Method and system for measuring citrate and creatinine concentrations by NMR spectroscopy

Abstract

Methods and systems for detecting the presence and concentration of biomarkers associated with kidney stone formation can be useful for determining personalized treatment approaches for subjects. Nuclear magnetic resonance (NMR) spectroscopy can be a valuable tool in detecting various biomarkers associated with various disease states. NMR spectroscopy can be used to examine biological samples obtained from subjects to determine the presence and concentration of relevant biomarkers.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE

[0001] This disclosure relates generally to methods and systems for determining citrate and creatinine concentrations from in vitro biological specimens, and more particularly to determining citrate and creatinine concentrations using NMR spectroscopy. [Background technology]

[0002]

[0002] The prevalence of nephrolithiasis, or kidney stones, in the United States based on surveys conducted from 2007 to 2014 was 10.1%, with a higher prevalence in men (12.6%) than in women (7.5%). The prevalence of kidney stones has been increasing over the past few decades, an increase consistent with the rise in obesity and type 2 diabetes. Therefore, identifying the causes of kidney stones and individualizing treatments to reduce kidney stone formation and recurrence are high priorities. Summary of the Invention [Means for solving the problem]

[0003]

[0003] Described herein are methods and systems that can aid in the identification and treatment of kidney stone disease. Understanding the composition of kidney stones is consistent with prioritizing their identification and treatment. Kidney stones may contain uric acid, cystine, or struvite. Other markers that may identify patients at risk for kidney stone formation and recurrence are citrate and creatinine. Methods and systems for accurately and rapidly determining citrate and creatinine levels may have useful implications for disease states such as nephrolithiasis and type 2 diabetes. The methods and systems described herein may accurately determine the amount of citrate and / or creatinine using nuclear magnetic resonance (NMR) spectroscopy.

[0004]

[0004] The present disclosure may be implemented in various ways. In some embodiments, methods and systems may include determining citrate and / or creatinine from a patient's urine sample. In some embodiments, a method for detecting the presence of citrate and / or creatinine from urine may include obtaining a urine sample from a subject, acquiring an NMR spectrum of the obtained urine sample, and determining the concentration of citrate and / or creatinine or other metabolites of interest in the sample based on the NMR spectrum of the sample. In some embodiments, the method may include deconvoluting the NMR spectrum and determining the concentration of citrate and / or creatinine or other metabolites of interest in the sample based on the deconvoluted NMR spectrum of the sample. The concentration of citrate and / or creatinine may be further calculated using the generated standard calibration curve. The calibration curve may be generated by relating peak amplitudes of urine enriched with at least one of the creatinine and / or citrate standards to the amount of standard added. In some embodiments, determining the concentration of citrate and / or creatinine in a sample can be used to determine a course of action for treating a subject. In some embodiments, the subject can be a patient undergoing treatment related to kidney stones.

[0005] In some embodiments, the method may include simultaneously detecting the presence of both citrate and creatinine based on their respective NMR signals. In some embodiments, the simultaneous detection step may include the use of a mathematical deconvolution step. In some embodiments, various chemical analytes, as described in more detail herein, may be tested or added to the urine sample to examine potential interference between the analyte NMR signals and the citrate and / or creatinine NMR signals. Thus, in some embodiments, the generation of citrate and / or creatinine test results may not be hindered or interfered with by other analytes.

[0006] Some embodiments may be directed to a system including an NMR analyzer. The NMR analyzer may be configured to acquire citrate and / or creatinine signal lineshapes for a biological sample. The NMR analyzer may include a computer program product that may store measured citrate and / or creatinine lineshapes and a reference spectrum. The computer program may be configured to derive citrate and / or creatinine concentrations by a deconvolution process. The NMR analyzer may further include an NMR spectrometer, a flow probe in communication with the spectrometer, and / or a controller in communication with the spectrometer configured to acquire NMR signals in defined peak regions of the NMR spectrum associated with citrate and creatinine in the flow probe. In some embodiments, the system may include a component for generating a patient report providing citrate and / or creatinine levels. The controller may include or be in communication with at least one local or remote processor, and the at least one processor may be configured to perform at least one of the following steps: (i) acquiring a composite NMR spectrum of a fitting region of the in vitro biological sample; (ii) deconvoluting the composite NMR spectrum using a defined deconvolution model and curve-fitting function; and / or (iii) mathematically calculating the concentrations of citrate and creatinine from the generated calibration curves.

[0007] In some embodiments, a clinical analyzer, such as a Vantera® clinical analyzer, may be used for marker determination, and the analyzer may be communicatively coupled to the NMR instrument for an automated, high-throughput, reagentless sampling process. In some examples, the clinical analyzer may autonomously mix each urine sample with an on-board buffer solution so that sample preparation is automated as the sample is obtained. In some examples, the urine sample may be mixed with the buffer solution at a 2:1 (v / v) ratio.

[0008]

[0008] Further features, advantages, and details of the present disclosure will be understood by those skilled in the art upon reading the following drawings and detailed description of the embodiments, which are merely exemplary of the present disclosure. Features described with respect to one embodiment may be incorporated into other embodiments without being specifically described therein. That is, it should be noted that aspects of the present disclosure described with respect to one embodiment may be incorporated into a different embodiment without being specifically described therein. That is, features of all embodiments and / or any embodiment may be combined in any manner and / or combination. The foregoing and other aspects of the present disclosure are described in detail in the specification set forth below.

[0009]

[0009] The present disclosure can be better understood with reference to the accompanying drawings, which may illustrate embodiments of the present disclosure. However, the present disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Furthermore, the flowcharts and block diagrams in certain figures herein illustrate the architecture, functionality, and operation of possible implementations of analytical models and evaluation systems and / or programs according to the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, operation, or portion of code, which may include one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in an order different from that noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. The present disclosure may be better understood with reference to the following non-limiting figures. [Brief explanation of the drawings]

[0010] [Figure 1]FIG. 1 is a flow diagram of a process for determining citrate and creatinine concentrations from a biological sample according to one embodiment of the present disclosure. [Figure 2] FIG. 1 is a schematic diagram of an NMR one-pulse, radio frequency pulse sequence experiment with WET solvent suppression, according to one embodiment of the present disclosure. [Figure 3] FIG. 1 is a schematic diagram of an NMR spectrum of a mixture of formate, creatinine, citrate, and TSP shown in accordance with one embodiment of the present disclosure. [Figure 4A] FIG. 1 is a schematic diagram of a single NMR peak of TSP for determining peak center position, according to one embodiment of the present disclosure. [Figure 4B] FIG. 1 is a schematic diagram of a single NMR peak of TSP for determining a peak baseline, according to one embodiment of the present disclosure. [Figure 4C] FIG. 1 is a schematic diagram of a single NMR peak of TSP for determining the peak linewidth at 50% amplitude, according to one embodiment of the present disclosure. [Figure 4D] FIG. 1 is a schematic diagram of a single NMR peak of a TSP for determining peak skew at 20% amplitude, according to an embodiment of the present disclosure. [Figure 5A] FIG. 1 is a schematic diagram of a single NMR peak of formate for determining peak center position, according to one embodiment of the present disclosure. [Figure 5B] FIG. 1 is a schematic diagram of a single NMR peak of formate for determining a peak baseline, according to one embodiment of the present disclosure. [Figure 5C] FIG. 1 is a schematic diagram of a single NMR peak of formate for determining the peak linewidth at 50% amplitude, according to one embodiment of the present disclosure. [Figure 5D] FIG. 1 is a schematic diagram of a single NMR peak of formate for determining peak skew at 10% amplitude, according to an embodiment of the present disclosure. [Figure 6] FIG. 10 is a flow diagram of a routine for improving a creatinine region fit by considering other peaks within the region, according to one embodiment of the present disclosure. [Figure 7]FIG. 1 is a flow diagram of a routine for improving the creatinine region fit by adjusting the creatinine peak linewidth, according to one embodiment of the present disclosure. [Figure 8A] FIG. 1 is a schematic diagram of an NMR spectrum showing the citrate region according to an embodiment of the present disclosure. [Figure 8B] FIG. 1 is a schematic diagram of an NMR spectrum showing citrate as a function of pH, according to an embodiment of the present disclosure. [Figure 9] FIG. 1 is a flow diagram for linear deconvolution of citrate NMR peaks according to one embodiment of the present disclosure. [Figure 10] FIG. 1 is a schematic diagram of an NMR spectrum representing various concentrations of creatinine, according to an embodiment of the present disclosure. [Figure 11] FIG. 1 is a schematic diagram of an NMR spectrum representing various concentrations of citrate, according to an embodiment of the present disclosure. [Figure 12] FIG. 1 is a schematic diagram of a block diagram of an NMR spectroscopy apparatus according to one embodiment of the present disclosure. [Figure 13] FIG. 1 is a schematic block diagram of a data processing system according to one embodiment of the present disclosure. [Figure 14] 1 is a plot depicting the limit of quantitation of creatinine according to one embodiment of the present disclosure. [Figure 15] 1 is a plot depicting the limit of quantitation of citrate according to one embodiment of the present disclosure. [Figure 16] 1 is a two plot display in which the first plot (left) is a linear scatter plot of creatinine and the second plot (right) is a residual plot of creatinine, according to an embodiment of the present disclosure. [Figure 17] 1 is a two-plot display in which the first plot (left) is a scatter plot of citrate linearity and the second plot (right) is a residual plot of citrate, according to an embodiment of the present disclosure. [Figure 18] 10A-10C are various plot displays comparing creatinine measurements made by NMR assay compared to chemical assays according to embodiments of the present disclosure. [Figure 19]10A-10C are various plots comparing citrate measurements made by NMR assay compared to chemical assays according to embodiments of the present disclosure. [Figure 20] FIG. 1 shows two calibration curves for citrate (left) and creatinine (right) used for amplitude to concentration conversion according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011]

[0037] Terms and Definitions Like numbers refer to like elements throughout. In the figures, the thickness of certain lines, layers, components, elements or features may be exaggerated for clarity. Dashed lines indicate optional features or operations unless otherwise stated. The order of operations and / or steps shown in the figures or listed in the claims is not intended to be limited to the order presented unless otherwise stated.

[0012]

[0038] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and / or "comprising," as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, phrases such as "between X and Y" and "between approximately X and Y" should be interpreted to include X and Y. As used herein, phrases such as "approximately X to Y" mean "approximately X to approximately Y."

[0013]

[0039] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. Terms, such as those defined in commonly used dictionaries, should be interpreted to have a meaning consistent with their meaning in the context of this specification and related art, and should not be interpreted in an idealized or overly formal sense unless explicitly defined as such in this specification. Well-known functions or configurations may not be described in detail for the sake of brevity and / or clarity.

[0014]

[0040] Terms such as "first," "second," and the like may be used herein to describe various elements, components, regions, layers, and / or portions, but it should be understood that these elements, components, regions, layers, and / or portions should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer, or portion from another region, layer, or portion. Thus, a first element, component, region, layer, or portion described below could be referred to as a second element, component, region, layer, or portion without departing from the teachings of the present disclosure. The order of operations (or steps) is not limited to the order presented in the claims or drawings unless otherwise specified.

[0015]

[0041] Various aspects of the present disclosure are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the present disclosure. Thus, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values ​​within that range. For example, the description of a range such as 1 to 6 should be considered to have specifically disclosed subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the width of the range.

[0016]

[0042] "Sample" or "patient sample" or "biological sample" or "specimen" are used interchangeably herein. Non-limiting examples of liquid samples for analysis by the disclosed systems and methods may include blood or blood products (e.g., serum, plasma, etc.), urine, nasal swabs, liquid biopsy samples (e.g., for cancer detection), or combinations thereof. The term "blood" includes whole blood, blood products, or any portion of blood as conventionally defined, such as serum, plasma, or buffy coat. Suitable samples include those that can be deposited on a substrate for collection and drying, including, but not limited to, blood, plasma, serum, urine, saliva, tears, cerebrospinal fluid, organ, hair, muscle, or other tissue samples or other liquid aspirates.

[0017]

[0043] The terms "patient" or "subject" are used broadly and refer to an individual who provides a biological sample for testing or analysis.

[0018]

[0044] The term "clinical disease state" refers to a medical condition at risk that may indicate the appropriateness of medical intervention, dietary adjustment and / or regimen, treatment, treatment adjustment, or exclusion and / or monitoring of a particular treatment (e.g., pharmaceutical). Identifying the likelihood of a clinical disease state may allow a clinician to treat, delay, or prevent the onset of the condition accordingly. Examples of clinical disease states include, but are not limited to, nephrolithiasis, CHD, CVD, stroke, type 2 diabetes, prediabetes, dementia, Alzheimer's disease, cancer, arthritis, rheumatoid arthritis (RA), kidney disease, lung disease, COPD (chronic obstructive pulmonary disease), peripheral vascular disease, congestive heart failure, organ transplant response, and / or medical conditions related to immune deficiency, abnormalities in biological functions in protein sorting, immunity, and receptor recognition, inflammation, pathogenesis, metastasis, and other cellular processes.

[0019]

[0045] The term "programmatically" means performed using a computer program and / or software, processor, or ASIC instructions. The term "electronic" and its derivatives refer to automated or semi-automated operations performed using devices with electrical circuits and / or modules, rather than through mental steps, typically referring to operations performed programmatically. The terms "automated" and "automatic" mean that an operation can be performed with minimal or no manual input. The term "semi-automated" refers to allowing some operator input or activation, but the calculation and signal acquisition, as well as the calculation of the concentration of ionized components, are performed electronically, typically by a program, without the need for manual input. The term "about" refers to + / - 10% (mean or average) of a specified value or number.

[0020]

[0046] The automated clinical NMR analyzer may be particularly suitable for analyzing metabolite and / or lipoprotein data in in vitro serum and / or plasma samples or urine samples. The term "circuit" refers to an entirely software embodiment or an embodiment combining software and hardware aspects, components, or features.

[0021]

[0047] The term "protocol" refers to an automated electronic algorithm (typically a computer program) with defined rules for mathematical calculations, data interrogation, and analysis that manipulates NMR data to compensate for temperature sensitivity.

[0022]

[0048] The term "computer network" includes one or more local area networks (LANs), wide area networks (WANs), and in certain embodiments may include a private intranet and / or the public Internet (also known as the World Wide Web or "web"). The term "networked" system means that one or more local analyzers can communicate with at least one remote (local and / or off-site) control system. The remote control system may be kept in a local "clean" room that is separate from the NMR clinical analyzer and is not subject to the same biohazard control requirements / concerns as the NMR clinical analyzer.

[0023]

[0049] As used herein, the term "integral" in reference to an NMR spectrometer refers to the resulting NMR signal (spectrum) of a sample. The integral can refer to the area of ​​a particular peak in the NMR spectrum. The area of ​​a peak is proportional to the concentration of that particular species. Therefore, when a (constant) concentration standard is measured, the integral value will be relatively constant if the NMR spectrometer / instrument is functioning properly; for example, the value will be within a target range, such as + / - 10%, or in some embodiments, + / - approximately 2%. Alternatively, the integral can be based on multiple peaks, or even all peaks, in the NMR spectrum, although it is more common to measure the area of ​​one defined peak. The term "ppm" (parts per million) can be used to describe the position of one or more peaks on the x-axis of an NMR spectrum, corresponding to the energy or frequency of the absorbed radio waves.

[0024]

[0050] The term "downfield" refers to the region / location on the NMR spectrum that is to the left of a particular peak / position / point (higher ppm scale relative to the reference). Conversely, the term "upfield" refers to the region / location on the NMR spectrum that is to the right of a particular peak / position / point.

[0025]

[0051] When known concentration standards are measured as stand-alone "calibration" samples, the integral provides a good test of (routine) performance, allowing quantitative NMR without the addition of an internal standard to each biological sample. Calibration samples can be aqueous or non-aqueous solutions of the concentration standards that can be used to calculate factors that normalize the integrals produced by an instrument or set of instruments so that the instrument(s) produce equivalent integrals for a known amount of concentration standard in a given volume of sample.

[0026]

[0052] The term "concentration standard" refers to a substance used to evaluate one or more peaks in an NMR spectrum. Examples of concentration standards include organic (non-polar) ethylbenzene solutions and aqueous sodium acetate solutions. In some embodiments, a TMA (trimethylacetic acid) solution may be used as a concentration standard. The TMA solution can have a specific ionic strength so that it behaves similarly to plasma / serum or other samples of interest in terms of NMR behavior. Furthermore, because the chemical shifts of citrate and creatinine can change with pH, ​​TSP (3-(trimethylsilyl)propionic acid-2,2,3,3-d4 acid, sodium salt) and formate signals can be used as references to identify signals of interest.

[0027]

[0053] Methods for measuring citrate and / or creatinine The present disclosure is described more fully below, where embodiments of the disclosure are set forth. However, the disclosure may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0028]

[0054] Described herein is a novel method, including an assay for identifying risk of nephrolithiasis obtained from a subject's biological sample, that utilizes NMR technology that can be paired with an NMR analyzer, such as the Vantera® clinical analyzer, for rapid, high-throughput results. The novel assay avoids alternative spectrophotometer-based assays for measuring citrate and creatinine. Traditionally, citrate and creatinine have been detected and quantified in biological samples by time-consuming chemical assays that can be prone to erroneous results in human-based assays. The chemicals used in this traditional assay include citrate lyase and creatinase, enzymes used to detect citrate and creatinine, respectively. Citrate can be quantified by a reduction-oxidation reaction that results in a change in absorbance of NADH, and creatinine can be quantified by the change in absorbance as a result of reaction with alkaline picrate. Limiting features of this traditional assay include limited access to enzymes and / or enzyme sources, limited enzyme shelf-life, and overly sensitive conditions for enzyme use. Thus, this disclosure describes a novel assay for the detection and quantification of citrate and creatinine using nuclear magnetic resonance.

[0029]

[0055] In embodiments of the present disclosure, a biological sample may be obtained from a subject and analyzed using analytical techniques to detect and quantify the presence of citrate and creatinine. An example embodiment may include a urine sample that can be analyzed using NMR technology.

[0030]

[0056] In some embodiments, the analyte to be measured may be citrate, and citrate may be found according to its respective signal in the NMR spectrum. In some embodiments, the analyte to be measured may be creatinine, and creatinine may be found according to its respective signal in the NMR spectrum. Furthermore, in other embodiments of the present disclosure, citrate and creatinine may be found simultaneously using the respective signals of each analyte in the NMR spectrum.

[0031]

[0057] In some embodiments, the method may include obtaining an NMR spectrum of a biological sample obtained from a subject and measuring the concentration of citrate and / or creatinine from the biological sample based on the NMR spectrum. The NMR analyzer may be particularly suited to obtaining data measurements from the biological sample, including qualitative and / or quantitative measurements that can be used for therapeutic or diagnostic purposes, typically meeting appropriate regulatory guidelines for accuracy, depending on the jurisdiction and / or test being performed. In some embodiments, an automatic temperature compensation protocol is useful for measuring NMR-quantifiable metabolites in types of human biological fluids, such as serum / plasma, urine, CSF, semen, sputum, and lavage fluids.

[0032]

[0058] In some embodiments, acquiring the NMR spectrum may include generating measured citrate and / or creatinine signal lineshapes from the NMR spectrum. Acquiring the NMR spectrum may further include generating calculated lineshapes for citrate and / or creatinine based on derived concentrations of citrate and / or creatinine expected to be present in the biological sample.

[0033]

[0059] In some embodiments, generating the calculated lineshape for citrate and / or creatinine may include calculating a plurality of reference coefficients for the calculated lineshape based on a linear least squares fitting technique.

[0034]

[0060] In some embodiments, identifying the citrate and / or creatinine concentration may include determining a degree of correlation between the initially calculated lineshape and the measured citrate and / or creatinine signal lineshape of the biological sample. Further, if the measured citrate and / or creatinine signal lineshape of the biological sample exceeds a calculated threshold, the concentration or presence of the analyte may be determined.

[0035]

[0061] In some embodiments, the NMR spectrum of a biological sample may include four citrate proton singlet signals in four different regions, with the four citrate proton singlet signals covering the range of 2.50 to 2.75 ppm. Additionally and / or alternatively, in some embodiments, the NMR spectrum of a biological sample may include two creatinine proton singlet signals in two different regions, with the two creatinine proton singlet signals covering the range of 3.0 to 4.20 ppm. For example, in some embodiments, citrate and creatinine can be quantified using the NMR signals at 2.50 to 2.75 ppm and 3.07 ppm, respectively. The respective chemical shifts may change position depending on pH. Therefore, in certain embodiments, TSP (3-(trimethylsilyl)propionic acid-2,2,3,3-d4 acid, sodium salt) and / or formate signals may be used as a relative reference for identifying the citrate and creatinine signals.

[0036]

[0062] In some embodiments, the NMR spectrum of a sample can include a spectrum representing citrate. Citrate can be identified by singlet proton peaks in four different regions of the NMR spectrum corresponding to four different chemical shifts. In particular, citrate-3, the signal with the highest signal-to-noise ratio, can be first identified based on two relative distances: the first distance from formate and TSP and the second distance from the creatinine-2 signal and TSP. Citrate-4 can then be located based on its coupling with citrate-3. Citrate-2 can then be located based on its coupling with citrate-3. Citrate-1 can then be located based on its coupling with citrate-2. In some embodiments, using the expected amplitude for a given signal can improve citrate specificity when the citrate signal is low compared to impurities or in the presence of adjacent and / or overlapping impurity peaks.

[0037]

[0063] In some embodiments, the NMR spectrum of a sample can include a spectrum representing creatinine. Creatinine can be identified by a proton singlet in a first downfield region and a proton singlet in a second upfield region. Creatinine can be identified using two signals, specifically the upfield signal and its relative distance from a TSP standard. A mathematical formula can relate the distance of the creatinine upfield signal from the TSP as a function of pH. The amplitudes of the two signals can be used to limit identification ambiguity. Both signals from creatinine can be used to determine quantification, but the upfield signal may interfere less with the water signal due to its higher intensity.

[0038]

[0064] In one embodiment, a mathematical algorithm may be used to accurately model the baseline for citrate and creatinine signals. Thus, in some embodiments, the method may include deconvolving signal data related to citrate and / or creatinine proton singlet signals. Furthermore, the method may include comparing data from multiple deconvoluted signal data sets with previous calibration data corresponding to standard biological samples with known concentrations of citrate and / or creatinine to determine the concentrations of citrate and / or creatinine in the biological sample. Furthermore, when mathematically modeling, a Lorentzian linear regression may be used, in which case a linear function and a constant offset may be incorporated into the algorithm. In some embodiments, the Lawson-Hanson nonnegative least-squares fitting algorithm may also be used for peak deconvolution.

[0039]

[0065] In some embodiments, the unit conversion of signal amplitude to analyte concentration in mg / dL or mg / L may include generating a linear calibration curve that plots the signal amplitude of citrate or creatinine against the concentration used to prepare the respective solutions. Calibration of the curve may be performed in triplicate, measuring a total of at least 12 samples. The linear plot may be used to generate a mathematical function that can convert a given signal amplitude to units of concentration according to the calibration curve. Accordingly, some embodiments may include a method for generating a report listing the concentrations of citrate and / or creatinine components present in a biological sample.

[0040]

[0066] In some embodiments, the NMR instrument may be interfaced with a clinical analyzer, such as a Vantera® clinical analyzer, for automated sampling. In such embodiments, samples may be collected and deposited into a fully automated analyzer, and a spectrum may be provided immediately thereafter. In some embodiments that do not use an automated sampling instrument, sample preparation may include creating a buffer solution and mixing the buffer solution with the sample before positioning it in the NMR instrument. Additionally, embodiments that utilize an automated sampling system may only involve positioning the sample in the instrument for sampling.

[0041]

[0067] In some embodiments, each urine sample can be mixed in the clinical analyzer with a 2:1 (v / v) ratio of 1.5 molar potassium phosphate dibasic buffer solution, further containing 38 mM sodium formate and 2.18 mM TSP. The pH of the buffer solution can be acidic for testing. The pH can be adjusted to approximately 6.0 using an acid. The sample can then be ready for spectral acquisition and processing.

[0042]

[0068] In some embodiments, acquisition and processing may further include a temperature increase above room temperature within the probe, at least four steady-state scans, a direct detection time of at least 2.0 seconds, a relaxation time of at least 1.95 seconds between scans, and an acquisition period of at least 64 seconds. Additionally, the free induction decay signal may be zero-filled with real and imaginary data points to be multiplied by an exponential window function that may correspond to a 0.5 Hz line broadening prior to Fourier transformation (FT). After FT, the spectrum may be restored for phase and baseline errors. Other corrections for each of these variables may also be used.

[0043]

[0069] In one embodiment, analytical validation and imprecision can be tested by combining several urine pools selected from specimen samples and collected in urine collection containers. Three-container urine samples containing low, intermediate, and high citrate and creatinine concentrations can be evaluated for assay imprecision. Inter-assay and intra-assay precision can be determined. Arithmetic means, standard deviations, and percent coefficients of variation can be calculated, and imprecision acceptance criteria for citrate and creatinine assays can be predetermined to be 10% and 12%, respectively. Furthermore, linearity can be assessed using serial dilutions of urine pools with low, intermediate, and high concentrations of citrate or creatinine. Assay linearity can be evaluated by linear and higher-order polynomial regression of assay results from serially mixed pools compared to expected concentrations using EP Evaluator® software. Acceptance criteria for linearity data can be defined as an acceptable nonlinearity of 3.5% for citrate and 8.6% for creatinine, corresponding to a slope between 0.9 and 1.10. Table 1 shows the intra- and interassay imprecision values ​​for citrate and creatinine. [Table 1]

[0044]

[0070] In another embodiment, the blank limit can be calculated using five deionized water samples. Additionally, the limit of detection can be calculated using five low citrate and creatinine concentration samples.

[0045]

[0071] Additionally, the temperature stability of citrate and creatinine from samples can be examined. Room temperature (20-25°C), refrigerated (2-8°C), and frozen (-20<-70°C) assays can be utilized to examine the effects of various temperatures, including freeze-thaw cycles on urine samples. Table 2 provides a summary of the time- and temperature-based stability of citrate and creatinine. [Table 2]

[0046]

[0072] In one embodiment of the present disclosure, a comparison of results between chemical-based and NMR-based assays for citrate and creatinine concentrations can be performed. The comparison can compare method results using the NMR-based assay compared to traditionally used citrate lyase and creatinase-based enzyme assays. The assay results can be compared to assess the accuracy of the new NMR-based assay for citrate and creatinine concentrations.

[0047]

[0073] One embodiment may further test for interference of other analytes with citrate and creatinine concentrations and results from an NMR-based assay. At least 10 or more substances may be used to test for interference, with three of the 10 being endogenous and seven of the 10 being exogenous. In one example of the present disclosure, the three endogenous substances may include, for example, urea, uric acid, and albumin. The seven exogenous substances may include, for example, acetaminophen, acetic acid, acetylsalicylic acid, ascorbic acid or vitamin C, boric acid, ibuprofen, and naproxen sodium. Boric acid or acetic acid may be used instead of hydrochloric acid as a preservative in urine samples. Substance concentrations may be tested at or above endogenous (naturally occurring) and exogenous (external and / or therapeutic) concentrations when compared to urinary citrate and creatinine concentrations.

[0048]

[0074] Figure 1 shows a flow diagram of a process for determining citrate and creatinine concentrations from a biological sample. The biological sample may first be obtained from a subject in a biological sample container. The biological sample may then be loaded into an analytical device, such as a Vantera® analyzer. The analytical device may then prepare the biological sample according to the following sample preparation procedure.

[0049]

[0075] In some embodiments, a predetermined dilution of the biological sample may be performed. For example, in some embodiments, each urine sample for a urinary citrate and creatinine (UCC) assay may be mixed with a 2:1 (v / v) ratio of buffer solution onboard an analyzer, such as a Vantera® clinical analyzer. The buffer solution may consist of 1.5 M dipotassium phosphate (KHPO; Sigma-Aldrich), 38 mM sodium formate (CHNaO; Sigma-Aldrich), and 2.18 mM 3-(trimethylsilyl)propionic acid-2,2,3,3-d4 acid, sodium salt (TSP; Sigma-Aldrich). The pH of the buffer solution may be adjusted to 6.0 ± 0.1 using 6 N HCl. The prepared sample may be delivered by the analyzer to an NMR flow cell for spectral acquisition and processing.

[0050]

[0076] Referring to Figure 1, sample preparation may be followed by NMR spectrum acquisition and spectral processing. One-dimensional proton NMR spectra can be collected using a single pulse sequence. A 90° flip angle can be used as the read pulse, with a total acquisition time of 64 seconds. Other acquisition parameters can be as follows: spectral width = 4496.4 Hz, steady-state scans = 4, direct detection time = 2.0 seconds, relaxation between scans = 1.95 seconds, and number of scans = 12. The free induction decay signal can be zero-filled to 16,384 pairs of real and imaginary data points and multiplied by an exponential window function corresponding to a line broadening of 0.5 Hz before Fourier transformation (FT). After FT, the spectrum can be corrected for phase and baseline errors.

[0051]

[0077] Reference peak measurements may be performed following spectrum acquisition and processing. The characteristics of the TSP, formate, and creatinine peaks can be used for pre-analytical quality control as chemical shift standards and as input to algorithms for quantification of citrate and creatinine. For TSP and formate, the peak center positions, amplitudes, line widths at 50% amplitude, and skews at 20% and 10% can be calculated, respectively, and the peak center positions of the two creatinine peaks at approximately 3.07 ppm and 4.12 ppm can be calculated.

[0052]

[0078] At this point, pre-analytical quality control can be used to detect instrument failure modes that can ensure that the urinary citrate and creatinine assays will not be run unless the input spectrum is properly acquired under specified conditions (Figure 1). Detected failure modes can include sample delivery failure and NMR shimming failure. If a given condition is found to exist, subsequent evaluations may not be performed. This provides specificity to the reason for pre-analytical QC failure. Pre-analytical QC evaluations may be performed in the order shown below. Alternatively, other sequences of these steps may be used.

[0053]

[0079] The software can detect complete sample delivery failure if the TSP amplitude is <4.5 au for any of the input spectra.

[0054]

[0080] The software can detect a partial sample delivery failure if the TSP skew is <-1.0 data points or if the TSP skew is >3.0 data points for any of the input spectra.

[0055]

[0081] The software can detect shimming failure if the TSP linewidth is >2.0 Hz and the formate linewidth is >2.0 Hz for any of the input spectra.

[0056]

[0082] The NMR spectrum of a sample may include a spectrum representing creatinine. Further referring to FIG. 1, creatinine may be identified by a proton singlet in a first downfield region and a proton singlet in a second upfield region. Creatinine may be identified by using two signals, specifically the upfield signal and its relative distance from a TSP standard. A mathematical formula may relate the distance of the creatinine upfield signal from the TSP as a function of pH. The amplitudes of the two signals may be used to limit identification ambiguity. While both signals from creatinine may be used to determine quantification, the upfield signal may interfere less with the water signal due to its higher intensity.

[0057]

[0083] In some embodiments, the location of citrate can be identified after creatinine analysis (Figure 1). As previously described, in some embodiments, the NMR spectrum of a sample can include a spectrum representing citrate. Citrate can be identified by singlet proton peaks in four different regions of the NMR spectrum corresponding to four different chemical shifts. In particular, citrate-3, the signal with the largest signal-to-noise ratio, can be first identified based on two relative distances: the first distance from formate and TSP and the second distance from the creatinine-2 signal and TSP. Citrate-4 can then be located based on its coupling with citrate-3. Citrate-2 can then be located based on its coupling with citrate-3. Citrate-1 can then be located based on its coupling with citrate-2. In some embodiments, using the expected amplitude for a given signal can improve citrate specificity when the citrate signal is low relative to the impurities or in the presence of adjacent and / or overlapping impurity peaks. In some embodiments, the citrate analysis is followed by post-analytical quality control as described herein (FIG. 1). The analytical results may then be output using mathematical models and linear functions, for example, to convert the amplitudes of the citrate and creatinine peaks into concentration units, such as mg / L or mg / dL, respectively.

[0058]

[0084] Figure 2 shows a schematic of an NMR one-pulse, radio frequency pulse sequence experiment with WET solvent suppression. Proton NMR spectra can be collected using a one-pulse sequence at 47 °C. The solvent signal can be attenuated by water suppression enhanced by the T1 effect (WET) module, applied for 68 ms.

[0059]

[0085] Figure 3 shows a schematic NMR spectrum of the indicated mixture of formate, creatinine, citrate, and TSP. The spectrum shows a magnified schematic of the four citrate singlet peaks ranging from 2.50 ppm to 2.75 ppm. The TSP peak can be seen at 0.0 ppm and can serve as an internal standard for detecting the creatinine and citrate peaks. The formate peak can also be seen in the range of 8.00 ppm to 9.00 ppm and can further serve as an internal standard for detecting creatinine and citrate. Peaks observed other than creatinine, citrate, TSP, and formate can be considered irrelevant to the detection and quantification of creatinine and citrate. Other identified peaks may contain analytes that can be found in biological samples, particularly urine. Additionally, other identified peaks may contain impurities.

[0060]

[0086] Figures 4A-4D show schematic diagrams of a single NMR peak of TSP for determining the peak center position, peak baseline, peak linewidth at 50% amplitude, and peak skew at 20% amplitude, respectively. The x-axis displays the position of the data points, and the y-axis displays the peak amplitude. The peak center position (iL) of the TSP peak is shown in Figure 4A. pts ) can be determined as the highest valued data point in the interval [14796, 15096]. Using the derivative of the signal amplitude in the data point interval, this location can be matched with the change in sign of the derivative to ensure it is an actual peak. The floating point peak center location (L pts ) can be determined by calculating the root mean square deviation (RMSD) between the observed TSP peak and a Lorentzian function centered at 0.01 data point increments around the integer peak center location. The location of the Lorentzian function with the smallest RMSD can be taken as the TSP floating point peak center location.

[0061]

[0087] In some embodiments, to calculate the TSP linewidth at 50% peak amplitude, the peak baseline and amplitude can first be determined. The baseline of the TSP peak can be determined as the average amplitude of the left and right baselines. The left and right baselines are respectively defined as the interval [iL pts -90,iL pts -60] and [iL pts +60,iL pts The TSP amplitude can be determined as the amplitude value at the integer peak center position data point minus the value of the baseline. The TSP linewidth can be calculated as the length of a line drawn at 50% of the peak amplitude. Linear interpolation can be used to determine the intersections of the 50% line with the left and right sides of the peak. If no 50% intersection exists on either the left or right side, the line can be assumed to extend to the end of the interval [14796, 15096] as needed.

[0062]

[0088] The TSP skew can be calculated as the difference between the floating-point peak center location and the midpoint of a line drawn at 20% of the peak amplitude. The TSP peak can have a linear profile with satellite peaks on either side of the center peak, and the 20% height can be above the amplitude of these satellite peaks so as not to contribute to skew measurement error. As with the line width, linear interpolation is used to determine the intersections of the 20% line with the left and right sides of the peak. If no 20% intersection exists on either the left or right side, the line can be assumed to extend to the end of the interval [14796, 15096] as needed.

[0063]

[0089] Figures 5A-5D show schematic diagrams of a single NMR peak of formate for determining the peak center position, peak baseline, peak linewidth at 50% amplitude, and peak skew at 10% amplitude, respectively. The x-axis displays the position of the data points, and the y-axis displays the peak amplitude. The integer peak center position (iL) of the formate peak is shown.pts ) can be determined as the maximum amplitude value in the data point interval [2350, 3050]. Using the derivative of the signal amplitude in the data point interval, this location can be matched with the change in sign of the derivative to ensure it is an actual peak. The floating point peak center location (L pts ) can be determined by calculating the root mean square deviation (RMSD) between the observed formate peak and a Lorentzian function centered at 0.01 data point increments around the integer peak center position. The position of the Lorentzian function with the smallest RMSD can be taken as the formate float peak center position.

[0064]

[0090] In some embodiments, to calculate the formate line width at 50% peak amplitude, the peak baseline and amplitude can be first determined. The baseline of the formate peak can be determined as the average amplitude of the left and right baselines. The left and right baselines are respectively defined as the interval [iL pts -70,iL pts -40] and [iL pts +40,iL pts The formate amplitude can be determined as the midpoint amplitude at [2350, 3050]. The 40 data points around the integer position can be excluded from baseline considerations, as these points (approximately) constitute the formate peak itself. The formate amplitude can be determined as the value of the integer peak center position data point minus the value of the baseline. The formate linewidth can be calculated as the length of a line drawn at 50% of the peak amplitude. Linear interpolation can be used to determine the intersections of the 50% line with the left and right sides of the peak. If no 50% intersection exists on either the left or right side, the line can be assumed to extend to the end of the interval [2350, 3050] as needed.

[0065]

[0091] Formate skew can be calculated as the difference between the floating-point peak center position and the midpoint of a line drawn at 10% of the peak amplitude. As with line width, linear interpolation can be used to determine the intersections of the 10% line with the left and right sides of the peak. If no 10% intersections exist on either the left or right side, the line can be assumed to extend to the end of the interval [2350, 3050] as needed.

[0066]

[0092] Figure 6 shows a flow diagram of a routine for improving the creatinine region fit by considering other peaks within the region. Once obtained, a basis set or design matrix can be generated as a sum of four Lorentzian lineshapes centered on the floating point of each of the analyte's positions on the peak.

[0067]

[0093] Although the method embodiments for analyzing creatinine described herein may be applied only to the upfield peak, analysis of each peak may generate a creatinine concentration value. Therefore, with further reference to FIG. 6, an iterative method using Lawson-Hanson nonnegative least-squares deconvolution can be used to fit each creatinine peak using a basis set (design matrix). The creatinine basis set can include five Lorentzian singlets centered on the floating-point creatinine position, two sloped lines, and a DC offset. The Lorentzian singlet lineshape can be divided into left and right linewidths. The default left and right linewidths may be half the measured creatinine linewidth. The area of ​​the Lorentzian lineshape may be normalized to a value of 1000.

[0068]

[0094] As further shown in Figure 6, the fit deviations resulting from each application of the deconvolution method can be evaluated to determine the location of additional Lorentzian components. These additional components can improve the accuracy of the fit by modeling additional or adjacent signals in the fitting region that may be highly variable for the sample.

[0069]

[0095] Figure 7 shows a flow diagram of a routine for improving the creatinine region fit by adjusting the creatinine peak linewidth. In addition to iterating the fit to resolve adjacent signals within the fitting region, the fit can be iterated to modify the linewidth of the Lorentzian lineshape used to fit the creatinine and adjacent signals. Ultimately, the final linear attributes (left and right linewidths) of the upfield creatinine peak may be utilized in downstream citrate modeling routines.

[0070]

[0096] In some embodiments, once the creatinine fit is optimized, the creatinine concentration (e.g., mg / dL) can be calculated. This may be done using the following analysis, or variations thereof: Thus, in one embodiment, the creatinine concentration may be calculated by summing the deconvolution coefficients of the creatinine Lorentzian components and multiplying by (8836.9*0.011312) for the upfield creatinine peak. The number 8836.9 can convert the value to μmol / L, and the number 0.011312 can convert the μmol / L value to mg / dL.

[0071]

[0097] The distance (difference) between the TSP position and the formate position can be an indicator of pH and can be useful for calculating other pH-dependent peak positions and peak couplings (distance between two peaks). The predicted position of the upfield creatinine peak (hereinafter referred to as CRE1), as well as the predicted coupling between the downfield and upfield creatinine peaks, can be calculated from the TSP and formate positions according to the formulas provided herein, according to examples of the present disclosure.

[0072]

[0098] The expected coupling between the two creatinine peaks can be calculated as follows: CRE_coupling=(a*exp(b*NMRpH)+c*exp(d*NMRpH))+1350 where a=-0.06187;b=0.02749;c=349.9;d=-0.0002149; NMRpH=tsp.fPosition-formate.fPosition-12050.

[0073]

[0099] The predicted location of the upfield creatinine peak can be calculated as follows: CRE1_location=tsp.fPosition-((a*exp(b*NMRpH)+c*exp(d*NMRpH))+4400) where a=-0.02564;b=0.02853;c=182.3;d=-0.0002739; NMRpH=tsp.fPosition-formate.fPosition-12050.

[0074]

[0100] The predicted position of the downfield creatinine peak (hereafter referred to as CRE2) can be calculated in two steps. First, the predicted position can be calculated from the floating-point TSP position and NMR-pH: CRE2_location=tsp.fPosition-((a*exp(b*NMRpH)+c*exp(d*NMRpH))+5900) where a=-0.07221;b=0.02848;c=382.4;d=-0.0003509; NMRpH=tsp.fPosition-formate.fPosition-12050.

[0075]

[0101] Second, it can be calculated independently from the predicted upfield creatinine position and coupling: CRE2_location=CRE1_location-CRE_coupling

[0076]

[0102] In some embodiments, the predicted location of CRE2 can be assigned as the most downfield result (either the leftmost or the result with the smallest data point value). The predicted CRE2 location can be used to define a creatinine doublet search region. This search region can be defined as the predicted CRE2 location minus 100 data points for the predicted CRE1 location plus 40 data points. Within this search region, pairs of peaks that match the predicted coupling ±50 data points can be identified. An empirically derived cost function can then be used to select the pair of peaks with the highest amplitude that best matches the known amplitude relationship of the creatinine peaks and their linear pH-dependent coupling relationship with TSP. The creatinine doublet search region can be smoothed using a Savitzky-Golay filter with a polynomial order of 3 and a frame length of 11. The derivative of the smoothed search region can be calculated. Peaks where the sign of the derivative changes from positive to negative can occur. Peak pairs separated by the predicted coupling ±50 data points can be identified.

[0077]

[0103] The pair of peaks that can maximize the following function can be selected as the creatinine doublet (CRE2 and CRE1 peaks): ((lamp+ramp) / 2) / (abs(0.6-lamp / ramp)*(abs(2.7629*(tsp_loc-rloc)-6379.2-(tsp_loc-lloc)))+1) where lamp = estimated amplitude of the left (downfield, CRE2) peak; ramp = estimated amplitude of right (upfield, CRE1) peak; lloc = location of left (downfield, CRE2) peak; rloc = location of right (upfield, CRE1) peak; tsp_loc = TSP peak location.

[0078]

[0104] The floating-point positions of individual creatinine peaks (CRE2 and CRE1) can be determined by calculating the root-mean-square deviation (RMSD) between the observed creatinine peak and a Lorentzian function centered at 0.01 data point increments around the integer peak position. Similar to TSP and formate, the baseline for each creatinine peak can be determined as the average amplitude of the left and right baselines.

[0079]

[0105] For the upfield creatinine peak (CRE1), the left and right baselines are separated by the interval [iL pts -80,iL pts -70] and [iL pts +70,iL pts +80], and iL pts refers to the integer peak position. The 70 data points around the peak position can be excluded from baseline consideration as those points (approximately) constitute the peak itself.

[0080]

[0106] For the downfield creatinine peak (CRE2), the left and right baselines are divided into sections [iL pts -20,iL pts -15] and [iL pts +15,iL pts +20] can be determined as the amplitude median (value), and iL pts refers to the integer peak position. The 15 data points around the peak position can be excluded from baseline consideration because those points (approximately) constitute the peak itself. The baseline region of a downfield creatinine peak can be smaller due to its proximity to residual water peaks, which can distort the baseline. The amplitude of each creatinine peak can be determined as the amplitude value at the integer peak position minus the value of its baseline.

[0081]

[0107] Figure 8A shows a schematic of an NMR spectrum representing citrate, and Figure 8B shows the dependence of the position on pH. The position of the citrate peak can be very sensitive to pH. The coupling of peaks 1 and 2 (the most downfield, labeled cit1 and cit2 in Figure 8A) and peaks 3 and 4 (the most upfield, labeled Cit3 and Cit4 in Figure 8A) can be relatively constant with pH. However, the coupling between the two pairs can be sensitive to pH. Citrate can be located using the peak positions of TSP and formate as indicators of pH, and the positions determined for the two creatinine peaks as a second indicator of pH.

[0082]

[0108] In some embodiments, upfield doublets may be identified first because their positions may be less pH-sensitive than downfield doublets. Given the positions of the upfield doublets, pH-based coupling can be used to identify the downfield doublets. The floating-point positions of the four citrate peaks may be used as input to a method for modeling the citrate region.

[0083]

[0109] A predicted Cit3 peak position can be calculated first based on the observed difference between the formate position and the TSP position, and then based on the observed difference between the downfield creatinine peak and the TSP position. The final predicted Cit3 peak position can be calculated as the average of the two separate estimates of the Cit3 position.

[0084]

[0110] For example, in one embodiment, the predicted Cit3 peak position based on TSP and formate can be calculated as follows: Citr3_formate_location=tsp.fPosition-((a0+a1*cos(NMRpH*w)+b1*sin(NMRpH*w)+a2*cos(2*NMRpH*w)+b2*sin(2*NMRpH*w)+a3*cos(3*NMRpH*w)+b3*sin(3*NMRpH*w))+3700) where NMRpH=tsp.fPosition-formate.fPosition-12050; a0=-805.5;a1=1407;b1=1258;a2=59.14;b2=-868.8;a3=-157; b3=94.78;w=0.00763. The predicted Cit3 peak position based on TSP and CRE2 can be calculated as follows: Citr3_cre2_location=tsp.fPosition-(((p1*NMRpH^2+p2*NMRpH+p3) / (NMRpH^3+q1*NMRpH^2+q2*NMRpH+q3))+3700) where NMRpH=tsp.fPosition-cre2.fPosition-5900;p1=-1.599e+09; p2=7.1e+11;p3=-5.406e+12;q1=-2.574e+06;q2=-4.598e+08; q3=6.171e+11.

[0085]

[0111] If the NMR-pH in the CRE2-based formula above is >380, the CRE2-based Cit3 position can be skipped. The predicted position of the third citrate peak (Cit3) can be calculated as the average of the predicted positions based on formate and CRE2: Citr3_location=(Citr3_formate_location+Citr3_cre2_location) / 2 If the CRE2 base position is skipped, Citr3_location=Citr3_formate_location

[0086]

[0112] In certain embodiments, the predicted Cit3 position can then be used to define an upfield citrate doublet search region. This search region can be defined as 40 data points downfield from the predicted Cit3 position to 97 data points upfield from the predicted Cit3 position. Within this search region, peak pairs that match the known coupling of the Cit3 and Cit4 peaks can be identified. An empirically derived cost function can then be used to select the peak pair with the highest amplitude that best matches the known amplitude relationship of the Cit3 and Cit4 peaks. Next, a Savitzky-Golay filter with a polynomial order of 3 and a frame length of 9 can be used to smooth the upfield citrate doublet search region.

[0087]

[0113] In one embodiment, the derivative of the smoothed search region may be calculated. Peaks occur where the sign of the derivative changes from positive to negative. For each identified peak, the estimated amplitude of the peak may be calculated by summing the values ​​of like signs of the derivative on either side of the peak location and dividing the sum by two.

[0088]

[0114] In one embodiment, for each identified peak, if a paired peak at 57±6 data points has not already been identified as a peak due to a change in derivative sign, the amplitude of the paired search region at 57 data points may be added as a "virtual" peak with an amplitude calculated as a one-sided sum of derivative values ​​of like sign at the paired points. Adding a "virtual" peak allows for the identification of doublets even when they are obscured by other non-citrate peaks. Pairs of peaks (real and virtual) separated by 57±6 data points may be identified. The pair of peaks that can maximize the following function is selected as the upfield citrate doublet (Cit3 and Cit4 peaks): (((lamp+ramp) / 2)^2) / abs(36.8 / 22.13-lamp / ramp) where lamp = estimated amplitude of the left (downfield, Cit3) peak; ramp = estimated amplitude of the right (upfield, Cit4) peak.

[0089]

[0115] This empirically derived function can effectively weight the amplitude relationships of peak pairs to favor peak pairs that can match the typical amplitude ratio of the Cit3 and Cit4 doublets. The floating-point positions of individual citrates (Cit3 and Cit4) can be determined by calculating the root-mean-square deviation (RMSD) between the observed citrate peak and a Lorentzian function centered at 0.01 data point increments around the integer peak position. Given the identified Cit3 floating-point position, the predicted coupling between the Cit2 and Cit3 peaks can be calculated based on the difference between the Cit3 position and the TSP position: Citr23_coupling=(p1*NMRpH^3+p2*NMRpH^2+p3*NMRpH+p4) / (NMRpH^3+q1*NMRpH^2+q2*NMRpH+q3) where NMRpH=tsp.fPosition-citr3.fPosition-3700;p1=665.5;p2=-3.613e+05; p3=8.034e+07;p4=1.251e+09;q1=-680.6;q2=4.383e+05;q3=1.074e+07. If the value of NMR-pH in the above equation is >300, the predicted coupling between Cit2 and Cit3 can be calculated based on the difference between the TSP and formate positions: Citr23_coupling=(p1*NMRpH^3+p2*NMRpH^2+p3*NMRpH+p4) / (NMRpH^3+q1*NMRpH^2+q2*NMRpH+q3) where NMRpH=tsp.fPosition-formate.fPosition-12050;p1=48.5;p2=-1110; p3=-1.203e+07; p4=2.466e+09; q1=-455.4; q2=1899; q3=1.383e+07. The predicted position of the Cit2 peak can be calculated as follows: Citr2_location=citr3.fPosition-Citr23_coupling

[0090]

[0116] In some embodiments, the predicted Cit2 position can be used to define a downfield citrate doublet search region. The downfield citrate doublet search region can be defined as 40 data points upfield from the predicted Citr2 position, downfield from 97 data points downfield from the predicted Citr2 position. Within this search region, peak pairs that match the known coupling of the Cit1 and Cit2 peaks can be identified. An empirically derived cost function can then be used to select the peak pair with the highest amplitude that best matches the known amplitude relationship of the Cit1 and Cit2 peaks.

[0091]

[0117] A Savitzky-Golay filter of polynomial order 3 and frame length 9 can then be used to smooth the downfield citrate doublet search region.

[0092]

[0118] The derivative of the smoothed search region can be calculated. A peak occurs where the sign of the derivative changes from positive to negative.

[0093]

[0119] In some embodiments, for each identified peak, the estimated amplitude of the peak may be calculated by summing the values ​​of like-signed derivatives on either side of the peak location and dividing the sum by two. Additionally, for each identified peak, if a paired peak at 57±6 data points has not already been identified as a peak due to a change in derivative sign, the amplitude of the paired search region at 57 data points may be added as a "virtual" peak with an amplitude calculated as a one-sided sum of the derivative values ​​of like signs at the paired points. The addition of a "virtual" peak allows for the identification of doublets even when they are obscured by other non-citrate peaks. A pair of peaks (real and virtual) separated by 57±6 data points is identified. The pair of peaks that maximizes the following function is selected as the downfield citrate doublet (Cit1 and Cit2 peaks): ((((lamp+ramp) / 2)^2) / abs(21 / 36-lamp / ramp)) / (abs(ref_lamp-lamp)+abs(ref_ramp-ramp)) where lamp = estimated amplitude of the left (downfield, Cit1) peak; ramp=estimated amplitude of right (upfield, Cit2) peak; ref_lamp=amplitude of selected Cit4 peak; ref_ramp = amplitude of selected Cit3 peak.

[0094]

[0120] This empirically derived function can effectively weight the amplitude relationships of peak pairs to favor peak pairs that match the typical amplitude ratio of the Cit1 and Cit2 doublets, while also matching the amplitudes of the peaks previously selected as Cit4 and Cit3, respectively.

[0095]

[0121] In certain embodiments, the floating-point positions of the individual citrates (Citr1 and Citr2) can be determined by calculating the root-mean-square deviation (RMSD) between the observed citrate peak and a Lorentzian function centered at 0.01 data point increments around the integer peak position.

[0096]

[0122] Figure 9 shows a flow diagram of an embodiment for linear deconvolution of a citrate NMR peak. A spectrum from a sample can be evaluated and deconvoluted via this flow diagram. After acquisition, a basis set or design matrix can be generated as the sum of four Lorentzian lineshapes centered on the floating point of each analyte's position on the peak. The Lawson-Hanson nonnegative least-squares algorithm can be used to deconvolute the citrate fitting region.

[0097]

[0123] In some embodiments, the final linear attributes of the upfield creatinine signal modeling (CRE1, left and right linewidth) can be applied to model the citrate signal. The citrate fitting region can be 50 data points downfield from the integer Cit1 position to 50 data points upfield from the integer Cit4 position.

[0098]

[0124] The citrate component used in the design matrix (basis set) for deconvolution can be constructed as the sum of four Lorentzian lineshapes centered on the floating-point positions of the four citrate peaks. The Lorentzian lineshapes can be divided into left and right linewidths.

[0099]

[0125] The left and right linewidths can be from the final model of the upfield creatinine signal (CRE1), adjusted for the nominal relationship between the creatinine and citrate linewidths: citrllw=1.1643*crellw+0.2347; citrrlw=1.1643*crerlw+0.2347 where citrllw and citrrlw can be the left and right citrate linewidths, respectively, and crellw and crerlw can be the left and right citrate linewidths of creatinine, respectively. The area of ​​the citrate component can be normalized to a value of 1000. By normalizing to a constant area, any increase or decrease in the measured area of ​​the citrate signal due to NMR shimming (line width) can be effectively eliminated.

[0100]

[0126] In some embodiments, the design matrix for deconvolution can include five citrate components (one centered on the floating-point citrate position and two shifted by two data points on either side), two slopes, and a DC offset. The Lawson-Hanson non-negative least squares algorithm can be used to deconvolute the citrate fitting region with this design matrix.

[0101]

[0127] The Lawson-Hanson deconvolution may be performed iteratively. With each iteration after the initial deconvolution, an additional Lorentzian signal is added to the design matrix, positioned at the location of the maximum fit deviation. Up to 20 additional Lorentzians may be added to the basis set until the ratio of the fit deviation to the area of ​​the citrate fit region meets the desired fit quality criteria. The fit may be complete if the fit region area divided by the fit deviation is ≥ 15. Up to two additional Lorentzians may be superimposed on the citrate peak, as defined by occurring within ±9 data points of any of the four citrate peak locations. The fit may be complete if the location of the maximum fit deviation is within ±9 data points of any of the four citrate peak locations and two additional overlapping Lorentzians are already present in the design matrix. The fit may be complete if up to 20 additional Lorentzians are added to the design matrix.

[0102]

[0128] From the completed fit, the citrate result (mg / L) can be calculated as the sum of the citrate component deconvolution coefficients * 5727 * 0.1921. The number 5727 can convert the value to μmol / L, and the number 0.1921 can convert the μmol / L value to mg / L, which may be the conventional unit for urinary citrate assays.

[0103]

[0129] 10 shows a schematic diagram of an NMR spectrum representing various concentrations of creatinine according to one embodiment of the present disclosure. Additionally, FIG. 10 shows three mathematically assisted curve-fitting plots corresponding to three different concentrations of creatinine and their respective percentiles. In some embodiments, data such as that shown in FIG. 10 can be used to visualize and determine the concentration of creatinine using the amplitude of creatinine from a sample.

[0104]

[0130] Figure 11 is a schematic diagram of an NMR spectrum representing various concentrations of citrate according to one embodiment of the present disclosure. Additionally, Figure 11 shows three mathematically assisted curve-fitting plots corresponding to three different concentrations of citrate and their respective percentiles. In some embodiments, data such as that shown in Figure 11 can be used to visualize and determine the concentration of citrate using the amplitude of citrate from a sample.

[0105]

[0131] System for measuring citrate and / or creatinine Also disclosed are systems for performing any of the steps of the disclosed methods, and computer-implemented instructions for performing any of the steps of the disclosed methods or operating any part of the disclosed systems.

[0106]

[0132] For example, a system may include one or more stations or components for performing any of the aforementioned method embodiments. In one embodiment of the present disclosure, a system for determining and measuring citrate and / or creatinine concentrations may include an NMR spectrometer configured to acquire measured citrate and / or creatinine signal lineshapes in an NMR spectrum of a biological sample. The NMR analyzer may include a computer program product capable of storing the measured citrate and / or creatinine lineshapes and a reference spectrum. The computer program may be configured to derive the citrate and / or creatinine concentrations through a deconvolution process, such as the method steps disclosed herein. The NMR analyzer may further include a controller in communication with the NMR spectrometer, a flow probe in communication with the spectrometer, and / or the spectrometer configured to acquire NMR signals in defined peak regions of the NMR spectrum associated with citrate and creatinine in the flow probe.

[0107]

[0133] Also disclosed is a computer program product tangibly embodied in a non-transitory machine-readable storage medium that includes instructions configured to cause one or more data processors to perform any of the steps of the disclosed methods or operate any of the components of the disclosed systems. For example, in certain embodiments, a computer program product tangibly embodied in a non-transitory machine-readable storage medium that includes instructions configured to cause one or more data processors to perform a process including (a) obtaining a sample from a subject, (b) detecting the presence of analyte(s) of interest in the sample, and (c) calculating the concentration of the analyte(s) of interest in the sample is disclosed.

[0108]

[0134] In some embodiments, the system may include components for generating a patient report providing citrate and / or creatinine levels.

[0109]

[0135] Figure 12 shows a schematic diagram of an example NMR analyzer. A system 207 for acquiring and calculating lineshapes of a selected sample is shown. The system 207 may include an NMR spectrometer 22 for performing NMR measurements on the sample. In one embodiment, the spectrometer 22 may be configured to perform NMR measurements at 400 MHz for proton signals; in other embodiments, measurements may be performed at 200 MHz to approximately 900 MHz or other suitable frequencies. Other frequencies corresponding to the desired operating magnetic field strength may also be used. A proton flow probe may be installed, as may a temperature controller for maintaining the sample temperature at 47°C + / - 0.5°C. The spectrometer 22 may be controlled by a digital computer 211 or other signal processing unit. The computer 211 may be capable of performing fast Fourier transforms. The computer 211 may also include a data link 212 to another processor or computer 213 and a direct memory access channel 214 that can be connected to a hard memory storage unit 215.

[0110]

[0136] Digital computer 211 may also include a set of analog-to-digital converters, digital-to-analog converters, and low-speed device I / O ports that connect to the operating elements of spectrometer 22 via pulse control and interface circuitry 216. These elements may include an RF transmitter 217 that can generate RF excitation pulses of duration, frequency, and magnitude guided by at least one digital signal processor that can be onboard or in communication with digital computer 211, and / or an RF power amplifier 218 that amplifies the pulses and couples them to an RF transmitter coil 219 that surrounds sample cell 220 and / or flow probe 220. NMR signals generated by the excited sample in the presence of a polarizing magnetic field (e.g., 9.4 Tesla) generated by superconducting magnet 221 may be received by coil 222 and applied to RF receiver 223. The amplified and filtered NMR signals may be demodulated at 224, and the resulting quadrature signals may be applied to interface circuitry 216, where they may be digitized and input via digital computer 211. The circuit 200 and / or module 350 may be located in one or more processors associated with the digital computer 211 and / or in a secondary computer 213 or other computer, which may be on-site or remote, accessible via a global network such as the Internet 227.

[0111]

[0137] After NMR data is acquired from the sample in measurement cell 220, processing by computer 211 may generate another file that can be stored in storage device 215, if desired. This second file may be a digital representation of the chemical shift spectrum, which can then be read into computer 213 for storage in its storage device 225 or in a database associated with one or more servers. Under the direction of a program stored in its memory or accessible by computer 213, computer 213, which may be a laptop computer, desktop computer, workstation computer, electronic notepad, electronic tablet, smartphone, or other device having at least one processor or other computer, may process the chemical shift spectrum in accordance with the teachings of the present disclosure to generate a report that can be output to printer 226 or stored electronically and relayed to a desired email address or URI. Alternatively, other output devices, such as a computer display screen, electronic notepad, smartphone, or the like, may be used to display the results.

[0112]

[0138] The functions performed by computer 213 and its separate storage device 225 may also be incorporated into the functions performed by the spectrometer's digital computer 211. In such cases, printer 226 may be connected directly to digital computer 211. Other interfaces and output devices may be used, as is well known to those skilled in the art.

[0113]

[0139] Certain embodiments of the present disclosure are directed to providing methods, systems and / or computer program products using citrate and creatinine assessments that may be particularly useful for automated screening tests for clinical disease states and / or risk assessment evaluations for screening of in vitro biological samples.

[0114]

[0140] Embodiments of the present disclosure may take the form of entirely software embodiments or embodiments combining software and hardware aspects, collectively referred to herein as "circuits" or "modules."

[0115]

[0141] The present disclosure may be embodied as an apparatus, a method, a data or signal processing system, or a computer program product. Accordingly, the present disclosure may take the form of an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, certain embodiments of the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code means embodied in the medium. Any suitable computer-readable medium may be utilized, including a hard disk, a CD-ROM, an optical storage device, or a magnetic storage device.

[0116]

[0142] A computer-usable or computer-readable medium may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (non-exhaustive list) of computer-readable media include an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). It should be noted that a computer-usable or computer-readable medium may also be paper or another suitable medium on which a program is printed, since the program can be captured electronically, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or processed in an appropriate manner as needed, and then stored in computer memory.

[0117]

[0143] Computer program code for carrying out operations of the present disclosure may be written in an object-oriented programming language such as Java7, Smalltalk, Python, Labview, C++, or Visual Basic. However, computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages ​​such as the "C" programming language or assembly language. The program code may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer. In the latter scenario, the remote computer may be connected to the user's computer via a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).

[0118]

[0144] The flowcharts and block diagrams in certain figures herein illustrate the architecture, functionality, and operation of possible implementations of the analytical model and evaluation system and / or program according to the present disclosure. In this regard, each block in the flowcharts or block diagrams represents a module, segment, operation, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

[0119]

[0145] 13 is a block diagram of an exemplary embodiment of a data processing system 305 illustrating systems, methods, and computer program products according to embodiments of the present disclosure. Processor 310 communicates with memory 314 via address / data bus 348. Processor 310 can be any commercially available or custom microprocessor. Memory 314 represents an overall hierarchy of memory devices containing software and data used to implement the functionality of data processing system 305. Memory 314 can include, but is not limited to, the following types of devices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

[0120]

[0146] 13, memory 314 may include several categories of software and data used by data processing system 305: operating system 352, application programs 354, input / output (I / O) device drivers 358, citrate and creatinine assessment module 350, and data 356. Citrate and creatinine assessment module 350 deconvolutes the NMR signals to reveal defined NMR signal peak regions in the proton NMR spectrum of each biological sample, allowing for identification of citrate and / or creatinine levels.

[0121]

[0147] Data 356 may include signal (component and / or composite spectral line shape) data 362, which may be obtained from a data or signal acquisition system 320 (e.g., NMR spectrometer 22 and / or analyzer 22). As will be appreciated by those skilled in the art, operating system 352 may be any operating system suitable for use in a data processing system, such as OS / 2, AIX, or OS / 390 manufactured by International Business Machines Corporation of Armonk, New York, Windows CE, Windows NT, Windows 95, Windows 98, Windows 2000, Windows XP, Windows 10 manufactured by Microsoft Corporation of Redmond, Washington, Palm OS manufactured by Palm, Inc., MacOS from Apple Computer, UNIX, FreeBSD, or Linux, a proprietary operating system, or a dedicated operating system for an embedded data processing system.

[0122]

[0148] I / O device drivers 358 typically include software routines accessed by application programs 354 via operating system 352 to communicate with devices such as I / O data port(s), data storage device 356 and certain memory 314 components, as well as signal acquisition system 320. Application programs 354 are illustrative of programs that implement various features of data processing system 305 and may include at least one application that supports operation according to embodiments of the present disclosure. Finally, data 356 represents static and dynamic data used by application programs 354, operating system 352, I / O device drivers 358, and other software programs that may reside in memory 314.

[0123]

[0149] Although the present disclosure has been illustrated with reference to, for example, application program module 350 of Figure 13, those skilled in the art will appreciate that other configurations may be utilized while still benefiting from the teachings of the present disclosure. Thus, the present disclosure should not be construed as limited to the configuration of Figure 13, which is intended to encompass any configuration capable of performing the operations described herein.

[0124]

[0150] In certain embodiments, module 350 includes computer program code for providing citrate and creatinine levels, which may be used as markers to assess the risk of kidney stone formation, and / or to indicate whether personalized therapeutic intervention is advisable, and / or to track the effectiveness of treatment or even unintended consequences of treatment. [Example]

[0125]

[0151] A method for testing for kidney stone formation risk is described herein. The urinary citrate and creatinine assay (UCC) analyzes urine biological samples in vitro. The process for measuring citrate and creatinine involves obtaining a sample from a subject, preparing the sample, acquiring a proton NMR spectrum from the sample, determining the citrate and creatinine concentrations using peak(s) present at 2.50-2.75 ppm and approximately 3.07 and 4.12 ppm, respectively, deconvoluting the peaks using an algorithm capable of identifying peaks in the spectral sample over other unrelated peaks, and finally, calculating and generating a concentration output using the respective peak amplitudes from an analyzer, such as a Vantera® clinical analyzer, after analysis of the sample.

[0126]

[0152] Comparison of results from the NMR-based assay for urinary citrate and creatinine with the chemistry-based assay revealed high correlation coefficients (0.98 and 0.96, respectively), small intercepts (4.7 and 0.97, respectively), and slopes of 0.971 and 0.968, respectively, suggesting that the NMR-based results can be substituted for chemistry-based results. Precision studies showed that the NMR-based assay had good precision (%CV for both assays <3.7%) and accurately measured citrate and creatinine. Finally, although urinary preservatives such as acetic acid and boric acid have been listed as limitations of chemistry-based assays, this interference was not found to be a limitation of the NMR-based assay. Thus, the NMR-based assay has performance characteristics that allow it to be used for clinical decision-making purposes.

[0127]

[0153] In addition to having good performance characteristics for quantifying citrate and creatinine, NMR-based assays have several advantages over chemical-based assays. Some of these advantages are not limited by the inability to test samples in which acetic acid or boric acid is used as a preservative. NMR assays are reagentless and therefore do not rely on reagents such as citrate lyase, which can be affected by supply chain issues. Furthermore, this NMR assay is high-throughput and reagentless, with no sample manipulation before testing (e.g., sample dilution with diluent buffer is performed on-board), and the turnaround time for testing and reporting results is <2 minutes. Furthermore, the NMR-based assay provides results for both citrate and creatinine simultaneously from the same spectral acquisition of the same specimen. One advantage of NMR is that it can simultaneously quantify several analytes, significantly reducing the time and resources required, as well as the cost of testing. The high-throughput and ease of use of NMR assays makes them amenable for testing samples from large-scale observational and interventional clinical studies. While current NMR assays can quantify citrate and creatinine in urine specimens, future applications of this technology may also include quantification of cystine and uric acid, allowing for a broader analysis of kidney stone formation risk. The newly developed high-throughput NMR assay demonstrates good performance, producing results comparable to currently utilized chemical tests and offering an alternative means of simultaneously quantifying urinary citrate and creatinine for clinical and research use.

[0128]

[0154] Figures 14 and 15 show plots showing the limits of quantitation for creatinine and citrate, respectively. The assay limits of quantitation were 5.9 mg / dL for creatinine and 17 mg / L for citrate. Eight urine samples were used to determine the limits of quantitation (LOQ). Four replicates per pool were tested per day for three days, following guidelines outlined by the Clinical and Laboratory Standards Institute (CLSI). The bias limits for citrate and creatinine were pre-determined to be 10% and 12.9%, respectively. Five deionized water samples and five low-concentration samples were tested to calculate the limit of blank (LOB) and limit of detection (LOD). Linearity is demonstrated far beyond the reference interval, as shown in Figures 16 and 17. Urinary creatinine and citrate measured by the NMR UCC assay compared well with results obtained on a chemistry analyzer.

[0129]

[0155] Figures 16 and 17 show two plots; the first (left) is a scatter plot of linearity, and the second (right) is a residual plot for creatinine and citrate, respectively. The linearity of the assay results was assessed using regression analysis of the assigned urinary citrate and creatinine concentrations versus the measured urinary citrate and creatinine concentrations. Citrate results were linear over the range of 6 to 2,040 mg / L. The equation of the best line for citrate was determined to be Y = 1.01X -0.18. The polynomial fit was not statistically better than the linear fit at the 5% significance level. For citrate, the limit of blank (LOB), analytical sensitivity or limit of detection (LOD), and functional sensitivity or limit of quantitation (LOQ) were determined to be 5, 9, and 17 mg / L, respectively. Creatinine results were linear over the range of 2.8 to 1,308 mg / dL. The equation for best fit for creatinine was determined to be Y = 1.00X - 0.24. The third-order polynomial fit was statistically better than the linear fit at the 5% significance level. For creatinine, the LOB, LOD, and LOQ were determined to be 5.5, 5.9, and 5.9 mg / dL, respectively.

[0130]

[0156] Figures 18 and 19 show various plots comparing creatinine and citrate measurements performed by the NMR assay compared to the chemical assay. A method comparison study was conducted to compare the NMR-based citrate test results with results generated using the chemical-based assay. Deming regression analysis of the citrate results from both assays (n = 297) yielded a correlation coefficient of 0.977, with a slope and intercept of 0.971 and 4.7, respectively. The bias plot revealed no systematic bias between the results of the two assays (mean bias = -3.0%). For creatinine, the NMR-based test results were compared to those generated using the chemical-based assay. Deming regression analysis of the creatinine results from both assays (n = 306) yielded a correlation coefficient of 0.960, with a slope and intercept of 0.968 and 0.97, respectively. The bias plot revealed no systematic bias between the results of the two assays (mean bias = -1.4%).

[0131]

[0157] Figure 20 shows two calibration curves for citrate (left) and creatinine (right) used for amplitude-to-concentration conversion. Analyte signal amplitudes from the deconvolution process were converted to concentration units using coefficients obtained from the calibration curves. Calibration curves were created by relating the peak amplitudes of urine spiked with creatinine or citrate standards to the amount of standard added. Urine samples were spiked with creatinine and citrate standards. A total of 12–13 samples spiked with known amounts of creatinine or citrate were tested in triplicate to establish standard curves for creatinine and citrate. The standard curves were used to convert creatinine and citrate from signal amplitude to concentration units.

[0132]

[0158] Substances (n = 10) were tested in vitro for potential interference with results generated by urinary citrate and creatinine assays (three endogenous and seven exogenous substances). Pooled urine samples with citrate concentrations ranging from 228.6 to 885.7 mg / L and creatinine concentrations ranging from 57.1 to 128.6 mg / dL were used to generate substance interference data during initial screening. Substances that showed interference during initial screening were tested in a dose-response format according to CLSI guidelines. For acetic acid and boric acid, which can be used as preservatives in urine, it is recommended to test five times the recommended concentration. For acetic acid, recommended concentrations of 0.5% to 2.5% were tested, and for boric acid, recommended concentrations of 1% to 5% were tested. The highest concentrations tested that did not interfere with citrate and creatinine results were defined as <10% bias for citrate and <12.9% bias for creatinine. Table 3 shows the highest substance concentrations tested that did not cause interference. [Table 3]

[0133]

[0159] Exemplary embodiments of suitable methods, systems, and programs As used hereinafter, any reference to methods, systems, and programs will be understood to refer to each of those methods, systems, and programs separately (e.g., "exemplary embodiments 1-4 shall be understood as exemplary embodiments 1, 2, 3, or 4").

[0134]

[0160] Exemplary embodiment 1 is a method that includes obtaining an NMR spectrum of a biological sample obtained from a subject, and measuring concentrations of citrate and / or creatinine from the biological sample based on the NMR spectrum.

[0135]

[0161] Exemplary Embodiment 2 is the method of any preceding or subsequent exemplary embodiment, wherein acquiring the NMR spectrum includes generating measured citrate and / or creatinine signal lineshapes from the NMR spectrum; and generating calculated lineshapes for citrate and / or creatinine, the calculated lineshapes being based on derived concentrations of citrate and / or creatinine expected in the biological sample.

[0136]

[0162] Exemplary Embodiment 3 is the method of any preceding or subsequent exemplary embodiment, wherein generating the calculated lineshapes for citrate and / or creatinine includes calculating a plurality of reference coefficients for the calculated lineshapes based on a linear least-squares fitting technique.

[0137]

[0163] Exemplary Embodiment 4 is the method of any preceding or subsequent exemplary embodiment, wherein the method further includes determining a degree of correlation between an initially calculated lineshape of the biological sample and a measured citrate and / or creatinine signal lineshape of the biological sample, and determining the presence of citrate and / or creatinine based on the calculated lineshape if the degree of correlation between the calculated lineshape and the measured citrate and / or creatinine signal lineshape of the biological sample is above a predetermined threshold.

[0138]

[0164] Exemplary Embodiment 5 is the method of any preceding or subsequent exemplary embodiment, wherein the NMR spectrum of the biological sample comprises four citrate proton singlet signals in four distinct regions, the four citrate proton singlet signal regions comprising the range of 2.50 to 2.75 ppm.

[0139]

[0165] Exemplary Embodiment 6 is the method of any preceding or subsequent exemplary embodiment, wherein the NMR spectrum of the biological sample comprises two creatinine proton singlet signals in two distinct regions, the two creatinine proton singlet signal regions comprising the range of 3.0 to 4.20 ppm.

[0140]

[0166] Exemplary Embodiment 7 is the method of any preceding or subsequent exemplary embodiment, wherein the method further includes deconvoluting signal data associated with citrate and / or creatinine proton singlet signals, and comparing data from the plurality of deconvoluted signal data to a priori calibration data corresponding to standard biological samples having known concentrations of citrate and / or creatinine to determine concentrations of citrate and / or creatinine in the biological sample.

[0141]

[0167] Exemplary Embodiment 8 is the method of any preceding or subsequent exemplary embodiment, wherein the method further includes generating a report listing the concentrations of citrate and / or creatinine components present in the biological sample.

[0142]

[0168] Exemplary Embodiment 9 is the method of any preceding or subsequent exemplary embodiment, wherein the biological sample comprises blood, serum, plasma, sputum, cerebrospinal fluid, urine, or a combination thereof.

[0143]

[0169] Exemplary Embodiment 10 is the method of any preceding or subsequent exemplary embodiment, wherein the method further includes identifying the subject as having a condition associated with abnormally elevated or decreased levels of citrate and / or creatinine.

[0144]

[0170] Exemplary embodiment 11 is a system comprising: an NMR spectrometer configured to obtain measured citrate and / or creatinine signal lineshapes of an NMR spectrum of a biological sample; a computer program product comprising instructions for storing the measured citrate and / or creatinine signal lineshapes of the biological sample; a computer program product comprising instructions for storing reference spectra for each of citrate and / or creatinine; a computer program product comprising instructions for calculating lineshapes based on a plurality of derived concentrations of citrate and / or creatinine from the biological sample and the reference spectra; and a computer program product comprising instructions for comparing the measured citrate and / or creatinine signal lineshapes with the calculated lineshapes to determine a degree of correlation between the calculated lineshapes and the measured citrate and / or creatinine signal lineshapes.

[0145]

[0171] Exemplary embodiment 11 is the system of any preceding or subsequent exemplary embodiment, wherein the system further comprises an output device for generating a report indicating the presence of citrate and / or creatinine.

[0146]

[0172] Exemplary embodiment 12 is a system of any preceding or subsequent exemplary embodiment configured to perform the method of any one of exemplary embodiments 1-10.

[0147]

[0173] Exemplary embodiment 13 is a computer program product tangibly embodied in a non-transitory machine-readable storage medium containing instructions configured to cause one or more data processors to perform a process, the computer program product comprising a non-transitory machine-readable storage medium containing instructions configured to cause the one or more data processors to perform a process including obtaining a sample from a subject, detecting the presence of an analyte of interest in the sample, and calculating a concentration of the analyte of interest in the sample.

Claims

1. obtaining an NMR spectrum of a biological sample obtained from the subject; determining the concentration of citrate and / or creatinine from the biological sample based on the NMR spectrum; A method comprising:

2. the step of acquiring the NMR spectrum comprises: generating measured citrate and / or creatinine signal lineshapes from the NMR spectrum; generating calculated lineshapes of citrate and / or creatinine, the calculated line shape is based on derived concentrations of citrate and / or creatinine expected in the biological sample. The method of claim 1 , comprising:

3. 3. The method of claim 2, wherein the step of generating a calculated lineshape for citrate and / or creatinine comprises calculating a plurality of reference coefficients for the calculated lineshape based on a linear least squares fitting technique.

4. determining a degree of correlation between the initially calculated lineshape of the biological sample and the measured citrate and / or creatinine signal lineshape of the biological sample; determining the presence of citrate and / or creatinine based on the calculated lineshape if the degree of correlation between the calculated lineshape and the measured citrate and / or creatinine signal lineshape of the biological sample exceeds a predetermined threshold; The method of claim 2 or 3, further comprising:

5. 5. The method of claim 1, wherein the NMR spectrum of the biological sample comprises four citrate proton singlet signals in four different regions, and the four citrate proton singlet signal regions comprise a range of 2.50 to 2.75 ppm.

6. 6. The method of claim 1, wherein the NMR spectrum of the biological sample comprises two creatinine proton singlet signals in two different regions, and the two creatinine proton singlet signal regions comprise the range of 3.0 to 4.20 ppm.

7. deconvoluting signal data relating to citrate and / or creatinine proton singlet signals; comparing data from the plurality of deconvoluted signal data with a priori calibration data corresponding to standard biological samples having known concentrations of citrate and / or creatinine to determine the concentrations of citrate and / or creatinine in the biological sample; 7. The method of claim 1, further comprising:

8. 8. The method of claim 7, further comprising generating a report listing the concentrations of citrate and / or creatinine components present in the biological sample.

9. 9. The method of any one of claims 1 to 8, wherein the biological sample comprises blood, serum, plasma, sputum, cerebrospinal fluid, urine, or a combination thereof.

10. 10. The method of any one of claims 1 to 9, further comprising identifying the subject as having a condition associated with abnormally elevated or decreased levels of citrate and / or creatinine.

11. an NMR spectrometer configured to obtain measured citrate and / or creatinine signal lineshapes of an NMR spectrum of a biological sample; a computer program product comprising instructions for storing the measured citrate and / or creatinine signal lineshapes of the biological sample; and a computer program product including instructions for storing a reference spectrum for each of citrate and / or creatinine; a computer program product comprising instructions for calculating lineshapes based on a plurality of derived concentrations of citrate and / or creatinine from the biological sample and a reference spectrum; a computer program product comprising instructions for comparing the measured citrate and / or creatinine signal lineshapes with the calculated lineshapes to determine a degree of correlation between the calculated lineshapes and the measured citrate and / or creatinine signal lineshapes; A system comprising:

12. 12. The system of claim 11, further comprising an output device for generating a report indicating the presence of citrate and / or creatinine.

13. one or more data processors, obtaining a sample from a subject; detecting the presence of an analyte of interest in said sample; calculating the concentration of the analyte of interest in the sample; tangibly embodied in a non-transitory machine-readable storage medium containing instructions configured to cause a process to be performed, including Computer program products.

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