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

Abstract

The present invention relates to methods and systems for detecting the presence and concentration of biomarkers associated with kidney stone formation may be used to determine personalized treatment pathways in a subject. Nuclear magnetic resonance (NMR) spectroscopy may be a valuable tool for detecting various biomarkers associated with various disease states. A biological sample obtained from a subject may be examined using NMR spectroscopy to determine the presence and concentration of associated biomarkers.

Description

Technical Field

[0001] The present disclosure generally relates to methods and systems for determining citrate and creatinine concentrations from an in vitro biological sample, and more particularly for determining citrate and creatinine concentrations by utilizing NMR spectroscopy. Background Art

[0002] Based on a survey conducted during 2007 - 2014, the prevalence of nephrolithiasis or kidney stones in the United States was 10.1%, with a higher prevalence in men (12.6%) than in women (7.5%). Over the past few decades, the prevalence of kidney stones has increased; this increase is consistent with the increase in obesity and type 2 diabetes. Thus, identifying the causes of kidney stones and personalized treatment to reduce kidney stone formation and recurrence are high priorities. Summary of the Invention

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

[0004] The present disclosure can be implemented in various ways. In some embodiments, the methods and systems can include determining citrate and / or creatinine in a urine sample from a patient. In some embodiments, a method for detecting the presence of citrate and / or creatinine from urine can include the steps of collecting a urine sample from a subject, collecting 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 can 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 can be further calculated using a generated standard calibration curve. The calibration curve can be generated by correlating the peak amplitude of urine concentrated with at least one of a creatinine and / or citrate standard with the amount of the added standard. In some embodiments, the determination of the concentration of citrate and / or creatinine in the sample can be used to determine a course of action for treating the subject. In some embodiments, the subject can be a patient undergoing treatment related to kidney stones.

[0005] In some embodiments, the method can include simultaneously detecting the presence of both citrate and creatinine based on their respective NMR signals. In some embodiments, the step of simultaneous detection can include using a mathematical deconvolution step. In some embodiments, various chemical analytes as described in more detail herein can 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 can relate to a system including an NMR analyzer. The NMR analyzer can be configured to acquire the citrate and / or creatinine signal line shapes of a biological sample. The NMR analyzer can include a computer program product that can store the measured citrate and / or creatinine line shapes and reference spectra. The computer program can be configured to derive the concentrations of citrate and / or creatinine through a deconvolution process. The NMR analyzer can additionally include an NMR spectrometer, a flow probe in communication with the spectrometer, and / or a controller in communication with the spectrometer, which is configured to obtain NMR signals of defined peak regions of the NMR spectra related to citrate and creatinine in the flow probe. In some embodiments, the system can include components for generating a patient report providing citrate and / or creatinine levels. The controller can include at least one local or remote processor or be in communication with at least one local or remote processor, where at least one processor can be configured to perform at least one of the following steps: (i) obtain a composite NMR spectrum of a fitted region of an in vitro biological sample; (ii) deconvolve the composite NMR spectrum using a defined deconvolution model and curve fitting function; and / or (iii) mathematically calculate the concentrations of citrate and creatinine from the generated calibration curve.

[0007] In some embodiments, a clinical analyzer, such as a clinical analyzer, can be used for biomarker determination, where the analyzer can be communicatively coupled to an NMR instrument for an automated, high-throughput, reagentless sampling process. In some instances, the clinical analyzer can be capable of automatically mixing each urine sample with a buffer on a plate, such that sample preparation is automated once the sample is obtained. In some instances, the urine sample can be mixed with the buffer in a 2:1 (v / v) ratio.

[0008] Those of ordinary skill in the art will understand additional features, advantages, and details of the present disclosure by reading the accompanying drawings and the following detailed description of the embodiments, such a description being merely illustrative of the present disclosure. Features described with respect to one embodiment may be combined with other embodiments, even though not specifically discussed therewith. That is, it should be noted that aspects of the present disclosure described with respect to one embodiment may be incorporated into different embodiments, even though not specifically described. That is, all embodiments and / or features of any embodiment may be combined in any manner and / or combination. The foregoing and other aspects of the present disclosure are explained in detail in the specification set forth below. Description of the Drawings

[0009] The present disclosure can be better understood with reference to the accompanying drawings, in which embodiments of the present disclosure may be shown. However, the present disclosure may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present disclosure will be thorough and complete and will fully convey the scope of the present disclosure to those skilled in the art. Additionally, the flowcharts and block diagrams of certain of the drawings herein illustrate the architecture, functionality, and operation of possible implementations of an analysis model and evaluation system and / or program in accordance with the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, operation, or portion of code that 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 recited in the blocks may occur in a different order than shown in the drawings. 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 upon the functionality involved. The present disclosure can be better understood by reference to the following non-limiting drawings.

[0010] Figure 1 A flowchart showing a method for determining the citrate and creatinine concentrations from a biological sample according to an embodiment of the present disclosure is shown.

[0011] Figure 2 A schematic diagram of an NMR single-pulse radiofrequency pulse sequence experiment with WET suppression according to an embodiment of the present disclosure is shown.

[0012] Figure 3 A schematic diagram of an NMR spectrum of a mixture of formate, creatinine, citrate, and TSP as indicated according to an embodiment of the present disclosure is shown.

[0013] Figure 4A A schematic diagram of a single NMR peak of TSP for determining the peak center position according to an embodiment of the present disclosure is shown.

[0014] Figure 4B Schematic diagram of a single NMR peak of TSP for determining the peak baseline according to an embodiment of the present disclosure.

[0015] Figure 4C Schematic diagram of a single NMR peak of TSP for determining the peak line width at 50% amplitude according to an embodiment of the present disclosure.

[0016] Figure 4D Schematic diagram of a single NMR peak of TSP for determining the peak skew at 20% amplitude according to an embodiment of the present disclosure.

[0017] Figure 5A Schematic diagram of a single NMR peak of formate for determining the peak center position according to an embodiment of the present disclosure.

[0018] Figure 5B Schematic diagram of a single NMR peak of formate for determining the peak baseline according to an embodiment of the present disclosure.

[0019] Figure 5C Schematic diagram of a single NMR peak of formate for determining the peak line width at 50% amplitude according to an embodiment of the present disclosure.

[0020] Figure 5D Schematic diagram of a single NMR peak of formate for determining the peak skew at 10% amplitude according to an embodiment of the present disclosure.

[0021] Figure 6 Flowchart of a routine for improving creatinine region fitting by considering other peaks in the region according to an embodiment of the present disclosure.

[0022] Figure 7 Flowchart of a routine for improving creatinine region fitting by adjusting the creatinine peak line width according to an embodiment of the present disclosure.

[0023] Figure 8A Schematic diagram of an NMR spectrum representing the citrate region according to an embodiment of the present disclosure.

[0024] Figure 8B Schematic diagram of an NMR spectrum representing citrate as a function of pH according to an embodiment of the present disclosure.

[0025] Figure 9 Flowchart of the line shape deconvolution of a citrate NMR peak according to an embodiment of the present disclosure.

[0026] Figure 10A schematic diagram showing NMR spectra representative of various concentrations of creatinine according to an embodiment of the present disclosure.

[0027] Figure 11 A schematic diagram showing NMR spectra representative of various concentrations of citrate according to an embodiment of the present disclosure.

[0028] Figure 12 A schematic diagram showing a block diagram of an NMR spectroscopy device according to an embodiment of the present disclosure.

[0029] Figure 13 A schematic diagram showing a block diagram of a data processing system according to an embodiment of the present disclosure.

[0030] Figure 14 A plot showing the limit of quantification of creatinine according to an embodiment of the present disclosure.

[0031] Figure 15 A plot showing the limit of quantification of citrate according to an embodiment of the present disclosure.

[0032] Figure 16 Two plots are shown according to an embodiment of the present disclosure, where the first plot (left) is a linear scatter plot of creatinine and the second plot (right) is a residual plot of creatinine.

[0033] Figure 17 Two plots are shown according to an embodiment of the present disclosure, where the first plot (left) is a linear scatter plot of citrate and the second plot (right) is a residual plot of citrate.

[0034] Figure 18 Various plots are shown comparing creatinine measurement values by NMR determination with chemical determination according to an embodiment of the present disclosure.

[0035] Figure 19 Various plots are shown comparing citrate measurement values by NMR determination with chemical determination according to an embodiment of the present disclosure.

[0036] Figure 20 Two calibration curves for citrate (left) and creatinine (right) in the conversion from amplitude to concentration are shown according to an embodiment of the present disclosure. Detailed Description

[0037] Terms and Definitions

[0038] Like reference numerals always refer to like elements. In the drawings, for clarity, the thickness of some lines, layers, components, elements or features may be exaggerated. Unless otherwise indicated, dashed lines denote optional features or operations. Unless otherwise stated, the order of operations and / or steps shown in the drawings or recited in the claims is not intended to be limited to the order presented.

[0039] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used herein, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms as well. It will be further understood that when used in this specification, the terms "comprises" and / or "comprising" specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not preclude 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 about X and Y" shall be construed to include X and Y. As used herein, phrases such as "about X to Y" mean "about X to about Y".

[0040] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for the sake of brevity and / or clarity.

[0041] It will be understood that although the terms first, second, etc. may be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the disclosure. Unless otherwise specifically indicated, the order of operations (or steps) is not limited to the order presented in the claims or the drawings.

[0042] 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. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as the individual values within that range. For example, a description of a range such as 1 to 6 should be considered to have specifically disclosed sub-ranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as the individual numbers within that range, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the width of the range.

[0043] The terms "sample" or "patient sample" or "biological sample" or "specimen" are used interchangeably herein. Non-limiting examples of liquid samples for analysis using the disclosed systems and methods can include blood or blood products (e.g., serum, plasma, etc.), urine, nasal swabs, liquid biopsy samples (e.g., for detecting cancer), or combinations thereof. The term "blood" encompasses whole blood, blood products, or any fraction of blood, such as serum, plasma, buffy coat, etc., as conventionally defined. Suitable samples include those that can be deposited onto a substrate for collection and drying, including but not limited to: blood, plasma, serum, urine, saliva, tears, cerebrospinal fluid, organs, hair, muscle, or other tissue samples, or other liquid aspirates.

[0044] The terms "patient" or "subject" are used broadly and refer to an individual who provides a biological sample for testing or analysis.

[0045] The term "clinical disease state" means a risk medical condition that may indicate appropriate medical intervention, dietary adjustment, and / or protocol, therapy, therapy adjustment, or exclusion of a therapy (e.g., a drug) and / or monitoring. The identification of the likelihood of a clinical disease state can allow a clinician to treat, delay, or inhibit the onset of the disorder 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 rejection, and / or medical conditions associated with immunodeficiency, abnormal biological function in protein sorting, immune and receptor discrimination, inflammation, pathogenicity, metastasis, and other cellular processes.

[0046] The term "programmatically" means to perform using a computer program and / or software, operations directed by a processor or ASIC. The term "electronic" and its derivatives refer to automated or semi-automated operations using a device having circuitry and / or modules rather than being performed via intellectual steps, and typically refers to operations performed programmatically. The terms "automated" and "automatic" mean that the operation can be performed with minimal or no manual labor or input. The term "semi-automated" means that some input or activation is allowed by the operator, but the calculations and signal acquisition and the calculation of the concentration of the ionized component(s) are done electronically, typically programmatically, without manual input. The term "about" means + / - 10% (mean or average) of the specified value or number.

[0047] An automated clinical NMR analyzer can 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 a fully software implementation, or an implementation that combines software and hardware aspects, components or features.

[0048] The term "protocol" refers to an automated electronic algorithm (typically, a computer program) having mathematical calculations, defined rules for data interrogation and analysis, which manipulates NMR data to compensate for temperature sensitivity.

[0049] The term "computer network" includes one or more local area networks (LANs), wide area networks (WANs), and in some 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 can be maintained in a local "clean" room separate from the NMR clinical analyzer and does not experience the same biological hazard control requirements / concerns as the NMR clinical analyzer.

[0050] As used herein, the term "integration" with respect to an NMR spectrometer refers to the acquired NMR signal (spectrum) of a sample. Integration can refer to the area of a specific peak in the NMR spectrum. The area of the peak is proportional to the concentration of that particular substance. Thus, if a (constant) concentration standard is measured, the integration value will be relatively constant if the NMR spectrometer / instrument is performing correctly, e.g., the value is within a target range, such as + / - 10%, and in some embodiments, + / - about 2%. Alternatively, integration can be based on more than one peak, or even on all the peaks of the NMR spectrum, but more commonly the area of a defined peak is measured. The term "ppm" (parts per million) can be used to describe the position of one or more peaks on the x-axis of the NMR spectrum and corresponds to the energy or frequency of the radio wave absorbed.

[0051] The term "low field" refers to the region / position to the left of a particular peak / position / point on the NMR spectrum (relative to a higher ppm scale reference). Conversely, the term "high field" refers to the region / position to the right of a particular peak / position / point on the NMR spectrum.

[0052] When measuring known concentration standards as independent "calibration" samples, integration provides a good test of (day-to-day) performance, which allows quantitative NMR without adding an internal standard to the corresponding biological sample. The calibration sample can be an aqueous or non-aqueous solution of a concentration standard, which can be used to calculate factors to normalize the integration produced by an instrument or group of instruments such that an equivalent integration of a known amount of concentration standard in a given volume of sample is produced for the (one or more) instruments.

[0053] The term "concentration standard" refers to a substance used to evaluate one or more peaks in an NMR spectrum. Examples of concentration standards include ethylbenzene solutions for organic systems (non-polar) and sodium acetate solutions for aqueous systems. In some embodiments, a TMA (trimethylacetic acid) solution can be used as a concentration standard. The TMA solution can have a specific ionic strength such that it behaves in terms of NMR behavior as plasma / serum or other samples of interest would. Additionally, because the chemical shifts of citrate and creatinine can vary with pH, TSP (sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4) and formate signals can be used as references to identify signals of interest.

[0054] Methods for measuring citrate and / or creatinine

[0055] The present disclosure will now be described more fully hereinafter, in which embodiments of the present disclosure are shown. However, the present 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 present disclosure to those skilled in the art.

[0056] New methods are described herein that include assays for determining the risk of nephrolithiasis obtained from a subject's biological sample, the assays utilizing NMR techniques, which NMR techniques can be used with an NMR analyzer, such as Clinical analyzers are paired to obtain rapid, high-throughput results. The new assay avoids alternative spectrophotometric-based assays for measuring citrate and creatinine. Traditionally, citrate and creatinine have been detected and quantified from biological samples by time-consuming chemical assays, which can be prone to human error results. The chemicals used for this traditional assay include citrate lyase and creatinase for detecting citrate and creatinine, respectively. Citrate can be quantified by a redox reaction that produces a change in NADH absorbance, while creatinine can be quantified by a change in absorbance resulting from a reaction with alkaline picrate. Limiting features of this traditional assay include limited access to the enzyme and / or enzyme supplier, limited shelf life of the enzyme, and overly sensitive conditions for using the enzyme. Accordingly, in the present disclosure, a new assay for detecting and quantifying citrate and creatinine using nuclear magnetic resonance is described.

[0057] In embodiments of the present disclosure, a biological sample can be obtained from a subject and analyzed using an analytical technique to detect and quantify the presence of citrate and creatinine. Examples of embodiments can include urine samples that can be analyzed using NMR techniques.

[0058] In some embodiments, the analyte being measured can be citrate, where citrate can be found based on its corresponding signal in the NMR spectrum. In some embodiments, the analyte being measured can be creatinine, where creatinine can be found based on its corresponding signal in the NMR spectrum. However, in other embodiments of the present disclosure, citrate and creatinine can be found simultaneously contemporaneously using the corresponding signals of each analyte in the NMR spectrum.

[0059] In some embodiments, the method can include: acquiring 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. An NMR analyzer can be particularly suitable for obtaining data measurements of biological samples, including qualitative and / or quantitative measurements, which can be used for therapeutic or diagnostic purposes and typically for diagnostic purposes that meet appropriate regulatory guidelines for accuracy, depending on the jurisdiction and / or the test being performed. In some embodiments, an automatic temperature compensation scheme would be beneficial for measuring NMR-quantifiable metabolites in types of human biological fluids such as serum / plasma, urine, CSF, semen, sputum, lavage fluid, etc.

[0060] In some embodiments, acquiring the NMR spectrum can include the step of generating a measured citrate and / or creatinine signal line shape from the NMR spectrum. The acquisition of the NMR spectrum can additionally include generating a calculated line shape of citrate and / or creatinine based on the derived concentration of citrate and / or creatinine predicted to be present in the biological sample.

[0061] In some embodiments, generating the calculated line shape for citrate and / or creatinine can include calculating multiple reference coefficients for the calculated line shape based on linear least squares fitting techniques.

[0062] In some embodiments, resolving the concentration of citrate and / or creatinine can include determining the degree of correlation between an initially calculated line shape of a biological sample and a measured citrate and / or creatinine signal line shape. Additionally, if the measured citrate and / or creatinine signal line shape of the biological sample is above a calculated threshold, the concentration or presence of an analyte can be measured.

[0063] In some embodiments, the NMR spectrum of a biological sample can include four singlet signals of citrate protons in four different regions, where the four singlet signals of citrate protons include the range of 2.50 - 2.75 ppm. Additionally, and / or alternatively, in some embodiments, the NMR spectrum of a biological sample can include two singlet signals of creatinine protons in two different regions, where the two singlet signals of creatinine protons include the range of 3.0 - 4.20 ppm. For example, in some embodiments, citrate and creatinine can be quantified using their respective NMR signals of 2.50 - 2.75 ppm and 3.07 ppm. The respective chemical shifts can vary in position according to the pH. Thus, in certain embodiments, the TSP (sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4) and / or formate signals can be used as relative references for discriminating the citrate and creatinine signals.

[0064] In some embodiments, the NMR spectrum of a sample can include a spectrum representative of 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, the signal citrate-3 with the highest signal-to-noise ratio can be identified first based on two relative distances: the first distance to formate and TSP and the second distance to the creatinine-2 signal and TSP. Then citrate-4 can be located based on the coupling of citrate-4 to citrate-3. Then citrate-2 can be located based on the coupling of citrate-2 to citrate-3. Then citrate-1 can be located based on the coupling of citrate-1 to citrate-2. In some embodiments, when the citrate signal is low relative to impurities, or in the presence of neighboring and / or (one or more) overlapping impurity peaks, using the predicted amplitude of a given signal can improve citrate specificity.

[0065] In some embodiments, the NMR spectrum of a sample can include a spectrum representative of creatinine. Creatinine can be identified by a proton singlet in a first low-field region and a proton singlet in a second high-field region. Creatinine can be identified by using two signals, particularly the high-field signal and its relative distance to the TSP standard. A mathematical equation can correlate the distance of the high-field signal from TSP to creatinine as a function of pH. The amplitudes of the two signals can be used to limit the ambiguity of the identification. Although both signals from creatinine can be used for quantification, due to the high intensity, the high-field signal can provide less interference to the water signal.

[0066] In one embodiment, a mathematical algorithm can be used for precise baseline modeling for citrate and creatinine signals. Thus, in some embodiments, the method can include deconvolving signal data associated with citrate and / or creatinine proton singlet signals. Additionally, the method can include comparing data from multiple deconvolved signal data with previously calibrated 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. Additionally, mathematical modeling can utilize the use of a Lorentzian line shape, where a linear function and a constant offset can be incorporated into the algorithm. In some embodiments, the Lawson-Hanson non-negative least squares fitting algorithm can also be used for peak deconvolution.

[0067] In some embodiments, the unit conversion of signal amplitude to analyte concentration in mg / dL or mg / L can include generating a linear calibration curve, plotting the signal amplitude of citrate or creatinine relative to the concentration used in preparing the corresponding solution. Calibration of the curve can be performed in triplicate, measuring at least 12 samples in total. Using linear plotting, a mathematical function can be generated that can convert a given signal amplitude to concentration units based on the calibration curve. Thus, some embodiments can include a method of generating a report listing the concentrations of citrate and / or creatinine components present in a biological sample.

[0068] In some embodiments, the NMR instrument can be connected to a clinical analyzer, such as a clinical analyzer, for automation of sampling. In such an embodiment, a sample can be collected and deposited into a fully automated analyzer, where a spectrum can be provided shortly thereafter. In some embodiments where an automated sampling instrument is not used, the preparation of the sample can include generating a buffer and mixing the buffer with the sample, then positioning it within the NMR instrument. However, embodiments utilizing an automated sampling system can include only positioning the sample within the instrument for sampling.

[0069] In some embodiments, each urine sample can be mixed within a clinical analyzer with a 2:1 (v / v) ratio of 1.5 molar dipotassium hydrogen phosphate buffer, which buffer additionally contains 38 millimoles of sodium formate and 2.18 millimoles of TSP. The pH of the buffer can be acidic for examination. The pH can be adjusted to a pH of about 6.0 with an acid. Then, the sample can be prepared for spectral acquisition and processing.

[0070] In some embodiments, the acquisition and processing can additionally include raising the temperature within the probe above room temperature, at least 4 steady-state scans, at least 2.0 seconds of direct detection time, a relaxation time of at least 1.95 seconds between scans, and at least a 64-second collection period. Additionally, the free induction decay signal can be zero-filled with real and imaginary data points to be multiplied by an exponential window function, which exponential window function can correspond to a line broadening of 0.5 Hz prior to Fourier transform (FT). After FT, the spectrum can be corrected for phase and baseline errors. Other modifications to each of these variables can be used.

[0071] In one embodiment, analytical verification and imprecision can be examined by combining several urine pools selected from the sample samples and collected in a urine collection container. The assay imprecision of three container samples of urine containing low, medium, and high citrate and creatinine concentrations can be evaluated. The between-assay and within-assay precision can be determined. The arithmetic mean, standard deviation, and coefficient of variation percentage can be calculated, wherein the acceptance criteria for citrate and creatinine assay imprecision can be pre-determined to be 10% and 12%, respectively. Additionally, serial dilutions of urine pools having low, medium, and high concentrations of citrate or creatinine, respectively, can be used to evaluate linearity. Using EP software, the linearity of the assay is evaluated by linear and higher-order polynomial regression of the assay results from the serial mixed pools compared to the predicted concentrations. The acceptance criteria for linearity data can be defined as an allowable non-linearity of 3.5% for citrate and 8.6% for creatinine, corresponding to a slope between 0.9 - 1.10. Table 1 shows the within-assay and between-assay imprecision values for citrate and creatinine.

[0072] Table 1. Between-assay / Within-assay Imprecision of Citrate and Creatinine Measured in Urine

[0073]

[0074] a Based on 1 run of 20 tests (n = 20)

[0075] b Based on CLSI EP5-A2 using duplicate 2 runs per day testing, run for 20 days (n = 80).

[0076] In another embodiment, five deionized water samples can be used to calculate the limit of blank. Additionally, five samples with low citrate and creatinine concentrations can be used to calculate the limit of detection.

[0077] Additionally, the temperature stability of citrate and creatinine from the samples can be examined. The effects from various temperatures, including the effects of freeze - thaw cycles from urine samples, can be examined using room temperature (20 - 25 °C), refrigerated (2 - 8 °C), and frozen (-20 < -70 °C) determinations. Table 2 shows a summary of the time - based / temperature - based stability of citrate and creatinine.

[0078] Table 2. Summary of citrate and creatinine stability in urine samples.

[0079]

[0080] In one embodiment of the present disclosure, a result comparison of a chemistry - based determination and an NMR - based determination of citrate and creatinine concentrations can be made. The result comparison examination can compare the method results using an NMR - based determination with the traditionally used enzymatic determination based on citrate lyase and creatinase. The determination results can be compared to evaluate the accuracy of the new NMR - based determination of citrate and creatinine concentrations.

[0081] One embodiment can additionally examine the interference of other analytes on the concentrations and results of citrate and creatinine from the NMR - based determination. At least 10 or more substances can be used for the interference examination, where 3 of the 10 substances are endogenous substances and 7 of the 10 substances are exogenous substances. In one example of the present disclosure, the 3 endogenous substances can include urea, uric acid, and albumin as examples. The 7 exogenous substances can include acetaminophen, acetic acid, acetylsalicylic acid, ascorbic acid or vitamin C, boric acid, ibuprofen, and naproxen sodium as examples. Boric acid or acetic acid can be used as a preservative in urine samples instead of hydrochloric acid. When comparing with urine citrate and creatinine concentrations, the substance concentrations can be examined at endogenous (naturally occurring) concentrations, exogenous (external and / or therapeutic) concentrations, and higher concentrations.

[0082] Figure 1 A flowchart showing the process for determining the citrate and creatinine concentrations from a biological sample is shown. A biological sample can first be obtained from a subject in a biological sample container. Then the biological sample can be placed on an analyzer, such as on the analyzer. Then, the analyzer can prepare the biological sample according to the following sample preparation procedures:

[0083] In some embodiments, there can be defined dilutions of biological samples. For example, in some embodiments, each urine sample for urine citrate and creatinine (UCC) determination can be mixed with a buffer at a ratio of 2:1 (v / v) on an analyzer, such as a clinical analyzer. The buffer can contain 1.5 M dipotassium hydrogen phosphate (K2HPO4; Sigma-Aldrich), 38 mM sodium formate (CHNaO2, Sigma-Aldrich), and 2.18 mM sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TSP; Sigma-Aldrich). The pH of the buffer can be adjusted to 6.0 ± 0.1 with 6 N HCl. The prepared sample can be delivered by the analyzer to an NMR flow cell for spectral acquisition and processing.

[0084] Still referring to Figure 1 , after sample preparation, NMR spectral acquisition and spectral processing can be performed. 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 s. Other acquisition parameters can be as follows: spectral width = 4496.4 Hz, steady-state scans = 4, direct detection time = 2.0 s, relaxation between scans = 1.95 s, 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 transform (FT). After FT, the spectra can be corrected for phase and baseline errors.

[0085] Reference peak measurements can follow spectral acquisition and spectral processing. The characteristics of the TSP, formate, and creatinine peaks can be used for pre-analytical quality control, as chemical shift references, and as inputs to algorithms for citrate and creatinine quantification. The peak center positions, amplitudes, line widths at 50% amplitude, and skewness at 20% and 10% can be calculated for TSP and formate, respectively, while the peak center positions of the two creatinine peaks at approximately 3.07 ppm and 4.12 ppm can be calculated.

[0086] At this point, pre-analytical quality control can be used to detect instrument failure modes, which can ensure that urine citrate and creatinine determinations are not performed unless input spectra have been correctly acquired under specified conditions ( Figure 1 ). Detected failure modes can include sample delivery failures and NMR shimming failures. If a given condition is found to exist, subsequent evaluations can be not performed. This provides specificity for the causes of pre-analytical QC failures. Pre-analytical QC evaluations can be performed in the following order. Or other sequences of these steps can be used.

[0087] If the TSP amplitude is < 4.5 au for any input spectrum, the software can detect a complete sample delivery failure.

[0088] If the TSP skewness is < -1.0 data points or the TSP skewness is > 3.0 data points for any input spectrum, the software can detect a partial sample delivery failure.

[0089] If the TSP linewidth is > 2.0 Hz and the formate linewidth is > 2.0 Hz for any input spectrum, the software can detect a shimming failure.

[0090] The NMR spectrum of a sample can include a spectrum representative of creatinine. Still referring Figure 1 , creatinine can be identified by a proton singlet in a first low-field region and a proton singlet in a second high-field region. Creatinine can be identified by using two signals, particularly the high-field signal and its relative distance to the TSP standard. A mathematical equation can correlate the distance of the high-field signal from TSP to creatinine as a function of pH. The amplitudes of the two signals can be used to limit the ambiguity of the identification. Although both signals from creatinine can be used for quantification, the high-field signal can provide less interference to the water signal due to its high intensity.

[0091] In some embodiments, the citrate position can be identified after creatinine analysis ( Figure 1 ). As described above, in some embodiments, the NMR spectrum of a sample can include a spectrum representative of 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, the citrate-3 signal with the highest signal-to-noise ratio can be identified first based on two relative distances: a first distance to formate and TSP and a second distance to the creatinine-2 signal and TSP. Then citrate-4 can be located based on the coupling of citrate-4 to citrate-3. Then citrate-2 can be located based on the coupling of citrate-2 to citrate-3. Then citrate-1 can be located based on the coupling of citrate-1 to citrate-2. In some embodiments, when the citrate signal is low relative to impurities, or in the presence of neighboring and / or overlapping impurity peaks, using the predicted amplitude of a given signal can improve citrate specificity. In some embodiments, the analysis of citrate is followed by post-analysis quality control as described herein ( Figure 1 ). Then a mathematical model and a line shape function can be used to output the analysis results to convert the amplitudes of the citrate and creatinine peaks into concentration units, such as mg / L or mg / dL, respectively.

[0092] Figure 2Shows a schematic diagram of an NMR single - pulse radio - frequency pulse sequence experiment with WET solvent suppression. A proton NMR spectrum can be collected at 47 °C using a single - pulse sequence. The solvent signal can be attenuated by applying a T1 - effect (WET) module for 68 milliseconds to enhance water suppression.

[0093] Figure 3 Shows a schematic diagram of the NMR spectrum of a mixture of the indicated formate, creatinine, citrate, and TSP. Embedded in the spectrum is an enlarged schematic of 4 single - peak citrate peaks in the position range of 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 creatinine and citrate peaks. The formate peak can also be noted in the range of 8.00 ppm to 9.00 ppm and can additionally be provided as an internal standard for detecting creatinine and citrate. Peaks noted other than creatinine, citrate, TSP, and formate can be considered irrelevant to the detection and quantification of creatinine and citrate. Other identified peaks may include analytes that can be found in biological samples, especially urine. Additionally, other identified peaks may include impurities.

[0094] Figures 4A - 4D Shows a schematic diagram of a single NMR peak of TSP used to determine the peak center position, peak baseline, peak linewidth at 50% amplitude, and peak skewness at 20% amplitude, respectively. The x - axis shows the data - point position and the y - axis shows the peak amplitude. The peak center position (iL pts ) of the TSP peak can be determined as the highest - value data point in the interval [14796, 15096]. Using the derivative of the signal amplitude in the data - point interval, this position can coincide with the change in the sign of the derivative to ensure that it is the actual peak. The floating - point peak center position (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 position. The position of the Lorentzian function with the minimum RMSD can be the TSP floating - point peak center position.

[0095] In some embodiments, to calculate the TSP linewidth at 50% peak amplitude, the peak baseline and amplitude can be determined first. The baseline of the TSP peak can be determined as the average amplitude of the left baseline and the right baseline. The left baseline and the right baseline can be determined as the average amplitude in the intervals [iL pts - 90, iL pts - 60] and [iL pts + 60, iL ptsThe median data point (value) in [+90]. Sixty data points around the integer position can be excluded from consideration of the baseline as these points constitute (approximate) the TSP peak itself. The TSP amplitude can be determined as the amplitude value at the data point at the center position of the integer peak minus the value of the baseline. The TSP line width can be calculated as the length of the 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 there is no intersection at 50% on the left or right side, it can be assumed that the line extends appropriately to the end of the interval [14796, 15096].

[0096] The TSP skewness can be calculated as the difference between the floating-point peak center position and the midpoint of the line drawn at 20% of the peak amplitude. The TSP peak can have a line shape including satellite peaks on either side of the central peak, and the 20% height can be above the amplitudes of these satellite peaks such that they do not cause errors in the measurement of skewness. 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 there is no intersection at 20% on the left or right side, it can be assumed that the line extends appropriately to the end of the interval [14796, 15096].

[0097] Figures 5A - 5D Schematic diagrams of a single NMR peak of formate for determining the peak center position, peak baseline, peak line width at 50% amplitude, and peak skewness at 10% amplitude are shown respectively. The x-axis shows the data point position and the y-axis shows the peak amplitude. The integer peak center position (iL pts ) of the formate peak can be determined as the highest amplitude value in the data point interval [2350, 3050]. Using the derivative of the signal amplitude in the data point interval, this position can coincide with the change in the derivative sign to ensure that it is the actual peak. The floating-point peak center position (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 minimum RMSD can be the floating-point peak center position of formate.

[0098] In some embodiments, to calculate the formate line width at 50% peak amplitude, the peak baseline and amplitude can be determined first. The baseline of the formate peak can be determined as the average amplitude of the left baseline and the right baseline. The left baseline and the right baseline can be determined as the intervals [iL pts -70, iL pts -40] and [iL pts +40, iL ptsMedian amplitude (value) at [[ID=]]. Forty data points around the integer position can be excluded from the baseline consideration as these points constitute the (approximate) formate peak itself. The formate amplitude can be determined as the value of the data point at the center position of the integer peak minus the value of the baseline. The formate linewidth can be calculated as the length of the 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 there is no intersection at 50% on the left or right side, it can be assumed that the line extends appropriately to the end of the interval [2350, 3050].

[0099] The formate skewness can be calculated as the difference between the floating-point peak center position and the midpoint of the line drawn at 10% of the peak amplitude. As with the linewidth, linear interpolation can be used to determine the intersections of the 10% line with the left and right sides of the peak. If there is no intersection at 10% on the left or right side, it can be assumed that the line extends appropriately to the end of the interval [2350, 3050].

[0100] Figure 6 A flowchart of a routine for improving the creatinine region fitting by considering other peaks in the region is shown. After acquisition, a basis set or design matrix can be generated as the sum of four Lorentzian line shapes centered on the floating point of the corresponding positions of the analyte on the peak.

[0101] Embodiments of the methods for analyzing creatinine described herein can be applied only to the high-field peaks, but the analysis of each peak can yield a concentration value of creatinine. Thus, still referring to Figure 6 , an iterative method using Lawson-Hanson non-negative least squares deconvolution can be used to fit each creatinine peak using the basis set (design matrix). The creatinine basis set can include five Lorentzian single peaks, two slant lines, and a DC offset centered on the floating-point creatinine position. The line shapes of the Lorentzian single peaks can be divided into a left linewidth and a right linewidth. The default left and right linewidths can be half of the measured creatinine linewidth. The area of the Lorentzian line shape can be normalized to a value of 1000.

[0102] As Figure 6 additionally shown, the fitting deviation generated by each application of the deconvolution method can be evaluated to determine the positions of additional Lorentz components. These additional components can improve the fitting accuracy by modeling additional or adjacent signals in the fitting region, which can vary highly between samples.

[0103] Figure 7A flowchart of a routine for improving the creatinine region fit by adjusting the creatinine peak linewidth is shown. In addition to iterative fitting to resolve adjacent signals in the fitting region, iterative fitting can also be performed to vary the linewidth of the Lorentzian line shape used to fit creatinine and adjacent signals. Ultimately, the final line shape attributes (left linewidth and right linewidth) of the high-field creatinine peak can be used in downstream citrate modeling routines.

[0104] In some embodiments, once the creatinine fit is optimized, the creatinine concentration (e.g., mg / dL) can be calculated. This can be done using the following analysis or a variant thereof. Thus, in one embodiment, the creatinine concentration can be calculated by summing the deconvolution coefficients of the creatinine Lorentzian components and multiplying by (8836.9 * 0.011312) of the high-field 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.

[0105] The distance (difference) between the TSP and the formate position can be an indication of pH and can be used to calculate other pH-dependent peak positions and peak couplings (distance between two peaks). The predicted position of the high-field creatinine peak (hereinafter referred to as CRE1) and the predicted coupling between the low-field creatinine peak and the high-field creatinine peak can be calculated from the TSP and formate positions according to the examples of the present disclosure using the formulas provided herein.

[0106] The predicted coupling between the 2 creatinine peaks can be calculated as:

[0107] CRE_coupling = (a * exp(b * NMRpH) + c * exp(d * NMRpH)) + 1350

[0108] where: a = -0.06187; b = 0.02749; c = 349.9; d = -0.0002149;

[0109] NMRpH = tsp.fPosition - formate.fPosition - 12050.

[0110] The predicted position of the high-field creatinine peak can be calculated as:

[0111] CRE1_position = tsp.fPosition - ((a * exp(b * NMRpH) + c * exp(d * NMRpH)) + 4400)

[0112] where: a = -0.02564; b = 0.02853; c = 182.3; d = -0.0002739;

[0113] NMRpH = tsp.fPosition - formate.fPosition - 12050.

[0114] The predicted position of the low-field creatinine peak (hereinafter 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:

[0115] CRE2_Position = tsp.fPosition - ((a * exp(b * NMRpH) + c * exp(d * NMRpH)) + 5900)

[0116] where: a = -0.07221; b = 0.02848; c = 382.4; d = -0.0003509;

[0117] NMRpH = tsp.fPosition - formate.fPosition - 12050.

[0118] Second, it can be calculated independently from the predicted high-field creatinine position and coupling:

[0119] CRE2_Position = CRE1_Position - CRE_Coupling

[0120] In some embodiments, the predicted position of CRE2 can be assigned as the lowest-field result (whichever result is the leftmost or smaller data point value). The predicted CRE2 position can be used to define the creatinine doublet search region. This search region can be defined as the predicted CRE2 position - 100 data points to the predicted CRE1 position + 40 data points. Within this search region, peak pairs that match the predicted coupling ±50 data points can be identified. Then an empirically derived cost function can be used to select the highest-amplitude peak pair that most closely 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. A peak may occur when the derivative sign changes from positive to negative. Peak pairs separated by the predicted coupling ±50 data points can be identified.

[0121] The peak pair that maximizes the following function can be selected as the creatinine doublet (CRE2 and CRE1 peaks):

[0122] ((lamp + ramp) / 2) / (abs(0.6 - lamp / ramp) * (abs(2.7629 * (tsp_loc - rloc) - 6379.2 - (tsp_loc - lloc))) + 1)

[0123] where: lamp = the estimated amplitude of the left (low-field, CRE2) peak;

[0124] ramp = the estimated amplitude of the right (high field, CRE1) peak;

[0125] lloc = the position of the left (low field, CRE2) peak;

[0126] rloc = the position of the right (high field, CRE1) peak;

[0127] tsp_loc = the position of the TSP peak.

[0128] The floating-point positions of the individual creatinine peaks (CRE2 and CRE1) can be determined by calculating the root mean square deviation (RMSD) between the observed creatinine peaks and Lorentzian functions centered at integer peak positions with 0.01 data point increments. Similar to TSP and formate, the baseline of each creatinine peak can be determined as the average amplitude of the left baseline and the right baseline.

[0129] For the high field creatinine peak (CRE1), the left baseline and the right baseline can be determined as the median amplitudes (values) in the intervals [iL pts -80, iL pts -70] and [iL pts +70, iL pts +80], where iL pts refers to the integer peak position. 70 data points around the peak position may be excluded from the consideration of the baseline because these points (roughly) constitute the peak itself.

[0130] For the low field creatinine peak (CRE2), the left baseline and the right baseline can be determined as the median amplitudes (values) at the intervals [iL pts -20, iL pts -15] and [iL pts +15, iL pts +20], where iL pts refers to the integer peak position. 15 data points around the peak position may be excluded from the consideration of the baseline because these points (roughly) constitute the peak itself. The baseline region of the low field creatinine peak may be smaller because it is close to the residual water peak, which may 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.

[0131] Figure 8A and 8B respectively show schematic diagrams representing the NMR spectra and the pH dependence of the position of citrate. The position of the citrate peak may be very sensitive to pH. Peaks 1 and 2 (lowest field, labeled cit1 and cit2 in Figure 8A and peaks 3 and 4 (highest field, in Figure 8AThe couplings labeled Cit3 and Cit4 in the figure may be relatively constant with respect to pH. However, the coupling between these two pairs may be sensitive to pH. The citrate can use the peak positions of TSP and formate as an indication of pH, as well as the positions determined for the two creatinine peaks as a second indication of pH for localization.

[0132] In some embodiments, the high-field doublets can be identified first because their positions can be less sensitive to pH than the low-field doublets. Given the positions of the high-field doublets, the pH-based couplings can be used to identify the low-field doublets. The floating-point positions of the four citrate peaks can be used as inputs to a method for modeling the citrate region.

[0133] The predicted Cit3 peak position can be calculated first based on the observed difference between the formate and TSP positions, and second based on the observed difference between the low-field creatinine peak and the TSP position. The final predicted Cit3 peak position can be calculated as the average of two separate estimates of the Cit3 position.

[0134] For example, in one embodiment, the predicted Cit3 peak position based on TSP and formate can be calculated as follows:

[0135] Citr3_formate_position = 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)

[0136] where: NMRpH = tsp.fPosition - formate.fPosition - 12050;

[0137] a0 = -805.5; a1 = 1407; b1 = 1258; a2 = 59.14; b2 = -868.8; a3 = -157; b3 = 94.78; w = 0.00763.

[0138] The predicted Cit3 peak position based on TSP and CRE2 can be calculated as follows:

[0139] Citr3_cre2_position = tsp.fPosition - (((p1 * NMRpH^2 + p2 * NMRpH + p3) / (NMRpH^3 + q1 * NMRpH^2 + q2 * NMRpH + q3)) + 3700)

[0140] 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

[0141] If the NMR-pH in the above CRE2-based equation is > 380, then 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:

[0142] Citr3_position = (Citr3_formate_position + Citr3_cre2_position) / 2

[0143] If the CRE2-based position is skipped, then

[0144] Citr3_position = Citr3_formate_position.

[0145] In certain embodiments, the predicted Cit3 position can then be used to define a high-field citrate doublet search region. The 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 couplings of the Cit3 and Cit4 peaks can be identified. An empirically derived cost function can then be used to select the highest-amplitude peak pair that most closely matches the known amplitude relationship of the Cit3 and Cit4 peaks. A Savitzky-Golay filter with a polynomial order of 3 and a frame length of 9 can then be used to smooth the high-field citrate doublet search region.

[0146] In one embodiment, the derivative of the smoothed search region can be calculated. A peak occurs where the derivative sign changes from positive to negative. For each peak identified, the estimated amplitude of the peak can be calculated by summing the like-signed values of the derivative on either side of the peak position and dividing the sum by 2.

[0147] In one embodiment, for each identified peak, if the peak paired with 57±6 data points has not been identified as a peak by a derivative sign change, the amplitude of the search region paired with 57 data points can be added as a "virtual" peak, where the amplitude is calculated as the one-sided sum of the derivative values of the same sign at the paired points. Adding the "virtual" peak allows for the possibility of identifying doublets even when obscured by other non-citrate peaks. Peaks pairs (actual and virtual) separated by 57±6 data points can be identified. The peak pair that maximizes the following function is selected as the high-field citrate doublet (Cit3 and Cit4 peaks):

[0148] (((lamp + ramp) / 2)^2) / abs(36.8 / 22.13 - lamp / ramp)

[0149] where: lamp = the estimated amplitude of the left (low-field, Cit3) peak;

[0150] ramp = the estimated amplitude of the right (high-field, Cit4) peak.

[0151] This empirically derived function can effectively weigh the amplitude relationship of the peak pair to prefer a peak pair that can match the typical amplitude ratio of the Cit3 and Cit4 doublets. The floating-point positions of the individual citrate (Cit3 and Cit4) peaks can be determined by calculating the root mean square deviation (RMSD) between the observed citrate peaks and a Lorentzian function centered at 0.01 data point increments around the integer peak positions. Given the identified floating-point position of Cit3, the predicted coupling between the Cit2 and Cit3 peaks can be calculated based on the difference between the Cit3 and TSP positions:

[0152] Citr23_coupling = (p1*NMRpH^3 + p2*NMRpH^2 + p3*NMRpH + p4) / (NMRpH^3 + q1*NMRpH^2 + q2*NMRpH + q3)

[0153] 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.

[0154] If the NMR-pH value 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:

[0155] Citr23_Coupling = (p1 * NMRpH^3 + p2 * NMRpH^2 + p3 * NMRpH + p4) / (NMRpH^3 + q1 * NMRpH^2 + q2 * NMRpH + q3)

[0156] 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.

[0157] The predicted position of the Cit2 peak can be calculated as:

[0158] Citr2_Position = citr3.fPosition - Citr23_Coupling.

[0159] In some embodiments, the predicted Cit2 position can be used to define a low-field citrate doublet search region. The low-field citrate doublet search region can be defined as 97 data points downfield from the predicted Citr2 position to 40 data points upfield from the predicted Citr2 position. Within this search region, peak pairs that match the known couplings of the Cit1 and Cit2 peaks can be identified. Then an empirically derived cost function can be used to select the highest amplitude peak pair that most closely matches the known amplitude relationship of the Cit1 and Cit2 peaks.

[0160] A Savitzky-Golay filter with a polynomial order of 3 and a frame length of 9 can be used to smooth the low-field citrate doublet search region.

[0161] The derivative of the smoothed search region can be calculated. Peaks occur where the derivative sign changes from positive to negative.

[0162] In some embodiments, for each identified peak, the estimated amplitude of the peak can be calculated by summing the same-sign values of the derivative on either side of the peak position and dividing the sum by 2. Additionally, for each identified peak, if a peak paired by 57 ± 6 data points has not been identified as a peak by a derivative sign change, the amplitudes of the search region paired by 57 data points can be added as a "virtual" peak, where the amplitude is calculated as the one-sided sum of the same-sign derivative values at the paired points. Adding the "virtual" peak allows the doublet to be identified even when obscured by other non-citrate peaks. Identify peak pairs (actual and virtual) separated by 57 ± 6 data points. Select the peak pair that maximizes the following function as the low-field citrate doublet (Cit1 and Cit2 peaks):

[0163] ((((lamp + ramp) / 2) ^ 2) / abs(21 / 36 - lamp / ramp)) / (abs(ref_lamp - lamp) + abs(ref_ramp - ramp))

[0164] Where: lamp = the estimated amplitude of the left (low field, Cit1) peak;

[0165] ramp = the estimated amplitude of the right (high field, Cit2) peak;

[0166] ref_lamp = the amplitude of the selected Cit4 peak;

[0167] ref_ramp = the amplitude of the selected Cit3 peak.

[0168] This empirically derived function can effectively weigh the amplitude relationship of the peak pairs to preferentially match the peak pairs with 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.

[0169] In some embodiments, the floating point positions of the individual citrate (Cit1 and Cit2) peaks can be determined by calculating the root mean square deviation (RMSD) between the observed citrate peaks and a Lorentzian function centered at 0.01 data point increments around the integer peak positions.

[0170] Figure 9 A flowchart of an embodiment of the line shape deconvolution of the citrate NMR peak is shown. The spectrum from the sample can be evaluated and deconvolved via the flowchart for line shape deconvolution. After acquisition, the basis set or design matrix can be generated as the sum of 4 Lorentzian line shapes centered at the floating points of the corresponding positions of the analyte on the peaks. The Lawson-Hanson non-negative least squares algorithm can be used to deconvolve the citrate fitting region.

[0171] In some embodiments, the final line shape attributes (CRE1, left line width and right line width) of the high field creatinine signal modeling can be applied to the modeling of the citrate signal. The citrate fitting region can be from 50 data points lower field from the integer Cit1 position to 50 data points higher field from the integer Cit4 position.

[0172] The citrate components in the design matrix (basis set) for deconvolution can be constructed as the sum of 4 Lorentzian line shapes centered at the floating point positions of the 4 citrate peaks. The Lorentzian line shapes can be divided into left line width and right line width.

[0173] The left line width and right line width can be those from the final model of the high field creatinine signal (CRE1) adjusted for the nominal relationship between the creatinine and citrate line widths:

[0174] citrllw = 1.1643 * crellw + 0.2347;

[0175] citrrlw = 1.1643 * crerlw + 0.2347.

[0176] Where citrllw and citrrlw can be the left and right citrate root line widths respectively, and crellw and crerlw can be the left and right creatinine citrate root line widths respectively.

[0177] The area of the citrate component can be normalized to a value of 1000. By normalizing to a constant area, the increase or decrease in the measured area of the citrate signal due to NMR shimming (line width) can be effectively eliminated.

[0178] In some embodiments, the design matrix for deconvolution can include 5 citrate components (one centered at the floating citrate position plus 2 shifted 2 data points on either side), 2 diagonal lines, and a DC offset. The Lawson-Hanson non-negative least squares algorithm can be used to deconvolve the citrate fitting region with this design matrix.

[0179] The Lawson-Hanson deconvolution can be performed iteratively. For each iteration after the initial deconvolution, an additional Lorentzian signal located at the position of the maximum fitting deviation is appended to the design matrix. Up to 20 additional Lorentzians can be added to the basis set until the ratio of the fitting deviation to the area of the citrate fitting region meets the desired fitting quality criteria. If the area of the fitting region divided by the fitting deviation is ≥ 15, the fitting can be completed. Up to 2 additional Lorentzians can be allowed to overlap with the citrate peaks, as defined by occurring within ±9 data points of any of the 4 citrate peak positions. If the position of the maximum fitting deviation is within ±9 data points of any of the 4 citrate peak positions and there are already 2 additional superimposed Lorentzians in the design matrix, the fitting can be completed. If up to 20 additional Lorentzians are added to the design matrix, the fitting can be completed.

[0180] Based on the completed fitting, 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 urine citrate determination.

[0181] Figure 10A schematic diagram of NMR spectra representing various concentrations of creatinine according to an embodiment of the present disclosure is shown. In addition, Figure 10 Three mathematically assisted curve fitting plots corresponding to three different concentrations of creatinine and their corresponding percentiles are shown. In some embodiments, the data, such as Figure 10 The data shown in can be used to visualize and determine the concentration of creatinine using the amplitude of creatinine from the sample.

[0182] Figure 11 A schematic diagram of NMR spectra representing various concentrations of citrate according to an embodiment of the present disclosure is shown. In addition, Figure 11 Three mathematically assisted curve fitting plots corresponding to three different concentrations of citrate and their corresponding percentiles are shown. In some embodiments, the data, such as Figure 11 The data shown in can be used to visualize and determine the concentration of citrate using the amplitude of citrate from the sample.

[0183] Systems for measuring citrate and / or creatinine

[0184] Also disclosed are systems for performing any steps of the disclosed methods and computer-implemented instructions for performing any steps of the disclosed methods or running any part of the disclosed systems.

[0185] For example, the system may include one or more stations or components for performing any of the aforementioned method embodiments. In one embodiment of the present disclosure, the system for determining and measuring the concentration of citrate and / or creatinine may include an NMR spectrometer, which is configured to collect the measured citrate and / or creatinine signal line shape of the NMR spectrum of the biological sample. The NMR analyzer may include a computer program product that can store the measured citrate and / or creatinine line shape and reference spectrum. The computer program may be configured to derive the concentration of citrate and / or creatinine through a deconvolution process, such as the method steps disclosed herein. The NMR analyzer may additionally include an NMR spectrometer, a flow probe communicating with the spectrometer, and / or a controller communicating with the spectrometer, which is configured to obtain the NMR signal of the limited peak area of the NMR spectrum associated with the citrate and creatinine in the flow probe.

[0186] Also disclosed is a computer program product tangibly embodied in a non-transitory machine-readable storage medium, the medium including 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 is disclosed that is tangibly embodied in a non-transitory machine-readable storage medium, the medium including instructions configured to cause one or more data processors to perform a process that includes: (a) obtaining a sample from a subject; (b) detecting the presence of an analyte(s) of interest in the sample; and (c) calculating the concentration of the analyte(s) of interest in the sample.

[0187] In some embodiments, the system can include components for generating a patient report providing citrate and / or creatinine levels.

[0188] Figure 12 An example of a schematic diagram of an NMR analyzer is shown. A system 207 for acquiring and calculating the line shape of a selected sample is depicted. The system 207 can include an NMR spectrometer 22 for performing NMR measurements on the sample. In one embodiment, the spectrometer 22 can be configured to perform NMR measurements at 400 MHz for proton signals; in other embodiments, measurements can be made between 200 MHz and about 900 MHz or other suitable frequencies. Other frequencies corresponding to the desired operating magnetic field strength can also be employed. Typically, a proton flow probe can be installed, as well as a temperature controller to maintain the sample temperature at 47 + / - 0.5 °C. The spectrometer 22 can be controlled by a digital computer 214 or other signal processing unit. The computer 211 can be capable of performing a fast Fourier transform. It can 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.

[0189] The digital computer 211 may also include a set of analog-to-digital converters, digital-to-analog converters, and slow device I / O ports, which are connected to the operating elements of the spectrometer 22 through the pulse control and interface circuit 216. These elements may include an RF transmitter 217 and / or an RF power amplifier 218. The RF transmitter 217 may generate RF excitation pulses of duration, frequency, and amplitude guided by at least one digital signal processor, which may be on or communicate with the digital computer 211, and / or the RF power amplifier 218 amplifies the pulses and couples them to the RE transmit coil 219 around the sample cell 220 and / or the flow probe 220. The NMR signal generated by the excited sample in the presence of the polarization magnetic field (e.g., 9.4 Tesla) generated by the superconducting magnet 221 may be received by the coil 222 and applied to the RF receiver 223. The amplified and filtered NMR signal may be demodulated at 224, and the resulting quadrature signals may be applied to the interface circuit 216, where they may be digitized and input through the digital computer 211. The circuit 200 and / or the module 350 may be located in one or more processors associated with the digital computer 211 and / or in an auxiliary computer 213 or other computer, which may be local or remote and accessible via a global network such as the Internet 227.

[0190] After NMR data is collected from the sample in the measurement cell 220, it may be processed by the computer 211 to generate another file that may be stored in the memory 215 as needed. This second file may be a digital representation of the chemical shift spectrum, which may then be read out to the computer 213 for storage in its memory 225 or in a database associated with one or more servers. Under the guidance of a program stored in its memory or accessible by the computer 213, the computer 213 (which may be a laptop computer, desktop computer, workstation computer, electronic notepad, electronic tablet, smart phone, or other device or other computer having at least one processor) may process the chemical shift spectrum according to the teachings of the present disclosure to generate a report, which may be output to a 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, smart phone, etc., may also be used to display the results.

[0191] The functions performed by the computer 213 and its separate memory 225 may also be incorporated into the functions performed by the digital computer 211 of the spectrometer. In such a case, the printer 226 may be directly connected to the digital computer 211. Other interfaces and output devices may also be employed, as are well known to those skilled in the art.

[0192] Certain embodiments of the present disclosure relate to providing methods, systems, and / or computer program products for evaluation using citrate and creatinine, which can be particularly used for automated screening tests for clinical disease states and / or for risk assessment evaluations of screening in vitro biological samples.

[0193] Embodiments of the present disclosure may take the form of a full software embodiment or an embodiment combining software and hardware aspects, all of which are generally referred to herein as "circuits" or "modules".

[0194] The present disclosure may be embodied as an apparatus, method, data or signal processing system, or computer program product. Thus, the present disclosure may take the form of a full software embodiment, or an embodiment combining software and hardware aspects. In addition, 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, CD-ROM, optical storage device, or magnetic storage device.

[0195] 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 the computer-readable medium would include the following: 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). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, and then stored in a computer memory.

[0196] Computer program code for performing the operations of the present disclosure may be written in an object-oriented programming language such as Java 7, Smalltalk, Python, Labview, C++, or Visual Basic. However, the computer program code for performing the operations of the present disclosure may also be written in a conventional procedural programming language, such as the "C" programming language or even assembly language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer. In the latter case, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0197] The flowcharts and block diagrams in certain of the figures herein illustrate the architecture, functionality, and operation of possible implementations of an analysis model and evaluation system and / or program in accordance with the present disclosure. In this regard, each block in the flowchart or block diagram represents a module, segment, operation, or portion of code that includes one or more executable instructions for implementing the specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order 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 upon the functionality involved.

[0198] Figure 13 is a block diagram of an exemplary implementation of a data processing system 305 that illustrates a system, method, and computer program product in accordance with an embodiment of the present disclosure. A processor 310 communicates with a memory 314 via an address / data bus 348. The processor 310 may be any commercially available or custom microprocessor. The memory 314 represents the overall hierarchy of memory devices containing software and data for implementing the functionality of the data processing system 305. The memory 314 may include, but is not limited to, the following types of devices: (high-speed) cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

[0199] As Figure 13 shown, the memory 314 may include several categories of software and data for the data processing system 305: an operating system 352; application programs 354; input / output (I / O) device drivers 358; a citrate and creatinine evaluation module 350; and data 356. The citrate and creatinine evaluation module 350 may deconvolve NMR signals to reveal NMR signal peak regions defined in the proton NMR spectrum of a corresponding biological sample, thereby discriminating the levels of citrate and / or creatinine.

[0200] The data 356 may include signal (constituent and / or composite spectral line shape) data 362 that may be obtained from a data or signal acquisition system 320 (e.g., NMR spectrometer 22 and / or analyzer 22). As will be understood by those skilled in the art, the operating system 352 may be any operating system suitable for use with a data processing system, such as OS / 2, AIX, or OS / 390 from International Business Machines Corporation of Armonk, N.Y., Windows CE, Windows NT, Windows 95, Windows 98, Windows 2000, Windows XP, Windows 10 from Microsoft Corporation of Redmond, Wash., Palm OS from Palm, Inc., Mac OS from Apple Computer, UNIX, FreeBSD, or Linux, a proprietary operating system, or a dedicated operating system, such as for an embedded data processing system.

[0201] The I / O device driver 358 typically includes software routines accessed by the application 354 through the operating system 352 to communicate with devices such as (one or more) I / O data ports, data memory 356, and certain memory 314 components, as well as the signal acquisition system 320. The application 354 is a description of a program that implements various features of the data processing system 305 and may include at least one application that supports the operations according to the embodiments of the present disclosure. Finally, the data 356 represents the static and dynamic data used by the application 354, the operating system 352, the I / O device driver 358, and other software programs that may reside in the memory 314.

[0202] Although the present disclosure is described, for example, with reference to the module 350 as an application in Figure 13 it will be understood by those skilled in the art that other configurations may also be utilized while still benefiting from the teachings of the present disclosure. Therefore, the present disclosure should not be construed as limited to the Figure 13 configuration, which is intended to cover any configuration capable of performing the operations described herein.

[0203] In certain embodiments, the module 350 includes computer program code for providing levels of citrate and creatinine, which may be used as markers for assessing the risk of kidney stone formation and / or indicating whether personalized therapeutic intervention is needed and / or tracking the efficacy of a therapy or even the unintended consequences of a therapy.

[0204] Examples

[0205] Methods for examining the risk of kidney stone formation are described herein. Urine citrate and creatinine determination (UCC) analyzes urine biological samples in vitro. The process of measuring citrate and creatinine involves obtaining a sample from a subject, sample preparation, acquisition of proton NMR spectra from the sample, determination of citrate and creatinine concentrations using (one or more) peaks located at 2.50 - 2.75 ppm and approximately 3.07 and 4.12 ppm, respectively, deconvolution of the peaks using an algorithm that can distinguish the peaks in the spectral sample from other non - relevant peaks, and finally, after analyzing the sample, using an analyzer such as a clinical analyzer to calculate and generate a concentration output of the corresponding peak amplitudes.

[0206] Comparison of the results of NMR - based determination of urine citrate and creatinine with those of chemistry - based determination 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, indicating that NMR - based results can replace chemistry - based results. Precision studies showed that the NMR - based determination had good precision (%CV < 3.7% for both determinations) and accurately measured citrate and creatinine. Finally, while urine preservatives such as acetic acid and boric acid were listed as limitations of chemistry - based determination, no such interference was found to be a limitation of NMR - based determination. Thus, the NMR - based determination has performance characteristics that allow its use for clinical decision - making purposes.

[0207] In addition to having good performance characteristics for quantifying citrate and creatinine, NMR-based assays have several benefits relative to chemistry-based assays. Some of these benefits do not have the same limitations as chemistry-based assays, which have the limitation of not being able to test samples that have used acetic acid or boric acid as preservatives. The NMR assay is reagent-free and thus does not rely on reagents such as citrate lyase that may be affected by supply chain issues. Additionally, the NMR assay is high-throughput and reagent-free, there is no manipulation of the sample prior to the assay (e.g., dilution of the sample with dilution buffer occurring on the instrument), and the turnaround time for the assay and reporting of results is <2 min. Further, the NMR-based assay provides results for both citrate and creatinine simultaneously from the same spectral acquisition of the same sample. One of the benefits of NMR is that several analytes can be quantified simultaneously, which significantly reduces the time, resources, and cost of the assay. The fact that the NMR assays are high-throughput and easy to use makes them suitable for testing samples from large observational and interventional clinical studies. While current NMR assays can quantify citrate and creatinine in urine samples, future applications of the technology could also include quantifying cystine and uric acid, which would allow for a more extensive analysis of the risk of kidney stone formation. The newly developed high-throughput NMR assay demonstrated good performance, yielding results comparable to the currently utilized chemical tests and providing an alternative means of simultaneously quantifying urinary citrate and creatinine for clinical and research use.

[0208] Figure 14 and 15 show plots demonstrating the limits of quantification for creatinine and citrate, respectively. The limits of quantification for the assay were 5.9 mg / dL for creatinine and 17 mg / L for citrate. For the limits of quantification (LOQ), eight urine samples were used to determine the LOQ. Each pooled sample was repeated four (4) times per day for 3 days according to guidelines developed 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. To calculate the limit of blank (LOB) and limit of detection (LOD), five deionized water samples and five low-concentration samples were tested, respectively. As Figure 16 and 17 shown, linearity was demonstrated to far exceed the reference interval. Creatinine and citrate in urine measured by the NMR UCC assay compared well with results obtained on a chemistry analyzer.

[0209] Figure 16 and 17Two plots are shown, where the first plot (left) is a linear scatter plot and the second plot (right) is a residual plot of creatinine and citrate. The linearity of the assay results was evaluated using regression analysis of the assigned and measured urinary citrate and creatinine concentrations. The citrate results were linear in 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. At the 5% significance level, no polynomial fit was statistically superior to the linear fit. 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. The creatinine results were linear in the range of 2.8 to 1,308 mg / dL. The equation of the best line for creatinine was determined to be Y = 1.00X - 0.24. At the 5% significance level, a third-order polynomial fit was statistically superior to the linear fit. For creatinine, the LOB, LOD, and LOQ were determined to be 5.5, 5.9, and 5.9 mg / dL, respectively.

[0210] Figure 18 and 19 Various plots comparing the measurement results of creatinine and citrate obtained by NMR determination and chemical determination are shown. A method comparison study was conducted to compare the citrate test results based on NMR with those generated using a chemistry-based assay. Deming regression analysis of the citrate results (n = 297) from both assays 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 with those generated using a chemistry-based assay. Deming regression analysis of the creatinine results (n = 306) from both assays yielded a correlation coefficient of 0.960, with a slope and intercept of 0.968 and 0.97, respectively. The bias plot showed no systematic bias between the results of the two assays (mean bias = -1.4%).

[0211] Figure 20 Two calibration curves of citrate (left) and creatinine (right) used in the conversion from amplitude to concentration are shown. Using the factors obtained from the calibration curves, the analyte signal amplitudes from the deconvolution process were converted to concentration units. The calibration curves were generated by correlating the peak amplitudes of urine spiked with creatinine or citrate standards with the amount of the added standards. Urine samples were spiked with creatinine and citrate standards. To establish the standard curves for creatinine and citrate, a total of 12 - 13 samples spiked with known amounts of creatinine or citrate were assayed in triplicate. The standard curves were used to convert creatinine and citrate from signal amplitude to concentration units.

[0212] In vitro testing of the potential interference of substances (n = 10) on the results generated from the determination of urinary citrate and creatinine (3 endogenous substances and 7 exogenous substances). During the initial screening, pooled urine with citrate concentrations between 228.6 - 885.7 mg / L and creatinine concentrations between 57.1 - 128.6 mg / dL was used to generate substance interference data. According to CLSI guidelines, substances showing interference during the initial screening were tested in a dose - response manner. For acetic acid and boric acid, which can be used as preservatives in urine, it is recommended to test at 5 times the recommended concentration. For acetic acid, test the recommended concentration from 0.5% to 2.5%, and for boric acid, test the recommended concentration from 1% to 5%. The highest detected concentration without interference on citrate and creatinine results was defined as citrate bias < 10% and creatinine bias < 12.9%. Table 3 shows the highest substance concentrations of the tests that did not cause interference.

[0213] Table 3. Interference test results showing the highest concentrations of the tested substances that do not interfere with the determination results of citrate or creatinine.

[0214] Substance Drug Name Concentration (mg / dL) Urea ─ 263.9 Uric Acid ─ 23.5 Protein (Albumin) ─ 175.0 Acetaminophen Tylenol 21.8 Acetic Acid Preservative 2500 Acetylsalicylic Acid Aspirin 66.4 Ascorbic Acid Vitamin C 6.0 Boric Acid Preservative 1250* Ibuprofen Sodium Advil 59.0 Naproxen Sodium Aleve 56.1

[0215] *Interferes with the determination result at this concentration, which is higher than the highest concentration of boric acid when used as a preservative.

[0216] Illustrative embodiments of suitable methods, systems, and procedures

[0217] As used below, any reference to a method, system, and procedure is understood to be a separate reference to each of those methods, systems, and procedures (e.g., "Illustrative embodiments 1 - 4 are understood to be Illustrative embodiments 1, 2, 3, or 4.").

[0218] Illustrative embodiment 1 is a method comprising: acquiring 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.

[0219] Illustrative embodiment 2 is the method of any of the foregoing or subsequent illustrative embodiments, wherein acquiring the NMR spectrum comprises: generating a measured citrate and / or creatinine signal line shape from the NMR spectrum; and generating a calculated line shape of citrate and / or creatinine, wherein the calculated line shape is based on the derived concentration of citrate and / or creatinine expected in the biological sample.

[0220] Illustrative embodiment 3 is the method of any of the foregoing or subsequent illustrative embodiments, wherein generating the calculated line shape of citrate and / or creatinine comprises calculating a plurality of reference coefficients of the calculated line shape based on linear least - squares fitting techniques.

[0221] Exemplary embodiment 4 is a method of any of the foregoing or subsequent exemplary embodiments, further comprising: determining the degree of correlation between an initially calculated line shape of a biological sample and a measured citrate and / or creatinine signal line shape of the biological sample; and if the degree of correlation between the calculated line shape of the biological sample and the measured citrate and / or creatinine signal line shape is higher than a predetermined threshold, determining the presence of citrate and / or creatinine based on the calculated line shape.

[0222] Exemplary embodiment 5 is a method of any of the foregoing or subsequent exemplary embodiments, wherein the NMR spectrum of the biological sample includes four citrate proton singlet signals in four different regions, and the four citrate proton singlet signal regions include the range of 2.50 - 2.75 ppm.

[0223] Exemplary embodiment 6 is a method of any of the foregoing or subsequent exemplary embodiments, wherein the NMR spectrum of the biological sample includes two creatinine proton singlet signals in two different regions, and the two creatinine proton singlet signal regions include the range of 3.0 - 4.20 ppm.

[0224] Exemplary embodiment 7 is a method of any of the foregoing or subsequent exemplary embodiments, further comprising: deconvolving signal data associated with the citrate and / or creatinine proton singlet signals; and comparing the data from the deconvolved signal data from multiple samples with prior calibration data corresponding to a standard biological sample having a known concentration of citrate and / or creatinine to determine the concentration of citrate and / or creatinine in the biological sample.

[0225] Exemplary embodiment 8 is a method of any of the foregoing or subsequent exemplary embodiments, further comprising the step of generating a report listing the concentrations of the citrate and / or creatinine components present in the biological sample.

[0226] Exemplary embodiment 9 is a method of any of the foregoing or subsequent exemplary embodiments, wherein the biological sample includes blood, serum, plasma, sputum, cerebrospinal fluid, urine, or a combination thereof.

[0227] Exemplary embodiment 10 is a method of any of the foregoing or subsequent exemplary embodiments, further comprising the step of identifying a subject as a subject suffering from a condition associated with an abnormal concentration of elevated or reduced citrate and / or creatinine.

[0228] Exemplary embodiment 11 is a system that includes: an NMR spectrometer configured to acquire measured citrate and / or creatinine signal line shapes of an NMR spectrum of a biological sample; a computer program product that includes instructions to store the measured citrate and / or creatinine signal line shapes of the biological sample; a computer program product that includes instructions to store a reference spectrum for each of citrate and / or creatinine; a computer program product that includes instructions to calculate a line shape 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 that includes instructions to compare the measured citrate and / or creatinine signal line shapes with the calculated line shapes to determine a degree of correlation between the calculated line shapes and the measured citrate and / or creatinine signal line shapes.

[0229] Exemplary embodiment 11 is the system of any of the foregoing or subsequent exemplary embodiments, further including an output device for generating a report indicating the presence of citrate and / or creatinine.

[0230] Exemplary embodiment 12 is the system of any of the foregoing or subsequent exemplary embodiments, configured to perform the method of any one of exemplary embodiments 1-10.

[0231] Exemplary embodiment 13 is a computer program product tangibly embodied in a non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform processing, the non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform processing including: obtaining a sample from a subject; detecting the presence of an analyte of interest in the sample; and calculating the concentration of the analyte of interest in the sample.

Claims

1. A method comprising: acquiring 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.

2. The method according to claim 1, wherein acquiring the NMR spectrum comprises: generating a measured citrate and / or creatinine signal line shape from the NMR spectrum; and generating a calculated line shape of citrate and / or creatinine, wherein the calculated line shape is based on a derived concentration of citrate and / or creatinine predicted to be present in the biological sample.

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

4. The method according to claim 2 or 3, further comprising: determining the degree of correlation between an initial calculated line shape of the biological sample and the measured citrate and / or creatinine signal line shape of the biological sample; and if the degree of correlation between the calculated line shape of the biological sample and the measured citrate and / or creatinine signal line shape is higher than a predetermined threshold, determining the presence of citrate and / or creatinine based on the calculated line shape.

5. The method according to any one of claims 1-4, 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 - 2.75 ppm.

6. The method according to any one of claims 1-5, 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 a range of 3.0 - 4.20 ppm.

7. The method according to any one of claims 1-6, further comprising: deconvolving signal data related to the citrate and / or creatinine proton singlet signals; and comparing data from the deconvolved signal data of multiple samples with prior calibration data corresponding to a standard biological sample having a known concentration of citrate and / or creatinine to determine the concentration of citrate and / or creatinine in the biological sample.

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

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

10. The method according to any one of claims 1-9, further comprising the step of identifying the subject as a subject suffering from a disorder associated with an abnormal concentration of elevated or reduced citrate and / or creatinine.

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

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

13. A computer program product tangibly embodied in a non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform processing, the non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform processing, the processing 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.

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