUROC = 0.993). This study describes TMAP as a novel potential biomarker of kidney function and dialytic clearance across several hemodialysis modalities. CKD primarily manifests as a secondary complication of diabetes and hypertension3. Progressive renal damage is irreversible and therefore, patients with CKD must manage the disease along with associated comorbidities. Complications from comorbidities are further exacerbated by the accumulation of toxins that follows declining renal function. These complications begin when the estimated glomerular filtration rate (eGFR) declines to <60 ml/min per 1.73 m2 in stage 3, which represents more than half of all CKD patients5. In advanced CKD, progression to end-stage renal disease (ESRD) requires renal replacement therapy, which can include various hemodialysis (HD) and peritoneal dialysis (PD) modalities or kidney transplantation to sustain life. Patients with late-stage CKD have a 3 to 6-fold increased risk of mortality, which further increases to 8-fold after initiation of dialysis compared to age-matched subjects with normal or moderately decreased kidney function6. Kidney transplantation significantly decreases the risk of mortality and is the only treatment that can effectively reverse toxin accumulation7.Chronic kidney disease (CKD) is estimated to affect 11–13% of the global population8. Several of these uremic toxins are gut-derived and recent studies have associated gut-derived metabolites with cardiovascular events in ESRD. Indeed, indoxyl sulfate, p-cresyl sulfate and phenylacetylglutamine are associated with cardiovascular events that contribute to elevated mortality in ESRD12. Indoxyl sulfate and p-cresyl sulfate are highly protein bound metabolites that are efficiently cleared by tubular secretion in patients with functioning kidneys13, but undergo minimal dialytic clearance8. Conversely, only 20% of phenylacetylglutamine is protein-bound suggesting a higher likelihood of clearance by dialysis14.The accumulation of small molecules that are normally cleared by the kidneys is defined as uremia. The European Uremic Toxin Work Group has identified over 90 uremic metabolites15. It is well established that the use of current biomarkers including creatinine and urea has several limitations; however, the basis for treatments and diagnosis of disease rely on the measurement of these metabolites. Alternatively, the use of a metabolic fingerprint, which includes several metabolites, may provide a more robust characterization of kidney disease status. Metabolites directly reflect genetic, physiological, and environmental changes and can provide a reliable prognostic and diagnostic readout for disease progression16. Several metabolites can be identified and measured in various biological matrices using high-throughput metabolomics. Therefore, using metabolomics to characterize the plasma metabolic fingerprint in kidney disease can provide insight into the complications and high mortality rates that beset patients, and potentially lead to novel treatments.Despite progress in uremic toxin identification, there is a need for new and reliable biomarkers to allow for more accurate estimation of GFR. This is especially important in children and the elderly. Biomarkers that enhance eGFR determination will help identify kidney disease patients earlier and help guide patient therapy during CKD progression, which has been recently highlighted19. Therefore, there is a lack of evidence on the fate of uremic solutes and their clearance across dialysis modalities.To date, studies evaluating uremic metabolites have focused on CKD patients or ESRD patients undergoing conventional HDIn this study, we aimed to identify early biomarkers of reduced kidney function by evaluating the plasma metabolic profiles of patients with a single kidney, which included living kidney donors and kidney transplant recipients, as compared to age-matched controls. Our second objective was to examine plasma metabolic perturbations in non-dialysis dependent (NDD) CKD patients and various dialysis modalities. In addition, we evaluated pre- and post-dialysis plasma to determine metabolites readily cleared by different dialysis modalities.2). All NIPD patients had residual renal function with a median residual volume of 712.5 (500–1250) mL/day. For all other dialysis dependent groups, patients were anuric except for one patient from each of the conventional HD, short daily HHD and intermittent nocturnal HHD groups.Ten subjects were recruited for each of the control, living kidney donor, kidney transplant, conventional HD and nocturnal intermittent peritoneal dialysis (NIPD) groups. There were 20 CKD patients and 5–6 patients in the short daily home hemodialysis (HHD), frequent nocturnal HHD, intermittent conventional HHD and intermittent nocturnal HHD groups Table . The majWe assessed the overall plasma metabolite variation between study groups by principal component analysis (PCA). Control, living donor and transplant plasma samples clustered together in both reverse phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) analysis Fig. . When plThe majority of variation in the PCA containing all groups was influenced by differences between normal renal function and severe renal impairment. Therefore, differences between control and patients with a single kidney (living donor and transplant groups) were further assessed by orthogonal partial least squares discriminant analysis (OPLS-DA). A number of uremic toxins were significantly increased in plasma from living kidney donors after nephrectomy, including N,N,N-trimethyl-L-alanyl-L-proline betaine (TMAP), p-cresyl sulfate, indoxyl sulfate, CMPF, phenylacetylglutamine, pyrocatechol sulfate and creatinine Fig. , Table 2P < 0.05, Fig. To assess the performance of metabolites significantly altered in living donor and transplant patients as biomarkers of altered kidney function, we performed receiver operating characteristic analysis. TMAP, pyrocatechol sulfate, indoxyl sulfate, bilirubin, phenyl sulfate, and dimethyluric acid more accurately predicted decreased kidney function in patients with a single kidney than creatinine Fig. . TMAP anTo assess plasma metabolic disturbances in various dialysis modalities as well as NDD-CKD, we compared NDD-CKD as well as pre-dialysis conventional HD, NIPD and frequent nocturnal HHD to subjects with normal kidney function. Comparisons for each CKD group to control patients were well modelled by OPLS-DA  and uridine were 100% accurate in predicting ESRD . Decreased plasma levels of several gut-derived uremic toxins were also found in post-dialysis hemodialysis samples. In NIPD patients, only the gut-derived uremic toxins phenylacetylglutamine and phenyl sulfate were significantly decreased in plasma after dialysis. All hemodialysis modalities caused a significant decrease in the same 25 plasma metabolites 21. Although creatinine was a consistent biomarker of reduced kidney function in ESRD, several metabolites including TMAP and pyrocatechol sulfate outperformed creatinine as biomarkers of reduced kidney function. Despite the limitations of creatinine as an early stage CKD biomarker, eGFR was more accurate than all other individual plasma biomarkers measured in the study  may allow for the development of improved equations to estimate GFR in earlier stages of CKD. Moreover, a combination of biomarkers identified in this study may provide an improved metabolic fingerprint of early stage CKD.The current guidelines for diagnosis and staging of CKD rely on creatinine as a biomarker to estimate GFRudy Fig. . Howeverudy Fig. . Indeed,23. Moreover, the variation in TMAP over one year for Control RV and Control 1YR was not significantly different than eGFR . Furthermore, indoxyl sulfate and p-cresyl sulfate were the most significantly increased metabolites one year after kidney donation in living donors Fig. . Indoxyl25. Interestingly, fewer metabolites underwent dialytic clearance during NIPD than all hemodialysis modalities. Unlike hemodialysis, NIPD relies more on passive diffusion of solutes, likely decreasing metabolite clearance. However, the dialysis dose, defined by Kt/V, was similar for NIPD compared to hemodialysis modalities. Therefore, the use of several metabolites may provide a more informative assessment of dialysis and aid clinicians when choosing an appropriate modality for their patients.As expected, dialytic clearance of several metabolites was demonstrated when comparing pre- and post-hemodialysis plasma samples. Patients receiving nocturnal hemodialysis had the greatest difference between pre- and post-dialysis samples Fig. ; however26. Therefore, MYL protein degradation may be responsible for the release of TMAP. Further study is necessary to determine the biological origin and potential physiological effects of TMAP.TMAP was the most consistently cleared metabolite by all hemodialysis modalities in our untargeted metabolomics analysis. Although the biological origin of TMAP has not been identified, we suggest that TMAP may be produced from degradation of myosin light chain (MYL) proteins. N,N,N-trimethylalanine is mainly found in myosin light chain (MYL) proteins and in each of the four MYL isoforms , the c-terminus of N,N,N-trimethylalanine forms a peptide bond with prolineThere are several limitations to this study. The sample size for each group was 5 to 20 patients, which limited our ability to control age and sex across groups. The home hemodialysis patient groups were most limited due to the small number of patients prescribed each home hemodialysis modality. In addition, relative quantification was performed for all features in the study followed by confirmation with known standards.27 injury. Our results suggest that TMAP, possibly along with other metabolites, may allow for the derivation of a new equation to provide more accurate estimates of GFR. Furthermore, we demonstrate the pronounced metabolic differences between transplantation and dialysis therapy and the notable inferior NIPD dialytic metabolite clearance compared to hemodialysis.In conclusion, this study provides evidence for novel plasma metabolic biomarkers of reduced kidney function in patients with a single kidney and ESRD as well as insight into the dialytic clearance of plasma metabolites in various hemodialysis modalities. Our key finding is the identification of TMAP as a potential novel plasma biomarker of reduced kidney function in early CKD, ESRD and hemodialytic clearance. Future study of TMAP in a larger patient population will be necessary to evaluate its use as a biomarker in the metabolic fingerprint of kidney disease. The potential impact of comorbidities, medications and other factors on TMAP interindividual variability also remain to be determined. Furthermore, a larger clinical study can determine the performance of TMAP in assessing CKD progression, inflammation and tubular injury including c-reactive protein, kidney injury molecule-1, IL-18, monocyte chemotactic protein-1 and YKL-40Conventional HD, NIPD, short daily HHD, frequent nocturnal HHD, intermittent conventional HHD, intermittent nocturnal HHD, kidney transplant, NDD-CKD, living kidney donors and controls were recruited from the Southwestern Ontario Regional Renal Program between 2010 and 2015. Eligible participants were over the age of 18 and excluded if they had evidence of gastrointestinal disease . Patients maintained their regular diet and were not asked to fast prior to the study. Participant demographic information was recorded on the day of sample collection from electronic health records. eGFR was calculated using the modified diet in renal disease (MDRD) equation. This study was approved by the Western University Health Sciences Research Ethics Board and was conducted according to the Declaration of Helsinki principles. Written informed consent was received from patients prior to inclusion in the study.For dialysis participants, blood was collected in EDTA coated tubes immediately prior to and following the dialysis session. CKD and transplant blood samples were collected during routine clinic visits. Blood from living kidney donors and matched control subjects were collected during a recruitment visit and one year following kidney donation or recruitment visit, respectively. Blood samples were centrifuged at 2000 × G to obtain plasma. Plasma was aliquoted and stored at −80C.29. The acetonitrile contained α-aminopimelic acid  and chlorpropamide  as internal standards for HILIC and RPLC, respectively. Metabolites were separated by HILIC and RPLC followed by Time-of-Flight mass spectrometry on a Waters Xevo-G2S QTof/MS.For metabolomics analysis, plasma samples were thawed at 4 °C and proteins were precipitated using a 3:1 ratio of ice-cold acetonitrile to sample as previously described29. Briefly, capillary and cone voltage were set at 2 kV and 40 V, respectively. The source temperature was 150 °C and the desolvation temperature was maintained at 600 °C. Nitrogen gas for desolvation and the cone were set at 1200 L/h and 50 L/h, respectively. An MSE method was used to acquire ions in the range of 50–1200 m/z alternating between MS1 (no collision energy) and MS2 (collision energy ramp of 15–50 V) with a scan time of 0.1 s. Leucine-enkephalin (100 ng/L) was used as a lockmass set to a flow rate of 10 μl/min. The lockmass was acquired every 10 seconds and averaged over 3 scans to ensure mass accuracy throughout the run.For HILIC analysis, 1 μL of sample was injected onto a Waters ACQUITY UPLC BEH Amide . Samples underwent a subsequent 1:5 dilution in water for RPLC analysis and 5 μL was injected onto a Waters ACQUITY UPLC HSS T3 column . Both columns were maintained at 45 °C and mobile phase flow was set to 0.45 mL/min using a Waters ACQUITY UPLC I-Class system . The mobile phase consisted of water containing 0.1% formic acid (A) and acetonitrile containing 0.1% formic acid (B). Mobile phase conditions for each column are described in Table All samples were run as a single batch for each chromatographic condition and ionization mode . A pooled sample was generated by combining the same volume of all samples into a single vial. The injection of samples was randomized and the pooled sample was injected every 6 samples.29 in R version 3.2.0. Isotopologue Parameter Optimization (IPO version 1.7.4) was performed on pooled injections to optimize peak-picking, retention time correction and grouping parameters prior to XCMS (version 1.42) analysis of samples31. The CAMERA package (version 1.26.0) was used to annotate adducts, isotopes and metabolites that ionize in both positive and negative mode32. Resulting features were normalized to internal standard. Features in pooled samples that exhibited a relative standard deviation greater than 30% over each sample batch were considered unreliable and removed from further analysis. Feature groups of adducts and isotopes were created and the feature with the maximum intensity in each group was chosen for analysis. For features that ionized in both positive and negative ESI modes, the more sensitive ion was used. Subsequently, positive and negative ESI mode data sets were combined for statistical analysis.Masslynx raw data files were converted to mzData files as previously describedData was analyzed using the Kruskal-Wallis ANOVA followed by Dunn’s post-hoc test with the R statistics and DescTools packages. P values were adjusted according to the Benjamini Hochberg procedure and q < 0.05 was considered significantly different. For pre and post-dialysis comparisons, a repeated measures ANOVA with Sidak’s correction was performed.33. Goodness-of-fit and predictive ability was determined based on R2Y and Q2Y values, respectively. Features with variable importance in projection (VIP) values > 1 and correlation (pcorr) values > 0.4 were considered significant and chosen as putative markers for identification.The data was processed by mean centering and pareto scaling in EZinfo 2.0 . PCA was used to visualize general trends in the data. Metabolic differences between individual groups were assessed by orthogonal partial least squares discriminant analysis (OPLS-DA). A multilevel PLS-DA was used for paired analysis to report the within patient differences for pre- and post-dialysis and pre and post kidney donation as previously describedPotential correlations with metabolite level and age were determined using Pearson correlation coefficients and corrected by Benjamini Hochberg procedure. Metabolite level association with sex was determined.34.Features considered for identification were searched in METLIN, Human Metabolome Database (HMDB), Lipid Maps and Chemspider. Spectral matching of feature fragmentation patterns to putative compound fragmentation was determined using MassFragment®. Standards were purchased to confirm metabolite identities, if available. Metabolite identification levels are presented according to the Chemical Analysis Working Group, as previously described35. For biomarker ROC analysis, control RV and living donor RV samples were combined and classified as normal kidney function. The performance of each significantly altered metabolite as a biomarker of early stage CKD was compared to the performance of eGFR. ROC analysis was also performed on metabolites significantly increased in ESRD and significantly cleared by hemodialytic clearance.Metabolites found to be significantly different by multivariate statistics were assessed by univariate ROC analysis using MetaboAnalystSupplementary Info


Do the acute kidney diseases and disorders (AKD) criteria from the Kidney Disease: Improving Global Outcomes guidelines identify patients who do not meet existing criteria for chronic kidney disease (CKD) or acute kidney injury (AKI), and what is the prognosis of these patients?In this cohort study of 1.1 million Canadian residents, AKD criteria identified many patients who did not meet existing criteria for CKD or AKI, and patients with AKD without AKI had overall modestly increased risks of incident and progressive CKD, end-stage kidney disease, and death.The incorporation of AKD into clinical and research initiatives for kidney disease would increase recognition of patients at risk of adverse outcomes who are not identified by current AKI and CKD criteria; however, the clinical importance of AKD remains to be determined. This cohort study investigates whether the Kidney Disease: Improving Global Outcomes guidelines can identify patients with acute kidney diseases and disorders who do not meet existing criteria for chronic kidney disease or acute kidney injury and whether these patients experience adverse outcomes. Abnormal measurements of kidney function or structure may be identified that do not meet criteria for acute kidney injury (AKI) or chronic kidney disease (CKD) but nonetheless may require medical attention. The Kidney Disease: Improving Global Outcomes Clinical Practice Guideline for AKI proposed criteria for the definition of acute kidney diseases and disorders (AKD), which include AKI; however, the incidence and prognosis of AKD without AKI remain unknown.To characterize the incidence and outcomes of AKD without AKI, with or without CKD.Retrospective cohort study including all adult residents in a universal health care system in Alberta, Canada, without end-stage kidney disease (ESKD) and with at least 1 serum creatinine measurement between January 1 and December 31, 2008, in a community or hospital setting. Data analysis took place in 2018.The Kidney Disease: Improving Global Outcomes guideline definitions for CKD, AKI, and AKD based on serum creatinine, estimated glomerular filtration rate, and albuminuria criteria were applied to estimate the proportion of patients with CKD, AKI, and AKD without AKI, and combinations of the conditions. Patients were followed up for up to 8 years  to characterize their risks of mortality, development of new CKD, progression of preexisting CKD, and ESKD.Among 1 109 099 Alberta residents included in the cohort, the mean (SD) age was 52.3 (17.6) years, and 43.0% were male. Findings showed that AKD without AKI was common . In Cox proportional hazards and competing risks models over a median (interquartile range) of 6.0 (5.7-6.3) years of follow-up, AKD without AKI (compared with no kidney disease) was associated with higher risks of developing new CKD , progression of preexisting CKD , ESKD , and death .Criteria for AKD identified many patients who did not meet the criteria for CKD or AKI but had overall modestly increased risks of incident and progressive CKD, ESKD, and death. The clinical importance of AKD remains to be determined. Recognizing this, the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for AKI proposed an operational definition for acute kidney diseases and disorders (AKD) as alterations in kidney function or structure for less than 3 months, which includes AKI  and chronic kidney disease (CKD); AKI is defined as decline in kidney function over 1 week or less, while CKD is defined as alteration of kidney function and structure for more than 3 months.5Identification of AKD, in addition to AKI and CKD, could enable creation of standardized diagnostic algorithms for prompt recognition and appropriate clinical evaluation of these disorders and facilitate comprehensive research, surveillance, and public health initiatives for kidney diseases. However, further research to characterize the incidence and outcomes of AKD has been recommended before adoption, as very few studies to date have evaluated the incremental value of AKD in epidemiological research on kidney disease.The purpose of this study was to characterize the frequency and prognosis of AKD without AKI across a geographic region with universal access to health care and clinical laboratory testing that included serum creatinine (sCr) and albuminuria measurements from all adult residents of the province of Alberta, Canada. We sought to compare the frequency of identification of AKD without AKI with that of CKD, AKI, and combinations of these conditions, as well as to compare the prognosis of these groups for the risks of development or progression of CKD or end-stage kidney disease (ESKD) and risk of death.STROBE) reporting guideline.6 The Conjoint Health Research Ethics Board of the University of Calgary approved the study and granted waiver of patient consent.We followed the Strengthening the Reporting of Observational Studies in Epidemiology  less than 15 mL/min/1.73 m2 or who were receiving treatment for ESKD prior to the index date, identified by physician claims or records for dialysis or a prior kidney transplant in the Northern or Southern or Alberta Renal programs,9 were excluded from the cohort.Outpatient and inpatient laboratory and administrative data with comprehensive coverage of all residents of the province of Alberta were linked as previous described.10 Information on black race was not available; however, misclassification was expected to be minimal, as less than 1% of the Alberta population is of African descent. Albuminuria measurements over the same period were categorized based on results from random urine albumin to creatinine ratio (ACR) or urine dipstick testing as unmeasured, none (ACR <3 mg/mmol or dipstick negative), moderate (ACR 3-30 mg/mmol or dipstick trace to ≥1), and severe (ACR >30 mg/mmol or dipstick ≥2) based on each patient’s median value when multiple measurements were made.11All inpatient and outpatient sCr measurements from January 1, 2004, to June 30, 2016, were identified from laboratories using isotope-dilution mass spectroscopy standardized creatinine assays, and eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation.3 and criteria described by the Acute Disease Quality Initiative consensus statement.5 First, the sCr and eGFR on the index date were compared with sCr and eGFR at least 3 months prior to the index date, and all albuminuria measurements prior to the index date were identified for each patient. Patients were classified as having (1) CKD if both the index eGFR and preceding eGFR were less than 60 mL/min/1.73 m2 or if albuminuria was present prior to the index date; (2) AKI if the index sCr had increased by more than 0.3 mg/dL  over 2 days or increased or decreased 1.5-fold or more from a nadir value over 7 days within the 3 months preceding the index date; (3) AKD without AKI if the index eGFR was less than 60 mL/min/1.73 m2 and the preceding measure was greater than or equal to 60 mL/min/1.73 m2 or there was no preceding sCr or eGFR measurement and albuminuria was absent or not measured prior to the index date; and (4) no kidney disease (NKD) if the index and prior eGFR were both greater than or equal to 60 mL/min/1.73 m2, albuminuria was absent or not measured, and AKI had not been detected. Second, the sCr, eGFR, and albuminuria measurements on the index date were compared with measurements over the 3 months after the index date. Patients with an increase in sCr greater than or equal to 0.3 mg/dL within a 2-day period or a 1.5-fold or greater increase or decrease in sCr from nadir value within a 7-day interval after the index date were classified as having AKI. For patients not already identified with AKI, those with a decrease in eGFR of 35%, an increase in sCr of 50%, or new onset of albuminuria were classified as having AKD without AKI. Those with AKD based on sCr and eGFR criteria but with an acute care diagnosis known to be associated with AKI  on a least 2 measurements separated by more than 3 months among those without preexisting CKD . Progression of CKD was defined as a decline of 35% or greater in eGFR from baseline more than 3 months after the index date among those with preexisting CKD .18 We identified ESKD with initiation of kidney replacement therapy using records for long-term dialysis or kidney transplantation from the Northern and Southern Alberta Renal Programs.Participants were followed up for 8.5 years  from the index date for the study outcomes of time to death of any cause, development of CKD, progression of CKD, and ESKD with initiation of kidney replacement therapy. Mortality was identified from Alberta provincial vital statistics records. For the primary analysis, development of CKD was defined as sustained eGFR less than 60 mL/min/1.73 m2 tests for categorical variables. We determined the proportion of patients with each condition based on the 2 ascertainment periods around the index date as functions of the number of adult residents (aged >18 years) of Alberta with an sCr measurement in 2008, and the estimated adult Alberta population based on 2008 provincial census data.We compared baseline characteristics according to the NKD, CKD, AKI, and AKD states using analysis of variance for continuous variables and χ19 competing risk regression to model death as a competing risk and obtain sub–hazard ratios for the outcomes of CKD development, CKD progression, and ESKD. Participants with preexisting CKD were excluded from the analyses for development of CKD, and patients without preexisting CKD were excluded from the analyses for progression of CKD. We fit unadjusted models, as well as models with stepwise progressive adjustment for age and sex only and adjusted for age, sex, social assistance, and individual comorbidities. We determined that the proportional hazard assumption for between-group comparisons with NKD was satisfied by using graphical methods and testing time-dependent effects. We compared the impact of adding AKD identification to improve discrimination for clinical outcomes based on the integrated discrimination improvement, and to reclassify patients into higher- or lower-risk categories based on risk thresholds of less than 1%, 1% to 4.9%, 5% to 9.9%, 10% to 19.9%, and greater than or equal to 20% using the continuous net reclassification index.20 Our threshold for statistical significance was set at 2-sided P < .05.Time from index date to each of the clinical outcomes, emigration from the province, or the study end date was determined and plotted using cumulative incidence curves. Outcomes of patients with CKD, AKI, AKD without AKI, and combinations of these states were examined relative to patients with NKD using Cox proportional hazards regression for time to all-cause mortality regardless of intermediate events. To avoid overestimating the incidence of nonfatal outcomes that can result from censoring for death using Cox regression, we used Fine and Gray2 with a decline greater than 10 mL/min/60 mL/min/1.73 m2 from previous baseline or subsequent increase in sCr by 50% within 7 days to 3 months. We also conducted 2 additional subgroup analyses to avoid potential misclassification of unrecognized CKD as AKD; the first restricted the cohort to those with prior eGFR greater than 60 mL/min/1.73 m2 (excluding patients with no prior sCr measurements). The second was restricted to patients without albuminuria at the index date .We performed a sensitivity analysis based on an alternative definition for AKD requiring an index eGFR less than 60 mL/min/1.73 m2, while 575 263 (51.9%) were identified without preexisting albuminuria.There were 1 109 099 eligible adult residents without ESKD with an index sCr measurement in Alberta in 2008 , progression of CKD (for those with preexisting CKD), and ESKD treated with kidney replacement therapy is shown in Identifying patients with AKD without AKI and CKD with AKD without AKI  improved the ability to correctly classify patients for risk of all outcomes (eTable 7 in the In this large cohort study of more than 1 million people with sCr and albuminuria measurements, we examined the incidence and outcomes of AKD without AKI in relation to CKD, AKI, and combinations of these disorders. We found that AKD without AKI was common and, similar to CKD and AKI, was associated with increased risks of death, frequent development or progression of CKD, and less frequent but increased risk of progression to ESKD compared with patients with NKD over 6 years following identification. The highest risks of death, progression of CKD, and ESKD were observed among patients with AKD without AKI in combination with CKD. Adding AKD without AKI to the classification of kidney disorders improved identification of patients who died or developed new onset or progressive CKD or ESKD. These findings illustrate that AKD without AKI is frequently observed, identifies patients not recognized by current AKI and CKD criteria, and is associated with overall modestly increased risk of long-term adverse outcomes.21 In principle, AKD without AKI might be caused by episodes of decreased kidney perfusion or parenchymal kidney diseases in which the decrement in GFR is too small or evolves too slowly to raise the sCr within the time limit of the AKI criteria, or to be detected with infrequent sCr measurements. Criteria for AKD have been applied previously in case series of patients who underwent kidney biopsy during hospitalization.22 Acute tubular necrosis was almost twice as common among patients with AKI as it was among those with AKD without AKI, while acute tubulointerstitial nephritis was more common among patients with AKD without AKI than among those with AKI. Crescentic glomerulonephritis and thrombotic microangiopathy were also observed in both AKI and AKD. Patients with biopsy-proven crescentic glomerulonephritis also presented with criteria for AKD without AKI in more than 10% of cases in another case series.23 These findings provide some insight into the nature of underlying kidney diseases that may fall within the spectrum of AKD and address points that have been emphasized in commentaries from the National Kidney Foundation Kidney Diseases Quality Initiative24 and Canadian Society of Nephrology,25 including recognition of the broad range of kidney diseases that may fall within AKD criteria and the need for clinical recognition and investigation to distinguish the array of underlying causes of disease and provide prompt treatment. However, the pathology identified in association with AKD in these reports is likely biased by selection of patients requiring a kidney biopsy. Our study provides novel information on the relatively high incidence of AKD, which suggests that most cases are unlikely to be attributable to glomerulonephritis or interstitial nephritis and highlights the prognostic significance of this presentation of kidney disease that is distinct from the criteria for AKI and CKD.Although AKD criteria were first published in the KDIGO clinical practice guideline for AKI in 2012, there is limited information on the causes and clinical characteristics of patients who develop AKD without AKI.2 resulting from physiological and measurement variability may contribute to identification of many cases of AKD based on the current KDIGO criteria. Albuminuria quantification may vary between measurements and was less frequently measured in our cohort, which could also lead to misclassification of CKD vs AKD and underestimate the prevalence of CKD while overestimating the prevalence of AKD. Hematuria, pyuria, other abnormalities of urine sediment, and imaging findings were not ascertained but are also important criteria for identification of kidney diseases.21 Like CKD and AKI, there may be a variety of causes of AKD, and not all reductions in eGFR may be associated with poor outcomes or require the same investigations or management. For example, some interventions, such as antihypertensive medication use for intensive blood pressure lowering, may reduce kidney function but improve outcomes.27 Third, 10% of patients in our study who met criteria for AKD did not have a preceding sCr measurement, so it is possible that some patients classified with AKD truly had AKI or CKD but did not have sufficient repeated sCr measurements to meet the diagnostic criteria for these conditions at time of testing. Patients with AKI and AKD were more likely to be hospitalized and have follow-up sCr and eGFR testing, which may have increased the opportunity to detect incident or progressive CKD in this cohort. However, our results reflect how abnormal kidney function is recognized in real-world care and provide comprehensive information for patients from a geographic region with universal access to health care. Fourth, the study was conducted in a Canadian province, and results may not be generalizable to populations with different ethnic distributions or determinants of access to care and laboratory testing.There are limitations to our study. First, our study does not characterize clinical features, including underlying causes and treatments for patients who met criteria for AKD, and thus does not identify causes of kidney disease or resolve how patients with AKD should be cared for in clinical practice. However, our findings do provide epidemiological insight into the incidence and prognostic relevance of AKD that can help inform whether changes to the classification systems for kidney diseases proposed in the KDIGO Clinical Practice Guideline for AKI should incorporate criteria for AKD within approaches to surveillance and detection of kidney disease in clinical care. Second, we focused on the use of sCr, eGFR, and albuminuria criteria for identifying CKD, AKI, and AKD without AKI in this study in keeping with the classification scheme proposed in the KDIGO guideline. Serum creatinine is frequently measured in clinical practice but may be influenced by non-GFR determinants, including age, sex, race, body composition, inflammation, diet, and medications. Relatively small changes in eGFR from a baseline eGFR greater than 60 mL/min/1.73 mWe found that AKD without AKI was common among patients in a universal health system who underwent sCr and albuminuria testing and, like CKD and AKI, AKD was associated with risk of adverse outcomes. These findings suggest that the incorporation of AKD into clinical and research initiatives for kidney disease would increase recognition of patients at risk of adverse outcomes who are not identified by current AKI and CKD criteria. However, the clinical characteristics of AKD remain to be determined, and further research is needed to characterize the clinical causes of AKD, identify their potential varying associations with outcomes, and understand how clinicians should care for people with AKD.


To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD).TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods . It was then validated using an external dataset of MRI scans from 65 patients with ADPKD.2) with ADPKD had a wide range of TKV (258–3680 ml) measured manually. The Sheffield TKV Tool was highly accurate , reproducible  and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology .Sixty-one patients  to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD.• Measuring TKV manually is time consuming and laborious.• TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.• The online version of this article (10.1007/s00330-018-5918-9) contains supplementary material, which is available to authorized users. Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease and fourth leading cause of end stage renal failure (ESRF) worldwide , 2. It iChanges in estimated glomerular filtration rate (eGFR) are conventionally used to measure loss of kidney function. In ADPKD, however, eGFR does not change until the later stages of disease due to compensatory glomerular hyperfiltration  thus limThe current gold standard method for measuring TKV from MRI involves manual tracing of the kidney boundary on each MRI slice using dedicated software and summing the product of area measurements and slice thickness . This isSeveral semi-automated methods and estimating equations have been developed to address the challenge of measuring TKV in ADPKD  summari. Fully aSheffield TKV Tool) for measuring TKV in a representative group of patients with ADPKD and a wide range of TKV.In this paper, we describe the development and validation of a rapid, high-performance semi-automated method (Sixty-one patients with ADPKD and stage 1–3 chronic kidney disease (CKD) attending a specialist PKD clinic at Sheffield Kidney Institute consented to an unenhanced abdominal MRI for measurement of TKV. Renal function (eGFR ) was meaKidney MRI scans were coronal true fast imaging with steady-state free precession (TRUFI) T2-weighted sequences (Siemens Avanto 1.5-T scanner) with the following parameters: 4 mm slice thickness, 0 mm slice gap, 2 ms echo time, 3.99 ms repetition time, 60° flip angle, 0.68 × 0.68 mm in plane resolution and 512 × 512 acquisition matrix. The TRUFI sequence acquisition time was 138 s. This imaging sequence was selected because its images enabled the clearest delineation of the kidney and associated cysts from neighbouring tissues.The Sheffield TKV Tool was implemented using a MATLAB 2016b (MathWorks) framework. The right or left kidney was segmented individually from coronal MRI slices using image processing techniques  used in hybrid LSM [ϕ is the level set function. I is the image to be segmented; H(ϕ) is smooth Heaviside function which considers area around contour; g is image edge (boundary) map where contour should be attracted and is set to be c controlling the slope. Ω is the image domain and α and β are predefined weights to balance two terms. The first term on the right-hand side of the equation defines that region to be segmented should have intensity greater than μ which is set to 50. Parameters α and β are set to 0.01 and 100, respectively. Evolving contour (level set function) is stopped after 100 iterations to obtain expected kidney region outline ) or class 2  based on their kidney morphology on MRI as defined in the Mayo Imaging classification . Class 1A). Analyst A was blinded to the development and TKV measurements of the Sheffield TKV Tool. Consistent with standard methods of manually measuring TKV [B) used the Sheffield TKV Tool (BSheffieldTKVTool1) to measure the right (R) and left (L) kidney volumes (KV) separately and compared its performance to that of the mid-slice method [To obtain reference TKV measurements, the gold standard method of manual segmentation was performed using MIM Maestro on T2 TRUFI coronal MR images of all 61 patients by an experienced image analyst  cases that were used to optimise the parameters of the MIROS Tool , particB manually  measured TKV for 40 kidneys from a representative subset of 20 patients (TKV 258–3680 ml) for comparison with TKV already measured in this dataset by analyst Amanual. Analyst B repeated the manual segmentation on the same dataset after 1 month  to assess the intra-operator variability.To assess the inter-operator variability of manual segmentation, analyst A measured TKV for the same dataset of 20 patients (ASheffieldTKVTool). The intra-operator variability of the Sheffield TKV Tool was assessed by analyst B on two occasions  separated by 1 month.To assess the inter-operator variability of the Sheffield TKV Tool, analyst T2-weighted MRI renal images of 65 ADPKD patients who participated in the DIPAK-1 study  were acqB) to measure TKV on this dataset in a blinded manner and its performance relative to the reference manual segmentation values was then assessed.Kidney boundaries were manually traced using commercially available software AnalyzeDirect 11.0  and kidney volumes were calculated from the set of contiguous images by summing the products of the area measurements within the kidney boundaries and slice thickness. Non-renal parenchyma, e.g. the renal hilum, was excluded from measurement. Importantly, all measurements were performed by readers blinded for patient number and previous TKV measurements . Separatρ (type I error), a power value (type II error), expected mean, SD and maximum acceptable percentage (%) volume difference between reference and TKV Tool measurements [ρ of 0.05, a power of 0.80, the minimum required number of TKV measurement pairs was 60. Bias (mean) obtained from different methods was assessed using paired sample t test.Baseline demographics are reported as mean ± standard deviation (SD). Agreement was assessed using Bland-Altman (BA) analyses to determine the mean difference between TKV for the various methods. Both actual and percentage (%) difference in volume were evaluated. For development of the Sheffield TKV Tool, sample size was calculated for BA agreement assessment  using thurements . Based ourements , 18, theurements . Thus, fSpatial overlap between segmentation outlines was determined using the dice similarity coefficient (DSC) . A DSC v2 (33–175 ml/min/1.73 m2) participated in this study. They represented a wide spectrum of disease with gold standard TKV ranging (mean ± SD) between 258 and 3680 ml (1167 ± 798 ml). Their kidneys had variable morphology   with a mean age of 45 ± 14 (20–77) years and eGFR of 76 ± 32 ml/min/1.73 mTable t test however showed no statistically significant difference (ρ (2-tailed) = 0.008) between bias obtained using the Sheffield TKV Tool and MIROS method. The Sheffield TKV Tool demonstrated no particular bias (± ≤ 0.5%) and had a narrower 95% confidence interval. In contrast, the estimating equations were less reliable with a positive bias (overestimation of TKV) and more variable results .In terms of volume error Table , Fig. 3,Figure 2 with a representative spectrum of disease with gold standard TKV ranging (mean ± SD) between 400 and 7431 ml (2408 ± 1806 ml) were used. These patients also had more severe associated polycystic liver disease. Table The MRI of 65 patients  with ADPKD, mean age 50 ± 8 (26–61) years and eGFR of 52 ± 13 (33–78) ml/min/1.73 mThe mean volume error Table , Fig. 4cThe average time taken to measure TKV by manual segmentation was 44 ± 18 min. In comparison, the Sheffield TKV Tool took 5.6 ± 1.5 min on the Sheffield cohort and 5 ± 3 min on the external validation cohort. The mean time to perform ellipsoid, mid-slice and MIROS methods was 4.5 ± 0.6, 3.2 ± 0.8 and 6.5 ± 2.2 min, respectively.Table We report a new semi-automated method (Sheffield TKV Tool) to measure TKV from MRI in ADPKD. The tool can run independently as a self-contained package and requires minimal user interaction to define a kidney outline from a coronal T2-weighted slice. Compared to manual segmentation, it performed with high accuracy in an unselected group of patients with a wider spectrum of disease than previously reported, as represented by kidneys with highly variable morphology, cyst burden, intensity distribution and extensive range of TKV (range 258–7431 ml). Importantly, it performed with high precision with no bias in measurements of the right or left kidneys, high agreement  and reproducibility (1.1 ± 2.9%) compared to the manual method. Validation in a representative external group of 65 patients with ADPKD confirmed good performance (mean volume error 3.45 ± 3.96%) with the positive bias caused by the method of manual segmentation which includes a slight border around the kidney.A direct comparison between the Sheffield TKV Tool and two estimation methods (ellipsoid and mid-slice) in the same patients showed that it was more accurate and precise than either. Unlike the estimation methods, it also clearly outlined the kidney boundaries: these could be used as a precursor for the segmentation of renal cysts . The SheThe Sheffield TKV Tool also performed better (TKV difference − 0.3 ± 3.8%) when compared to other published semi-automated methods , 28 Tab. Turco eIn terms of efficiency, the reduced time required for the Sheffield TKV Tool would enable 8–10 TKV measurements to be performed in the time taken for a single manual TKV measurement. It performed particularly well on larger kidneys and in a wider range of kidney volumes (258–7431 ml) than previously reported largest 2837 ml) , 28.37 ml , 2Liver cysts can cause considerable challenges when measuring TKV because of the close proximity of the liver with the right kidney and less often the left kidney (with much enlarged polycystic livers), since the distribution of cysts between the two neighbouring organs can be indistinguishable. However, in most cases, the Sheffield TKV Tool was able to distinguish between liver and kidney cysts even when the visual boundaries appeared vague. There was no influence of imaging classification on the performance of the Sheffield TKV Tool: it performed equally well in class 1 and class 2 patients. This is a considerable advantage since no patients requiring TKV measurements need to be excluded.The current limitations of the Sheffield TKV Tool are a slight undersegmentation and measurement of TKV in kidneys associated with exophytic cysts or oversegmentation associated with large blood vessels especially when the kidney regions are small Fig. j. It hasIt has not yet been tested for serial measurements of TKV measurements to monitor natural history or response to treatment. A future goal is therefore to apply image registration techniques for this purpose . FinallyIn summary, the accuracy, reproducibility and rapidity of the Sheffield TKV Tool highlight its potential for wider adoption to measure TKV as a prognostic marker routinely in patients with ADPKD.ESM 1(DOCX 529 kb)