Creatinine is a metabolic product of creatine phosphate in muscles, which provides energy to muscle tissues. Creatinine has been considered as indicator of renal function specifically after dialysis, ...thyroid malfunction and muscle damage. The normal level of creatinine in the serum and its excretion through urine in apparently healthy individuals is 45–140 μM and 0.8–2.0 gm/day respectively. The level of creatinine reaches >1000 μM in serum during renal, thyroid and kidney dysfunction or muscle disorder. A number of conventional methods such as colorimetric, spectrophotometric and chromatographic are available for determination of creatinine. Besides the advantages of being highly sensitive and selective, these methods have some drawbacks like time-consuming, requirement of sample pre-treatment, high cost instrumental set-up and skilled persons to operate. The sensors/biosensors overcome these drawbacks, as these are fast, easy, cost effective and highly sensitive. This review article describes the classification, operating principles, merits and demerits of various creatinine sensors/biosensors, specifically nanomaterials based biosensors. Creatinine biosensors work optimally within 2–900 s, potential range 0.1–1.0 V, pH range 4.0–10.0, temperature range 25–35 °C and had linear range, 0.004–30000 µM for creatinine with the detection limit between 0.01.01 µM and 520 µM. These biosensors measured creatinine level in sera and urine samples and had storage stability between 4 and 390 days, while being stored dry at 4 °C. The future perspective for further improvement and commercialization of creatinine biosensors are discussed.
•Review illustrates classification of creatinine biosensors with their merits and demerits.•Creatinine biosensors work ideally within 2–900 s, between pH, 4.0–10.0 and temp 25–35 °C and linear range, 0.004–30,000 µM.•Detection limits of creatinine biosensors are between 0.01 μM and 520 µM.•Fabrication of low cost NPs based creatinine biosensors along with their improved sensitivity and stability has been discussed.•The future research could be focused on miniaturization of creatinine biosensors.
Albuminuria is a well-known predictor of chronic kidney disease in patients with type 2 diabetes mellitus (DM). However, proteinuria is associated with chronic complications in patients without ...albuminuria. In this retrospective cohort study, we explored whether non-albumin proteinuria is associated with all-cause mortality and compared the effects of non-albumin proteinuria on all-cause mortality between patients with and without albuminuria. We retrospectively collected data from patients with type 2 DM for whom we had obtained measurements of both urinary albumin-to-creatinine ratio (UACR) and urinary protein-to-creatinine ratio (UPCR) from the same spot urine specimen. Urinary non-albumin protein-creatinine ratio (UNAPCR) was defined as UPCR-UACR. Of the 1809 enrolled subjects, 695 (38.4%) patients died over a median follow-up of 6.4 years. The cohort was separated into four subgroups according to UACR (30 mg/g) and UNAPCR (120 mg/g) to examine whether these indices are associated with all-cause mortality. Compared with the low UACR and low UNAPCR subgroup as the reference group, multivariable Cox regression analyses indicated no significant difference in mortality in the high UACR and low UNAPCR subgroup (hazard ratio HR 1.189, 95% confidence interval CI 0.889-1.589, P = 0.243), but mortality risks were significantly higher in the low UACR and high UNAPCR subgroup (HR 2.204, 95% CI 1.448-3.356, P < 0.001) and in the high UACR with high UNAPCR subgroup (HR 1.796, 95% CI 1.451-2.221, P < 0.001). In the multivariable Cox regression model with inclusion of both UACR and UNAPCR, UNAPCR ≥ 120 mg/g was significantly associated with an increased mortality risk (HR 1.655, 95% CI 1.324-2.070, P < 0.001), but UACR ≥ 30 mg/g was not significantly associated with mortality risk (HR 1.046, 95% CI 0.820-1.334, P = 0.717). In conclusion, UNAPCR is an independent predictor of all-cause mortality in patients with type 2 DM.
Purpose
To evaluate equations for estimation of glomerular filtration rate (GFR) and measured urinary creatinine clearance, compared to measured GFR in critically ill patients.
Methods
GFR was ...measured using inulin clearance. Multiple blood samples were collected per patient for determination of serum creatinine, cystatin C and inulin. GFR was estimated by the use of the following estimation equations (eGFR): four commonly used creatinine-based equations Cockcroft–Gault, Modification of Diet in Renal Disease (both the short and long formula) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), five cystatin C based estimation equations (Hoek, Larsson, Filler, Le Bricon, CKD-EPIcys) and one equation combining cystatin C and serum creatinine (CKD-EPIcr-cys). In addition we measured urinary creatinine clearance. Bias, precision and accuracy of all estimates were compared to those of the inulin clearance.
Results
Data were collected from 83 patients, of whom 68 were considered evaluable. The median age was 58 years interquartile range (IQR) 39–68. The median inulin clearance was 80 mL/min/1.73 m
2
(IQR 31–114). Equations based on creatinine had much bias and poor precision and accuracy. Measured urinary creatinine clearances overestimated GFR. Equations based on cystatin C were free of bias, but also had limited precision and accuracy.
Conclusions
In this cohort of patients, estimates of GFR had low accuracy and precision. Cystatin C based formulas, especially CKD-EPIcr-cys, showed limited bias; however, the accuracy and precision of these estimates were still insufficient. Measured urinary creatinine clearance overestimates GFR, but may provide a cheap alternative, when this is taken into account.
Aims/hypothesis
We examined whether candidate biomarkers in serum or urine can improve the prediction of renal disease progression in type 1 diabetes beyond prior eGFR, comparing their performance ...with urinary albumin/creatinine ratio (ACR).
Methods
From the population-representative Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) we sampled 50% and 25% of those with starting eGFR below and above 75 ml min
−1
1.73 m
−2
, respectively (
N
= 1629), and with median 5.1 years of follow-up. Multiplexed ELISAs and single molecule array technology were used to measure nine serum biomarkers and 13 urine biomarkers based on our and others’ prior work using large discovery and candidate studies. Associations with final eGFR and with progression to <30 ml min
−1
1.73 m
−2
, both adjusted for baseline eGFR, were tested using linear and logistic regression models. Parsimonious biomarker panels were identified using a penalised Bayesian approach, and their performance was evaluated through tenfold cross-validation and compared with using urinary ACR and other clinical record data.
Results
Seven serum and seven urine biomarkers were strongly associated with either final eGFR or progression to <30 ml min
−1
1.73 m
−2
, adjusting for baseline eGFR and other covariates (all at
p
<2.3 × 10
−3
). Of these, associations of four serum biomarkers were independent of ACR for both outcomes. The strongest associations with both final eGFR and progression to <30 ml min
−1
1.73 m
−2
were for serum TNF receptor 1, kidney injury molecule 1, CD27 antigen, α-1-microglobulin and syndecan-1. These serum associations were also significant in normoalbuminuric participants for both outcomes. On top of baseline covariates, the
r
2
for prediction of final eGFR increased from 0.702 to 0.743 for serum biomarkers, and from 0.702 to 0.721 for ACR alone. The area under the receiver operating characteristic curve for progression to <30 ml min
−1
1.73 m
−2
increased from 0.876 to 0.953 for serum biomarkers, and to 0.911 for ACR alone. Other urinary biomarkers did not outperform ACR.
Conclusions/interpretation
A parsimonious panel of serum biomarkers easily measurable along with serum creatinine may outperform ACR for predicting renal disease progression in type 1 diabetes, potentially obviating the need for urine testing.
Background
Since the daily creatinine excretion rate (CER) is directly affected by muscle mass, which varies with age, gender, and body weight, using the spot protein/creatinine ratio (Spot P/Cr) ...follow‐up of proteinuria may not always be accurate. Estimated creatinine excretion rate (eCER) can be calculated from spot urine samples with formulas derived from anthropometric factors. Multiplying Spot P/Cr by eCER gives the estimated protein excretion rate (ePER). We aimed to determine the most applicable equation for predicting daily CER and examine whether ePER values acquired from different equations can anticipate measured 24 h urine protein (m24 h UP) better than Spot P/Cr in pediatric kidney transplant recipients.
Methods
This study enrolled 23 children with kidney transplantation. To estimate m24 h UP, we calculated eCER and ePER values with three formulas adapted to children (Cockcroft‐Gault, Ghazali‐Barratt, and Hellerstein). To evaluate the accuracy of the methods, Passing‐Bablok and Bland‐Altman analysis were used.
Results
A statistically significant correlation was found between m24 h UP and Spot P/Cr (p < .001, r = 0.850), and the correlation was enhanced by multiplying the Spot P/Cr by the eCER equations. The average bias of the ePER formulas adjusted by the Cockcroft‐Gault, Ghazali‐Barratt, and Hellerstein equations were −0.067, 0.031, and 0.064 g/day, respectively, whereas the average bias of Spot P/Cr was −0.270 g/day obtained by the Bland‐Altman graphics.
Conclusion
Using equations to estimate eCER may improve the accuracy and reduce the spot urine samples’ bias in pediatric kidney transplantation recipients. Further studies in larger populations are needed for ePER reporting to be ready for clinical practice.
To compare the efficacy of tacrolimus (TAC) and mycophenolate mofetil (MMF) for the initial therapy of lupus nephritis (LN).
This is an open randomised controlled parallel group study.
Adult patients ...with biopsy-confirmed active LN (class III/IV/V) were randomised to receive prednisolone (0.6 mg/kg/day for 6 weeks and tapered) in combination with either TAC (0.06-0.1 mg/kg/day) or MMF (2-3 g/day) for 6 months. Good responders were shifted to azathioprine for maintenance. The primary outcome was the rate of complete renal response (CR) at 6 months and the secondary outcomes included partial renal response, renal flares and decline of renal function over time.
150 patients (92% women; aged 35.5±12.8 years; 81% class III/IV) were randomised (76 MMF, 74 TAC). At month 6, the rate of CR was 59% in the MMF and 62% in the TAC group (treatment difference: 3.0% (-12%, 18%); p=0.71). Major infective episodes occurred in 9.2% patients treated with MMF and in 5.4% patients treated with TAC (p=0.53). Maintenance therapy with azathioprine was given to 79% patients. After 60.8±26 months, proteinuric and nephritic renal flares developed in 24% and 18% of patients in the MMF group and 35% (p=0.12) and 27% (p=0.21) in the TAC group, respectively. The cumulative incidence of a composite outcome of decline of creatinine clearance by ≥30%, development of chronic kidney disease stage 4/5 or death was 21% in the MMF and 22% in the TAC group of patients (p=0.35).
TAC is non-inferior to MMF, when combined with prednisolone, for induction therapy of active LN. With azathioprine maintenance for 5 years, a non-significant trend of higher incidence of renal flares and renal function decline is observed with the TAC regimen.
Hospital Authority Research Ethics Committee Clinical Trial Registry (HARECCTR0500018; Hong Kong) and US ClinicalTrials.gov (NCT00371319).
Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and ...longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.
This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation.
Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation.
These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.
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