Patent Application
20230037869
The present invention relates to a method for estimating kidney function of a subject being evaluated, and to a system for estimating kidney function of a subject being evaluated.
Glomerular filtration rate (GFR) is a typical marker for indication of kidney function. The glomerular filtration rate represents the liquid volume filtered per minute from blood by the glomeruli, with inulin clearance considered to be the international gold standard. However, measurement of inulin clearance requires continuous drip infusion of inulin over a period of 2 hours as well as urine and blood collection multiple times, which creates a burden for both the patient and the practitioner. For routine practice in the clinic, therefore, measurement of inulin clearance is only carried out for limited situations such as donors for living kidney transplant, while for most cases it is substituted by measurement of other markers such as creatinine. Most marker values, however, diverge significantly from the actual glomerular filtration rate according to the gold standard of inulin clearance, thus interfering with accurate diagnosis of kidney disease.
Creatinine is routinely measured in the clinic as a marker for kidney function. Creatinine is the final metabolite of creatine which is necessary for muscle contraction. Creatine formed in the liver is taken up into muscle cells and partially metabolized to creatinine, transported to the kidneys through the blood, filtered by the glomeruli, and then excreted into urine in the kidney tubules without being reabsorbed. It is utilized for evaluation of kidney function because it can serve as an advantageous marker for uremia, since reduced glomerular filtration capacity leads to impaired excretion and accumulation in the blood causing its numerical value to increase. However, the amount of creatinine in blood does not appear as a clearly abnormal value until GFR has reduced by 50% or greater, and it therefore cannot be considered to be a sensitive marker.
Cystatin C is a protein of 13.36 kDa molecular weight that is produced in a fixed proportion by systemic nucleated cells, and is completely filtered out by the glomeruli and subsequently catabolized in the kidneys via reabsorption in the kidney tubules, and since it is therefore thought to be removed from the blood depending on the filtration rate, its amount in blood serves as a GFR marker. When kidney function is greatly reduced, however, the amount of increase in blood cystatin C reaches a plateau, and in end-stage kidney disease it becomes difficult to accurately evaluate kidney function.
Thus, no biomarker has yet existed that can adequately meet clinical demands for accurately measuring glomerular filtration rate for individual patients in a wide range from early to end stage by blood collection alone, without a large burden on subjects or patients.
Conventionally, D-amino acids had been considered to be absent from a mammalian body but have since been shown to be present in various tissues and to play physiological functions. It has been shown that the amounts of D-serine, D-alanine, D-proline, D-glutamic acid and D-aspartic acid in blood can serve as kidney failure markers since they vary in kidney failure patients and correlate with creatinine (NPL 1, NPL 2, NPL 3, NPL 4). It has also been disclosed that amino acids selected from the group consisting of D-serine, D-threonine, D-alanine, D-asparagine, D-allothreonine, D-glutamine, D-proline and D-phenylalanine serve as pathology index values for kidney disease (PTL 1). The aforementioned publications merely disclose that the fluctuation of D-amino acids in the blood of patients suffering from kidney disease compared to healthy persons can be used as markers for diagnosis of kidney disease, or that D-serine blood levels of subjects correlate with creatinine levels or with estimated corrected creatinine levels, but nowhere suggest that D-amino acid blood levels directly correlate with the gold standard of inulin clearance and allow estimation of glomerular filtration rate. Incidentally, while urine L-FABP, blood NGAL and urine KIM-1 have been disclosed as kidney disease markers in recent years, these do not correlate with glomerular filtration capacity.
There is a demand for a method for accurately estimating kidney function in subjects across a wider range than with conventionally known kidney function markers such as blood creatinine.
The present inventors, focusing on D- and L-amino acids in blood and analyzing their correlation with GFR (inulin clearance), found surprisingly that D- and L-amino acid levels in blood samples from healthy persons and kidney disease patients in the early to end stages are more highly correlated with GFR (inulin clearance) than creatinine or cystatin C levels throughout all stages, and have completed this invention based on this finding.
The present invention relates to the following:
[1] A method for estimating kidney function of a subject being evaluated, wherein the method includes:
a step of estimating the kidney function of the subject being evaluated based on the value of Y calculated from the levels of D- and L-amino acids in a biological sample from the subject being evaluated, using the following formula (I):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (I)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
wherein formula (I) is predetermined by the regression analysis using D- and L-amino acid levels in a biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables.
[2] The method according to [1] above, wherein the step of estimating the kidney function of the subject being evaluated is a step of estimating the kidney function of the subject being evaluated by estimating the glomerular filtration rate of the subject being evaluated based on the Y value.
[3] The method according to [1] or [2] above, wherein the biological sample is blood, plasma or serum.
[4] The method according to any one of [1] to [3] above, wherein formula (I) with a correlation coefficient of R≥0.5, is used.
[5] The method according to any one of [1] to [3] above, wherein formula (I) with a correlation coefficient of R≥0.8, is used.
[6] The method according to any one of [1] to [5] above, wherein the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C, and
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis.
[7] The method according to any one of [1] to [5] above, wherein
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X1 to Xn.
[8] The method according to [7] above, wherein
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C, and
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn.
[9] The method according to any one of [1] to [8] above, wherein X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine.
[10] The method according to any one of [1] to [9] above, wherein the glomerular filtration rate of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance, creatinine clearance, 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance.
[11] The method according to any one of [1] to [10] above, wherein the subject being evaluated is a subject who has been assessed to have suspected kidney disease by a conventional examination method.
[12] The method according to any one of [1] to [11] above, wherein treatment intervention is carried out for a subject being evaluated who has been assessed to have reduced kidney function.
[13] The method according to [12] above, wherein the treatment intervention is selected from the group consisting of lifestyle habit improvement, dietary guidance, blood pressure management, anemia management, electrolyte management, uremia management, blood sugar level management, immune management and lipid management.
[14] The method according to [12] or [13] above, wherein the treatment intervention includes administration to a subject of one or more drugs selected from the group consisting of diuretic drugs, calcium antagonists, angiotensin converting enzyme inhibitors, angiotensin receptor antagonists, sympatholytic drugs, SGLT2 inhibitors, sulfonylurea drugs, thiazolidine drugs, biguanide drugs, α-glucosidase inhibitors, glinide drugs, insulin formulations, NRF2 activators, immunosuppressive agents, statins, fibrates, anemia treatments, erythropoietin formulations, HIF-1 inhibitors, iron agents, electrolyte regulators, calcium receptor agonists, phosphorus adsorbents, uremic toxin adsorbents, DPP4 inhibitors, EPA formulations, nicotinic acid derivatives, cholesterol nicotinic acid derivatives, cholesterol transporter inhibitors and PCSK9 inhibitors.
[15] A system for estimating kidney function of an subject being evaluated, the system including a storage unit, an analytical measurement unit, a data processing unit and an output unit, wherein:
the storage unit stores the following formula (II):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (II)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
wherein formula (II) is predetermined the regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables,
the analytical measurement unit quantifies the levels of D- and L-amino acids in a biological sample from the subject being evaluated,
the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y and estimates the kidney function of the subject being evaluated, based on the value of Y, and
the output unit outputs information regarding the estimated kidney function of the subject being evaluated.
[16] The system according to [15] above, wherein the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y, and estimates the glomerular filtration rate of the subject being evaluated based on the value of Y, thereby estimating the kidney function of the subject being evaluated.
[17] The system according to [15] or [16] above, wherein the biological sample is blood, plasma or serum.
[18] The system according to any one of [15] to [17] above, wherein formula (II) with a correlation coefficient of R≥0.5, is used.
[19] The system according to any one of [15] to [17] above, wherein formula (II) with a correlation coefficient of R≥0.8, is used.
[20] The system according to any one of [15] to [19] above, wherein the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C, and
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis.
[21] The system according to any one of [15] to [19] above, wherein the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X1 to Xn.
[22] The system according to [21] above, wherein
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C, and
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn.
[23] The system according to any one of [15] to [22] above, wherein X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine.
[24] The system according to any one of [15] to [23] above, wherein the glomerular filtration rates of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance, creatinine clearance, 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance.
[25] The system according to any one of [15] to [24] above, wherein the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method.
The D- and L-amino acid levels in blood used for the method for estimating kidney function of the invention correlate better with GFR (such as inulin clearance) than conventionally used blood creatinine or cystatin C levels. According to the invention it is therefore possible to use a biological sample to conveniently estimate kidney function of a subject being evaluated.
The tables in
The present invention relates to a method for estimating kidney function of a subject being evaluated, and to a system for estimating kidney function of a subject being evaluated, based on D- and L-amino acid levels in a biological sample.
According to one embodiment, the invention provides a method for estimating kidney function of a subject being evaluated, wherein the method includes:
a step of estimating the kidney function of the subject being evaluated based on the value of Y calculated from the levels of D- and L-amino acids in a biological sample from the subject being evaluated, using the following formula (I):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (I)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
wherein formula (I) is predetermined by the regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables. The method may be used to provide preliminary data for diagnosis by a physician, and may therefore be considered a preliminary method to diagnosis or a diagnostic aid method.
As used herein, “arbitrary subjects” mean subjects being measured both for levels of D- and L-amino acids (often including creatinine and/or cystatin C levels) in a biological sample, and glomerular filtration rate, used for calculating the regression formula, and it may also include “a subject being evaluated”. The number of arbitrary subjects used to calculate the regression formula is preferably a number sufficient to calculate a statistically significant regression formula, and for the purpose of the invention a number of, for example, 3, 5, 10, 20, 30, 50, 100 or greater may be used. The arbitrary subjects measured for calculating the regression formula preferably include a subject with kidney disease and a healthy subject (such as a non-kidney disease subject).
As used herein, a “biological sample” is a sample derived from an organism, and it may be blood, plasma, serum, saliva, urine, ascites fluid, amnionic fluid, lymph, semen, spinal fluid, nasal discharge, sweat, milk or tears, although blood, plasma or serum is preferred as the biological sample for the invention.
As used herein, “D- and L-amino acids” refer to amino acids that are constituents of “D-form” and “L-form” proteins, as well as amino acids that include glycine which has no stereoisomer, and specifically they include glycine, D, L-alanine, D, L-histidine, D, L-isoleucine, D, L-alloisoleucine, D, L-leucine, D, L-lysine, D, L-methionine, D, L-phenylalanine, D, L-threonine, D, L-allothreonine, D, L-tryptophan, D, L-valine, D, L-arginine, D, L-cysteine, D, L-glutamine, D, L-proline, D, L-tyrosine, D, L-aspartic acid, D, L-asparagine, D, L-glutamic acid and D, L-serine. Since D, L-cysteine in a biological sample is oxidized ex vivo and converted to D, L-cystine, one embodiment of the invention allows measurement of D, L-cystine instead of D, L-cysteine, calculating the level of D, L-cysteine in the biological sample.
As used herein, “glomerular filtration capacity” refers to the ability of the glomeruli to filter blood. According to one aspect, it is represented as the glomerular filtration rate (GFR), but the glomerular filtration capacity is not limited to the actual glomerular filtration rate and may be determined in arbitrary units. As an example, the blood D- and L-amino acid levels may be represented as the glomerular filtration capacity either directly or after correction by an arbitrary value depending on the case. According to the invention, the glomerular filtration capacity may be the glomerular filtration capacity corrected for body surface area, or it may be the glomerular filtration capacity without correction for body surface area. Because glomerular filtration capacity requirement differs depending on physical size, correction for body surface area is often used for comparison, statistical processing or screening diagnosis.
The glomerular filtration rate is represented in units of “mL/min”, indicating the liquid volume filtered per minute from blood by the glomeruli. Since the glomerular filtration rate requirement varies according to physical size, it is corrected to the glomerular filtration rate per standard body surface area of 1.73 m2 for statistical processing, comparison and screening diagnosis, and units of “mL/min/1.73 m2” are used. While it is common to use the body surface area-corrected value for comparison and screening diagnosis of kidney function, the value without body surface area correction is used for individual kidney function diagnosis and dosage determination of kidney excretion drugs. The glomerular filtration rates of arbitrary subjects used as the response variables of the invention may be calculated from inulin clearance, or it may be calculated from creatinine clearance, 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance.
The D- and L-amino acids used as markers for the invention are strictly regulated in the tissues and blood, but D- and L-amino acid levels in blood vary in cases of kidney damage.
The “D- and L-amino acid levels in a biological sample” referred to herein may be the D- and L-amino acid levels in a specified amount of biological sample, or it may be their concentrations. The D- and L-amino acid levels in a biological sample are measured as the amounts in a harvested biological sample that has been treated by centrifugal separation, sedimentation separation or other pretreatment for analysis. Therefore, the D- and L-amino acid levels in the biological sample can be measured as the amount in a blood sample derived from sampled whole blood, serum or plasma, for example. For analysis using HPLC, as one example, the D- and L-amino acid levels in a predetermined amount of blood are represented in a chromatogram, and the peak heights, areas and shapes may be quantified by analysis based on standard sample comparison and calibration. It is possible to measure the D- and L-amino acid concentrations in blood by comparison with samples of known D- and L-amino acid concentrations, allowing the D- and L-amino acid concentrations in blood to be used as the D- and L-amino acid levels in blood. With an enzyme method, the amino acid concentration can be calculated by quantitative analysis using a standard calibration curve.
The D- and L-amino acid levels may be measured by any method, such as chiral column chromatography, or measurement using an enzyme method, or quantitation by an immunological method using a monoclonal antibody that distinguishes between optical isomers of amino acids. Measurement of the D- and L-amino acid levels in a sample according to the invention may be carried out using any method well known to those skilled in the art. Examples include chromatographic and enzyme methods (Y. Nagata et al., Clinical Science, 73 (1987), 105. Analytical Biochemistry, 150 (1985), 238, A. D'Aniello et al., Comparative Biochemistry and Physiology Part B, 66 (1980), 319. Journal of Neurochemistry, 29 (1977), 1053, A. Berneman et al., Journal of Microbial & Biochemical Technology, 2 (2010), 139, W. G. Gutheil et al., Analytical Biochemistry, 287 (2000), 196, G. Molla et al., Methods in Molecular Biology, 794 (2012), 273, T. Ito et al., Analytical Biochemistry, 371 (2007), 167.), antibody methods (T. Ohgusu et al., Analytical Biochemistry, 357 (2006), 15), gas chromatography (GC) (H. Hasegawa et al., Journal of Mass Spectrometry, 46 (2011), 502, M. C. Waldhier et al., Analytical and Bioanalytical Chemistry, 394 (2009), 695, A. Hashimoto, T. Nishikawa et al., FEBS Letters, 296 (1992), 33, H. Bruckner and A. Schieber, Biomedical Chromatography, 15 (2001), 166, M. Junge et al., Chirality, 19 (2007), 228, M. C. Waldhier et al., Journal of Chromatography A, 1218 (2011), 4537), capillary electrophoresis methods (CE) (H. Miao et al., Analytical Chemistry, 77 (2005), 7190, D. L. Kirschner et al., Analytical Chemistry, 79 (2007), 736, F. Kitagawa, K. Otsuka, Journal of Chromatography B, 879 (2011), 3078, G. Thorsen and J. Bergquist, Journal of Chromatography B, 745 (2000), 389.), and high-performance liquid chromatography (HPLC) (N. Nimura and T. Kinoshita, Journal of Chromatography, 352 (1986), 169, A. Hashimoto et al., Journal of Chromatography, 582 (1992), 41, H. Bruckner et al., Journal of Chromatography A, 666 (1994), 259, N. Nimura et al., Analytical Biochemistry, 315(2003), 262, C. Muller et al., Journal of Chromatography A, 1324 (2014), 109, S. Einarsson et al., Analytical Chemistry, 59 (1987), 1191, E. Okuma and H. Abe, Journal of Chromatography B, 660 (1994), 243, Y. Gogami et al., Journal of Chromatography B, 879 (2011), 3259, Y. Nagata et al., Journal of Chromatography, 575 (1992), 147, S. A. Fuchs et al., Clinical Chemistry, 54 (2008), 1443, D. Gordes et al., Amino Acids, 40 (2011), 553, D. Jin et al., Analytical Biochemistry, 269 (1999), 124, J. Z. Min et al., Journal of Chromatography B, 879 (2011), 3220, T. Sakamoto et al., Analytical and Bioanalytical Chemistry, 408 (2016), 517, W. F. Visser et al., Journal of Chromatography A, 1218 (2011), 7130, Y. Xing et al., Analytical and Bioanalytical Chemistry, 408 (2016), 141, K. Imai et al., Biomedical Chromatography, 9 (1995), 106, T. Fukushima et al., Biomedical Chromatography, 9 (1995), 10, R. J. Reischl et al., Journal of Chromatography A, 1218 (2011), 8379, R. J. Reischl and W. Lindner, Journal of Chromatography A, 1269 (2012), 262, S. Karakawa et al., Journal of Pharmaceutical and Biomedical Analysis, 115 (2015), 123.).
The separative analysis system for optical isomers according to the invention may be a combination of multiple separative analysis methods. More specifically, the D-/L-amino acid level in a sample can be measured using an optical isomer analysis method comprising a step of passing a sample containing a component with optical isomers through a first column filler as the stationary phase, together with a first liquid as the mobile phase, to separate the components in the sample, a step of separately holding each of the components in the sample in a multi loop unit, a step of passing each of the components in the sample that are separately held in the multi loop unit through a flow channel in a second column filler having an optically active center, as the stationary phase, together with a second liquid as the mobile phase, to separate the optical isomers among each of the sample components, and a step of detecting the optical isomers in each of the sample components (Japanese Patent No. 4291628). In HPLC analysis, D- and L-amino acids are sometimes pre-derivatized with a fluorescent reagent such as o-phthalaldehyde (OPA) or 4-fluoro-7-nitro-2,1,3-benzoxadiazole (NBD-F), or diastereomerized using an agent such as N-tert-butyloxycarbonyl-L-cysteine (Boc-L-Cys) (Hamase, K. and Zaitsu, K., Bunseki Kagaku, Vol. 53, 677-690(2004)). Alternatively, the D-amino acids may be measured by an immunological method using a monoclonal antibody that distinguishes optical isomers of amino acids, such as a monoclonal antibody that specifically binds to D- and L-amino acids. When the total of the D-form and L-form is to be used as the marker it is not necessary to separate the D-form and L-form, allowing the amino acids to be analyzed without separating the D-form and L-form. In such cases as well, separation and quantitation may be carried out using an enzyme method, antibody method, GC, CE or HPLC.
The following formula (I):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (I)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
is predetermined by regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables. According to the invention it is possible to use formula (I) to estimate kidney function of a subject being evaluated.
As used herein, “predetermined” means determined before the point at which the Y value is calculated based on levels of D- and L-amino acids in a biological sample from a subject being evaluated (often including creatinine and/or cystatin C levels). Therefore, the point at which formula (I) is determined is not particularly restricted so long as it is before the point at which the Y value is calculated. Formula (I) used for the invention may be a formula determined by the person who calculates the value of Y, or it may be a formula determined by a third party who does not calculate the value of Y. That is, the invention encompasses cases of calculating the value of Y using any formula within the range of formula (I) of the invention and carrying out the step of estimating the kidney function of the subject being evaluated, based on the obtained value of Y.
As used herein, “regression analysis” refers to a method of estimating a formula representing the relationship between an explanatory variable (also known as “independent variable”) and a response variable (also known as “dependent variable”) in a statistical manner, and for example, it is a solution method by regression using the least square method, moving average method or kernel method. Regression analysis is a well-known method and any regression analysis may be used for the invention. Regression used in regression analysis for the invention may be linear regression, or it may be nonlinear regression (such as “n” th polynomial regression analysis). The regression used for the invention may be simple linear regression or multiple regression. According to the invention, a formula for determining the value of Y, is determined by regression analysis using levels of D- and L-amino acids (often including creatinine and/or cystatin C levels) in biological samples of arbitrary subjects as the explanatory variables, and glomerular filtration rates of the arbitrary subjects as the response variables.
As used herein, “a1 to an” represents the constants obtained by regression analysis using levels of D- and L-amino acids (often including creatinine and/or cystatin C levels) in biological samples of arbitrary subjects as the explanatory variables, and glomerular filtration rates of the arbitrary subjects as the response variables.
As used herein, “X1 to Xn” represents the variables of D- and L-amino acid levels (often including creatinine and/or cystatin C levels) selected by regression analysis using levels of D- and L-amino acids (often including creatinine and cystatin C levels) in biological samples of arbitrary subjects as the explanatory variables, and glomerular filtration rates of the arbitrary subjects as the response variables, and depending on the regression analysis employed, they may be expressed as exponential variables of a first-order function, second-order function, third-order function . . . nth order function (where n is a natural number). As used herein, the “n” subscript in “an” and “Xn” represents a natural number of 1≤n≤45, as numbers assigned to distinguish factors selected from the group consisting of D- and L-amino acids, and sometimes creatinine and cystatin C, used for the invention. Therefore, “n” is equivalent to the number of D- and L-amino acids, creatinine and cystatin C selected by regression analysis.
As used herein, “b” represents the constant (also referred to as intercept) obtained by regression analysis using levels of D- and L-amino acids (often including creatinine and/or cystatin C levels) in biological samples of arbitrary subjects as the explanatory variables, and glomerular filtration rates of the arbitrary subjects as the response variables.
Formula (I) derived by regression analysis is preferably a formula with correlation coefficient R≥0.5, more preferably correlation coefficient R≥0.6, even more preferably correlation coefficient R≥0.7 and most preferably correlation coefficient R≥0.8.
Since the blood creatinine level to be used for comparison with the invention is significantly affected by the amount of muscle from which it is derived, sports athletes, acromegaly patients and persons that have ingested large amounts of meat will exhibit higher values, while patients suffering from neuromuscular disease (such as muscular dystrophy), emaciation, prolonged bed rest, frailty, sarcopenia, locomotive syndrome or amputation, or persons that have restricted their protein intake, will exhibit lower values, and therefore accurate kidney function cannot be reflected. Moreover, since blood creatinine levels have been found to have circadian variation of about 10%, with higher values in the morning, care must also be taken in that regard. Since blood cystatin C level increases sharply compared to blood creatinine level with moderate reduction in kidney function, it is considered to be advantageous for discovering early impaired kidney function. However, it is also known that the levels are affected by the use of steroids and cyclosporins, and by patient conditions such as diabetes, hyperthyroidism, inflammation, hyperbilirubinemia and hypertriglyceridemia. For examination of kidney disease, therefore, it is necessary to make a comprehensive diagnosis in combination with other markers such as urea nitrogen (BUN) and urine proteins.
The accuracy of the glomerular filtration rate determined based on conventional kidney function markers such as blood creatinine levels is low, and while accurate glomerular filtration rate measurement is possible based on the gold standard of inulin clearance, it involves complex methods and a burden on patients and health care professionals, and is therefore limited in its practicality. The method of determining glomerular filtration capacity according to the invention at least allows the glomerular filtration capacity to be more accurately determined than by blood creatinine levels, and even allows glomerular filtration capacity to be determined more accurately than by blood cystatin C levels. When analysis was made in correlation with inulin clearance in groups classified according to glomerular filtration rate, D-serine exhibited higher correlation coefficient R values and higher correlation with inulin clearance, than both blood cystatin C and creatinine levels across all the groups. Future experiments should be conducted to determine accuracy, but it has the potential to equal or even surpass the performance obtained by the glomerular filtration rate determining method based on inulin clearance, which is the international standard for measurement. According to another aspect of the invention, therefore, it is possible to use blood D- and L-amino acid levels as a substitute marker for inulin clearance. A substitute marker is a marker whose relationship with a final evaluation can be scientifically proven. Being a substitute marker for inulin clearance, therefore, means that glomerular filtration rate can be determined based on D- and L-amino acid levels instead of using a GFR determining method based on inulin clearance, as a result of having used blood D- and L-amino acid levels to statistically demonstrate a relationship with evaluation based on inulin clearance.
While it is not our intention to be limited to any particular theory, D- and L-amino acids have the advantage of not being affected by muscle mass and thus not requiring correction for physical size as is necessary for blood creatinine. According to one embodiment of the invention, the method for estimating kidney function is characterized by not correcting for one or more physical size-related factors selected from the group consisting of gender, age and muscle mass.
The explanatory variable used for calculation of formula (I) according to one embodiment of the invention may be the “D- and L-amino acid level”, or the level of a factor selected from the group consisting of creatinine and cystatin C. In this case, X1 to Xn represent the variables for levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis.
According to another embodiment of the invention, the explanatory variables for calculation of formula (I) are the standardized values for the logarithm of the D- and L-amino acid levels in biological samples of arbitrary subjects, while the response variables may be the standardized values of the logarithm of the glomerular filtration rates for the arbitrary subjects. In this case, the standardized values for the logarithm of the D- and L-amino acid levels selected by regression analysis are applied for X1 to Xn in formula (I).
As used herein, “logarithm of the D- and L-amino acid levels” is the value for the D- and L-amino acid levels converted to the natural logarithm. As used herein, “standardized value for the logarithm of the D- and L-amino acid levels” is the value calculated using the following formula (A):
The “parent population” here is a group including all of the arbitrary subjects quantified for regression analysis.
As used herein, “logarithm of the glomerular filtration rate” is the value of the glomerular filtration rate converted to the natural logarithm. The “standardized value of the logarithm of the glomerular filtration rate” is the value calculated using the following formula (B):
For conversion from the value of Y obtained for this embodiment to an estimated value for the glomerular filtration rate, the opposite order from formula (B) can be used, i.e. by multiplying the Y value by the standard deviation of the logarithm of the glomerular filtration rate for the parent population and adding the average value of the logarithm of the glomerular filtration rate for the parent population, and then transforming the obtained value to an exponent.
According to another aspect, the explanatory variables may further include the standardized level of the logarithm of the amount of a factor selected from the group consisting of creatinine and cystatin C, in addition to D- and L-amino acids (the standardized levels of the logarithms of the amounts of creatinine and cystatin C being calculated by the same method as formula (A) above). In this case, X1 to Xn of formula (I) represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithms of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn.
According to one embodiment of the invention, X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine. This is preferred for higher correlation of formula (I) of the invention with the glomerular filtration rate.
According to one aspect of the invention, the kidney function of an subject being evaluated can be estimated based on the value of Y obtained by inserting blood D- and L-amino acid levels of an subject being evaluated into the formula derived from the correlation between the inulin clearance and blood D- and L-amino acid levels of the arbitrary subject, and a corresponding table or graph. The correlation between inulin clearance and D- and L-amino acid levels has been shown to be higher than the correlation between inulin clearance and blood creatinine level, and therefore glomerular filtration capacity estimated by substituting D- and L-amino acid level of an subject being evaluated into a formula, or a corresponding table or graph, derived from the correlation between inulin clearance and blood D- and L-amino acid levels, is more accurate than the conventional glomerular filtration rate estimated from blood creatinine level. The inulin clearance used for correlation analysis may be the inulin clearance after correction for body surface area or the inulin clearance before correction for body surface area. Glomerular filtration capacity before or after correction for body surface area may be selected according to the need.
A correspondence table derived from the correlation between inulin clearance and blood D- and L-amino acid levels may list values for glomerular filtration capacity corresponding to D- and L-amino acid levels, or it may list kidney functions corresponding to numerical ranges, i.e. categories for severity of kidney disease.
The severity categories for chronic kidney disease (CKD) are the 6 levels of G1, G2, G3a, G3b, G4 and G5, according to the numerical ranges for glomerular filtration rate. Specifically, the definitions are normal or high value for 90 mL/min/1.73 m2 or greater (G1), normal or mildly low for 60 to 89 mL/min/1.73 m2 (G2), mildly to moderately low for 45 to 59 mL/min/1.73 m2 (G3a), moderately to severely low for 30 to 44 mL/min/1.73 m2 (G3b), severely low for 15 to 29 mL/min/1.73 m2 (G4) and end-stage kidney disease for less than 15 mL/min/1.73 m2 (G5) (Japanese Society of Nephrology Guidelines).
Various formulas have been devised for blood creatinine level or cystatin C level which is observed to correlate with physical size, estimating the glomerular filtration rate by correcting for race, age and gender using large-scale patient data. The major estimation formulas for glomerular filtration rate are the Cockcroft-Gault formula, MDRD formula and CKD-EPI formula, and currently the estimation formula (eGFR) used for routine examination for Japanese is the following.
However, the eGFR determined in this manner is a marker created for health examination screening or for convenient evaluation in epidemiologic research for comparison of numerous subjects, with the values being intended to be calculated with correction to average physical size, and therefore it is still recommended to use inulin clearance for accurate evaluation of kidney function for individual patients including excessively lean elderly (Japanese Society of Nephrology Guidelines).
The present invention allows estimation of kidney function, such as the severity of kidney disease, based on the determined Y value. As an example, the invention allows categorization of severity on the 6 levels of G1, G2, G3a, G3b, G4 and G5, as classifications for chronic kidney disease patients based on the value of Y. Treatment intervention is used for subjects classified in categories corresponding to G2 to G5. The treatment intervention is selected as appropriate for each category. Treatment intervention is guidance for one or a combination from among lifestyle habit improvement, dietary guidance, blood pressure management, anemia management, electrolyte management, uremia management, blood sugar level management, immune management or lipid management. Lifestyle habit improvement may be a recommendation to stop smoking or to reduce the BMI value to below 25. Dietary guidance may be salt or protein restriction. For blood pressure management, anemia management, electrolyte management, uremic toxin manage, blood sugar level management, immune management or lipid management in particular, treatment may involve administration of a drug. Blood pressure management may involve general management or administration of an antihypertensive drug, to reach below 130/80 mmHg. Antihypertensive drugs include diuretic drugs (thiazide diuretics such as trichlormethiazide, benzylhydrochlorothiazide and hydrochlorothiazide, thiazide-like diuretics such as meticrane, indapamide, tribamide and mefluside, loop diuretics such as furosemide, and potassium-sparing diuretics and aldosterone antagonists such as triamterene, spironolactone and eplerenone), calcium antagonists (dihydropyridine-based antagonists such as nifedipine, amlodipine, efonidipine, cilnidipine, nicardipine, nisoldipine, nitrendipine, nilvadipine, barnidipine, felodipine, benidipine, manidipine, azelnidipine and aranidipine, benzodiazepine-based antagonists, and diltiazem), angiotensin converting enzyme inhibitors (such as captopril, enalapril, acelapril, delapril, cilazapril, lisinopril, benazepril, imidapril, temocapril, quinapril, trandolapril, perindopril and erbumine), angiotensin receptor antagonists (angiotensin II receptor antagonists such as losartan, candesartan, valsartan, telmisartan, olmesartan, irbesartan and azilsartan), and sympatholytic drugs (n-blockers, such as atenolol, bisoprolol, betaxolol, metoprolol, acebutolol, celiprolol, propranolol, nadolol, carteolol, pindolol, nipradilol, amosulalol, arotinolol, carvedilol, labetalol, bevantolol, urapidil, terazosin, prazosin, doxazosin and bunazosin). Erythropoietin formulations, iron agents and HIF-1 inhibitors are used as anemia treatments. Calcium receptor agonists (such as cinacalcet and etelcalcetide) and phosphorus adsorbents are used as electrolyte regulators. Active carbon is used as a uremic toxin adsorbent. Blood glucose level is managed to Hbalc of <6.9%, and in some cases a hypoglycemic agent is administered. Hypoglycemic agents that are used include SGLT2 inhibitors (such as ipragliflozin, dapagliflozin, luseogliflozin, tofogliflozin, canagliflozin and empagliflozin), DPP4 inhibitors (such as sitagliptin phosphate, vildagliptin, saxagliptin, alogliptin, linagliptin, teneligliptin, trelagliptin, anagliptin, omarigliptin), sulfonylurea agents (such as tolbutamide, acetohexamide, chlorpropamide, glyclopyramide, glibenclamide, gliclazide and glimepiride), thiazolidine agents (such as pioglitazone), biguanide agents (such as metformin and buformin), α-glucosidase inhibitors (such as acarbose, voglibose and miglitol), glinide agents (such as nateglinide, mitiglinide and repaglinide), insulin formulations and NRF2 activators (such as bardoxolonemethyl). Immunosuppressive agents (such as steroids, tacrolimus, anti-CD20 antibody, cyclohexamide and mycophenolate mofetil (MMF)) are used for immune management. Lipid management includes management to lower LDL-C to below 120 mg/dL, or in some cases dyslipidemia treatments are used, including statins (such as rosuvastatin, pitavastatin, atorvastatin, cerivastatin, fluvastatin, simvastatin, pravastatin, lovastatin and mevastatin), fibrates (such as clofibrate, bezafibrate, fenofibrate and clinofibrate), nicotinic acid derivatives (such as nicotinic acid derivatives (tocopherol nicotinate, nicomol and niceritrol), cholesterol transporter inhibitors (such as ezetimibe), PCSK9 inhibitors (such as evolocumab) and EPA formulations. All of these drugs may be used as single dosage forms or mixtures. Depending on the degree of kidney function impairment, kidney replacement therapy such as peritoneal dialysis, hemodialysis, continuous hemodialysis filtration, blood apheresis (such as plasma exchange or plasma adsorption) or kidney transplant may also be carried out.
According to another aspect of the invention, the step of estimating the kidney function of the subject being evaluated may be a step of estimating the kidney function of the subject being evaluated by estimating the glomerular filtration rate of the subject being evaluated, based on the Y value.
According to another aspect, the invention relates to a system and program for carrying out the aforementioned method for estimating kidney function of a subject being evaluated.
More specifically, in the sample analysis system 10 of the invention,
the storage unit 11 stores a predetermined formula (II):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (II)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
wherein formula (II) is predetermined by the regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables, as inputted through the input unit 12,
the analytical measurement unit 13 quantifies the levels of D- and L-amino acids in a biological sample from the subject being evaluated,
the data processing unit 14 inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y and estimates the kidney function of the subject being evaluated, based on the value of Y, and
the output unit 15 outputs information regarding the estimated kidney function of the subject being evaluated. Formula (II) is the same as formula (I) used in the method for estimating kidney function of an subject being evaluated described above, and since the explanation for the method for estimating kidney function of an subject being evaluated is applicable to the system and program for estimating kidney function of an subject being evaluated, it will not be explained in full here.
According to a more preferred aspect, the sample analysis system of the invention may further include a step in which the storage unit 11 stores a threshold value inputted from the input unit 12, and a step in which the data processing unit 14 compares the Y value with the threshold value. Comparison between the Y value and the threshold allows kidney function of the subject being evaluated to be estimated and allows the output unit 15 to output information regarding the estimated kidney function of the subject being evaluated.
The storage unit 11 has a portable storage device which may be a memory device such as a RAM, ROM or flash memory, a fixed disk device such as a hard disk drive, or a flexible disk or optical disk. The storage unit stores data measured by the analytical measurement unit, data and instructions inputted from the input unit, and results of computation processing by the data processing unit, as well as the computer program and database to be used for processing by the information processing equipment. The computer program may be a computer readable recording medium such as a CD-ROM or DVD-ROM, or it may be installed via the internet. The computer program is installed in the storage unit using a commonly known setup program, for example. The storage unit stores the following formula (II):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (II)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis],
wherein formula (II) is predetermined by regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables, inputted in advance through the input unit 12. It may also store kidney function categories corresponding to Y values.
The input unit 12 is an interface and also includes operating devices such as a keyboard and mouse. This allows the input unit to input data measured by the analytical measurement unit 13 and instructions for computation processing to be carried out by the data processing unit 14. When the analytical measurement unit 13 is external, for example, the input unit 12 may also include an interface unit allowing input of measured data through a network or storage medium, separately from the operating device.
The analytical measurement unit 13 carries out a step of measuring the D- and L-amino acids in a biological sample. The analytical measurement unit 13 may therefore have a construction allowing separation and measurement of the D-forms and L-forms of amino acids. The amino acids may be analyzed one at a time, or some or all of the amino acid types may be analyzed at once. With no intention to be limitative, the analytical measurement unit 13 may be a chiral chromatography system comprising a sample introduction inlet, an optical resolution column and a detector, for example, and it is preferably a high-performance liquid chromatography system. From the viewpoint of detecting the levels of only specific amino acids, quantitation may be carried out by an enzyme method or immunological method. The analytical measurement unit 13 may be constructed separately from the sample analysis system, and measured data may be inputted through the input unit 12 using a network or storage medium.
The data processing unit 14 can calculate the Y value from the measured D- and L-amino acid levels by inputting them into formula (II) which is predetermined by regression analysis using the D- and L-amino acid levels in biological sample of an arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subject as the response variables. Formula (II) requires other correction values such as age, body weight, gender or body height, and such information may also be inputted beforehand through the input unit and stored in the storage unit. During calculation of the glomerular filtration rate, the data processing unit may access the information and input it into the formula, or read out a value from the corresponding table or graph, to calculate the glomerular filtration rate. The data processing unit 14 may also determine a kidney disease or kidney function category from the determined glomerular filtration capacity. The data processing unit 14 carries out various computation processing operations on the data measured by the analytical measurement unit 13 and stored in the storage unit 11, based on a program stored in the storage unit. The computation processing is carried out by a CPU in the data processing unit. The CPU includes a functional module that controls the analytical measurement unit 13, input unit 12, storage unit 11 and output unit 15, with the functional module performing various control operations. Each of the units may be constructed by independent integrated circuits, microprocessors and firmware.
The output unit 15 is constructed so as to output the glomerular filtration capacity which is the result of the computation processing by the data processing unit. The output unit 15 may be output means such as a display device with a liquid crystal display that directly displays the computation processing results, or a printer, or it may be an interface unit for output to an external memory unit or output to a network. It may output the Y value either together with the glomerular filtration rate and/or kidney function of the subject being evaluated, or independently.
to store in the storage unit a D- and L-amino acid level previously inputted through the input device,
to read out the formula (II):
Y=a
1
·X
1
+a
2
·X
2
+ . . . +a
n
·X
n
+b (II)
[where
a1 to an represent constants obtained by regression analysis,
X1 to Xn represent variables for the D- and L-amino acid levels selected by the regression analysis, and
b represents a constant obtained by the regression analysis], previously derived by regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables, and the D- and L-amino acid level previously stored in the storage unit, and determine the Y value in the data processing unit,
to store the determined Y value in the storage unit, and
to output the stored Y value to the output unit. The program of the invention may be stored in a storage medium, or it may be provided via electronic transmission such as the internet or a LAN.
When the information processing device comprises an analytical measurement unit, it may include a command for causing the information processing device to take the value for the blood sample measured by the analytical measurement unit and store it in the storage unit, instead of having the D- and L-amino acid level inputted from an input device.
All of the publications mentioned throughout the present specification are incorporated herein in their entirety by reference.
The examples of the invention described below are intended to serve merely as illustration and do not limit the technical scope of the invention. The technical scope of the invention is limited solely by the description in the Claims. Modifications of the invention, such as additions, deletions or substitutions to the constituent features of the invention, are possible so long as the gist of the invention is maintained.
Group of Subjects
Eleven patients were used in a retrospective study, from among a cohort of chronic kidney disease (CKD) patients admitted to Osaka University Hospital, Department of Nephrology for diagnosis and/or treatment between 2016 and 2017. Separately, 15 healthy volunteers of age 20 and older were recruited by the National Institutes of Biomedical Innovation, Health and Nutrition. The test protocol was approved by the ethics committee of each facility, and written informed consent was obtained from all of the subjects.
The information for the healthy subjects and chronic kidney disease patients were as follows.
[Table 1]
Method of Measuring Inulin Clearance
Subject's inulin clearance (Cin) was calculated from the blood and urine inulin concentrations, and urine volume, according to the standard method described in Clin Exp Nephrol 13, 50-54(2009). In brief, 1% inulin (INULEAD injection, Fuji Yakuhin Co., Ltd.) was given by continuous intravenous drip infusion over a period of 2 hours while in a state of fasting, medication postponement and water load, and blood and urine samples were taken at 3 different time points during the period. The subjects ingested 500 mL of water orally at 30 minutes before drip infusion. In order to maintain water load, 60 mL of water was ingested 40, 60 and 90 minutes after starting inulin drip infusion. The initial rate of drip infusion was 300 mL/h for the first 30 minutes, and 100 mL/h for the following 90 minutes. Blood samples were taken at 45, 75 and 105 minutes after the start of inulin drip infusion. The subjects urinated to completely empty bladder at 30 minutes after the start of drip infusion. Urine samples were then taken during the period of 30 to 60 minutes, 60 to 90 minutes and 90 to 120 minutes thereafter. Inulin was measured using an enzyme method. The average of three Cin values was used as Cin (Cin-ST) according to a standard method (
Measurement of Blood D- and L-Amino Acids
Sample Preparation
Sample prepare from human plasma was carried out as follows: First a 20-fold volume of methanol was added to and completely mixed with the plasma. After centrifugation, 10 μL of supernatant obtained from the methanol homogenate was transferred to a brown tube and dried under reduced pressure. To the residue there were added 20 μL of 200 mM sodium borate buffer (pH 8.0) and 5 μL of fluorescent labeling reagent (40 mM 4-fluoro-7-nitro-2,1,3-benzooxadiazole (NBD-F) in anhydrous MeCN), and the mixture was then heated at 60° C. for 2 minutes. The reaction was suspended by addition of 75 μL of aqueous 0.1% TFA (v/v), and 2 μL of the reaction mixture was supplied to two-dimensional HPLC.
Quantitation of Amino Acid Optical Isomers by Two-Dimensional HPLC
The amino acid optical isomers were quantified using the following two-dimensional HPLC system. NBD derivatives of the amino acids were separated and eluted using a reversed-phase column (KSAA RP, 1.0 mm i.d.×400 mm; Shiseido Co., Ltd.), in the mobile phase (5 to 35% MeCN, 0 to 20% THF, 0.05% TFA). The column temperature was 45° C. and the mobile phase flow rate was 25 μL/min. The separated amino acid fraction was separated off using a multi loop valve, and optically resolved in a continuous manner with a chiral column (KSAACSP-001S, 1.5 mm i.d.×250 mm; Shiseido Co., Ltd.). The mobile phase used was a MeOH/MeCN mixed solution containing citric acid (0 to 10 mM) or formic acid (0 to 4%), according to the amino acid retention. NBD-amino acids were detected by fluorescence detection at 530 nm using excitation light of 470 nm. The NBD-amino acid retention time was identified from standard amino acid optical isomers and quantified by a calibration curve (
Derivation of Formula for Estimating Kidney Function by Regression Analysis
The D- and L-amino acids, creatinine and cystatin C levels in plasma of each subject were used for regression analysis against a dataset of inulin clearance results. The regression analysis was carried out by OPLS using the ropls package of free statistical analysis software “R” (URL: <https://cran.r-project.org/>).
The D- and L-amino acids, creatinine, cystatin C and inulin clearance were converted to logarithms and further standardized. The standardized inulin clearance was used as the response variables and different combinations of the D- and L-amino acids, creatinine and cystatin C were used as the explanatory variables, for all of the methods of OPLS regression analysis. A formula with high correlation coefficient R (R2 value) was extracted (
The dataset for D-serine, D-alanine, D-proline and D-asparagine detected at high frequency in blood, and inulin clearance (
Formulas derived from combinations of D- and L-amino acids, creatinine and cystatin C which are not shown in
| Number | Date | Country | Kind |
|---|---|---|---|
| 2019-239749 | Dec 2019 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2020/048977 | 12/25/2020 | WO |
