Noninvasive measurement of endogenous S-nitrosothiols

Abstract

The present invention provides a noninvasive measurement for endogenous S-nitrosothiols and related measurement systems and methods. In one embodiment, one or more sensors noninvasively measure a set of one or more biocharacteristic parameters in a region of interest of a subject to provide a time series measurement of each biocharacteristic parameter in the set of biocharacteristic parameters. The medium stores device readable instructions that, when executed by an associated processor, perform a process including receiving the time series measurements of the biocharacteristic parameters, using a predictive model to generate a value indicative of endogenous S-nitrosothiol content of tissue in the region of interest from the time series measurements of the biocharacteristic parameters, and providing the value indicative of endogenous S-nitrosothiol content of tissue in the region of interest to a user via a user interface.

Description

Detailed Description of the Invention

[0001] (Related Applications) This application claims priority under 35 U.S.C. § 119(e) to U.S. Patent Application No. 63 / 232,686 filed August 13, 2021, U.S. Patent Application No. 63 / 289,470 filed December 14, 2021, U.S. Patent Application No. 63 / 339,871 filed May 9, 2022, and U.S. Patent Application No. 63 / 347,661 filed June 1, 2022, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] The present invention relates to diagnostic systems, and more particularly to the non-invasive measurement of endogenous S-nitrosothiols. [Background technology]

[0003] Nitric oxide (NO) is involved in many physiological functions, including smooth muscle relaxation, vasodilation, inflammatory responses, and inhibition of platelet adhesion and aggregation. Identifying natural reservoirs of NO and methods to regulate the levels of biologically available NO and its alternative biologically active forms could provide a means to control these physiological functions. Nitric oxide (NO) and S-nitrosothiols (SNOs) are transported by hemoglobin along with oxygen. Because NO itself cannot escape from red blood cells, SNOs are the biologically active form of NO and the only endogenous active form of NO remaining in blood. For example, during oxygen deprivation or exercise, SNOs are released from hemoglobin in tissues to dilate blood vessels, thereby delivering oxygen to tissues. Therefore, SNOs released from RBCs regulate microvascular blood flow in tissues, while without SNOs, tissues cannot deliver oxygen (Zhang PNAS 2015, Premont Circ Res 2019). Therefore, SNO levels are an important component of VO2 (oxygen consumption by tissues). Although non-invasive means are available to detect oxygenated hemoglobin, no means exist that can detect endogenous levels of NO or SNO. Summary of the Invention

[0004] One example provides a system comprising: at least one sensor configured to noninvasively measure a bio-characteristic parameter within a region of interest of a subject to provide a time series of measurements of the bio-characteristic parameter; at least one processor in communication with the sensor; and at least one non-transitory computer-readable medium storing device-readable instructions, the at least one processor, when executing the device-readable instructions, performing processes including receiving the time series of measurements of the bio-characteristic parameter, using a predictive model to generate a value indicative of endogenous S-nitrosothiol content of tissue within the region of interest from the time series measurements of the bio-characteristic parameter, and providing the value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest to a user via a user interface.

[0005] Another example provides a method for noninvasive measurement of endogenous S-nitrosothiols. Time-series measurements of a bio-characteristic parameter are provided by noninvasively measuring the bio-characteristic parameter in a region of interest of a subject via at least one sensor. Values indicative of the endogenous S-nitrosothiol content of tissue in the region of interest are generated from the time-series measurements of the bio-characteristic parameter. The values indicative of the endogenous S-nitrosothiol content of tissue in the region of interest are stored in a non-transitory computer-readable medium. In one embodiment, the endogenous S-nitrosothiol values are capable of predicting tissue reoxygenation rate.

[0006] Another example provides another method for noninvasively measuring endogenous S-nitrosothiols. Blood volume and oxygen saturation are noninvasively measured in a region of interest of a subject via at least one sensor, thereby providing time-series measurements of oxygen saturation and time-series measurements of blood volume. A linear relationship between the time-series measurements of blood volume and the time-series measurements of oxygen saturation is identified using a predictive model. Based on the identified linear relationship, a value indicative of endogenous S-nitrosothiol content of tissue in the region of interest is determined from the time-series measurements of oxygen saturation and the time-series measurements of blood volume. The value indicative of endogenous S-nitrosothiol content of tissue in the region of interest is stored in a non-transitory computer-readable medium.

[0007] In one embodiment, the present invention provides a method for identifying the presence or risk of dementia or cognitive loss in a subject. The method includes calculating a UO2 measurement by measuring the subject's maximum nitric oxide level during an exercise period. For subjects without dementia, a low UO2 measurement indicates the presence of a risk of dementia or cognitive loss. In one aspect, the dementia or cognitive loss is associated with Alzheimer's disease.

[0008] In another embodiment, the present invention provides a method for identifying dementia or improvement in cognitive function in a subject. The method includes measuring the subject's maximum nitric oxide level during exercise using UO2 measurements at a first time point and a second time point, the second time point being a time after the proposed exercise following the measurement at the first time point. For subjects without dementia, a lower UO2 measurement indicates dementia or cognitive loss at the first time point, and an increase in UO2 measurement at the second time point relative to the first time point indicates improvement in the subject's dementia or cognitive function. In one embodiment, the dementia or cognitive loss is associated with Alzheimer's disease. [Brief explanation of the drawings]

[0009] [Figure 1]1 shows one example of a system for generating a value indicative of the endogenous S-nitrosothiol content of tissue within a region of interest in a subject. [Figure 2] Another example of a system for generating a value indicative of the endogenous S-nitrosothiol content of tissue within a region of interest in a subject is shown. [Figure 3] 1 is a graph showing PNO levels in patients during exercise. [Figure 4] 1 is a graph showing a patient's VO2 level during exercise. [Figure 5] 10 is a graph of a time series of UO2 measurements and a time series of VO2 measurements as an athlete operates a full-body fitness bicycle using sensors to record blood volume and oxygen saturation. [Figure 6] 10 is a graph of a time series of UO2 measurements and a time series of VO2 measurements as an athlete operates a full-body fitness bicycle using sensors to record blood volume and oxygen saturation. [Figure 7] This is a graph of the athlete's maximum NO (MAX-NO) power recorded each week over a six-month period. [Figure 8] The relationship between the athletes' maximum NO (MAX-NO) tolerance and their critical power recorded within a 6-week period is shown as a scatter plot. [Figure 9] 1 illustrates one exemplary method for generating a value indicative of the endogenous S-nitrosothiol content of tissue within a region of interest in a subject. [Figure 10] Another example of a method for generating a value indicative of the endogenous S-nitrosothiol content of tissue within a region of interest in a subject is provided. [Figure 11] Another method is presented for generating a value indicative of the endogenous S-nitrosothiol content of tissue within a region of interest in a subject. [Figure 12] This section shows indicators and usage examples of professional sports platforms that use (NO). [Figure 13] Shown are bar graphs of player recovery measurements for Max(NO)-Recovery Level under the three group numbers. [Figure 14]Individual maximum nitric oxide (PNO) over time and (NO) regeneration over time are shown. [Figure 15] FIG. 1 shows the effects of exercise on muscle oxygenation (SmO2) and s-nitrosothiol (PNO). [Figure 16] 1 is a curve graph showing the effect of exercise on muscle oxygenation (SmO2) and s-nitrosothiols (PNO), where (+) correlations are nested within (-) correlations, and (+) correlations are nested within (+) correlations. [Figure 17A] Representative tracings of control βC93 mice and corresponding βC93A mutant animals are shown. [Figure 17B] Basal pO2 in the gastrocnemius muscle of control βC93 mice and corresponding βC93A mutant animals is shown. [Figure 17C] 1 shows the percent recovery after blockade of muscle pO2 in control βC93 mice and corresponding βC93A mutant animals. [Figure 18A] SNO-Hb isolated from fresh arterial blood of a patient group is shown. [Figure 18B] FeNO levels in the patient groups are shown. [Figure 18C] Total HbNO for the patient group is shown. [Figure 18D] The ratio of SNO to total HbNO for the patient groups is shown. [Figure 19A] Representative near-infrared sensor measurements of recovery of Hb oxygenation over time are shown for healthy controls and PAD patients. [Figure 19B] Reperfusion recovery half-life in a patient population measured at the leg using an ankle cuff is shown. [Figure 19C] Reperfusion recovery half-life in a patient population measured at the leg using a thigh cuff is shown. [Figure 19D] Reperfusion recovery half-life in a patient population measured at the lower leg using a thigh cuff is shown. [Figure 19E] Correlation between SNO-Hb levels and recovery half-life is shown. [Figure 20] Correlation between NOHb measurements and clinical chemistry results is shown. [Figure 21]1 shows the correlation between NOHb measurements and hyperemic NIRS half-life. [Figure 22] Examples of measuring equipment are shown below. [Figure 23] FIG. 1 is a schematic block diagram illustrating an exemplary system of hardware components in which embodiments of the systems and methods disclosed herein may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0010] As used herein, the term "comprising" means including, but not limited to, including; the term "based on" means based at least in part on; and references to "a," "first," "another," or equivalents thereof in the specification or claims should be interpreted as including one or more of those elements, and not excluding two or more of those elements.

[0011] As used herein, the "amount" of a biological material can refer to either the volume, mass, saturation, or concentration of the material.

[0012] As used herein, a "physiological parameter" refers to a continuous, categorical, or ordinal value of a physiological state of a subject.

[0013] As used herein, a "predictive model" is a mathematical model for predicting the future state of a parameter or estimating the current state of a parameter that is not directly measured.

[0014] As used herein, a "subject" is a human or other mammal.

[0015] As used herein, "biometric parameter" refers to a measured parameter that is indicative of the health or fitness of a subject.

[0016] As used herein, a measurement is made "non-invasively" if the measurement does not require the extraction of blood or tissue from the subject.

[0017] As used herein, "providing treatment to a subject" may include applying a therapeutic agent, applying mechanical force or electrical energy to a subject, or instructing a subject to perform a particular behavior or task that provides a therapeutic benefit.

[0018] As used herein, "PNO" or individual nitric oxide (PNO) refers to an individual's level of the bioactive form of NO in blood, identified as s-nitrosothiol in hemoglobin, derived from red blood cells (RBCs). Note that SNO in RBCs is in equilibrium with other SNO, and PNO may be formed from different NO-derived (and related NOx) forms, and s-nitrosothiol released from RBCs may be generated in different ways to produce NO and s-nitrosothiol in tissues. Therefore, PNO represents the bioactivity derived from NO and includes any bioactive form of NO formed in RBCs or other processes for oxygenating tissues. PNO is a relative measure that demonstrates a direct correlation between hemoglobin oxygen saturation and total hemoglobin. By way of example, individuals with higher levels of SNO may be able to re-oxygenate tissues more quickly than individuals with lower levels of SNO, e.g., muscle recovery during periods of regular exercise.

[0019] As used herein, "UO2" is the amount of oxygen available to the tissues in a region of interest measured based on a nitric oxide calculation, which is estimated as the product of the PNO index and the amount of oxygen available to the tissues.

[0020] As used herein, "maximal NO power" is a nitric oxide measurement that reflects an individual's maximum energy utilization rate. This maximum energy utilization rate directly correlates to a true measure of energy utilization in watts and is derived from the maximum PNO index and the maximum energy utilization rate of muscle tissue during exercise. Finally, "maximal NO tolerance" reflects an individual's maximum energy delivery rate, based on nitric oxide measurements, that directly correlates to critical power gains, a key standard measure of endurance performance, derived from the maximum rate of change in PNO index generated during the rest period following exercise.

[0021] As used herein, a calculation or determination that a user obtains within one minute of a corresponding measurement is “real time.” In one embodiment, a real time calculation is performed within 10 seconds of a measurement.

[0022] The volume of oxygen consumed by an individual (VO2) is currently a key measure of physical performance used by physicians and physiologists worldwide. VO2 represents the combined capacity of the pulmonary, cardiovascular, and muscular systems to absorb, transport, and consume oxygen. Conventional systems and methods for measuring VO2 are invasive and / or require closely controlled conditions. For example, conventional VO2 measurements require athletes to wear a mask in a laboratory, and measurement tools can cost over $35,000.

[0023] In the embodiments described herein, the release of nitric oxide determines how much oxygen is available during exercise, and monitoring an individual's nitric oxide levels can be used to determine physical performance and other physiological characteristics. One measurable value, called personalized nitric oxide (PNO), is a measurement of the amount of activated nitric oxide released from circulating red blood cells during exercise. Because activated nitric oxide, represented by S-nitrosothiols in the blood, opens blood vessels that deliver oxygen to tissues (including the heart and brain), a patient's nitric oxide levels are closely related to their health. By monitoring this metric, along with others described below, the system can determine how much nitric oxide an exerciser releases in response to exercise, how intensely they need to exercise, how long they should exercise, and what type of exercise is most appropriate, thereby providing improvements in strength, function, and health. Because oxygen saturation measured in small blood vessels in muscles is individual- and environment-specific, the PNO metric derived from the measurement is also patient- and environment-specific. However, measurements can be relative, but only to a certain extent, and an individual's PNO measurement can be used as a reliable indicator of the individual's health and fitness.

[0024] Sensors for calculating UO2 blood volume and oxygen saturation are portable and lightweight, accounting for less than 5 percent of the devices currently used for VO2 measurement on the market. Standard VO2 testing and measurement tools rely on exhaled gas concentrations to measure system oxygen consumption. However, these centralized measurements miss important information about metabolic processes occurring in muscles and therefore cannot reveal why an individual's VO2 max is not high. Furthermore, because UO2 measurements are performed at the muscle level (affected by nitric oxide concentrations), they not only measure oxygen consumption but also allow for the identification of rate-limiting factors for increasing it (e.g., insufficient blood flow or low muscle utilization of oxygen). This not only serves as a diagnostic measurement tool for assessing physical performance, but can also suggest the type of exercise needed to improve health and physical performance. For example, the cause of a change in UO2 may be identified from time-series measurements and identified as limited oxygen supply (representing limited blood flow) or limited oxygen utilization (representing poor muscle function).

[0025] FIG. 1 illustrates an example system 100 for generating a value indicative of endogenous S-nitrosothiol content in tissue within a region of interest in a subject. It should be understood that the system 100 can noninvasively and in real time determine a value indicative of endogenous S-nitrosothiol content in tissue. This can be used to match the tissue S-nitrosothiol content and indices derived therefrom to performance or biometric parameters of the subject during or after exercise, or when muscle tissue blood flow is otherwise physiologically or externally interrupted. The system 100 may include a group of at least one sensor 102 that noninvasively measures biometric parameters within the region of interest to provide at least one time-series measurement of the biometric parameter. In one example, if the biometric parameters include blood volume and oxygen saturation, the group of sensors 102 may include a single sensor that measures both blood volume and oxygen saturation or multiple sensors that commonly provide these measurements. In one embodiment, a single optical sensor measures oxygen saturation and blood volume using the near-infrared light spectrum and determines blood flow based on changes in total hemoglobin concentration and oxygen saturation, although the group of sensors 102 may include additional sensors that typically record multiple biometric parameters within the subject's region of interest.

[0026] Each sensor interface 104, predictive model 106, and user interface 108 may be implemented as device-readable instructions stored on a non-transitory computer-readable medium 110 and executed by a corresponding processor 112. The sensor interface 104 may receive time-series measurements of biometric parameters from the group of sensors 102 and condition the data for use by the predictive model 104. The predictive model 104 may also use data about the subject stored on the computer-readable medium 110, such as age, gender, genomic data, nutritional information, medication intake, relevant medical history, etc.

[0027] The predictive model 110 may utilize one or more pattern recognition algorithms, each analyzing the data provided via the sensor interface 104 and any additional data to assign a continuous or categorical parameter to a region of interest indicative of the amount of endogenous S-nitrosothiol present in that region of interest. When multiple classification or regression models are used, a mediation element may be utilized to provide coherent results from the multiple models. The training process for a given classifier varies depending on its implementation, but training typically involves statistically aggregating training data into one or more parameters associated with an output classification. Rule-based models, such as decision trees, may substitute or supplement training data using domain knowledge provided by one or more human experts, for example, and use the extracted features to select rules for classifying users. Any one of a variety of techniques may be used for the classification algorithm, including support vector machines (SVMs), regression models, self-organizing mapping, fuzzy logic systems, data fusion processes, bootstrap integration and suckback methods, rule-based systems, or artificial neural networks (ANNs).

[0028] For example, an SVM classifier may utilize multiple functions called hyperplanes to conceptually demarcate boundaries in an N-dimensional feature space, where each of the N dimensions represents a relevant feature of the feature vector. The boundaries may define a range of eigenvalues associated with each classification. A continuous or classification output value for a given input feature vector may therefore be identified based on the vector's location relative to the boundaries in the feature space. In one embodiment, an SVM may be implemented using a kernel method using a linear or nonlinear kernel. A trained SVM classifier may converge to an optimal hyperplane to obtain a solution with maximized margins for correlated features.

[0029] An ANN classifier may include multiple nodes connected to each other. Values from a feature vector may be fed to multiple input nodes. The input nodes may provide these input values to one or more layers of intermediate nodes, respectively. A given intermediate node may receive one or more output values from the previous node. The received values may be weighted according to a set of weights established during classifier training. The intermediate node may convert the received values into a single output based on a transfer function at the node. For example, the intermediate node may sum the received values and perform a rectification function operation on the sum. The output of the ANN may be a continuous or categorized output value. In one example, the final layer of nodes provides a confidence value for the ANN's output classification, and each node has a correlation value indicating the confidence of one of the classifier's associated output classifications. The confidence value may be based on a loss function, such as a cross-entropy loss function. The loss function may be used to optimize the ANN. In one example, the ANN may be optimized to minimize the loss function.

[0030] Many ANN classifiers are fully connected and feedforward. However, convolutional neural networks contain convolutional layers, where nodes from previous layers are connected to only a subset of nodes in the convolutional layer. A recurrent neural network is a neural network in which the connections between nodes form a directed graph along time-series data. Unlike feedforward networks, recurrent neural networks may incorporate state feedback from earlier inputs. This allows the output of a recurrent neural network for a given input to be a function of not only the input but also one or more previous inputs. As an example, a long-short-term memory (LSTM) network is a modified version of a recurrent neural network that is better able to store past data in memory.

[0031] A rule-based classifier can select an output classification by applying a set of logical rules to extracted features. Rules can be applied in sequence, with the logical outcome of each step affecting the analysis of subsequent steps. Specific rules and their sequence can be identified based on any or all training data, simulated inference from previous situations, or existing domain knowledge. One example of a rule-based classifier is a decision tree algorithm, which selects a classification for a feature vector by comparing the values of features in a feature set with corresponding thresholds in a hierarchical tree structure. A random forest classifier modifies a decision tree algorithm using a bootstrap integration or "bagging" method. In this method, multiple decision trees can be trained on random samples of a training set and average (e.g., mean, median, or pattern) results across multiple decision trees can be returned. For classification tasks, modal results can be used because the results from each tree are classified.

[0032] The output of the predictive model 106 may be a continuous parameter, such as the amount of endogenous S-nitrosothiol present in the region of interest, or a categorical parameter indicating an increase, decrease, or range of amounts of endogenous S-nitrosothiol present in the region of interest. The output of the predictive model 106 may be stored, for example, in an electronic health record database and / or provided to a user on an associated display via the user interface 108.

[0033] FIG. 2 illustrates another example of a system 200 for noninvasively and in real time generating values of endogenous S-nitrosothiol content in tissue within a region of interest of a subject. In one example, the tissue is muscle tissue, the endogenous S-nitrosothiol is endogenous S-nitrosothiol from hemoglobin, and the amount of endogenous S-nitrosothiol is determined during muscle training or after physiological or external blockage of blood flow from the muscle tissue. System 200 may include a near-infrared spectroscopy (NIRS) sensor 202. The near-infrared spectroscopy sensor 202 noninvasively measures blood volume and oxygen saturation within the region of interest to provide time-series measurements of blood volume and oxygen saturation. In one embodiment, the time-series measurements of blood volume may be expressed as time-series data of a total hemoglobin index. The spectroscopic sensor may include a cable tie that contacts the sensor with the skin. The cable tie may be flexible and / or elastic. As an example, the sensor may be coupled to a wristband. Those skilled in the art will recognize that a cable tie or any other part may be arranged to connect the measurement devices described herein to any relevant area of interest.

[0034] Nitric oxide (NO) calculation unit 210 may be implemented as device readable instructions stored on a non-transitory computer readable medium 212 and executed by a corresponding processor 214. NO calculation unit 210 may include a sensor interface 222, a regression model 224, and a user interface 226. Sensor interface 222 may receive time series measurements of blood volume and time series measurements of oxygen saturation from a NIRS sensor and condition the data for use by regression model 224.

[0035] The regression model 224 may identify a relationship between the time series of blood volume values and the time series of oxygen saturation values and provide at least one parameter indicative of the identified relationship. In one example, the relationship is linear, and an ordered pair of the two time series data can be fitted with a best-fit line. The parameter provided here is the slope of the best-fit line, and the amount of S-nitrosothiol in the tissue is derived from the slope. In another example, the parameter is derived from the correlation coefficient between oxygen saturation and blood volume. The value can then be provided to the user via the user interface 226. In one example, as an alternative or supplement to directly displaying the value, the value can be used to calculate other indices indicative of the subject's health and physical performance in real time.

[0036] 3 is a graph 300 showing a patient's PNO level during a period of exercise. The vertical axis 302 shows the PNO level, and the horizontal axis 304 shows the duration of the exercise in seconds, with the PNO level at each time shown as a hatched area 306. At approximately 100 seconds, the patient is provided with a rest period 308, during which the PNO level 306 remains constant during that period and for a short period thereafter.

[0037] As mentioned above, VO2 measurement is an important standard measure of physical performance used by physicians. Traditionally, VO2 measurement requires invasive testing and expensive laboratory equipment, but system 200 can perform such measurements noninvasively within local tissue and during activities of daily living (defined as UO2). FIG. 4 is a graph 400 showing a patient's UO2 levels during exercise. Vertical axis 402 represents the UO2 level, and horizontal axis 404 represents the duration of exercise in seconds, with the UO2 level at each time shown as a hatched area 406 on the graph. At approximately 100 seconds, the patient is provided with a rest period 408, during which the UO2 level 406 can be seen to drop sharply.

[0038] UO2 measurements are a local muscle oxygen consumption measurement related to nitric oxide, and their behavioral patterns are similar to true VO2 measurements. Specifically, UO2 is generated based on a function of local nitric oxide measurements and blood flow, and can provide a measure of available oxygen in muscle tissue in a region of interest. As shown in Figures 5 and 6, UO2 measurements are an excellent surrogate for VO2, and UO2 can be measured using a single sensor at approximately 100% of the cost of low-end VO2 measurement devices.

[0039] FIG. 5 shows a graph 500 of a time series of UO2 measurements 502 and VO2 measurements 504 as an athlete operates a full-body fitness bicycle using sensors to record blood volume and oxygen saturation. The left vertical axis 506 represents VO2 in mL / kg / min, the right vertical axis 508 represents UO2 in arbitrary units, and the horizontal axis 506 represents elapsed time. A very strong correlation (r=0.95) was observed between measured UO2 502 and measured VO2 504. As can be seen, an estimated VO2 max can be derived from the UO2 measurements. The VO2 max derived from the graph is 68 mL / kg / min.

[0040] Similarly, Figure 6 is a graph 600 of a time series of UO2 measurements 602 and a time series of VO2 measurements 604 as an athlete operates a full-body fitness bicycle using two sensors on different limbs to record blood volume and oxygen saturation. The left vertical axis 606 represents VO2 in mL / kg / min, the right vertical axis 608 represents UO2 in arbitrary units, and the horizontal axis 610 represents elapsed time. A very strong correlation (r=0.95) was observed between measured UO2 602 and measured VO2 604.

[0041] Activated nitric oxide levels reflect oxygen availability. The better the oxygen availability and utilization, the better the function. Maximum NO (MAX-NO) power and maximum NO (MAX-NO) tolerance are nitric oxide-related measurements that can be generated by the system and are closely related to an individual's actual power output and maximum endurance level.

[0042] FIG. 7 shows a graph 700 of a player's maximum NO power recorded each week over a six-month period. The left vertical axis 702 shows maximum NO (MAX-NO) power in arbitrary units, the right vertical axis 704 shows the player's maximum power output in watts, and the horizontal axis 706 shows elapsed time in weeks. As a player increases (measured by adding their maximum power output in watts 708), their maximum (MAX-NO) power 710 also increases. There is a very strong correlation (R) between the measured maximum NO (MAX-NO) power 710 and their maximum power output in watts 708. 2 = 0.95), establishing MAX-NO power as a good biomarker of expression.

[0043] 8 shows a graph 800 plotting the relationship between the improvement in maximal NO tolerance (MAX-NO) recorded over a six-week period for a group of 21 athletes and their critical power (a key measure of endurance, measured in watts). The vertical axis 802 shows the percentage improvement in maximal NO tolerance for the 21 athletes, and the horizontal axis 804 shows the improvement in the athletes' critical power, also in watts. The graph demonstrates the significant correlation between the improvement in maximal NO tolerance and the improvement in critical power, identifying maximal NO tolerance as a non-invasive measure of endurance.

[0044] Specific training and other therapies can be designed based on the patient's values for these indices. For example, individuals with higher maximum NO power can extract oxygen from the blood and utilize it in skeletal muscles at a faster rate than individuals with higher maximum NO tolerance. These individuals achieve optimal benefits from an exercise regimen by performing low-intensity, prolonged exercise in a continuous fashion. For example, Mr. Jones, 28 years old, with a maximum NO power of 6 and a maximum NO tolerance of 3, could be prescribed to run 20 minutes at 50-55% of his maximum UO2 three days per week. This would likely improve his maximum NO tolerance over the course of several weeks.

[0045] Alternatively, individuals with high maximum NO tolerance relative to maximum NO power can extract oxygen from the blood and deliver it to working muscles at a rate much faster than their energy production. These individuals benefit optimally from high-intensity, short-duration exercises with intervening rest periods. In these cases, UO2 increases sharply and scores are relatively low between exercise sessions. For example, Mrs. Bennett, a 48-year-old cyclist with a maximum NO tolerance of 7 and a maximum NO power of 2.5, may be prescribed to perform near-maximal intensity spurts two days a week until her UO2 stops increasing, followed by a three-minute rest period, for a total of six cycles. This can be expected to improve both maximum NO power and maximum UO2 over the course of several weeks.

[0046] In another example, an individual whose upper-body PNO level is higher than their lower-body PNO level may be instructed to reallocate their power output to decrease the amount of exercise performed by their upper body and increase the amount of exercise performed by their lower body. This increases their lower-body PNO level, resulting in a greater total-body PNO value. This can be sustained for a longer period of time. This increases oxygen delivery to the brain, heart, and muscles. In another example, an individual with early-onset Alzheimer's disease may be trained to improve their PNO level, thereby improving cerebral blood flow. For example, Mrs. Levy, age 70, suffers from early-onset Alzheimer's disease. She may be prescribed a 30-minute daily walk, with the goal of increasing her PNO to 5. Mrs. Levy may also be prescribed a 3-minute daily cycling routine and the amount of exercise adjusted to increase her PNO to 15 during the exercise process. After six months, her memory and baseline PNO are expected to improve.

[0047] As discussed, VO2 is currently the standard measure of physical performance used by physicians and physiologists worldwide, indicating the combined capacity of the pulmonary, cardiovascular, and muscular systems for absorption, transport, and oxygen consumption. UO2 measurements are local muscle oxygen consumption rates relative to nitric oxide, and their behavioral patterns are similar to true VO2 measurements. Specifically, UO2 may be generated based on a function of local nitric oxide measurements and blood flow to provide a measure of available oxygen to muscle tissue in a region of interest. As shown in Figures 5 and 6, UO2 measurements are excellent surrogates for VO2.

[0048] Individuals suffering from Alzheimer's disease are known to have low VO2. VO2 is highly dependent on microvascular blood flow, and erythrocyte-derived nitric oxide regulates blood flow. Therefore, as nitric oxide-mediated measurement of UO2 is described herein as VO2, PNO can predict an individual's VO2 max. Therefore, in another embodiment, the present invention provides a method for determining the risk of Alzheimer's disease or early-onset disease by using UO2 as a biomarker. Improving UO2 (and PNO) can improve or prevent Alzheimer's disease. Meanwhile, the present invention provides a method for identifying the risk of diseases associated with reduced blood flow (e.g., dementia or other declines in cognitive function related to blood flow or cardiovascular / cardiometabolic disease). For example, an exercise regimen can be established for an individual suffering from Alzheimer's disease, and UO2 measurements can be taken over time to identify improvement in the disease. For example, UO2 is measured at a starting time point before the exercise scheme begins, and at a second time point (and other selectable time points) to determine whether the UO2 value increases, e.g., reflecting an improvement in cognitive function or dementia. Other complementary tests, including cognitive tests known to those skilled in the art, can be used to further assess improvement in an individual's disease state.

[0049] In another example, values indicative of endogenous S-nitrosothiol content in tissue (e.g., UO2 measurements) can be used to identify the presence or risk of neurodegenerative diseases (e.g., Parkinson's disease). For example, in subjects without neurodegenerative diseases, low UO2 measurements indicate risk of neurodegenerative diseases. For example, the systems and methods disclosed herein monitor a patient's NO and / or sNO and compare them to standard or personalized thresholds that correlate blood-brain barrier function with a state that provides adequate amounts of O2, blood nutrients, and trophic factors (e.g., brain-derived neurotrophic factor (BDNF)) to maintain normal brain cell function. In particular, Parkinson's disease has been shown to respond to strenuous exercise, which affects sNO levels. For example, Salgado, Sanjay, Nori Williams, Rima Kotian, and Miran Salgado. 2013. “An Evidence—Based Exercise Regimen for Patients with Mild to Moderate Parkinson's Disease” Brain Sciences 3, no. 1: 87—100. https: / / doi.org / 10.3390 / brainsci3010087; Maggie Fox (Dec. 11, 2017). "Vigorous exercise can slow Parkinson's" NBC News. https: / / www.nbcnews.com / health / health―news / vigorous―exercise―can―slow―parkinson―s―n828521 ; and Schenkman, Margaret et al. "Effect of High-Intensity Treadmill Exercise on Motor Symptoms in Patients With De Novo Parkinson Disease: A Phase 2 Randomized Clinical Trial." JAMA neurology vol. 75,2 (2018): 219-226. doi: 10.1001 / jamaneurol.2017.3517. Each of these references is incorporated herein by reference.

[0050] In another example, Jack is a 60-year-old businessman with heart disease. He may be prescribed an exercise regimen of 30 minutes of daily exercise to improve his PNO level to 13. Over time, the regimen increases to 40 minutes, doubling his PNO, indicating an increased ability to provide oxygenated blood to the heart muscle. In another example, Mrs. Stevenson is a long-term mother of three young children who strives to keep up with her children's pace in daily life and has a maximum UO2 of 43. He may be prescribed an exercise regimen of two days a week. Day one involves 20 to 30 minutes of moderate-intensity training at 50%-60% of her maximum UO2, and day two involves three 5-minute bouts of exercise at 75%-85% of her maximum UO2. This is expected to increase his strength, energy, and his maximum UO2. Similarly, in another example, James is a 60-year-old businessman with diabetes. His baseline blood glucose level is 200. An exercise regimen of 20 minutes of exercise daily may be prescribed to increase PNO levels to 12. In this example, this can be increased over time to 40 minutes of exercise, doubling PNO, indicating an increased ability to provide oxygenated blood to muscles and subsequently reduce resting blood glucose.

[0051] In view of the above structural and functional features, the exemplary methods are better understood with reference to FIGS. 9-11. However, for ease of explanation, the exemplary methods of FIGS. 9-11 are shown as being performed serially. It should be understood that the present embodiments are not limited to the illustrated order. Some operations may occur multiple times and / or simultaneously in a different order than shown herein in other examples. Also, not all of the described operations need to be performed to implement the methods. For example, each of these methods may be performed by system 100 of FIG. 1 and / or system 200 of FIG. 2.

[0052] FIG. 9 illustrates an example method 900 for generating a value indicative of endogenous S-nitrosothiol content of tissue within a region of interest of a subject. At 902, a time series of measurements of a biometric parameter may be provided by noninvasively measuring the biometric parameter with a sensor 102 / 202 within the region of interest of the subject. In one example, these measurements are taken while the subject is participating in exercise. In another example, these measurements are taken during a rest period after the subject has engaged in exercise. In other examples, measurements may be taken after physiological or external interruption of blood flow in the region of interest, or immediately after physiological oxygen depletion. In one embodiment, measuring an overshoot response of one of blood flow and oxygen saturation above baseline after inducing oxygen deprivation provides one of a time series of measurements of oxygen saturation and a time series of measurements of blood volume. A predictive model uses the overshoot or rate value to generate a value indicative of endogenous S-nitrosothiol content of tissue within the region of interest.

[0053] At 904, a value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest can be generated from the time series data by the processor 112 / 214 via the predictive model 106 / 224. In one embodiment, a linear relationship between first time series data indicative of blood volume and second time series data indicative of oxygen saturation is determined, and the value is determined based on the linear relationship. For example, a linear regression model is fed with the two time series data to provide a best-fit line between oxygen saturation and blood volume over time. The value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest can be derived from the slope of the best-fit line. At 906, the value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest can be stored by the processor 112 / 214 in memory embodied as the non-transitory computer-readable medium 110 / 212. In one example, method 900 may be performed before and after a treatment is provided to a subject to determine the effect of the treatment on endogenous S-nitrosothiol content in a region of interest by comparing values generated after the treatment with stored values generated before the treatment, and the stored values may be used to generate one or more of the subject's maximum nitric oxide tolerance index, maximum nitric oxide power index, usable oxygen consumption index, and personalized nitric oxide index.

[0054] FIG. 10 illustrates another example method for generating a value indicative of endogenous S-nitrosothiol levels in tissue within a region of interest. At 1002, a sensor 102 / 202 may noninvasively measure blood volume and oxygen saturation within the region of interest to provide an oxygen saturation measurement in a first time series of data and a blood volume measurement in a second time series of data. For example, near-infrared spectroscopy may determine both blood volume and oxygen saturation in the tissue. In one example, these measurements are taken while the subject is participating in exercise. At 1004, a linear relationship between the first and second time series of data may be determined by the processor 112 / 214 via a predictive model 106 / 224. For example, the two time series of data may be subjected to a linear regression model to provide a best-fit line between oxygen saturation and blood volume over time.

[0055] At 1006, the processor 112 / 214 may generate a value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest from the linear relationship between the first and second time series data. In one embodiment, the linear relationship is represented as a best-fit line between the first and second time series data, and the value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest may be derived from the slope of the best-fit line. At 1008, the value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest may be stored by the processor 112 / 214 in memory embodied as the non-transitory computer-readable medium 110 / 212. The stored value may also be used to generate one or more of the subject's maximum nitric oxide tolerance index, maximum nitric oxide power index, usable oxygen consumption index, and personalized nitric oxide index.

[0056] 11 illustrates another method 1100 for generating values indicative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject. At 1102, the subject is instructed to participate in aerobic exercise. In one example, the subject may be instructed to ride a bicycle. At 1104, blood volume and oxygen saturation in the region of interest of the subject's muscle affected by the exercise may be measured noninvasively by sensor 102 / 202 to provide a first time series of oxygen saturation measurements and a second time series of blood volume measurements.

[0057] At 1106, the processor 112 / 214 may adjust at least one of the time series data of blood volume and the time series data of oxygen saturation within the region of interest to remove extraneous influences. Blood flow in tissue is mediated by many factors, including prostaglandins, catecholamines, nitric oxide, temperature, kinases, adenosine triphosphate (ATP), and oxygen deficiency. For example, kinases increase flow during inflammation, and NO mediates shear- and Ach-induced vasodilation. To facilitate measuring the effect of NO released by hemoglobin on blood flow, the collected blood flow data may be adjusted to remove the influence of these factors. For example, most aerobic exercise involves repeated muscle contractions over a fairly predictable period of time. Such blood volume reductions may be quantified as a periodic signal and presented as a separate time series that can be removed from the blood volume time series measurement. Other physiological effects on either or both blood volume and oxygen saturation vary with position and specific movements, and adding signals indicative of these effects can remove nonlinear effects from the time series data that describe the relationship between blood volume and oxygen saturation.

[0058] At 1108, the processor 112 / 214 may perform statistical processing to identify a linear relationship between blood flow and oxygen saturation derived from the time-series measurements of blood volume, and generate a value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest from the linear relationship. For example, a linear regression analysis may be performed, and the slope of the best-fit line generated in the regression analysis may be used to quantify the linear relationship between blood flow and oxygen saturation. A correlation coefficient between the two parameters may also be generated, particularly to evaluate a linear relationship where muscle oxygen depletion reduces the slope. At 1110, the value indicative of the endogenous S-nitrosothiol content of the tissue within the region of interest may be stored by the processor 112 / 214 in memory embodied as the non-transitory computer-readable medium 110 / 212.

[0059] FIG. 12 illustrates metrics and usage examples for professional athletes. Specific metrics may relate to different positive athletic outcomes, such as improvements in maximal speed, power, or endurance. Additionally, metrics can be useful for assisting in monitoring, predicting, and planning for athletes' recovery and regeneration. For example, measuring nitric oxide can be used to support the progression of athletic activity and maximize the endogenous increase in nitric oxide in a person's blood supply. In another example, monitoring nitric oxide regeneration can identify how much rest an athlete needs during a match or between games. In a third example, adaptive training can monitor and / or improve nitric oxide regeneration to reduce injury risk and return to the field after injury.

[0060] FIG. 13 shows an exemplary measurement of MAX NO Recovery versus the number of groups performed in an exercise routine. Max(NO)-Recovery derives the rate of reoxygenation of muscle tissue after exercise and is affected by various factors (e.g., NO concentration, breathing mode, and aerobic fitness level). Max(NO)-Recovery can inform how an athlete recovers in real time and when they are fully recovered after exercise. Individuals may also be trained to increase their Max(NO)-Recovery score. This allows for faster recovery after training or in gaps in training. As shown in the graph, the athlete's MAX NO Recovery peaks at group 2. A threshold for the decline from peak MAX NO Recovery may be set to identify the maximum amount of groups an athlete should perform. Adjustments may allow an athlete to peak with a greater number of groups and / or slow the rate of decline after the peak.

[0061] FIG. 14 shows exemplary measurements of PNO and MAX NO Regeneration over time. PNO measurements are plotted as an athlete alternates between exercise and rest periods during an exercise period. As an athlete exercises, PNO increases. During recovery periods, PNO falls back. The more PNO an athlete generates, the better their health and function. PNO may also be used to reduce injury risk and reduce an individual's risk of re-injury during the regeneration period. Comparing PNO levels between healthy and injured legs can inform an athlete's load capacity.

[0062] By comparing the nitric oxide levels received by an injured person at different times, the degree to which tissue damage has been repaired relative to complete recovery can be determined. Measurements of maximum NO regeneration (MAX NO Registration) can be calculated during rest. To record maximum NO regeneration (MAX NO Registration), a cuff can be placed on the individual's upper arm or upper thigh, and then a biosensor can be placed on a large muscle distal to the cuff. The cuff can be inflated with gas until pressure is cut off from the blood in the limb. Once blood flow is cut off, the individual can maintain rest, allowing the cuff to inflate for a set period of time, after which the cuff automatically releases gas, allowing blood flow back to the limb. Biomarker measurements can be recorded during reperfusion after ischemia and used to calculate NO levels.

[0063] In an exemplary measurement, a player's MAX NO Registration score is plotted for their left and right legs 12 weeks after right ACL surgery. The difference between the healthy left leg and the injured right leg can be easily tracked throughout the recovery process. This metric can be used in combination with traditional metrics (e.g., strength). This metric may be used to optimize a player's training method to return to the field as quickly as possible and without undue risk of injury.

[0064] As shown in Figure 15, different trends emerge simultaneously when measuring PNO or muscle oxygenation (SmO2). The macro trend indicates autoregulation of blood flow. The availability of oxygen to skeletal muscle results in a compensatory increase in muscle blood flow. In this case, an inverse linear correlation is observed between total hemoglobin (THb), an index of muscle blood volume (not shown), and muscle oxygen. Therefore, a significant increase in nitric oxide and s-nitrosothiols (PNO) was observed. This response can last from seconds to minutes.

[0065] The microtrends show active hyperemia. When muscles contract, blood flow is restricted, causing a drop in oxygen levels. Then, during the muscle relaxation phase before the next contraction, blood flow and oxygen saturation increase. During the active hyperemia period, SmO2 and THb are linearly correlated. The microtrends in Figure 15 show an active hyperemia response accompanied by a rapid increase and decrease in PNO.

[0066] Those skilled in the art should be aware that while the macrotrend indicates an overall increase in PNO during exercise, within the macrotrend there is a microtrend consisting of small increases / decreases in PNO, and both of these trends are used to regulate muscle blood flow.

[0067] As shown in Figure 16, there are cases where the macro- and micro-trends exhibit a positive linear correlation, such as when muscle contraction has a stronger than normal effect on blood flow. This makes it difficult to clearly see the autoregulatory response. However, because a linear correlation is being considered, both positive and negative correlations result in an increase in PNO.

[0068] In Figure 16, there is a (+) correlation nested within a (-) correlation, and a (+) correlation nested within a (+) correlation. Although they cannot be visually distinguished, it is possible (the high PNO values in Group 2 are not due to the + / + correlation).

[0069] After tissue injury, an injured person may believe that the tissue injury has already fully healed and place all of their weight on the injured tissue, and / or believe that the injured body part has fully healed, when in fact the tissue injury has not. Actual full tissue injury recovery can be determined by comparing nitric oxide levels from the injured limb (i.e., arm or leg) with a corresponding uninjured limb of the same type. Based on the indicators described herein, an injured person can continue full activity while reducing the likelihood of re-injury.

[0070] <Example: Study> To determine the clinical significance of SNOs, particularly SNO-Hb-βCys93, in measuring hypoxic vasodilation, we used mice expressing human hemoglobin (Hb). SNO-Hb in mice is depleted and exhibits numerous cardiovascular defects resulting from hypoxic vasodilation damage. In this study, we examined the functional outcomes of a standard clinical protocol of simulated reactive hyperemia with Cys93 SNO. After occlusion of the femoral artery for 5 minutes, we measured reoxygenation (pO2) in the gastrocnemius muscle using a needle electrode.

[0071] Figure 17A shows representative tracings of control βC93 mice and the corresponding βC93A mutant animals. In βC93 mice, which have normal hypoxic vasodilator activity, tissue pO2 responses rapidly recovered above baseline after arterial occlusion (i.e., restoration of femoral artery blood flow). In contrast, βC93A animals showed a delayed response, with muscle oxygen not returning to baseline levels during the 5-minute recording interval following release. As shown in Figure 17B, comparison of group data reveals a significant decrease in basal pO2 in the gastrocnemius muscle of βC93A mice (p=0.032), consistent with our previous studies. Comparison of group data further reveals that muscle pO2 recovered more slowly after 5 minutes in βC93A mice compared with the βC93 control group (46±17 vs. 28±15 mm Hg; p=0.004). As shown in Figure 17C, a comparison of group data revealed that the rate of post-occlusion recovery of muscle pO2 was significantly reduced in γβC93 mice compared with γβC93 mice, approximately half the normal rate (0.23 ± 0.15 vs. 0.13 ± 0.11 mm Hg / sec, respectively), p = 0.036. Thus, SNO-Hb deficiency reduces the rate and overall efficiency of tissue oxygenation following brief interruptions of blood flow.

[0072] Erythrocyte SNO levels were measured in age-matched healthy controls and patients diagnosed with systemic (i.e., heart failure and chronic obstructive pulmonary disease (COPD)) or peripheral (i.e., peripheral vascular disease and sickle cell disease (SCD)) oxygenation-deficient diseases (i.e., diabetic peripheral arterial disease (PAD), heart failure with reduced expulsion fraction (HF), COPD, and sickle cell disease (SCD)). Specific uptake criteria and disease status for each group are described in the Extended Methods. Of the 53 subjects, 49 were studied (13 healthy controls, 13 PAD, 6 HF, 9 COPD, and 8 SCD).

[0073] RBCs were processed on-site and quantified for SNO-Hb and ferrous nitroso-Hb by mercury-binding photolysis-chemiluminescence within approximately 1 hour of collection from the radial artery. Values for nine subjects were discarded due to equipment failure (i.e., the diagnosis and decision to discard these data were made by a technician blinded to the patient's physiological status). The results for the remaining samples are shown in Figure 18A-D.

[0074] As shown in Figure 18A, in normal volunteers (n = 10), arterial hemoglobin SNO-Hb levels were 2.6 ± 1.3 per 1000 Hb, a concentration similar to that recorded in other healthy subjects. SNO-Hb levels in the HF group (n = 5; 2.5 ± 0.6 per 1000 Hb) and COPD group (n = 5; 2.0 ± 1.3 per 1000 Hb) were not different from those in the control group. However, the amount of SNO-Hb in the blood of patients with PAD (n = 11; 1.5 ± 1.2) and SCD (n = 5; 0.9 ± 0.6) was significantly lower than that in normal controls (p < 0.05). The decrease in SNO-Hb levels may be due to a general decrease in NO production, or a defect in the process within the Hb molecule that prevents the intramolecular transfer of NO from hemoglobin to thiols, reflected in an increased number of inactive FeNO, as previously reported in healthy subjects with SCD, PH, and hypoxia. As shown in Figure 18C, the total amount of NO bound to Hb (HbNO) was not distinguishable from normal in any patient group. However, as shown in Figure 18B, HbFeNO concentrations were significantly higher than normal in subjects with PAD, COPD, and SCD, and the ratio of SNO to total HbNO was significantly reduced in all groups except HF: from 0.69 ± 0.13 and 0.67 ± 0.16 in normal volunteers and HF patients, respectively, to 0.36 ± 0.30 in PAD, 0.36 ± 0.22 in COPD, and 0.24 ± 0.20 in SCD. Correlations between SNO-Hb and various clinical chemistry parameters will also be analyzed. Figure 18C plots total HbNO, and Figure 18D plots the ratio of SNO to total HbNO for the identified patient groups. Analysis of the dataset, as shown in Figure 20 (n=33), revealed negative correlations between plasma nitrite levels and SNO-Hb, and between nitrite and the SNO-Hb / total HbNO ratio. The negative correlations between the difference between SNO-Hb and FeNO levels, the ratio of SNO to total HbNO, and plasma nitrite and NO bioactivity all suggest the presence of NO processing defects in red blood cells from patients with PAD, COPD, and SCD.

[0075] This study aimed to demonstrate the effect of SNO-Hb on reactive hyperemia in βC93A mice. After blood collection and short-term limb blood flow occlusion, tissue oxygenation in the lower leg and foot was measured using a near-infrared spectroscopy (NIRS) device. Because leg ischemia may induce occlusive crises, SCD patients were excluded from this study group. In the other group, subjects were placed in a semi-recumbent position, with pneumatic cuffs wrapped around the upper thigh and lower leg near the ankle. Each cuff was rapidly inflated in 1 second to halt arterial blood flow (target pressure = systolic blood pressure + approximately 130 mm Hg; maximum 300 mm Hg). The occlusion period was maintained for 5 minutes, after which the cuff pressure was released and tissue oxygenation was measured for 5 minutes. There was a 5-minute recovery interval between the two cuff inflation / recording processes. First, the ankle cuff was used to record the leg status, and then the thigh cuff was used to record the leg and upper leg status, respectively. The occlusion test was conducted on 45 subjects. NIRS curves were analyzed offline by a person blinded to the subjects' disease status and RBC SNO levels. Thirteen subjects were excluded from the independent analysis group because their recorded near-infrared spectra were deemed unreadable due to leg movement and / or differential signal resolution. Data from the remaining 11 healthy subjects, eight PAD patients, six HF patients, and seven COPD patients were used for comparison.

[0076] The experimental endpoint was the half-life (t) of tissue oxygenation recovery (i.e., recovery to 50% of baseline) measured in seconds. 1 / 2 ) and the results are shown in Figures 19A-E. Figure 19A shows representative leg tissue oxygenation recovery trajectories after thigh cuff removal in one healthy subject and one PAD patient. The reoxygenation response in the healthy subject was rapid, with t 1 / 2 is 10 seconds, and the tissue oxygenation response in PAD patients is delayed, t 1 / 2 The time to reoxygenation of the leg after ankle and thigh cuff occlusion is 22 seconds. As shown in Figure 17A, this is very similar to the rapid recovery of tissue pO2 after release of the femoral artery occlusion in βC93 control mice and the slow recovery in βC93A mice. Figures 19B, 19C, and 19D show the time to reoxygenation of the leg after ankle and thigh cuff occlusion, respectively. 1 / 2 and crural reoxygenation after femoral blockade 1 / 2Quantification group data (mean ± SD) are shown. The mean t recorded by healthy subjects for all three measurements 1 / 2 The value was approximately 10 seconds, consistent with previous findings. Importantly, as shown in Figure 19E, the SNO-Hb level in Group 1 was directly related to the reperfusion rate. The mean t 1 / 2 Although the values improved, only the PAD patient group showed a significant prolongation of the foot reperfusion half-life after ankle or cuff inflation. Also, as shown in Figure 21, the leg reperfusion time 1 / 2 There is a clear inverse correlation between SNO-Hb levels and the ratio of SNO-Hb to total HbNO, but not FeNO levels, thereby relating RBC SNO to the reoxygenation response.

[0077] In many clinical diseases, microvascular blood flow is impaired, leading to tissue ischemia. However, drugs that increase blood flow do not improve tissue oxygenation. Clinical measurements of blood flow emphasize endothelial components, particularly NO, but NO does not affect tissue oxygenation. Conversely, solid evidence suggests that blood flow, which regulates tissue oxygenation, is regulated by S-nitrosohemoglobin (S-Hb). In other words, blood flow, which promotes blood pressure, is regulated by endothelial NO, while blood flow, which regulates tissue oxygenation, is controlled by RBC-SNO. Reactive hyperemia is an increase in blood flow after brief periods of ischemia that restores tissue oxygenation. While reactive hyperemia is caused by endothelial NO, RBC-SNO plays a major role in vasodilation and blood flow responses in mice. This study involved direct measurement of tissue oxygenation in mice and humans. Importantly, this study demonstrated that SNO-Hb is required to oxygenate oxygen-deficient tissues and that a deficiency in SNO-Hb can cause impaired oxygenation. Furthermore, SNO-Hb levels in patients can also predict tissue oxygenation after short-term local oxygen deprivation, indicating that this is the primary biomarker of microcirculatory blood flow.

[0078] This study presents a three-gas model of the respiratory cycle, in which O2 and NO are simultaneously loaded into Hb, followed by the release of vasodilatory SNO from SNO-Hb, which regulates blood flow and tissue oxygenation. Mutant mice are unable to transport or allocate SNO from βCys93, resulting in generally impaired tissue oxygenation. As shown in Figure 17B, in addition to tissue defects under basal conditions, mutant mice also exhibit impaired tissue oxygenation under global hypoxia and regional ischemia. Conversely, hypoxia, whether affecting oxygen loading or other aspects of Hb heterotranslocation, also affects S-nitrosation. This is manifested as a decrease in SNO-Hb levels or a decrease in the ratio of SNO-Hb to total HbNO, simply because NO is still bound to the heme iron and therefore cannot be transferred to Cys93. Accordingly, as shown in Figure 18A–D, patients with disease states characterized by pathological changes in oxygen (COPD, PAD, and SCD) had low SNO-Hb and SNO / HbNO levels, confirming previous reports. The ratio of NO relative to SNO-Hb and total Hb (SNO-Hb + Hb FeNO; total HbNO), indicating the absence of accumulation of active FeNO, is expected to be a more sensitive indicator of loss of bioactivity than SNO-Hb alone. Thus, NO / SNO processing defects are observed in several diseases in which tissue oxygen is insufficient and may be associated with the inability of Hb to convert FeNO to SNO-βCys93-Hb. Similarly, we found that the ratios of SNO-Hb to nitrite and the ratio of SNO-Hb / HbNO to blood nitrite were actually inversely related, which is consistent with previous findings, i.e., high nitrite inhibits the formation of SNO-Hb, as shown in Figure 20. Therefore, nitrite levels are independent of blood flow or tissue oxygenation.

[0079] PAD patients have clear characteristics of microvascular dysfunction. As shown in Figure 19A-E, when the test subjects recovered from transient ischemia of the lower limbs, reperfusion in the group was significantly delayed, and in both cases, the time to reoxygenation was significantly longer compared to healthy controls. 1 / 2 Importantly, in other groups, t 1 / 2Although no statistically significant differences were observed between SNO-Hb levels and reoxygenation, there was a significant correlation between SNO-Hb levels and oxygenation ratios in all patient groups. Altogether, combined with genetic validation in mice, these results indicate that SNO-Hb is a key driver of blood flow autoregulation, whereby tissue blood flow controls tissue oxygenation.

[0080] These findings have various clinical implications. Reactive hyperemia, which has a significant RBC SNO fraction, has previously been considered an indicator of endothelial function. In general, the roles of endothelial NO and erythrocyte SNO are distinct, with the former affecting vascular health and the latter affecting tissue health. Our results demonstrate that erythrocyte SNO-Hb is a biomarker of tissue oxygenation, and in particular, SNO-Hb is directly related to the reperfusion rate. This study also demonstrates that the reactive hyperemia test is an effective measurement for assessing SNO-Hb function in patients. Enhancing RBC SNO activity can improve tissue oxygenation and have a wide range of clinical benefits.

[0081] Example: Measuring device FIG. 22 illustrates an example of a measurement device 2200, where the measurement device 2200 is wearable by a patient. The measurement device 2200 may include one or more light sources 2201 (e.g., light-emitting diodes, organic light-emitting diodes, lasers, or a single light source emitting multiple wavelengths). The one or more light sources 2201 may form an array. A control circuit and / or a processor may control the one or more light sources 2201. For example, one or more of the systems 100 and / or 200 may be incorporated within, coupled to, or in communication with the measurement device 2200. Thus, the processor 112 / 214 may control the one or more light sources 2201. The one or more light sources 2201 may generate light at multiple wavelengths. As an example, the one or more light sources 2201 may generate light at 525 nm, 650 nm, 750 nm, 810 nm, 850 nm, and 970 nm. In another example, at least one light source 2201 produces green light, at least one light source produces red light, and at least one light source produces infrared light.

[0082] The measurement device 2200 may include one or more optical receivers 2202 (e.g., photodiodes or optical sensors). The one or more optical receivers 2202 may form an array. The one or more optical receivers 2202 may be positioned at a predetermined distance from the one or more light sources 2201. The one or more optical receivers 2202 may be used as one or more of the sensors 102 / 202 of the system 100 and / or 200. Information received from the one or more optical receivers 2202 may be converted in a digital converter. The measurement device 2200 may include one or more processors (e.g., processor 112 / 214) for processing data from the one or more optical receivers 2202. The measurement device 2200 may include a communication interface (e.g., Bluetooth, WiFi, 5G, etc.) for transmitting data to an external system and / or processor. Measurement device 2200 may include one or more memory devices (eg, computer-readable media 110 / 212) for storing data.

[0083] The measurement device 2200a includes a display and / or audio output 2203 for presenting data on the device. Alternatively, the data may be presented to an external system. For example, the measurement device 2200 may communicate with a personal computer or mobile device via a communications interface and output to a user via the personal computer or mobile device. In one example, the personal computer or mobile device may be used as the system 100 and / or 200.

[0084] The measurement device 2200 may include one or more additional sensing elements, including, but not limited to, a thermometer and a bioimpedance sensor, which may be included in the sensor 102 / 202. The data collected by the additional sensing elements can provide enhanced measurement and post-processing analysis.

[0085] Operation of the measurement device 2200 may include initiating a data capture sequence. The data capture sequence may include activating the one or more light sources 2201 in a timing sequence of on and off. The data capture sequence may include activating the one or more light sources 2201 coupled at a predetermined distance to activate the one or more light receivers 2202. Different wavelengths of generated light may be positioned to target different substances within the body. Different predetermined distances between the light receiver 2202 and the light source 2201 can target different depths within the body. As an example, green light (approximately 530 nm) can be targeted at a pulse rate over a distance of 3 mm. Red light (approximately 660 nm) can target deoxygenated hemoglobin, and infrared light (approximately 940 nm to 980 nm) can target oxyhemoglobin at various depths within the body given the distance of the light receiver 2202 from the light source 2201. Infrared light at 790-810 nm targets isoploid hemoglobin. The measurement device 2200 can capture signals from one or more optical receivers 2202. The captured signals may be pre-processed (i.e., by digital conversion and / or filtering). The captured signals may be converted into biomarkers. The biomarkers may be further processed, stored, and / or communicated (e.g., on the device and / or to an external system).

[0086] Example: Computer System 23 is a schematic block diagram illustrating an example system 2300 in which example hardware components of the systems and methods disclosed herein may be implemented. System 2300 may include various systems and subsystems. System 2300 may include one or more of a personal computer, a laptop, a mobile computing device, a workstation, a computer system, an appliance, an application specific integrated circuit (ASIC), a server, a blade server, a cluster of servers, etc.

[0087] The system 2300 may include a system bus 2302, a processing means 2304, a system memory 2306, memory devices 2308 and 2310, a communication interface 2312 (e.g., a network interface), a communication link 2214, a display 2316 (e.g., a video screen), and input devices 2318 (e.g., a keyboard, touch screen, and / or a mouse). The system bus 2302 may be in communication with the processing means 2304 and the system memory 2306. Additional memory devices 2308 and 2310, such as a hard disk drive, a server, an independent database, or other non-volatile memory, may also be in communication with the system bus 2302. The system bus 2302 interconnects the processing means 2304, the memory devices 2306 and 2310, the communication interface 2312, the display 2316, and the input device 2318. In some examples, the system bus 2302 further connects additional ports (not shown), such as Universal Serial Bus (USB) ports.

[0088] The processing means 2304 may be a computing device or may include an application specific integrated circuit (ASIC). The processing means 2304 executes a set of instructions to implement the operations of the embodiments disclosed herein. The processing means may include a processing core.

[0089] Additional memory devices 2306, 2308, and 2310 may store data, programs, instructions, database queries, and any other information necessary to operate a computer in text or compiled form. Memory 2306, memory 2308, and 2310 may be implemented as a computer-readable medium (integrated or removable), such as a memory card, magnetic disk drive, optical disk (CD), or server accessible over a network. In some examples, memory 2306, memory 2308, and memory 2310 may include text, images, video, and / or audio, some of which is available in a human-understandable format.

[0090] Additionally or alternatively, the system 2300 can access external data or query sources via a communication interface 2312 , which can be in communication with the system bus 2302 and a communication link 2314 .

[0091] In operation, system 2300 may be configured to implement one or more portions of the systems of the present invention, such as system 100, system 200, and / or measurement device 2200. According to some examples, a computer for implementing a diagnostic system may logically reside in the system memory 2306 and one or more of the memory devices 2308 and 2310. Processing means 2304 executes one or more computer-executable instructions from the system memory 2306 and memory devices 2308 and 2310. As used herein, the term "computer-readable medium" refers to a medium that provides instructions to processing means 2304 for execution. The medium may be distributed over multiple discrete components operatively connected to a common processor or group of related processors.

[0092] Specific details are provided in the above description to provide a thorough understanding of the embodiments. However, it should be understood that the embodiments may be practiced without these specific details. For example, physical components may be shown in block diagrams to avoid unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

[0093] The implementation of the above techniques, blocks, steps and devices may be completed in various ways. For example, these techniques, blocks, steps and devices may be implemented by hardware, software or a combination thereof. With regard to a hardware implementation, the processing means may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processors (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors and / or other electronic units designed to perform the above functions.

[0094] Additionally, the embodiments may be described as a process that depicts a flow diagram, a flowchart, a data flowchart, a structure diagram, or a block diagram. Note that while a flowchart illustrates operations as a series of processes, multiple operations may occur in parallel or simultaneously. The order of operations may also be rearranged. A process is terminated when an operation of the process is completed but there are additional steps not included in the diagram. A process may correspond to a method, a function, a process, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to the function returning to the calling function or the main function.

[0095] Additionally, embodiments may be implemented using hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and / or any combination thereof. When implemented in software, firmware, middleware, scripting languages, and / or microcode, program code or code segments to perform the necessary tasks may be stored in a machine-readable medium, such as a storage medium. Code segments or machine-readable instructions may represent any combination of processes, functions, subprograms, programs, routines, subroutines, modules, packages, scripts, sets of instructions, data structures, and / or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc. may be communicated, forwarded, or transmitted via any suitable means, including memory sharing, message transfer, handover, network transmission, etc.

[0096] For a firmware and / or software implementation, these methods may be implemented with modules (e.g., processes, functions, etc.) that perform the functions described herein. Any machine-readable medium embodying instructions may be used to implement the methods described herein. For example, software code may be stored in memory. Memory may be implemented within or external to the processor. As used herein, the term "memory" refers to any type of long-term, short-term, volatile, non-volatile, or other storage medium, and is not limited to any type of memory or number of memories or types of media storing the memory.

[0097] Also, as disclosed herein, the term "storage medium" can refer to one or more memories for storing data, including read-only memory (ROM), random-access memory (RAM), magnetic RAM, core memory, magnetic disk storage media, optical storage media, flash memory devices, and / or other machine-readable media for storing information. The term "machine-readable medium" includes, but is not limited to, portable or fixed storage devices, optical storage devices, wireless channels, and / or various other storage media that contain or are portable with instructions and / or data.

[0098] The present invention includes the following examples. In the foregoing description, specific details have been set forth to provide a thorough understanding of the exemplary embodiments of the invention described herein. However, it will be apparent that various embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form to avoid obscuring the examples in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the examples. While the description of the exemplary embodiments provides those skilled in the art with exemplary embodiments for implementing the invention, various changes can be made in the function and arrangement of elements without departing from the spirit and scope of the invention. Accordingly, the present invention is intended to embrace all such changes, modifications, and variations that fall within the scope of the claims.

[0099] Although the invention has been described with reference to the above examples, it should be understood that modifications and variations are encompassed within the spirit and scope of the invention. Accordingly, the invention is limited only by the following claims.

Claims

1. at least one sensor configured to non-invasively measure a biometric parameter within a region of interest of a subject to provide a time series measurement of the biometric parameter; at least one processor in communication with the sensor; at least one non-transitory computer-readable medium storing machine-readable instructions; The at least one processor, when executing the device-readable instructions, receiving a time series of measurements of a biometric characteristic parameter; generating a value indicative of endogenous S-nitrosothiol content of tissue within a region of interest from the time series measurements of the biometric parameter using a predictive model; and providing a value indicative of the endogenous S-nitrosothiol content of the tissue in the region of interest to a user via a user interface.

2. The system of claim 1 , wherein the at least one sensor includes a near-infrared spectrum sensor.

3. 10. The system of claim 1, wherein the at least one sensor comprises a sensor configured to non-invasively measure oxygen saturation and blood volume within the region of interest to provide a time series measurement of oxygen saturation and a time series measurement of blood volume.

4. The generation using the predictive model includes: Identifying a linear relationship between time series measurements of blood volume and time series measurements of oxygen saturation; and generating a value indicative of the endogenous S-nitrosothiol content of the tissue in the region of interest based on the linear relationship.

5. the processing further includes measuring an overshoot response of one of blood flow and oxygen saturation above baseline after one of a physiological blockage to blood flow to the region of interest and / or an external blockage to blood flow to the region of interest to provide one of a time series measurement of oxygen saturation and a time series measurement of blood volume; The system of claim 3, wherein generating using a predictive model includes generating a value indicative of endogenous S-nitrosothiol content of tissue within the region of interest by using a rate value or an overshoot value.

6. non-invasively measuring biometric parameters within a region of interest of the subject with at least one sensor; said at least one sensor providing a time series of measurements of biometric parameters; generating, from the time series measurements of the biometric parameter, a value indicative of endogenous S-nitrosothiol content of tissue within the region of interest, by at least one processor in communication with the at least one sensor; and storing, by the at least one processor, a value indicative of the endogenous S-nitrosothiol content of tissue within the region of interest in a non-transitory computer-readable medium.

7. 7. The method of claim 6, wherein the measuring includes non-invasively measuring blood volume and oxygen saturation in the region of interest to provide time series measurements of oxygen saturation and time series measurements of blood volume.

8. 8. The method of claim 7, wherein non-invasively measuring blood volume and oxygen saturation in the region of interest of the subject comprises measuring blood volume and oxygen saturation in the region of interest during one of a period of exercise of the subject, a period of physiological occlusion to blood flow to the region of interest, and a period of external occlusion to blood flow to the region of interest.

9. the region of interest includes muscle tissue; non-invasively measuring blood volume includes measuring a total hemoglobin index in muscle tissue to provide time series measurements of blood volume as time series data of a total hemoglobin index; 8. The method of claim 7, wherein the generating includes using a predictive model to generate a value indicative of endogenous S-nitrosothiol content in muscle tissue from time-series measurements of total hemoglobin and time-series measurements of oxygen saturation.

10. 8. The method of claim 7, further comprising generating one of a personalized nitric oxide index, a usable oxygen consumption (UO2) index, a maximum nitric oxide tolerance index, and a maximum nitric oxide power index from the value indicative of the endogenous S-nitrosothiol content of tissue in the region of interest.

Images