The present disclosure relates to treating obesity (or other gastric/metabolic disorders), and, more specifically, to systems and methods for the treatment of obesity (or other gastric/metabolic disorders) via closed-loop vagus nerve stimulation (VNS).
Obesity, defined as a body mass index (BMI) over 30, is a global epidemic. As of 2016 13% of adults worldwide are considered obese, a number that has nearly tripled since 1975. Elevated BMI significantly increases risk of premature death and chronic diseases such as heart disease, stroke, diabetes, and come cancers. Obesity can be prevented or reduced by changes in diet and exercise habits; however, these behavioral changes are often not adequate for sustained weight loss. Gastric surgery can be effective in the long-term but can have deleterious effects because it is an invasive surgery. Given the increasing rates of adult and child obesity, new therapies are needed which can provide alternatives to major surgery.
The vagus nerve contains a variety of bidirectional signaling pathways between the brain and internal organs, but the majority of vagal fibers are gastric afferents, which are involved in the regulation of food intake via signaling of hunger and satiety (feeling full). Open-loop vagus nerve stimulation (VNS) has been used to induce weight loss in obese patients, though the mechanism by which these therapies cause weight loss is not well understood. In fact, most VNS studies have showed inconsistent results.
Closed-loop vagus nerve stimulation (VNS) may be a key to improving consistency and effectiveness in the treatment of obesity (or other gastric/metabolic disorders).
In one aspect, the present disclosure includes a system for closed-loop VNS for the treatment of obesity (or other gastric/metabolic disorders). The system includes one or more implantable recording electrodes to record signals from a portion of one or more subdiaphragmatic branches of a patient’s vagus nerve. The system also includes a processing device configured to: receive the signals from the portion of the one or more subdiaphragmatic branches of the patient’s vagus nerve, perform signal processing to decode the signals from the portion of the one or more subdiaphragmatic branches of the patient’s vagus nerve, and configure a stimulation to decrease the patient’s hunger and/or increase the patient’s satiety based on the decoded signals. The system also includes one or more implantable stimulating electrodes to deliver the configured stimulation to another portion of one or more subdiaphragmatic branches of the patient’s vagus nerve.
In another aspect, the present disclosure includes a method for closed-loop VNS for the treatment of obesity (or other gastric/metabolic disorders). Steps of the method can be performed by a system that includes a processor. The steps include: receiving signals recorded by one or more implanted recording electrode positioned in a portion of one or more subdiaphragmatic branches of a patient’s vagus nerve; performing signal processing to decode the signals from the portion of the one or more subdiaphragmatic branches of the patient’s vagus nerve; and configuring a stimulation to decrease the patient’s hunger and/or increase the patient’s satiety based on the decoded signals when delivered to another portion of one or more subdiaphragmatic branches of the patient’s vagus nerve by one or more implanted stimulating electrodes.
The foregoing and other features of the present disclosure will become apparent to those skilled in the art to which the present disclosure relates upon reading the following description with reference to the accompanying drawings, in which:
Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains.
As used herein, the singular forms “a,” “an,” and “the” can also include the plural forms, unless the context clearly indicates otherwise.
As used herein, the terms “comprises” and/or “comprising,” can specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups.
As used herein, the term “and/or” can include any and all combinations of one or more of the associated listed items.
As used herein, the terms “first,” “second,” etc. should not limit the elements being described by these terms. These terms are only used to distinguish one element from another. Thus, a “first” element discussed below could also be termed a “second” element without departing from the teachings of the present disclosure. The sequence of operations (or acts/steps) is not limited to the order presented in the claims or figures unless specifically indicated otherwise.
As used herein, the term “vagus nerve” can refer to the longest cranial nerve, passing through the neck and thorax to the abdomen. The vagus nerve contains efferent and afferent fibers from the autonomic nervous system related to various bodily organs.
As used herein, the term “subdiaphragmatic”, when describing the vagus nerve, refers to branches of the vagus nerve located beneath the diaphragm and serves as a major modulatory pathway between the brain and the gut. In fact, one function the subdiaphragmatic vagus nerve is responsible for is regulating gastric functions, including digestion.
As used herein the term “branches” refer to portions of the vagus nerve innervating various organs. An example of the different branches of the vagus nerve is shown in
As used herein, the term “closed-loop” refers to a system that receives feedback and configures a stimulation based on the feedback.
As used herein, the term “patient” refers to a mammal (e.g., a human) suffering from obesity (or other gastric/metabolic disorders or conditions like irritable bowel disease, diabetes, hypertension, trouble eating, or the like).
The vagus nerve innervates nearly every internal organ, providing sensory input to the brain and parasympathetic-control inputs to the viscera. Therefore, abnormal vagus-nerve activity has been linked to many chronic diseases, such as epilepsy, diabetes, hypertension, and cancer. The majority of vagal afferent fibers come from the gut, and abnormal vagal activity has been clearly implicated in obesity and other gastric/metabolic disorders. Open-loop vagus nerve stimulation (VNS) has been used to induce weight loss in obese patients, though the mechanism by which these therapies cause weight loss is not well understood; however, most VNS studies have showed inconsistent results. Described herein are closed-loop VNS systems and methods for the treatment of obesity and other gastric/metabolic disorders. The closed-loop VNS systems can record vagal activity (in the form of electrical signals) related to gastric events from a subdiaphragmatic branch of the vagus nerve, decode the vagal activity related to these events (which can involve matching to a previously decoded signal), and deliver a stimulation to another subdiaphragmatic branch of the vagus nerve.
An aspect of the present disclosure can include a system 100 for closed-loop vagus nerve stimulation for obesity control (and/or treatment of other metabolic/gastric conditions). The system 100 includes one or more implantable recording electrodes 102 to record signals from a portion of one or more subdiaphragmatic branches of a patient’s vagus nerve; a processing device 104 configured to: receive 108 the signals from the portion of the one or more subdiaphragmatic branches of the patient’s vagus nerve, perform signal processing 110 to decode the signals from the portion of the one or more subdiaphragmatic branches of the patient’s vagus nerve, and configure 112 a stimulation to decrease the patient’s hunger and/or increase the patient’s satiety based on the decoded signals; and one or more implantable stimulating electrodes 106 to deliver the configured stimulation to another portion of one or more subdiaphragmatic branches of the patient’s vagus nerve. In other words, the recording electrode(s) 102 can recording of spontaneous vagal activity in order to detect eating-related signals and decode patient condition (by the processing device 104, for example the patient condition can be eating, not eating, should be full, should be hungry, full, hungry, or the like), and a stimulation can be configured based on the decoded patient condition and delivered by the stimulating electrode(s) 106. In some instances, the cycle can keep going after the stimulation is delivered when a new reading is acquired until a preset value is reached at the recording and the cycle stops. The system 100 can provide closed-loop vagus nerve stimulation to reduce stimulation time, power requirements, and adverse side effects.
The recording electrode(s) 102 and stimulating electrode(s) (stimulation electrode(s) 106) can be implanted proximal to or within the subdiaphragmatic branches of the vagus nerve. Such implantation can minimize off-target recordings and stimulation side-effects. The recording electrode(s) 102 and/or stimulating electrode(s) can be, for example, cuff electrodes, loose electrodes, or intrafascicular electrodes (at least a portion of which can be configured to be implanted intrafascicularly). As an example, at least a portion of intrafascicular electrodes can be made of a carbon nanotube yarn or other material that can be used in an intrafascicular electrode. The processing device 104 can be located outside the body of the patient (e.g., a computing device such as a mobile device or computer) and/or implanted within the body of the patient (e.g., as a signal processing chip) because the signal processing and stimulus control could be done via a fully implantable system, or with the aid of external processing. Because the regulation of food intake is a relatively slow process (changes occurring on the order of seconds or minutes), the processing device (comprising at least a processor, such as a hardware processor and a memory, such as a non-transitory memory) can be outside of the patient, such as on a mobile device or computer, in some instances. As another examples, each of the components can be implantable as separate pieces in communication with each other or even combined in a single implantable device.
For example, signals recorded from the nerve by the recording electrode(s) 102 are sent to the processing device 104, which analyzes (or decodes) data and also may save the data and/or the results of the analysis for further use. As another example, after implantation and surgery recovery the system 100 would require a training period, where the patient would indicate their times of hunger, amount, and length of food intake, and/or satiety feelings after meals. The training period can be used to match vagal activity with specific eating/satiation events. After the training period, recorded vagal signals would be decoded by the signal processing device 104, which would then control stimulation to produce the desired effect (e.g., reduction in hunger when a certain vagal activity is decoded and a certain stimulation is delivered) while lessening off-stream reactions and unrelated/undesired consequences by minimizing the amount of stimulation used.
In treating obesity, for example, the system 100 is intended to stimulate the vagus nerve to either decrease hunger or induce satiety in order to reduce food intake.
Another aspect of the present disclosure can include methods 200, 300 for closed-loop vagus nerve stimulation for obesity control (and/or treatment of other metabolic/gastric conditions) shown in
For purposes of simplicity, the methods are shown and described as being executed serially; however, it is to be understood and appreciated that the present disclosure is not limited by the illustrated order as some steps could occur in different orders and/or concurrently with other steps shown and described herein. Moreover, not all illustrated aspects may be required to implement the method, nor is the method necessarily limited to the illustrated aspects. Additionally, at least the processing device 104 is a computer-related entity that includes hardware, including a memory (which is a non-transitory memory) and a processor (e.g., a microprocessor, a computing device, a state machine, a signal processing chip, or the like, and communicates with hardware (e.g., recording electrodes 102 and stimulating electrodes 106) to facilitate the performance the closed-loop system shown in
A method 200 for closed-loop vagus nerve stimulation for treatment of metabolic/gastric conditions is shown in
A method 300 for treatment of obesity by delivering a closed-loop stimulation is shown in
In either method 200 or 300, the processor can be trained during a training period to recognize the signals that indicate times of hunger, amount, and length of food intake, and/or satiety feelings after meals. During the training period, the patient can indicate the times of hunger, the amount, and the length of food intake, and/or the satiety feelings after meals. This can help the processing device 104 better recognize/decode the signals.
The following experiment shows chronic recording and decoding of activity in the vagus nerve of freely moving animals enabled by the axon-like properties of the CNTY biosensor in both size and flexibility and provides an important step forward in the ability to understand spontaneous vagus-nerve function.
In this study, spontaneous vagal-spiking activity from awake, freely moving rats were continuously recorded for >48 hours up to two weeks after implantation. It is thought that this is the first time this has been successfully demonstrated. The neural-recording data was synchronized with continuous video recording of the subjects. Spike sorting is used to separate semi-distinct spike clusters, which are then correlated to animal behavior identified from the video recordings. Interspike interval distributions are also found to change in response to food intake, presenting another neural feature that can be used to decode spontaneous vagal activity. Several spike clusters that show tuning to animal eating are reported, and the firing dynamics of multiple decoded spike clusters can be used to classify eating compared to drinking, grooming, and resting behaviors.
CNT yarns were manufactured at Case Western Reserve University, as described previously. CNTYs were then connected to 35NLT®-DFT® wire (FortWayne Metals, FortWayne, IN, USA) with silver conductive epoxy (H20E, EPO-TEK), creating a CNTYDFT® junction. Dacron mesh and silicone elastomer (MED-4211/MED-4011, NuSil Silicone Technology, Carpinteria, CA, USA) were added to seal the junction, confirmed by measuring the impedance of the junction at 1 kHz in a saline bath. The free end of the CNTY was tied to the end of an 11-0 nylon suture (S&T 5V33) using a fisherman’s knot, as shown in
All surgical and experimental procedures were done with the approval and oversight of the Case Western Reserve University Institutional Animal Care and Use Committee to ensure compliance with all federal, state, and local animal welfare laws and regulations. Electrodes were implanted in male Sprague Dawley rats between 7-12 weeks of age.
To expose the left cervical vagus nerve, a midline incision was made along the neck. The muscles and salivary glands were separated and held in place, revealing the carotid sheath which contains the carotid artery and vagus nerve. The vagus nerve was carefully separated from the carotid artery using blunt dissection and held in slight tension using a glass hook. CNTY electrodes were implanted by sewing the suture through the nerve for ~2 mm, then pulling the suture until the CNTY-suture knot was pulled through. Then, the electrode was pulled back so that the knot sat against the epineurium, ensuring the recording site remained inside the nerve, as shown in
Recordings were carried out continuously in awake, behaving animals for 56 and 40 hours (Rat 1 and 2, respectively). A custom-built PCB with an Intan RHD2216 recording chip was attached to the headcap connector, which was secured to the animal with a 3D-printed locking mechanism and attached to a PlasticsOne® (Roanoke, VA, USA) commutator, allowing the rat to move around the cage without tangling or pulling on the connector cable. Input signals were routed to eight amplifier channels, using 8-channel hardware averaging to decrease amplifier noise. Output from the amplifier board was run through the commutator into an Intan RHD USB Interface board (Intan part #C3100), which is powered by an external battery supplying 5V DC power. Signals are then routed to a computer where they are saved for offline analysis and can be viewed in real time.
Neural recordings were sampled at 20 kHz with a 5 kHz low-pass filter. Recordings were started around 10 AM (approximately four hours after the start of the light cycle). During ENG recording, a video camera was used for simultaneous video recording. The camera was equipped with an infrared light and infrared sensor, allowing for filming even during the dark cycle. The camera was connected to the recording computer and manually synced to the recording. A diagram of the recording setup can be seen in
ENG data were imported into MATLAB, where they were further processed. ENG was band-pass filtered from 500-5000 Hz to minimize interference from EMG, ECG, or other possible sources. The filter bandwidth was kept relatively wide to minimize distortion of spike waveforms. Spikes were detected and sorted into clusters using the UltraMega-Sort2000 software in MATLAB, using a threshold of eight times the RMS of baseline. Spike waveforms (3 ms long) were transformed into the principal component space, and principal components accounting for 95% of the total waveform variance were used for spike clustering. Spike clustering was done using k-means clustering of spike waveform principal components, with a maximum of k = 256 clusters. Using the UMS2000 software, clusters were further analyzed for better separation and exclusion of artifacts. First, outliers were removed if they had a z-score greater than 500 on the x2 distribution of distance to the cluster center. Clusters were removed from analysis if the spike waveform contained a second, larger threshold crossing (i.e., removing of spikes which were detected twice due to threshold-crossing of the spike tail). Clusters were also removed if spike width was less than 0.3 ms and amplitude was greater than 1mV (presumed recording artifacts) or if spike width was greater than 2 ms. Spike waveform values were used to calculate spike amplitudes (difference between the maximum and minimum voltage values) and the spike RMS. Spike-cluster-firing timings were also used to calculate cluster-firing rates and interspike intervals (ISI). Average spike amplitudes over time are shown in
Toluidine blue staining: the image shown in
Fluorescent staining: the image shown in
Where relevant, results are reported as mean ± standard deviation. Average spike waveforms in
Table 1. Firing rates of cluster groups relative to eating. Sorted clusters are separated into five cluster groups based on their response to eating. Table 1 shows the number of clusters of each group recorded in both animals and the behavior of those cluster groups before, during, and after eating: up arrow means an increased firing rate, dash means no change in firing rate, and down arrow means a decreased firing rate for the cluster group.
Table 2. Differences in ISI distributions for before, during, and after eating periods, compared to non-eating periods, for all clusters which had at least one group with a significant change. Cluster groups are shown for each cluster (see Table 1), and non-significant p-values are not shown.
It has previously been shown that CNTY electrodes can record spikes from the glossopharyngeal and vagus nerves in anesthetized rats and can be used to measure vagal tone in freely moving animals. Here, a novel continuous chronic-recording setup is demonstrated (shown in
Identifying the function of spontaneous spikes in freely moving animals is important to understanding how vagal fibers modulate their activity during normal animal behavior. Given the high ratio of gastric afferents in the vagus, most vagal spiking is involved with gastric signaling.
After animal-eating times were identified from video recordings, they were compared to the firing rates of individual spike clusters. In both animals, several clusters show a significant increase in firing rate that occurs <25 min before eating. Some clusters also had increased or decreased firing that occurred during eating, while others had increased firing that occurred <10 min after eating.
Many clusters exhibited a mix of behaviors, showing firing rates before, during, or after eating that were significantly different from baseline activity (p < 0.01 with Bonferroni correction). To analyze cluster behavior related to eating, clusters were sorted into groups based on their firing rate response before eating (from 25 min before, until the start of eating), during eating, and after eating (end of the eating event, until 10 min after eating). These data are summarized in Table 1 for both rats, which show how the cluster-firing rates changed for each group and the number of clusters from each animal which make up each group. The table shows the direction of change and associated p-value for the changes in firing rate of each group in the different eating-related periods (sum of the spiking activity in all clusters within a group compared to the baseline firing activity for the clusters in that group). Only 3 of the 132 recorded clusters did not showing any significant tuning to eating behavior. While specific spiking correlations are unique to each subject, they are consistent within each animal, and
Spikes are often observed exhibiting bursting behavior, where fibers tend to fire at specific frequencies. Bursting behavior can be seen in
periods for one example cluster (Cluster 1.8, which is part of Group II and has increased activity before and after eating). In
These data are summarized in Table 2 which shows p-values comparing noneating and eating-related ISI distributions for any cluster which showed a significant change. The 18 clusters in Rat 2 only showed a change in ISI distribution during eating, with no changes either before or after. The 10 clusters in Rat 1 each showed changes before eating, while some also had a significantly different ISI distribution during or after eating as well.
In addition to showing that individual spike clusters are correlated with food intake, it was also examined whether spike-firing rates are sufficient to classify the times during which the animal is eating, compared to other behaviors, such as drinking, grooming, and resting. A multinomial logistic regression model was constructed, with behaviors and spike-cluster-firing rates averaged over 30 s. The model uses firing rates from each of the recorded clusters, as well as firing rates during peak delayed or preceding correlations with eating. The models were trained on the first ⅔ of recording data and tested on the final ⅓ of recording data.
The autonomic nervous system is a vital part of regulating homeostasis and overall health. The vagus nerve is the largest autonomic nerve, serving to both sense and control internal organ function. This has led to a variety of neuromodulation therapies targeting the vagus nerve for treatment of chronic diseases, though with somewhat mixed results. Many studies of the vagus nerve focus on the effects of the efferent vagal fibers, with heart rate variability and “vagal tone” chief among them. However, as many as 80% of the fibers in the vagus are afferent, with a majority coming from the gut. Thus, a greater analysis of the behavior of afferent vagal fibers and their impact on chronic health and disease is important, both for improvement of current therapies, and for the development of new therapies. Recent advancement in chronic recording from small peripheral nerves allows for long-term recording from awake, non-anesthetized rats and investigation of spontaneous vagal signals. In this study, spontaneous vagal firing is used to classify several animal behaviors
Carbon nanotube yarn (CNTY) electrodes were prepared as described previously1. Two CNTY electrodes were implanted in the left cervical vagus of a 10-week old male Sprague Dawley rat, attached to a connector mounted on the skull. After a two-week recovery, vagus nerve ENG was recorded for 72 consecutive hours with simultaneous video recording. Spike detection and classification was performed using the UltraMegaSort2000 software in MATLAB2. Artifacts, outliers, and clusters with fewer than 2000 spikes during the 72-hour period were removed from the analysis. Several rat behaviors were classified based on the recorded video: eating, drinking, resting, grooming, and other activity. Cluster spike rates and rat behaviors were averaged in 30-second time windows. Then, the mutual information between cluster firing rates and classified behaviors were calculated with varying delays from -1 hour to +1 hour. The first 48 hours of data were used to train a multinomial logistic regression model, with cluster firing rates and peak delayed firing rates found from mutual information analysis used as input variables. The model was then fed the last 24 hours of data, generating a list of classification probabilities for each behavior state. Probabilities greater than 0.5 were categorized and compared to the behaviors classified from recorded video.
56 spike clusters were identified which matched the criteria designated above. Of these clusters, 7 exhibited a maximum mutual information occurring with a 10-13 minute delay after eating. An additional 3 clusters showed a maximum mutual information occur-ring with either a positive or a negative time delay compared to drinking (-2.5, -5, and +3.5 minutes). Thus, 66 total input variables were used to create the multinomial logistic regression.
From the above description, those skilled in the art will perceive improvements, changes, and modifications. Such improvements, changes and modifications are within the skill of one in the art and are intended to be covered by the appended claims. All patents, patent applications, and publications cited herein are incorporated by reference in their entirety.
This application claims the benefit of U.S. Provisional Application No. 63/317,997 filed Mar. 9, 2022, entitled, “CLOSED-LOOP VAGUS NERVE STIMULATION FOR THE TREATMENT OF OBESITY”. This application is hereby incorporated by reference in its entirety for all purposes.
This invention was made with government support under W81XWH-18-1-0581 awarded by Congressionally Directed Medical Research Programs and 5R01 NS032845-22 awarded by National Institutes of Health. The government has certain rights in the invention.
Number | Date | Country | |
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63317997 | Mar 2022 | US |