8.7% were on cyclosporine and 63.6% were on tacrolimus. There has also been a trend towards decreasing prednisone dose after kidney transplantation; corticosteroids are well known to promote bone loss (4, 5). In our study the median steroid dose in the first 90 days after transplant in 1997 was 27.6 mg/day compared to 20.2 mg/day in 2009. In recent years there may be an increase in the number of recipients prescribed fracture prevention therapy (bisphosphonates and vitamin D); the KidneyDisease Improving Global Outcomes for Chronic KidneyDisease-Mineral and Bone Disorder guidelines recommend that bisphosphonates and vitamin D are prescribed to recipients who have an estimated glomerular filtration rate >30 mL/min/1.73 m 2 and low bone mineral density (1). In our study, of recipients eligible for prescription drug use, 5.5% of recipients who received their transplant in 1997 were prescribed bisphosphonates in the first three years after transplant compared to 11.5% in 2009, but the number of fracture events was too small to detect any impacts from these interventions. Therefore, including recipients who more recently transplanted may have decreased the overall incidence rate. Second, to increase the accuracy of our fracture definition it was necessary that hip and forearm fracture diagnostic codes were accompanied by associated procedural codes (37, 41, 42); failure to include procedural codes may lead to over-ascertainment of fractures.
The glomerular filtration rate was estimated using the Chronic KidneyDiseaseEPIdemiology collaboration equation (CKD-EPI) . This was analysed as a con- tinuous variable and in three classes eGFR < 90; 90–105; > 105 ml/min/1.73m 2 ; the threshold 90 ml/min/1.73m 2 was chosen as it is conventionally used to indicate kid- ney disease without chronic kidney failure ; the group with eGFR > 105 ml/min/1.73m 2 includes half of our healthy population. At baseline, the UACR was de- termined on two separate occasions several weeks apart, and the mean UACR calculated and analysed in three classes: undetected, detected below and detected above the sex-specific median.
Abstract: Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidneydisease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidneydisease and a control. These metabolites are likely to be robust indicators of kidneydisease processes, and therefore potential biomarkers, warranting further investigation.
BMI: Body mass index; CISTA: Research center on health, work and environment; CKD: Chronic kidneydisease; CKD-EPI: Chronic kidneydiseaseepidemiology collaboration; CKDu: Chronic kidneydisease of undetermined cause; eGFR: Estimated glomerular filtration rate; ESRD: End-stage renal disease; GFR: Glomerular filtration rate; IQR: Interquartile range; ISICv4: International standard industrial classification of all economic activities, Rev4; MeN: Mesoamerican nephropathy; NCDs: Non-communicable diseases; NSAIDs: Non-steroidal anti-inflammatory drugs; SCr: Serum creatinine; SD: Standard deviation; UK: United Kingdom; UNAN- León: National Autonomous University of Nicaragua, León; USD: United States Dollars; UTI: Urinary tract infection
ACR- Albumin to creatinine ratio; BUN – Blood urea nitrogen; CKD – Chronic Kidneydisease; CKD-EPI – Chronic Kidneydisease – Epidemiology Collaboration; CKDu – Chronic kidneydisease of unknown aetiology; COPCORD – Community acquired program for the control of rheumatic disease; E-GFR – Estimated glomerular filtration rate; ESRD – End stage renal disease; IDMS – Isotope dilution mass spectroscope; JOABPEQ – Japanese orthopaedic association back pain evaluation questionnaire; MDRD – Modification of Diet in Renal disease; MOH – Ministry of Health; NCD – Non-communicable diseases; NCP – North Central Province; NSAID’s – Non-steroidal anti-inflammatory drugs; NWP – North Western Province; WHO – World Health Organization.
Clinical data were obtained by medical record review, including demographic factors, liver disease etiology, and co-morbid conditions (all defined by documentation in the medical record). Uric acid and serum creatinine levels assayed by the John Hopkins Hospital Laboratory using standard clinical laboratory techniques were re- trieved from the laboratory database. The Model for End-Stage Liver Disease (MELD) score at OLT was cal- culated in accordance with the United Network for Organ Sharing (UNOS) formula . To calculate the MELD score, serum bilirubin, creatinine, and Inter- national Normalized Ratio (INR) values less than 1.0 were set to 1.0 to preclude negative values, and serum creatinine upper-limit values were set at 4.0 if the pa- tient required renal replacement therapy prior to trans- plantation. Renal function was assessed by estimating glomerular filtration rate (eGFR) using the Chronic KidneyDiseaseEpidemiology Collaboration (CKD-EPI) formula . Categories of eGFR were created according to standard classifications . Information regarding the primary calcineurin inhibitor (CNI) used after
We developed a structured survey instrument designed to test different factors related to TM practices among community members (Additional file 1: Appendix 1). The development of this survey has been described else- where, but in brief, the instrument was drafted by local and non-local experts from multiple disciplines includ- ing medicine, epidemiology, sociology, anthropology, and public health. It was independently translated into Swahili by two native speakers, and we conducted joint reviews of each version with a focus on the codability of words and concepts with difficult translations. To ensure the content validity of the survey instrument, we piloted it through multiple qualitative sessions. This was an it- erative process that involved several adjustments to the instrument as new themes and ideas emerged through- out the sessions. Many of the survey items and response categories were added directly based on the results of these qualitative piloting sessions . In its final form, the survey instrument included nine items. It comprised open-ended questions related to types of TMs used by community members as well as close-ended questions related to frequency of use, reasons for use, modes of use, modes of access, and conditions treated by TMs.
Patients were included only if they had at least one outpatient estimated glomerular filtration rate (eGFR) estimated by the Chronic KidneyEpidemiology (CKD- EPI) equation during their observation period. For the CKD study, we excluded patients with an eGFR < 60 ml/ min/1.73 m 2 at entry (pre-existing CKD patients). Cases were identified as those patients who were subsequently diagnosed with CKD (ie, had an observed eGFR < 60). Controls were identified as those patients who were not diagnosed with CKD during their observation period. Records were retrospectively reviewed starting from the time of CKD diagnosis for cases, and from the time of last observation for controls to assess whether or not risk factors existed in the history of each patient. For the mortality study, cases were identified as individuals who died during the observation period and controls as indi- viduals who were alive on March 31, 2008.
5 mL fasting peripheral venous blood was collected from subjects for various biochemical investigations. Routine biochemical tests included blood urea, serum creatinine, sodium, potassium, calcium and uric acid were carried out in the hospital laboratory. Estimated glomerular filtration rate (eGFR) was calculated by using Modification of Diet in Renal Disease (MDRD)  and Chronic KidneyDiseaseEpidemiology Collaboration (CKD-EPI) equation . Morning spot urine samples were collected for urine albumin and urine creatinine test. Serum creatinine and urine creatinine were measured by alkaline picrate jaffee´s kinetic method . Urine micro-albumin was estimated by nephelometer (nephstar®, Goldsite Diagnostics). Albumin/creatinine ratio (ACR) was calculated by using urine micro-albumin and urine creatinine and were expressed in mg/g creatinine.
The estimated GFR (eGFR), calculated by different equa- tions, is commonly used for clinical care and research. The Modification of Diet in Renal Disease (MDRD) equation, initiated in 1999 and based on the serum creatinine level, is still applied clinically after several modifications [7, 8]. Recently, new equations such as the Chronic KidneyDiseaseEpidemiology Collaboration (CKD-EPI) equations based on cystatin C and/or serum creatinine have been recommended for clinical applications [9, 10]. Some reports showed an improved accuracy of the eGFR using the cystatin C-based eq. [11, 12]. However, it is still controversial whether cystatin C-based GFR-estimating formulae are superior to serum creatinine-based ones .
Kidney function is usually measured by estimating glomerular filtration rate (GFR), which is currently considered to be the best index. A direct measurement of GFR is possible, such as by assessing urinary iothalamate or inulin clearance, but this is cum- bersome and not suitable for route clinical or population screening. Several equations have been proposed to estimate GFR (eGFR) from serum creatinine and the currently recommended equation for adults is the Chronic KidneyDisease-Epidemiology Collab- oration (CKD-EPI) equation . The CKD-EPI equation also takes age, sex and race into account, because of their association with muscle mass, which influences the gen- eration of creatinine. It is particularly challenging to accurately estimate eGFR in older adults, because the increase in serum creatinine reflecting reduced kidney function is paralleled by an age-related decrease in muscle mass . Another issue is the need to calibrate serum creatinine assays across laboratories to use them to estimate GFR [8, 9]. Because creatinine depends on muscle mass and other factors, such as diet, that influence creatinine generation, there have been efforts to identify a marker of glomerular filtration that does not suffer from these limitations. Cystatin C, an endogenous protein produced by nearly all human cells that is freely filtered by the glomeruli, has recently been pro- posed as a new marker. Cystatin C-based equations to estimate GFR are now available [10–14]. Compared to creatinine, cystatin C-based equations better predicted all-cause mortality and cardiovascular events in people older than 65 years  as well as all-cause mortality and end-stage renal disease (ESRD) in general adult populations . Cystatin C may be combined with creatinine to estimate GFR , as demonstrated by some recently published equations cited above [13, 14]. Markers of glomerular filtration (e.g. serum cre- atinine and cystatin C) and markers of kidney damage (e.g. albuminuria, renal biopsy find- ings) are also part of the tests used to define CKD-staging.
In people with chronic kidneydisease (CKD), hyperten- sion is an important modifiable risk factor for both car- diovascular (CV) events and progressive renal dysfunction [1–5]. Delaying the progression of CKD is important because it enables patients to live longer with- out significant complications from CKD (malnutrition, bone and electrolyte disorders, anemia, higher risk of CV events) and the need for renal replacement therapy. Despite the significance of attaining blood pressure (BP) targets, the estimated prevalence of hypertension among clinical populations with CKD remains high at 67–86% [6, 7]. This is despite the use of multiple anti-hyperten- sive drugs and access to dietary counseling on low so- dium intake (more than 90% of Canadian CKD clinics have a dietician on staff ) .
There was evidence of increased lipid lowering agent use (indicative of increased statin use) and a small fall in population lipid levels. There is some evidence of reno- protective effects of statins in patients with CKD; A lower rate of decline in GFR was found in patients with renal disease who took antilipemic agents. 38 In the Heart Protection Study, the use of the hypolipidemic drug sim- vastatin reduced the rise in slightly elevated creatinine over time in participants with diabetes and non-diabetic CKD. 39 In the SHARP trial, allocation of the lipid lower- ing ezetimibe plus simvastatin in participants not already on dialysis at randomisation reduced the outcome of ESRD or a doubling of creatinine with an OR of 0.93, though this was not statistically signi ﬁ cant. 40 In the GREACE trial, statin treatment prevented a decline in renal function in people with high blood lipids and cor- onary heart disease; patients not treated with statins showed a 5.2% decrease in creatinine clearance, while patients treated with statins showed a 4.9% increase in creatinine clearance. 41 However, our period changes were not altered by adjusting for statins (lipid lowering drugs) or lipid levels (HDL, total cholesterol). 37
The recruitment of inflammatory cells into renal tissue has a pivotal role in the progression of various renal dis- eases by promoting a microenvironment that amplifies tis- sue injury and fibrosis [20, 21]. MCP-1-mediated macrophage accumulation and activation are critical events in the development of diabetic renal injury in animal models [20, 21]. MCP-1 protein and mRNA were detected in cortical tubuli, and infiltrating mononuclear cells in the kidneys of patients with DKD , Urinary MCP-1 levels correlated with the severity of both the tubulointerstitial and glomerular lesions. Other studies have also shown correlations between urinary MCP-1 with baseline proteinuria and renal function in DKD [10, 22, 23]. In line with previous studies, we also found that UMCP-1 strongly correlated with the level of albu- minuria. In addition, we also found that high UMCP-1 was a predictor of rapid GFR loss in DKD. The relationship between urinary MCP-1 levels and subsequent GFR decline had been observed in earlier studies. In a small study, urine MCP-1 levels was found to correlate with the rate of renal function decline in DKD patients over a 6 year period , but the authors did not adjust for conventional risk factors in the study. Recently, urinary MCP-1 was found to be independently associated with the rate of GFR decline in a Canadian cohort with advanced stage 3 to 4 DKD . Our study extended previous observations by showing that high UMCP-1 was predict- ive of rapid renal function loss across a broad spectrum of kidney function that was independent of conventional fac- tors in Asian patients with stage 1 to 5 DKD.
A majority of participants who were identified as having CKD had stage one or two CKD (i.e., albuminuria only). As we did not repeat assessment of albuminuria, we do not have population-specific data on persistent albumin- uria. However extrapolating data from NHANES on spot versus persistent albuminuria, 51% of participants with microalbuminuria and stage one CKD and 75% of partici- pants with microalbuminuria and stage two CKD would be estimated to have persistent microalbuminuria, yielding an adjusted (approximate) CKD prevalence equal to 19%, rather than 26%. Thus, the prevalence of CKD in the urban Bangladeshi population would still be higher than that in the U.S., Europe, and Japan. Strikingly, we identi- fied four persons (1%) with stage five CKD (eGFR below 15 ml/min/1.73 m 2 ). If we extrapolate only to the >30 year old population of Mohammedpur, a stage five CKD preva- lence of 1% would imply that roughly 1,800 persons would be at risk for imminent kidney failure in a district that rep- resents 0.3% of the Bangladeshi population. Even these 1,800 persons represent an immense burden in a country where kidney transplants are performed in only one and dialysis in just four of 13 government hospitals .
To decide which measure of proteinuria is most predict- ive of the primary composite outcome, we constructed a Cox regression model and calculated the Harrell’s C- statistic and Akaike’s Information Criterion (AIC) for each measure of proteinuria. The C-statistic is a measure of pre- dictive power (where 0.5 = random concordance and 1 = perfect concordance) and the AIC is a measure of the model’s goodness-of-fit. For the Cox model, we first per- formed a univariable analysis using the predictor variables. For the multivariable models, we added variables that were known risk factors for progression of CKD including age, baseline eGFR, hypertension and diabetes mellitus and any predictor variable with a p-value of less than 0.1 in the univariable analysis. To check whether the association between the predictor variables and the primary outcome was modified by whether the patient had a functioning transplant at the time of study enrolment, we created an interaction term between each of the predictor variables and the transplant status. We checked the proportional hazard assumption of each model by plotting the Schoenfield residuals against time. We considered a two- tailed P value of < 0.05 as statistically significant. We per- formed a sensitivity analysis by excluding patients who had a functioning kidney transplant at study recruitment. We assessed the linearity of each predictor variable using Martingale Residuals and by plotting the residuals against the predictor variable using LOWESS (locally weighted scatterplot smoothing). We analysed the data using Stata, version 14.1 (StataCorp LP, College Station TX).
Risk factors for kidney failure in ADPKD patients have been suggested to include the PKD1 gene mutation, hypertension, large kidney size, male gender, proteinuria, and a younger age at diagnosis [8,9]. However, some studies have investigated the impact of UTI on renal function in ADPKD patients [9,10]. In 2006, Ahmed et al. reported that UTI is a risk factor for deteriorating renal function in ADPKD patients along with other trad- itional risk factors . In another retrospective study, UTI was suggested to be a cause of renal deterioration based on the finding that renal function was preserved better in the group of patients who used prophylactic antibiotics than in the control group .