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Single Nephron Glomerular Filtration Rate In Healthy Adults
Department of Medicine, Division of Nephrology, Medical University of South Carolina, 96 Jonathan Lucas Street, MSC 629, CSB 822, Charleston, SC 29425, USA
Chronic kidney disease (CKD) is generally regarded as a final common pathway of several renal diseases, often leading to end-stage kidney disease (ESKD) and a need for renal replacement therapy. Estimated GFR (eGFR) has been used to predict this outcome recognizing its robust association with renal disease progression and the eventual need for dialysis in large, mainly cross-sectional epidemiological studies. However, GFR is implicitly limited as follows: (1) GFR reflects only one of the many physiological functions of the kidney; (2) it is dependent on several non-renal factors; (3) it has intrinsic variability that is a function of dietary intake, fluid and cardiovascular status, and blood pressure especially with impaired autoregulation or medication use; (4) it has been shown to change with age with a unique non-linear pattern; and (5) eGFR may not correlate with GFR in certain conditions and disease states. Yet, many clinicians, especially our non-nephrologist colleagues, tend to regard eGFR obtained from a simple laboratory test as both a valid reflection of renal function and a reliable diagnostic tool in establishing the diagnosis of CKD. What is the validity of these beliefs? This review will critically reassess the limitations of such single-focused attention, with a particular focus on inter-individual variability. What does science actually tell us about the usefulness of eGFR in diagnosing CKD?
Key Contribution: This banner paper for this Special Edition of Toxins reviews and assess the implicit limitations of using eGFR snapshots for CKD diagnosis; a comprehensive approach to establish individual risk profile in CKD patients including age, eGFR slope, proteinuria, GFR variability, nutritional and metabolic profile suggested.
Concordance Of Estimated Glomerular Filtration Rate According To The Formulas Used In Colombia For Patients With Chronic Kidney Disease Not On Dialysis
Glomerular filtration rate (GFR) was originally introduced to estimate glomerular function by calculating the amount of fluid filtered through the renal glomeruli per unit of time. It is not a measure of single-nephron glomerular function; rather, it is a measure reflecting the summation of the filtration of all glomerular capillaries in the human kidney [1]. When a solute is freely filtered through the glomeruli and is neither reabsorbed nor secreted by tubules, as in the case of inulin, then the clearance of that solute may be used to measure GFR. Thus, GFR has been determined by finding the volume of blood glomeruli clear of insulin per minute and is calculated by the formula urine concentration times urine flow per plasma concentration [2, 3]. In clinical practice, creatinine is substituted for inulin as creatinine is present naturally in the body so it does not need to be injected like inulin [4, 5]. However, creatinine is not an ideal marker for estimating GFR due to its tubular secretion that increases through the course of renal disease; the more advanced the disease the larger the ratio of secreted to freely filtered creatinine [6]. In addition, creatinine being a waste product of protein metabolism in muscles, one must assume that creatinine clearance as a true reflection of intrinsic renal pathology may theoretically depend on the condition of a steady state with a constant and stable generation of creatinine in the muscle unaffected by catabolism (1), no changes in muscle activity (2), or dietary influences (3). It may also be assumed that creatinine should have a stable distribution with a relatively constant concentration in the serum (4) and adequate delivery to the glomerular capillaries using the so-called one compartment model (5) necessitating stable cardiovascular status and good vascular supply to the kidneys with the absence of acute changes in cardiac output or hydration status, current administration of non-steroid anti-inflammatory drugs (NSAID) or of any other medication acutely affecting renal blood flow (RBF) including blood pressure lowering agents, especially angiotensin converting enzyme (ACE) inhibitors or angiotensin II type 1 receptor blockers (ARBs). Functionally, the human kidneys can be simplified into two conceptual compartments—one of a filter and the other one the repressor; however, it is only the latter one that is energy expensive in terms of O
It is important to recognize that creatinine clearance as a reflection of decreased filtration due to actual renal disease is dependent on all these assumptions and that especially if the autoregulation of the afferent arterioles is affected by chronic disease such as diabetes mellitus, hypertension, congestive heart failure, or many others [7, 8], creatinine clearance will be highly variable creating a snapshot effect should creatinine clearance be measured, like in many studies, only a few times a year.
1.2. The Concept of the Estimation of Glomerular Filtration Rate by Estimating the Clearance of a Marker: The Estimation of an Estimation
Improving Accuracy Of Estimating Glomerular Filtration Rate Using Artificial Neural Network: Model Development And Validation
Since the calculation of creatinine clearance by a 24-hour urine collection has been cumbersome and is subject to much imprecision during collection, mGFR (measured GFR) as measured by urine collection of filtered creatinine has been largely replaced by eGFR, derived from approximations obtained through large epidemiological studies establishing correlations between serum creatinine and mGFR as influenced by parameters known to modify this relationship including age, sex, race, and others [9, 10, 11]. One such formula is CKD-EPI [12, 13], which was obtained from large cross-sectional studies of patients with or without renal disease where correlation between serum creatinine values and mGFR as measured by clearance of exogenous filtration markers such as iothalamate has been established using the formula GFR = 141 × min (S
× 1.018 (if female) × 1.159 (if black), accounting for variables of age, sex, and race (black vs. non-black). Yet, in the study populations used for establishing this formula, the elderly, especially subjects above 65 years of age, black subjects, and patients with an actual diagnosis of CKD were largely under-represented [12]. Furthermore, the equation obtained is based on a population-based average ignoring such unique individual factors as muscle mass, body composition, or the presence or absence of steady state; the latter commonly occurring in states of catabolism. The most recent development is the acceptance of a race-free formula in the United States [14]; however, none of these concepts fully consider the slow evolvement of the human body composition and significant changes of diet, lifestyle, and physical activity with the post-industrial era, all potentially impacting the eGFR formula and rendering any formulas less accurate 1 or 2 decades later.
It is commonly accepted that the use of mGFR would provide higher accuracy than most eGFR equations. The performance goal is for eGFR to be within 30% of mGFR values 90% of the time per KDIGO 2012 clinical practice guidelines [15]. Whether any of eGFR equations that use creatinine or Cystatin C accurately reflect kidney function has been debated. Porrini et al. [16] analyzed 70 studies comparing eGFR with mGFR involving 40, 000 kidney transplant patients and showed that eGFR often differed from mGFR by ±30% or more, which incorrectly staged CKD in 60% of patients, with eGFR and mGFR showing different rates of GFR decline. Some authors believe that this discrepancy might be partially mitigated by the incorporation of more filtration markers or using their combination to increase predicting value of eGFR. A combination of cystatin C-based eGFR, the inverse of β2-microglobulin concentration and creatinine-based eGFR was found to be a stronger predictor of ESKD than creatinine-based eGFR alone [17]. In addition, cystatin C-based formulae may obviate the need for race-based correction and may more accurately reflect actual GFR in the context of sarcopenia [18]. This may be especially important in the elderly where functional renal decline may be associated with a complex change in metabolic profile [19] including weight loss and changes in serum albumin, C-reactive protein, and a host of other important parameters.
Estimated Gfr In Autosomal Dominant Polycystic Kidney Disease: Errors Of An Unpredictable Method
It is obvious that more research is needed to determine the accuracy of GFR estimations; these research efforts may include more precise assessments of novel filtration markers [20, 21]. In the meantime, clinicians should keep in mind the limitations of using currently available equations.
An additional real-life problem is the precision of estimating GFR in the elderly leading to a potential bias
Since the calculation of creatinine clearance by a 24-hour urine collection has been cumbersome and is subject to much imprecision during collection, mGFR (measured GFR) as measured by urine collection of filtered creatinine has been largely replaced by eGFR, derived from approximations obtained through large epidemiological studies establishing correlations between serum creatinine and mGFR as influenced by parameters known to modify this relationship including age, sex, race, and others [9, 10, 11]. One such formula is CKD-EPI [12, 13], which was obtained from large cross-sectional studies of patients with or without renal disease where correlation between serum creatinine values and mGFR as measured by clearance of exogenous filtration markers such as iothalamate has been established using the formula GFR = 141 × min (S
× 1.018 (if female) × 1.159 (if black), accounting for variables of age, sex, and race (black vs. non-black). Yet, in the study populations used for establishing this formula, the elderly, especially subjects above 65 years of age, black subjects, and patients with an actual diagnosis of CKD were largely under-represented [12]. Furthermore, the equation obtained is based on a population-based average ignoring such unique individual factors as muscle mass, body composition, or the presence or absence of steady state; the latter commonly occurring in states of catabolism. The most recent development is the acceptance of a race-free formula in the United States [14]; however, none of these concepts fully consider the slow evolvement of the human body composition and significant changes of diet, lifestyle, and physical activity with the post-industrial era, all potentially impacting the eGFR formula and rendering any formulas less accurate 1 or 2 decades later.
It is commonly accepted that the use of mGFR would provide higher accuracy than most eGFR equations. The performance goal is for eGFR to be within 30% of mGFR values 90% of the time per KDIGO 2012 clinical practice guidelines [15]. Whether any of eGFR equations that use creatinine or Cystatin C accurately reflect kidney function has been debated. Porrini et al. [16] analyzed 70 studies comparing eGFR with mGFR involving 40, 000 kidney transplant patients and showed that eGFR often differed from mGFR by ±30% or more, which incorrectly staged CKD in 60% of patients, with eGFR and mGFR showing different rates of GFR decline. Some authors believe that this discrepancy might be partially mitigated by the incorporation of more filtration markers or using their combination to increase predicting value of eGFR. A combination of cystatin C-based eGFR, the inverse of β2-microglobulin concentration and creatinine-based eGFR was found to be a stronger predictor of ESKD than creatinine-based eGFR alone [17]. In addition, cystatin C-based formulae may obviate the need for race-based correction and may more accurately reflect actual GFR in the context of sarcopenia [18]. This may be especially important in the elderly where functional renal decline may be associated with a complex change in metabolic profile [19] including weight loss and changes in serum albumin, C-reactive protein, and a host of other important parameters.
Estimated Gfr In Autosomal Dominant Polycystic Kidney Disease: Errors Of An Unpredictable Method
It is obvious that more research is needed to determine the accuracy of GFR estimations; these research efforts may include more precise assessments of novel filtration markers [20, 21]. In the meantime, clinicians should keep in mind the limitations of using currently available equations.
An additional real-life problem is the precision of estimating GFR in the elderly leading to a potential bias
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