A Novel Sweat Sensor Detects Specific Inflammatory Circadian Patterns in Inpatients and Outpatients with Cirrhosis

A Novel Sweat Sensor Detects Specific Inflammatory Circadian Patterns in Inpatients and Outpatients with Cirrhosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Sweat Sensor Detects Specific Inflammatory Circadian Patterns in Inpatients and Outpatients with Cirrhosis Brian C. Davis, Kai-Chun Lin, Sarah Shahub, Annapoorna Ramasubramanya, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5146199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in npj Digital Medicine → Version 1 posted 13 You are reading this latest preprint version Abstract Cirrhosis is the end stage result of chronic liver injury and clinical events are driven by worsening of complex inflammatory pathways leading to frequent hospitalizations and death or need for liver transplantation. Inflammatory biomarkers such as c-reactive protein (CRP), tumor necrosis factor alpha (TNFα) and interleukin-6 (IL6) are typically elevated in serum of cirrhosis patients and associated with worse outcomes. These markers are not routinely checked due to the invasive nature of blood draws and difficulty in interpretation of a single measurement. Therefore, we designed a study to measure these biomarkers using a continuous monitor of passively expressed sweat in a well characterized cohort of subjects with cirrhosis. We enrolled 32 patients with cirrhosis and 12 controls. The AWARE sweat sensor was placed on each subject with the sensor staying in continuous contact with the skin and exchanged daily for 3 days. Serum lab draws to measure CRP, TNFα, IL6, and liver function were performed along with quality-of-life surveys and hepatic encephalopathy testing. We found that CRP, TNFα, and IL6 were correlated in sweat and serum among cirrhosis and controls. All three biomarkers in sweat and serum were elevated in inpatients compared with outpatients or controls. IL6, whether measured in sweat or serum, was associated with lower transplant-free survival. Continuous monitoring of sweat showed nocturnal elevations of CRP and IL6 when compared to healthy controls. Outpatients with cirrhosis were consistently found to have inflammation levels starting to elevate during the evening periods and peaking towards the early night periods. The levels start to fall much later in the night periods and early morning periods. These data suggest that further investigation of continuous measurement of sweat biomarkers in patients with cirrhosis is warranted. Health sciences/Medical research/Translational research Health sciences/Diseases/Gastrointestinal diseases/Liver diseases/Liver cirrhosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Liver cirrhosis is the end stage of chronic liver injury from conditions such as alcohol use disorder, viral hepatitis, and metabolic dysfunction, which is a major cause of morbidity and mortality worldwide 1 – 5 . Cirrhosis consists of a subclinical “compensated” stage, where liver function is largely preserved, then followed by a more advanced “decompensated” stage characterized by clinical manifestations such as infections, hepatic encephalopathy (HE), variceal bleeding, and ascites—resulting in hospitalizations and death 6 – 8 . Systemic inflammation represents the major pathophysiological phenomenon in the progression of cirrhosis from the compensated to decompensated state 9 – 14 . Translocation of bacterial components known as pathogen associated molecular patterns (PAMPs) from the intestinal lumen into the portal circulation leads to chronic overactivation of the innate immune system by stimulating production of pro-inflammatory cytokines such as interleukin-6 (IL6), tumor necrosis factor alpha (TNFα), interferons, and others. 12 These cytokines play a key role in the host response to infections, but overstimulation may lead to further organ damage and secondary infections via an anti-inflammatory compensatory response and immune exhaustion. 15 , 16 C-reactive protein (CRP), an acute phase reactant, is elevated in the serum of patients with cirrhosis 17 . Increased serum levels of tumor necrosis factor alpha (TNFα) and interleukin-6 (IL6) are also found in cirrhosis 18 – 20 . Therefore, numerous studies have linked inflammatory markers to poor outcomes in decompensated cirrhosis and development of multi-organ failure, termed acute on chronic liver failure (ACLF) 21 – 23 . Emerging evidence links systemic inflammation and sleep disturbance in the general population and sleep issues are quite common in people with cirrhosis 24 – 26 . Systemic inflammation worsens neuropsychiatric test results and severity of HE episodes 9 , 10 . Furthermore, significant sleep disturbances have been described in covert or minimal HE patients 27 . Therefore, changes in sleep architecture may be of clinical significance and portend worsening of underlying systemic inflammation 28 . Despite the research and advances in the systemic inflammation theory of cirrhosis progression, current clinical practice does not involve measuring cytokines and clinical guidelines do not support obtaining biomarkers to make clinical decisions 29 . First, these serum tests are invasive. Second, it is unclear of the optimal time(s) and impact of cirrhosis inflammatory biomarkers on circadian variation. Third, interpretation can be a challenge, and cut-offs may vary depending on the reference lab. Thus, current standard of care involves obtaining cultures to rule out infection, which often take several days to result and can lead to delays in diagnosis and treatment. 6 , 15 Use of noninvasive sensors that can detect cytokines at more frequent intervals could shed greater light into the pathophysiological basis of inflammation in subclinical cirrhosis and earlier identification high-risk hospitalized patients 30 . A novel, Sweat AWARE perspiration-based biosensing platform that non-invasively detect inflammation levels and changes over time, would serve as an important interface between the patient and provider to improve prediction of outcomes by linking these with patient reported-outcomes (PROs). The AWARE sweat sensor has been shown to track systemic inflammation markers in sweat among patients with other gastrointestinal disorders such as inflammatory bowel disease, but its application in cirrhosis is unclear 31 . The primary aim of this study was to characterize common cytokines in sweat such as TNFα, IL6, and CRP 32 , 33 . We also hypothesized that the AWARE sweat sensor would delineate diurnal patterns among subjects with cirrhosis due to the continuous measurement of cytokines when compared with healthy controls, these sweat cytokines signatures may vary based on inpatient or outpatient clinical status and would be linked with cognitive testing and outcomes in these patients. Methods We prospectively enrolled healthy controls, outpatients with cirrhosis, and inpatients with cirrhosis. Subjects were enrolled from the University of Texas at Dallas and Richmond VA Medical Center. All subjects needed to be > 18 years of age and able to provide informed consent. Healthy controls were recruited from the community and were free of chronic diseases and were not on prescription medications. For the two cirrhosis groups, cirrhosis was diagnosed by either biopsy, imaging, transient elastography, presence of varices and/or platelet count 1 in chronic liver disease patients, or those with frank decompensation [ascites, hepatic encephalopathy (HE), hepato-pulmonary syndrome, jaundice, variceal bleeding]. We excluded those with an unclear diagnosis of cirrhosis, with concomitant IBD, and those on immunosuppressive therapy. We only included subjects who could come in daily or be available daily for 3 days. We recorded demographics, disease severity, course and complications, and concomitant medications at baseline and at each study visit. After consenting, we analyzed routine daily labs (basic metabolic and hepatic panel [BMP], complete blood count [CBC]), serum inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL6], and tumor necrosis factor alpha [TNFα]) in those with cirrhosis. Patients with cirrhosis were also administered the Sickness Impact Profile (SIP), a generic quality of life (QOL) instrument that inquires about daily function related to health over the last 24 hours on the day of enrollment 34 . SIP consists of physical and psychosocial domains and a high SIP indicates poor QOL. Both SIP and blood draw were in the mornings. The sensor was then fitted to their arm, and subjects added in a de-identified manner in the study iPad. Sensors were replaced every 24 hours, and their capture of the data was checked before reloading. During the study period if the inpatients were considered stable for discharge, the study was continued as outpatients with daily return till day 3 post-enrollment. We did not withdraw those who developed infections or require antibiotics during the study period. Subjects who became confused during the study continued to have clinical data recorded but were withdrawn from active study participation. Subjects were followed for up to one year to calculate transplant-free survival i.e. percentage of subjects who did not die or undergo liver transplantation. Ethics declaration : Healthy control human subject studies were approved by the Institutional Review Board of the University of Texas at Dallas (IRB number UTD IRB 19–146) and Richmond VA Medical Center for patients with cirrhosis (IRB number 1649873 mIRB 02701 BAJAJ0028). All subjects signed informed consent. Sweat AWARE Device : This comprises a replaceable sweat-sensing strip tailored for specific target biomarkers, affixed to a wearable electronic reader (Fig. 1 ). This reader translates the sensor's impedance into a calibrated concentration of measured biomarker levels in sweat. The sensor response was measured through non-faradaic electrochemical impedance spectroscopy (EIS), recording the resulting impedance at a frequency range of 100 Hz to 1 KHz using a low sinusoidal input voltage of 1-100 mV. The sensor electrode underwent a functionalization process involving the application of a thiol cross-linker. This cross-linker was specifically chosen for its molecular properties. The opposite end of the cross-linker was meticulously bonded with a concentration of monoclonal capture antibodies, each tailored for the biomarkers CRP, IL6, and TNFα. This careful selection of monoclonal antibodies was deliberate, aiming to achieve a high degree of specificity in detecting the target biomarkers. We have previously outlined the fabrication process for both the Sweat AWARE device and sweat sensor. The sensor fabrication process has been adapted from Munje et al., and Jagannath et al., and has been described in detail previously 30 , 31 . Measurements of sweat CRP, IL6, and TNFα were obtained on the body at one-minute intervals throughout each 24-hour period. These recorded measurements were then averaged for every consecutive 2-hour period, commencing from the start of each collection period. Following this, the averaged values were compared to serum levels to analyze the correlation between sweat and serum concentrations. Temporal graphs of the actual and average levels of CRP, IL6, and TNFα were generated for all subjects, with categorization based on different subject groups. Statistical Analysis : GraphPad Prism version 10.2.1 software was utilized to conduct analyses and generate figures. P-values were computed based on the average data of sweat and serum across all days. The Mann-Whitney test and one-tailed analysis were employed. Sweat value in this analysis was calculated by averaging sweat CRP, IL6, and TNFα measurements collected every 1-minute. Additionally, a heatmap was generated using the correlation matrix function within the GraphPad analysis tool. Circadian patterns : An effect size in statistics is a sample-based estimate of the number that quantifies the strength of the association between two variables in a population. It can be used to describe the value of a parameter for a fictitious population, the value of a statistic derived from a sample of data, or the equation that operationalizes the relationship between parameters and statistics and the effect size value. The correlation between two variables, the regression coefficient in a regression, the mean difference, or the likelihood that a certain event (like a heart attack) would occur are a few examples of effect sizes. In addition to serving as a supplement to statistical hypothesis testing, effect sizes are crucial for power analyses, sample size planning, and meta-analyses. The group of techniques for analyzing data related to effect sizes is known as estimate statistics. Hedge's G is comparable to Glass's G and Cohen's D statistics. Usually, an experimental dataset and a control dataset are compared using these statistics. We adopted in here the Hedge’s G technique to assess the circadian characteristics of the sweat biomarker expression by calculating the impact magnitude of the mean difference across the 24-hour time period i.e., Morning (6am to 2pm), Evening (2pm to 10pm), and Night (10pm to 6am) and by subject cohorts i.e., Control, Inpatient, and Outpatient for each of the sweat biomarkers measured. The 24-hour time period was divided into three 8-hour periods by taking into consideration the relative half-life of the inflammatory biomarkers for assessing effect size. The Hedge’s G was calculated was calculated by comparing 2 time periods for each of the subject cohorts and using the formula as below 35 : g=(y1-y2)/sp, where, y1 and y2 are the standard means of the 2 samples and sp is the pooled standard deviation given by: sp= √(((n1-1) 〖s1〗^2+(n2-1) 〖s2〗^2)/(n1 + n1-2)). Results A total of 12 controls (no-cirrhosis) and 32 veterans with cirrhosis (22 inpatients [IP] and 10 outpatients [OP]) were included in the study. Analyses were performed between groups at baseline as well as within groups over time. The median age of OP with cirrhosis was 64 years (range 42-74) and the median age of IP with cirrhosis was 65 years (range 35-77). Most cirrhosis subjects were male (100% outpatient and 91% outpatient) and half of each cirrhosis cohort identified as non-white. Table 1. Baseline Characteristics in Study Population of healthy controls (no-cirrhosis) and cirrhosis subjects sweat biomarkers grouped by time of day (Morning vs. Evening vs. Night). Blood samples were not collected in healthy controls. Data are presented as mean±standard deviation unless otherwise noted. Abbreviations: CRP: c-reactive protein; I6: interleukin-6; TNFα: tumor necrosis factor alpha. Parameter Control, No-Cirrhosis (n=12) Outpatient Cirrhosis (n=10) Inpatient Cirrhosis (n=22) P value between Cirrhosis groups Age (years; median; range) 44.5 (40-47) 64; 42-74 65; 35-77 0.02 Sex (% male) 67% 100% 91% 0.91 Race (% white) 0% 50% 50% 0.82 Enrollment Serum and Sweat Biomarkers Serum CRP (mean, mg/dL) - 0.48 ± 0.37 2.60 ± 2.20 <0.0001 Sweat CRP. Morning (mean, pg/mL) 1835.50 ± 1492.90 1685.33 ± 1062.72 2672.72 ± 1363.90 <0.0001 Sweat CRP. Evening (mean, pg/mL) 1423.99 ± 1113.26 2011.91 ± 1396.74 1300.98 ± 479.88 <0.0001 Sweat CRP. Night (mean, pg/mL) 1858.20 ± 1673.31 4063.02 ± 1458.81 3010.48 ± 1522.54 <0.0001 Serum IL6 (mean, pg/mL) - 2.13 ± 1.53 24.5 ± 18.2 <0.0001 Sweat IL6, Morning (mean, pg/mL) 3.87 ± 2.37 4.99 ± 3.12 7.05 ± 4.40 <0.0001 Sweat IL6, Evening (mean, pg/mL) 3.33 ± 1.98 4.72 ± 3.09 5.08 ± 2.53 0.0002 Sweat IL6, Night (mean, pg/mL) 3.77 ± 2.07 10.16 ± 5.53 7.17 ± 3.71 <0.0001 Serum TNFα (mean, pg/mL) - 4.22 ± 1.46 11.02 ± 8.04 0.004 Sweat TNFα, Morning (mean, pg/mL) 4.92 ± 1.76 6.01 ± 1.37 6.38 ± 1.68 <0.0001 Sweat TNFα, Evening (mean, pg/mL) 4.82 ± 1.64 6.00 ± 1.35 6.27 ± 1.47 <0.0001 Sweat TNFα, Night (mean, pg/mL) 4.71 ± 1.76 6.64 ± 1.59 6.38 ± 1.68 <0.0001 All inpatients were admitted for cirrhosis-related complications with a mean length of stay 5.5 ± 0.81 days. Sixteen of the subjects had alcohol-related liver disease, nine with viral hepatitis, and seven with metabolic-dysfunction as the primary etiology of cirrhosis. Fourteen IP were given antibiotics and seven had documented infections: three urinary tract infections, one bacteremia, one Helicobacter pylori, one S. aureus hand wound infection, and one Coronavirus disease-2019 infection. Four inpatients had overt HE on admission. SIP was administered to 9 of 10 outpatients and 15 of 22 inpatients—SIP total, physical, and psychosocial scores were all higher in the inpatient cohort. Liver function, as measured by MELD-Na, was significantly worse on each day of the study in the inpatient group. The transplant-free survival rate after one year was 0.56 and significantly lower in the inpatient group (Table 2). Table 2. Inpatients with cirrhosis have worse liver function and functional status than outpatients. Data are presented as means±SD unless otherwise noted. The SIP scores are a quality-of-life summary assessments and higher scores indicated lower quality of life. The MELD-Na score ranges from 6-40 with higher scores associated with higher risks of short-term mortality. Abbreviations: HE: hepatic encephalopathy; SIP: sickness impact profile; MELD-Na: Model for End Stage Liver Disease Sodium. Outpatient Cirrhosis (n=10) Inpatient Cirrhosis (n=22) P value Total SIP 14.8±8.3 (n=9) 32.2±16.6 (n=15) 0.005 Physical SIP 12.6±7.4 34.5±20.2 0.002 Psychosocial SIP 13.4±9.1 23.3±21.8 0.16 Day 0 MELD-Na 9.4±2.2 22.1±10.6 <0.0001 Day 1 MELD-Na 9.7±2.5 23.4±10.4 <0.0001 Day 2 MELD-Na 9.1±1.9 26.6±9.7 <0.0001 Transplant-free survival (1 year) 9 (90%) 9 (41%) 0.001 The coefficient of determination (R 2 ) of sweat and serum biomarkers was examined across different cirrhosis groups (Figure 2A-F). Subjects in the inpatient group exhibited higher levels of each biomarker, regardless of the fluid source. The overall R² for CRP was 0.392 (Figure 2A), while the R² for CRP for outpatient group improved to 0.666 (Figure 2D). Subjects in the outpatient group had serum CRP expression levels below 15 mg/mL, while subjects in the IP group had serum CRP expression levels up to 100 mg/mL. Higher serum CRP values did not correspond to a significant increase in sweat CRP levels. A similar pattern of differences in the coefficient of determination between serum and sweat levels was observed with TNF-a (Figure 2B and 2E) and IL6 (Figure 2C and 2F) biomarkers. These differences in the coefficient of determination between serum and sweat levels could be attributed to the optimization of the dynamic range of the sweat sensor assay performance for each of the biomarkers respectively. The Sweat AWARE device is designed for enabling patient centered clinical decision support system and optimized for use as a remote patient monitoring system. The current dynamic range of the sweat sensor assay performance matching with the inflammation levels of outpatient subjects would make the device optimal for primarily for use in a remote outpatient monitoring setup. Clinical Outcomes and Sweat Analysis When sweat biomarker averages were compared among clinical status and controls, all sweat CRP, IL6, and TNFα control levels were lower compared to outpatient and inpatient group. (Figure 3A-C). The measurements of CRP, IL6, and TNFα via the Sweat AWARE device were assessed to determine its utility in classifying inpatient and outpatient individuals versus control. The Sweat AWARE device effectively distinguished the healthy control group from outpatient cirrhosis based on the measured sweat biomarkers. Correlation heatmap analysis of sweat and serum biomarker levels was examined on these groups (Figure 3D-F). Sweat levels were averaged by time of the day, in three parts as, Morning sweat levels between 6 am – 2 pm; Evening sweat levels between 2 pm – 10 pm; and Night sweat levels between 10 pm – 6 am. Sickness Impact Profile (SIP) scores showed both positive and negative correlations with inflammatory markers (CRP, IL6, TNFα) in sweat based on the time of the day. Moderately positive correlations were found for inflammatory marker levels in serum to sweat inflammatory levels for CRP and IL6 during morning time periods between 6 am – 2 pm, while a slight negative correlation was found for serum to sweat TNFα levels. In contrast, moderately negative correlations were found between serum and sweat inflammatory levels for all 3 biomarkers for the Evening time periods between 2 pm – 10 pm and for the Nighttime periods between 10 pm – 6 am. It should be noted that blood sampling in the study and SIP scores were all done during the morning time period. Correlation heatmap analysis suggested a positive association with morning time period sweat CRP and IL6 and SIP-physical quality of life. Serum CRP had moderate correlations with SIP physical (0.51), serum IL6 (0.56), and serum TNFα (0.47). Morning time period sweat CRP had moderate correlations with SIP physical (0.32), serum IL6 (0.58), serum CRP (0.35), and serum TNFα (0.28). Serum IL6 had moderate to strong correlations with SIP physical (0.56), serum CRP (0.55), and serum TNFα (0.65). Morning time period sweat IL6 had moderate correlations with SIP physical (0.52), serum CRP (0.22) and serum IL6 (0.44). Serum TNFα had moderate correlations with SIP physical (0.47), serum CRP (0.51), and serum IL6 (0.65). Morning time period sweat TNFα showed very low correlations with all the other variables. Overall, Inflammatory markers (CRP, IL-6, TNFα) in serum generally show moderate correlations with the SIP physical subscale, suggesting a potential link between inflammation and physical symptoms. Inflammatory markers CRP and IL-6 in morning sweat had generally moderate correlations with other variables, indicating that changes in these markers may reflect the SIP measures. Outpatients with cirrhosis when analyzed independently were consistently found to have sweat inflammation levels starting to elevate during the evening periods and peaking towards the early night periods. The sweat inflammation levels start to fall much later in the night periods and early morning periods. Moderately positive correlations were found for inflammatory marker levels in sweat to SIP scores for the evening time periods between 2 pm – 10 pm and moderately negative correlations for the morning time periods between 6 am – 2 pm. The correlations were mixed in the night periods between 10 pm – 6 am. These trends can be clearly seen in the temporal plots of the sweat inflammatory biomarkers (Figure 4A-D). Among compensated versus decompensated cirrhosis, the average decompensated sweat biomarker values measured during the study period were a slightly higher in both CRP and IL6 (Figures 5A-D). In subjects who received antibiotics, the sweat CRP and IL6 levels were lower than subjects who did not receive antibiotics, where the area of curve of antibiotic of CRP is 4384945 and non-antibiotic is 4557502 (Figure 6A-D). In subjects who died or received a liver transplant, the admission sweat CRP and IL-6 levels were elevated compared with subjects who survived (Figure 7A-D). Circadian characteristics of sweat biomarker expression: Any physiological or behavioral characteristic exhibit circadian rhythm and quantifying this rhythm has inherent value. 36 Inflammation is elevated due to chronic conditions and the relative changes in the inflammation levels over time are due to the illness states and the physiological state of the subject. Diurnal refers to a characteristic elevation of biomarker expression mainly in the day time while nocturnal refers to a characteristic elevation of biomarker expression mainly during night time. 37 Hedge’s G was used to determine the circadian expression of the sweat inflammatory biomarker levels over time. The Hedge's G statistic expresses the difference of the means in units of the pooled standard deviation. It is typically used in the context where one of the samples is a control sample. That is, we are interested in the effect size of the sample being analyzed relative to a control sample. A Hedge’s G value of 0.2 or lower (small effect); 0.2 – 0.5 (medium effect); and 0.8 or greater (large effect). In this case, we use this Hedge’s G statistic to compare the means of the sweat inflammatory biomarker levels averaged by time of the day, in three parts as, Morning sweat levels between 6 am – 2 pm; Evening sweat levels between 2 pm – 10 pm; and Night sweat levels between 10 pm – 6 am and comparing between subjects with cirrhosis i.e., inpatients and outpatients who are inflamed due to cirrhosis and healthy control subjects with no inflammation. The Hedge’s G for sweat CRP and IL6 in Figure 8A and 8C shows that the patients with cirrhosis have higher Hedge’s G value at night when compared to morning or evening periods hence, the significance of the biomarker is higher in the night. For TNFa the Hedge’s G values are all below 0.5 indicating that the effect size is low to medium as compared to CRP and IL6 which had Hedge’s G values above 0.8 indicating a larger effects size in CRP and IL6 expressions in sweat. From the Hedge’s G values for the three sweat biomarkers, it can be concluded that CRP and IL6 show a significant effect size between evening and night periods in cirrhosis subjects compared to morning periods, while in the same significance in effect size is not observed in control subjects. Discussion In this study, we demonstrated a novel sweat sensor device can safely be applied to outpatients and inpatients with cirrhosis and generate discriminatory data when compared to healthy controls. We showed that serum and sweat inflammatory markers are correlated in patients with cirrhosis. We found that decompensated patients had dampened diurnal variation of these cytokine levels compared to compensated patients and healthy controls. Sweat-based inflammatory biomarkers on average were higher in infected patients and in those who had a poor transplant-free survival. A key attribute of this study is the demonstration that continuous sweat monitoring illustrates important clinical manifestations of a chronic inflammatory disease state, such as cirrhosis. For instance, sleep cycle disturbances were first described in decompensated cirrhosis in the 1950s and complaints such as difficulty falling asleep, insomnia, and excessive daytime sleepiness are quite common in the cirrhosis population. 24 , 26 , 28 , 38 , 39 However, the pathophysiology is complex and incompletely understood with various internal and external factors playing a role. For instance, the release of melatonin is disrupted in cirrhosis, which leads to higher rates of insomnia and excessive daytime sleepiness and naps. 40 , 41 Furthermore, co-morbid conditions such as sleep apnea also play a role in cirrhosis patients’ sleep disruption. 42 Emerging data show circadian disorders are linked with chronic inflammation. 25 Our study provides intriguing evidence that continuous noninvasive monitoring of inflammatory biomarkers may predict outcomes – as patients with cirrhosis had higher evening and nocturnal levels of sweat CRP and IL6 when compared with healthy controls. Serum IL-6 is associated with poor outcomes in patients with cirrhosis using blood levels in prior studies. 11 However, those studies have largely focused on blood levels at discrete timepoints without focusing on variations in levels over time. Cirrhosis is a disease of chronic inflammation requiring frequent hospitalizations and numerous studies have looked at serum biomarkers as predictors of inflammation and decompensation 43 . We chose sweat CRP, IL6, and TNFα as the target cytokines in this study based on previous research of serum studies linking these biomarkers to important clinical outcomes. Perdigoto et al 44 showed that serum CRP can predict infections in a prospective cohort of inpatients with cirrhosis. A recent meta-analysis documented serum IL6 may discriminant bacterial infections and predict hepatic encephalopathy in subjects with cirrhosis 45 , 46 TNFα also is elevated in decompensated cirrhosis and correlates with severity of ACLF, although a clinical trial of anti-TNFα therapy did not show a benefit in patients with ACLF due to increased secondary infections. 14 , 18 – 20 , 47 However, the unique patterns of cytokines throughout the day in decompensated and more advanced patients compared to healthy people and outpatients could be important in monitoring patients over time than one-time blood draws. This follows the circadian rhythms in these cytokine levels and extends them into compensated and decompensated cirrhosis patients 48 . In addition to survival and infections, investigating quality-of-life impairment and inflammation is critical in cirrhosis 43 , 49 . SIP inquires about QOL over the last 24 hours and was administered in the morning, as was the blood draw. As expected more advanced patients (inpatients) had a worse QOL compared to outpatients. Interestingly, while there was a moderate correlation with morning blood inflammatory markers and SIP, this pattern was distinct across time periods in the sweat. Morning sweat inflammatory markers were more positively correlated with SIP but not the evening and night-time levels. This circadian variation is important in case these questionnaires are administered during different times of the day. Previous sweat sensor studies used healthy controls and to identify potential chronic inflammatory markers and showed correlation with serum and sweat CRP and IL-6 as distinguishing factors in patients with inflammatory bowel disease (IBD) 31 , 50 . The Sweat AWARE device showed direct correlation of serum and sweat markers among inpatients with IBD-related complications. Early data also suggest a pattern in sweat biomarkers among active IBD versus healthy controls. This study had several limitations. The sample size was small as this was a pilot study, thus larger and more heterogenous cohorts are needed to further define sweat inflammatory patterns. We were unable to analyze sweat data between compensated and decompensated outpatients based on the small sample size. Longer clinical outcome data are also needed, although we did show the SIP scores were worse among inpatients and correlated with sweat cytokines and elevated IL6 was associated with reduced TFS. The sweat sensor data were collected for a short interval (up to 3 days) and longer monitoring may identify distinct patterns in the natural history of hospitalized patients. While the sweat sensor was noninvasive and easy to apply, further modification of the sensor to generate point-of-care results (i.e., wider dynamic range) would be useful to obtain clinical applicability as patients with cirrhosis express high levels of inflammation as observed by the serum levels analyzed in this study. Given the large amount of data produced by the AWARE sensor, future studies should apply deep-learning models for analysis. Finally, serum data were drawn daily as opposed to continuous monitoring with the sweat sensor so the correlations may be over or underestimated depending on timing of the lab draw. We attempted to correct this discrepancy by trying to average sweat data over 2 hours based on the half-life of each biomarker. In conclusion, a novel, noninvasive device detects inflammatory cytokines in the sweat of patients with cirrhosis. The sweat biomarkers correlated with serum values and distinct sweat patterns are seen in outpatients and inpatients with cirrhosis. Sweat Inflammation biomarker levels are elevated in those with cirrhosis and follow a circadian behavior and there as differences in expression between outpatients and inpatients. Such an analysis would not have been possible to assess with serum collections. However, this is a limited sample size and these need to be validated in a larger cohort. Specifically, sweat cytokines are elevated in hospitalized patients who are at higher risk for infections. Elevated sweat IL-6 may be an important predictor of mortality and larger studies are indicated to further characterize inflammatory sweat biomarkers in cirrhosis. Declarations Competing Interests SM and SP reports a significant interest in EnLiSense LLC, a companythat may have a commercial interest in the results of this research and technology, no other COI for other authors Funding Partly supported by VA Merit Review 2I01CX001076 and I01CX002472 to JSB and a Richmond Institute for Veterans Research Pilot Award to BCD. Author Contribution JSB conceptualized the study with SP, JSB, SS, SP, AR, SR, BCD and AF were involved in study conduct, BCD and JSB obtained funding, KL and SM provided technical support and all authors were involved in analysis and drafting of the manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to IRB restrictions but are available from the corresponding author on reasonable request. References Asrani, S.K., Devarbhavi, H., Eaton, J. & Kamath, P.S. Burden of liver diseases in the world. J Hepatol 70 , 151-171 (2019). Terrault, N.A. et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology 67 , 1560-1599 (2018). Bhattacharya, D., Aronsohn, A., Price, J., Lo Re, V. & Panel, A.-I.H.G. Hepatitis C Guidance 2023 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection. Clin Infect Dis (2023). Crabb, D.W., Im, G.Y., Szabo, G., Mellinger, J.L. & Lucey, M.R. Diagnosis and Treatment of Alcohol-Associated Liver Diseases: 2019 Practice Guidance From the American Association for the Study of Liver Diseases. Hepatology 71 , 306-333 (2020). Rinella, M.E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 78 , 1966-1986 (2023). Bajaj, J.S., Kamath, P.S. & Reddy, K.R. The Evolving Challenge of Infections in Cirrhosis. N Engl J Med 384 , 2317-2330 (2021). Vilstrup, H. et al. Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. Hepatology 60 , 715-735 (2014). Biggins, S.W. et al. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology 74 , 1014-1048 (2021). Shawcross, D.L., Davies, N.A., Williams, R. & Jalan, R. Systemic inflammatory response exacerbates the neuropsychological effects of induced hyperammonemia in cirrhosis. J Hepatol 40 , 247-254 (2004). Shawcross, D.L. et al. Infection and systemic inflammation, not ammonia, are associated with Grade 3/4 hepatic encephalopathy, but not mortality in cirrhosis. J Hepatol 54 , 640-649 (2011). Arroyo, V. et al. The systemic inflammation hypothesis: Towards a new paradigm of acute decompensation and multiorgan failure in cirrhosis. J Hepatol 74 , 670-685 (2021). Bernardi, M., Moreau, R., Angeli, P., Schnabl, B. & Arroyo, V. Mechanisms of decompensation and organ failure in cirrhosis: From peripheral arterial vasodilation to systemic inflammation hypothesis. J Hepatol 63 , 1272-1284 (2015). Moreau, R. et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. Gastroenterology 144 , 1426-1437, 1437 e1421-1429 (2013). Trebicka, J. et al. The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology. J Hepatol 73 , 842-854 (2020). Tandon, P. & Garcia-Tsao, G. Bacterial infections, sepsis, and multiorgan failure in cirrhosis. Semin Liver Dis 28 , 26-42 (2008). Sriskandan, S. & Altmann, D.M. The immunology of sepsis. J Pathol 214 , 211-223 (2008). Tilg, H. et al. Serum levels of cytokines in chronic liver diseases. Gastroenterology 103 , 264-274 (1992). Albillos, A. et al. Tumour necrosis factor-alpha expression by activated monocytes and altered T-cell homeostasis in ascitic alcoholic cirrhosis: amelioration with norfloxacin. J Hepatol 40 , 624-631 (2004). Lee, F.Y. et al. Plasma interleukin-6 levels in patients with cirrhosis. Relationship to endotoxemia, tumor necrosis factor-alpha, and hyperdynamic circulation. Scand J Gastroenterol 31 , 500-505 (1996). Zhang, W., Yue, B., Wang, G.Q. & Lu, S.L. Serum and ascites levels of macrophage migration inhibitory factor, TNF-alpha and IL-6 in patients with chronic virus hepatitis B and hepatitis cirrhosis. Hepatobiliary Pancreat Dis Int 1 , 577-580 (2002). Claria, J. et al. Systemic inflammation in decompensated cirrhosis: Characterization and role in acute-on-chronic liver failure. Hepatology 64 , 1249-1264 (2016). Albillos, A. et al. Cirrhosis-associated immune dysfunction. Nat Rev Gastroenterol Hepatol 19 , 112-134 (2022). Butterworth, R.F. The liver-brain axis in liver failure: neuroinflammation and encephalopathy. Nat Rev Gastroenterol Hepatol 10 , 522-528 (2013). Cordoba, J. et al. High prevalence of sleep disturbance in cirrhosis. Hepatology 27 , 339-345 (1998). Irwin, M.R., Olmstead, R. & Carroll, J.E. Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. Biol Psychiatry 80 , 40-52 (2016). Montagnese, S., Middleton, B., Skene, D.J. & Morgan, M.Y. Night-time sleep disturbance does not correlate with neuropsychiatric impairment in patients with cirrhosis. Liver Int 29 , 1372-1382 (2009). Bajaj, J.S. et al. Disruption of sleep architecture in minimal hepatic encephalopathy and ghrelin secretion. Aliment Pharmacol Ther 34 , 103-105 (2011). Montagnese, S. et al. Sleep-wake abnormalities in patients with cirrhosis. Hepatology 59 , 705-712 (2014). Bajaj, J.S. et al. Acute-on-Chronic Liver Failure Clinical Guidelines. Am J Gastroenterol 117 , 225-252 (2022). Munje, R.D., Muthukumar, S., Jagannath, B. & Prasad, S. A new paradigm in sweat based wearable diagnostics biosensors using Room Temperature Ionic Liquids (RTILs). Sci Rep 7 , 1950 (2017). Jagannath, B. et al. A Sweat-based Wearable Enabling Technology for Real-time Monitoring of IL-1beta and CRP as Potential Markers for Inflammatory Bowel Disease. Inflamm Bowel Dis 26 , 1533-1542 (2020). Liu, C. et al. Cytokines: From Clinical Significance to Quantification. Adv Sci (Weinh) 8 , e2004433 (2021). Pepys, M.B. & Hirschfield, G.M. C-reactive protein: a critical update. J Clin Invest 111 , 1805-1812 (2003). Bergner, M., Bobbitt, R.A., Carter, W.B. & Gilson, B.S. The Sickness Impact Profile: development and final revision of a health status measure. Med Care 19 , 787-805 (1981). , Vol. 2024 ( Dijk, D.J. & Duffy, J.F. Novel Approaches for Assessing Circadian Rhythmicity in Humans: A Review. J Biol Rhythms 35 , 421-438 (2020). Wang, C., Lutes, L.K., Barnoud, C. & Scheiermann, C. The circadian immune system. Sci Immunol 7 , eabm2465 (2022). Sherlock, S., Summerskill, W.H., White, L.P. & Phear, E.A. Portal-systemic encephalopathy; neurological complications of liver disease. Lancet 267 , 454-457 (1954). Mostacci, B. et al. Sleep disturbance and daytime sleepiness in patients with cirrhosis: a case control study. Neurol Sci 29 , 237-240 (2008). Steindl, P.E. et al. Disruption of the diurnal rhythm of plasma melatonin in cirrhosis. Ann Intern Med 123 , 274-277 (1995). Montagnese, S., Middleton, B., Mani, A.R., Skene, D.J. & Morgan, M.Y. On the origin and the consequences of circadian abnormalities in patients with cirrhosis. Am J Gastroenterol 105 , 1773-1781 (2010). Bajaj, J.S. et al. Effects of obstructive sleep apnea on sleep quality, cognition, and driving performance in patients with cirrhosis. Clin Gastroenterol Hepatol 13 , 390-397 e391 (2015). Kronsten, V.T. & Shawcross, D.L. Clinical implications of inflammation in patients with cirrhosis. Am J Gastroenterol (2024). Perdigoto, D.N., Figueiredo, P.N. & Tome, L.F. Clarifying the role of C-reactive protein as a bacterial infection predictor in decompensated cirrhosis. Eur J Gastroenterol Hepatol 30 , 645-651 (2018). Wu, Y., Wang, M., Zhu, Y. & Lin, S. Serum interleukin-6 in the diagnosis of bacterial infection in cirrhotic patients: A meta-analysis. Medicine (Baltimore) 95 , e5127 (2016). Labenz, C. et al. Raised serum Interleukin-6 identifies patients with liver cirrhosis at high risk for overt hepatic encephalopathy. Aliment Pharmacol Ther 50 , 1112-1119 (2019). Naveau, S. et al. A double-blind randomized controlled trial of infliximab associated with prednisolone in acute alcoholic hepatitis. Hepatology 39 , 1390-1397 (2004). Nakao, A. Temporal regulation of cytokines by the circadian clock. J Immunol Res 2014 , 614529 (2014). Montagnese, S. & Bajaj, J.S. Impact of Hepatic Encephalopathy in Cirrhosis on Quality-of-Life Issues. Drugs 79 , 11-16 (2019). Robert P. Hirten, K.-C.L., Jessica Whang, Sarah Shahub, Nathan K.M. Churcher, Drew Helmus, Sriram Muthukumar, Bruce Sands, Shalini Prasad Longitudinal monitoring of IL-6 and CRP in inflammatory bowel disease using IBD-AWARE. Biosensors and Bioelectronics: X 16 (2024). Additional Declarations Competing interest reported. SM and SP reports a significant interest in EnLiSense LLC, a company that may have a commercial interest in the results of this research and technology, no other COI for other authors Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in npj Digital Medicine → Version 1 posted Editorial decision: Revision requested 15 Oct, 2024 Reviews received at journal 13 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviews received at journal 07 Oct, 2024 Reviews received at journal 02 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers invited by journal 27 Sep, 2024 Editor assigned by journal 25 Sep, 2024 Submission checks completed at journal 25 Sep, 2024 First submitted to journal 24 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5146199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":366448786,"identity":"6c20dc86-978d-4b10-b8f2-572ac983b5d3","order_by":0,"name":"Brian C. Davis","email":"","orcid":"","institution":"Richmond Veterans Affairs (VA) Medical Center and Virginia Commonwealth University","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"C.","lastName":"Davis","suffix":""},{"id":366448789,"identity":"0972b9cf-826c-4f58-bed3-800cfd4e21af","order_by":1,"name":"Kai-Chun Lin","email":"","orcid":"","institution":"University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Kai-Chun","middleName":"","lastName":"Lin","suffix":""},{"id":366448790,"identity":"c670d23b-8794-43d6-bf92-cddd55e939ca","order_by":2,"name":"Sarah Shahub","email":"","orcid":"","institution":"University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Shahub","suffix":""},{"id":366448791,"identity":"09832cd6-94d6-4494-b731-7a25a56c29ac","order_by":3,"name":"Annapoorna Ramasubramanya","email":"","orcid":"","institution":"University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Annapoorna","middleName":"","lastName":"Ramasubramanya","suffix":""},{"id":366448792,"identity":"06515664-861e-4acf-a81f-a831f3a4658d","order_by":4,"name":"Andrew Fagan","email":"","orcid":"","institution":"Richmond Veterans Affairs (VA) Medical Center and Virginia Commonwealth University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Fagan","suffix":""},{"id":366448793,"identity":"1b160f7a-4e17-4f53-8a45-f410c38382f3","order_by":5,"name":"Sriram Muthukumar","email":"","orcid":"","institution":"EnLiSense LLC","correspondingAuthor":false,"prefix":"","firstName":"Sriram","middleName":"","lastName":"Muthukumar","suffix":""},{"id":366448794,"identity":"cc696e53-5a15-4b01-9313-be5f930a06dd","order_by":6,"name":"Shalini Prasad","email":"","orcid":"","institution":"University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Shalini","middleName":"","lastName":"Prasad","suffix":""},{"id":366448795,"identity":"396ea627-a9a6-49b5-8ecd-4161df34eb75","order_by":7,"name":"Jasmohan S. Bajaj","email":"data:image/png;base64,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","orcid":"","institution":"Richmond Veterans Affairs (VA) Medical Center and Virginia Commonwealth University","correspondingAuthor":true,"prefix":"","firstName":"Jasmohan","middleName":"S.","lastName":"Bajaj","suffix":""}],"badges":[],"createdAt":"2024-09-24 15:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5146199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5146199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41746-024-01404-1","type":"published","date":"2024-12-28T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69381536,"identity":"408860ef-8cb2-4941-b8a5-b98c565e7cf7","added_by":"auto","created_at":"2024-11-19 19:05:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":400345,"visible":true,"origin":"","legend":"\u003cp\u003eSweat AWARE perspiration-based biosensing platform that non-invasively detect inflammation levels and changes over time from sweat in a passive manner. The collected data is transmitted instantly through Bluetooth to the user's smartphone and subsequently sent to the cloud server. This facilitates the immediate reporting of biomarker levels to both patients and healthcare providers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/67c617a72f8893a77c73145f.png"},{"id":69381535,"identity":"889207b4-0d67-43c0-bc01-5f523410a271","added_by":"auto","created_at":"2024-11-19 19:05:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-F\u003c/strong\u003e: Coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) between CRP, TNFα, and IL6 measured in serum (lab-based) and sweat (2hr average of sweat CRP levels and 30min average of sweat TNFα and sweat IL6 levels from the Sweat AWARE device) were highly significant across groups;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/68f1b6dad77d36983716a4fd.png"},{"id":69381542,"identity":"2f85db0a-f4b0-4339-86f4-9bffa261e6a2","added_by":"auto","created_at":"2024-11-19 19:05:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-C)\u003c/strong\u003e Sweat values of inflammatory biomarkers in Inpatient and outpatients with cirrhosis compared to controls; (D-F) Correlation heatmap analysis of SIP scores with serum and sweat biomarkers analyzed by the sweat levels averaged by the time of the day.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/8cbb62f5742c0161a327ce5b.png"},{"id":69381541,"identity":"3147bca3-b4bf-4e47-9c9a-f545a1f38afe","added_by":"auto","created_at":"2024-11-19 19:05:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210143,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal plots of sweat inflammatory biomarkers, CRP (A and C) and IL6 (B and D) in outpatients with cirrhosis compared to controls.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/19210bfda2bfd0cdd7e4b798.png"},{"id":69381537,"identity":"f9be621e-e999-41c5-86b4-e78f92fb783b","added_by":"auto","created_at":"2024-11-19 19:05:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":164524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-D:\u003c/strong\u003e Sweat biomarkers temporal plots in cirrhosis cohort based on clinical status. Compensated (n=5) was defined as clinically stable without HE, EVB, ascites, jaundice, or HRS. Decompensated status (n=27) was defined as history or evidence of liver clinical events at study enrollment. (A and B) Sweat CRP and IL6 levels are plotted at 1min intervals in subjects with decompensated (red) and compensated cohort (blue). Each line represents a subject’s sweat values over the follow up period. (C and D) The mean sweat CRP and IL6 levels for the cohort of decompensated (red) and compensated (blue) over the follow up period. Abbreviations: CRP: c-reactive protein; IL6: interleukin-6; TNF: tumor necrosis factor alpha;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/3d1c6deb49e135b18dcd0383.png"},{"id":69381539,"identity":"3ed301c2-4360-43f1-beb5-ea31f4534774","added_by":"auto","created_at":"2024-11-19 19:05:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-D\u003c/strong\u003e: Sweat biomarker temporal plots in inpatients with cirrhosis based on antibiotic exposure. Fourteen subjects receive antibiotics and eight did not. (A and B) Sweat CRP and IL6 levels are plotted at 1min intervals in subjects receiving antibiotics (red) and non-antibiotic cohort (blue). Each line represents a subject’s sweat values over the follow up period. (C and D) The mean sweat CRP and IL6 levels for the cohort of subjects with antibiotic (red) and non-antibiotic cohort (blue). over the follow up period.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/c4c8e726b9f9770ee128e95d.png"},{"id":69381841,"identity":"d7a29bc5-302a-4fd9-82dd-db405574cbab","added_by":"auto","created_at":"2024-11-19 19:13:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA-D:\u003c/strong\u003e Sweat biomarker temporal plots in subjects based on transplant-free survival. (A and B) Sweat CRP and IL6 levels are plotted at 1min intervals in subjects with non-survival (red) and survival cohort (blue). Each line represents a subject’s sweat values over the follow up period. (C and D) The mean sweat CRP and IL6 levels for the cohort of non-survival (red) and survival (blue) over the follow up period.\u003c/p\u003e","description":"","filename":"7AD.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/fdbc986827733d955cd2d225.png"},{"id":69381840,"identity":"5299e992-7189-4721-9e21-dd0fc6cac623","added_by":"auto","created_at":"2024-11-19 19:13:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7\u003c/strong\u003e: Hedge’s G analysis of the effect size of the sweat biomarker expression over time segmented by the time of the day i.e., Morning, Evening, and Night and by cohort i.e. Control, Inpatients, and Outpatients for CRP (A), TNF⍺ (B), and IL6 (C). A relatively small or no-circadian effect is observed in Control cohort across all sweat biomarkers, while a significant effect is observed for outpatients for CRP and IL6 expression in sweat. Inpatients also show a circadian expression in sweat CRP and IL6 albeit to a much smaller effect than outpatients. Sweat TNF⍺ expression across all patient cohorts was smaller than that observed with sweat CRP and IL6 expressions respectively.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/3b930e6bc382716d4b2bc9ba.png"},{"id":72640580,"identity":"99cc98c3-2068-465a-bf80-0ca08127d2b7","added_by":"auto","created_at":"2024-12-30 16:07:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2391483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5146199/v1/82944a91-8c8a-4a5c-a3da-64296ba355ac.pdf"}],"financialInterests":"Competing interest reported. SM and SP reports a significant interest in EnLiSense LLC, a company\nthat may have a commercial interest in the results of this research and technology, no other COI for other authors","formattedTitle":"A Novel Sweat Sensor Detects Specific Inflammatory Circadian Patterns in Inpatients and Outpatients with Cirrhosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cirrhosis is the end stage of chronic liver injury from conditions such as alcohol use disorder, viral hepatitis, and metabolic dysfunction, which is a major cause of morbidity and mortality worldwide\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Cirrhosis consists of a subclinical \u0026ldquo;compensated\u0026rdquo; stage, where liver function is largely preserved, then followed by a more advanced \u0026ldquo;decompensated\u0026rdquo; stage characterized by clinical manifestations such as infections, hepatic encephalopathy (HE), variceal bleeding, and ascites\u0026mdash;resulting in hospitalizations and death\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSystemic inflammation represents the major pathophysiological phenomenon in the progression of cirrhosis from the compensated to decompensated state\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Translocation of bacterial components known as pathogen associated molecular patterns (PAMPs) from the intestinal lumen into the portal circulation leads to chronic overactivation of the innate immune system by stimulating production of pro-inflammatory cytokines such as interleukin-6 (IL6), tumor necrosis factor alpha (TNFα), interferons, and others.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These cytokines play a key role in the host response to infections, but overstimulation may lead to further organ damage and secondary infections via an anti-inflammatory compensatory response and immune exhaustion.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e C-reactive protein (CRP), an acute phase reactant, is elevated in the serum of patients with cirrhosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Increased serum levels of tumor necrosis factor alpha (TNFα) and interleukin-6 (IL6) are also found in cirrhosis\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, numerous studies have linked inflammatory markers to poor outcomes in decompensated cirrhosis and development of multi-organ failure, termed acute on chronic liver failure (ACLF)\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmerging evidence links systemic inflammation and sleep disturbance in the general population and sleep issues are quite common in people with cirrhosis\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Systemic inflammation worsens neuropsychiatric test results and severity of HE episodes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, significant sleep disturbances have been described in covert or minimal HE patients\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Therefore, changes in sleep architecture may be of clinical significance and portend worsening of underlying systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the research and advances in the systemic inflammation theory of cirrhosis progression, current clinical practice does not involve measuring cytokines and clinical guidelines do not support obtaining biomarkers to make clinical decisions\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. First, these serum tests are invasive. Second, it is unclear of the optimal time(s) and impact of cirrhosis inflammatory biomarkers on circadian variation. Third, interpretation can be a challenge, and cut-offs may vary depending on the reference lab. Thus, current standard of care involves obtaining cultures to rule out infection, which often take several days to result and can lead to delays in diagnosis and treatment.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUse of noninvasive sensors that can detect cytokines at more frequent intervals could shed greater light into the pathophysiological basis of inflammation in subclinical cirrhosis and earlier identification high-risk hospitalized patients\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A novel, Sweat AWARE perspiration-based biosensing platform that non-invasively detect inflammation levels and changes over time, would serve as an important interface between the patient and provider to improve prediction of outcomes by linking these with patient reported-outcomes (PROs). The AWARE sweat sensor has been shown to track systemic inflammation markers in sweat among patients with other gastrointestinal disorders such as inflammatory bowel disease, but its application in cirrhosis is unclear\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The primary aim of this study was to characterize common cytokines in sweat such as TNFα, IL6, and CRP\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We also hypothesized that the AWARE sweat sensor would delineate diurnal patterns among subjects with cirrhosis due to the continuous measurement of cytokines when compared with healthy controls, these sweat cytokines signatures may vary based on inpatient or outpatient clinical status and would be linked with cognitive testing and outcomes in these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe prospectively enrolled healthy controls, outpatients with cirrhosis, and inpatients with cirrhosis. Subjects were enrolled from the University of Texas at Dallas and Richmond VA Medical Center. All subjects needed to be \u0026gt;\u0026thinsp;18 years of age and able to provide informed consent.\u003c/p\u003e \u003cp\u003eHealthy controls were recruited from the community and were free of chronic diseases and were not on prescription medications. For the two cirrhosis groups, cirrhosis was diagnosed by either biopsy, imaging, transient elastography, presence of varices and/or platelet count\u0026thinsp;\u0026lt;\u0026thinsp;150,000 and AST/ALT\u0026thinsp;\u0026gt;\u0026thinsp;1 in chronic liver disease patients, or those with frank decompensation [ascites, hepatic encephalopathy (HE), hepato-pulmonary syndrome, jaundice, variceal bleeding]. We excluded those with an unclear diagnosis of cirrhosis, with concomitant IBD, and those on immunosuppressive therapy. We only included subjects who could come in daily or be available daily for 3 days.\u003c/p\u003e \u003cp\u003eWe recorded demographics, disease severity, course and complications, and concomitant medications at baseline and at each study visit. After consenting, we analyzed routine daily labs (basic metabolic and hepatic panel [BMP], complete blood count [CBC]), serum inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL6], and tumor necrosis factor alpha [TNFα]) in those with cirrhosis. Patients with cirrhosis were also administered the Sickness Impact Profile (SIP), a generic quality of life (QOL) instrument that inquires about daily function related to health over the last 24 hours on the day of enrollment\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. SIP consists of physical and psychosocial domains and a high SIP indicates poor QOL. Both SIP and blood draw were in the mornings. The sensor was then fitted to their arm, and subjects added in a de-identified manner in the study iPad. Sensors were replaced every 24 hours, and their capture of the data was checked before reloading.\u003c/p\u003e \u003cp\u003eDuring the study period if the inpatients were considered stable for discharge, the study was continued as outpatients with daily return till day 3 post-enrollment. We did not withdraw those who developed infections or require antibiotics during the study period. Subjects who became confused during the study continued to have clinical data recorded but were withdrawn from active study participation. Subjects were followed for up to one year to calculate transplant-free survival i.e. percentage of subjects who did not die or undergo liver transplantation.\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEthics declaration\u003c/span\u003e: Healthy control human subject studies were approved by the Institutional Review Board of the University of Texas at Dallas (IRB number UTD IRB 19\u0026ndash;146) and Richmond VA Medical Center for patients with cirrhosis (IRB number 1649873 mIRB 02701 BAJAJ0028). All subjects signed informed consent.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSweat AWARE Device\u003c/span\u003e: This comprises a replaceable sweat-sensing strip tailored for specific target biomarkers, affixed to a wearable electronic reader (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This reader translates the sensor's impedance into a calibrated concentration of measured biomarker levels in sweat. The sensor response was measured through non-faradaic electrochemical impedance spectroscopy (EIS), recording the resulting impedance at a frequency range of 100 Hz to 1 KHz using a low sinusoidal input voltage of 1-100 mV. The sensor electrode underwent a functionalization process involving the application of a thiol cross-linker. This cross-linker was specifically chosen for its molecular properties. The opposite end of the cross-linker was meticulously bonded with a concentration of monoclonal capture antibodies, each tailored for the biomarkers CRP, IL6, and TNFα. This careful selection of monoclonal antibodies was deliberate, aiming to achieve a high degree of specificity in detecting the target biomarkers. We have previously outlined the fabrication process for both the Sweat AWARE device and sweat sensor. The sensor fabrication process has been adapted from Munje et al., and Jagannath et al., and has been described in detail previously\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Measurements of sweat CRP, IL6, and TNFα were obtained on the body at one-minute intervals throughout each 24-hour period. These recorded measurements were then averaged for every consecutive 2-hour period, commencing from the start of each collection period. Following this, the averaged values were compared to serum levels to analyze the correlation between sweat and serum concentrations. Temporal graphs of the actual and average levels of CRP, IL6, and TNFα were generated for all subjects, with categorization based on different subject groups.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Analysis\u003c/span\u003e: GraphPad Prism version 10.2.1 software was utilized to conduct analyses and generate figures. P-values were computed based on the average data of sweat and serum across all days. The Mann-Whitney test and one-tailed analysis were employed. Sweat value in this analysis was calculated by averaging sweat CRP, IL6, and TNFα measurements collected every 1-minute. Additionally, a heatmap was generated using the correlation matrix function within the GraphPad analysis tool.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCircadian patterns\u003c/span\u003e: An effect size in statistics is a sample-based estimate of the number that quantifies the strength of the association between two variables in a population. It can be used to describe the value of a parameter for a fictitious population, the value of a statistic derived from a sample of data, or the equation that operationalizes the relationship between parameters and statistics and the effect size value. The correlation between two variables, the regression coefficient in a regression, the mean difference, or the likelihood that a certain event (like a heart attack) would occur are a few examples of effect sizes. In addition to serving as a supplement to statistical hypothesis testing, effect sizes are crucial for power analyses, sample size planning, and meta-analyses. The group of techniques for analyzing data related to effect sizes is known as estimate statistics. Hedge's G is comparable to Glass's G and Cohen's D statistics. Usually, an experimental dataset and a control dataset are compared using these statistics. We adopted in here the Hedge\u0026rsquo;s G technique to assess the circadian characteristics of the sweat biomarker expression by calculating the impact magnitude of the mean difference across the 24-hour time period i.e., Morning (6am to 2pm), Evening (2pm to 10pm), and Night (10pm to 6am) and by subject cohorts i.e., Control, Inpatient, and Outpatient for each of the sweat biomarkers measured. The 24-hour time period was divided into three 8-hour periods by taking into consideration the relative half-life of the inflammatory biomarkers for assessing effect size. The Hedge\u0026rsquo;s G was calculated was calculated by comparing 2 time periods for each of the subject cohorts and using the formula as below\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e: g=(y1-y2)/sp, where, y1 and y2 are the standard means of the 2 samples and sp is the pooled standard deviation given by: sp= \u0026radic;(((n1-1) 〖s1〗^2+(n2-1) 〖s2〗^2)/(n1\u0026thinsp;+\u0026thinsp;n1-2)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 12 controls (no-cirrhosis) and 32 veterans with cirrhosis (22 inpatients [IP] and 10 outpatients [OP]) were included in the study. Analyses were performed between groups at baseline as well as within groups over time. The median age of OP with cirrhosis was 64 years (range 42-74) and the median age of IP with cirrhosis was 65 years (range 35-77). Most cirrhosis subjects were male (100% outpatient and 91% outpatient) and half of each cirrhosis cohort identified as non-white.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline Characteristics in Study Population of healthy controls (no-cirrhosis) and cirrhosis subjects sweat biomarkers grouped by time of day (Morning vs. Evening vs. Night). Blood samples were not collected in healthy controls. Data are presented as mean\u0026plusmn;standard deviation unless otherwise noted. Abbreviations: CRP: c-reactive protein; I6: interleukin-6; TNF\u0026alpha;: tumor necrosis factor alpha.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl, No-Cirrhosis (n=12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutpatient Cirrhosis (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInpatient Cirrhosis (n=22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value between Cirrhosis groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eAge (years; median; range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e44.5 (40-47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e64; 42-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e65; 35-77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSex (% male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eRace (% white)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 618px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrollment Serum and Sweat Biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSerum CRP (mean, mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.48 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.60 \u0026plusmn; 2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat CRP. Morning (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1835.50 \u0026plusmn; 1492.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1685.33 \u0026plusmn; 1062.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2672.72 \u0026plusmn; 1363.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat CRP. Evening (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1423.99 \u0026plusmn; 1113.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2011.91 \u0026plusmn; 1396.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1300.98 \u0026plusmn; 479.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat CRP. Night (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1858.20 \u0026plusmn; 1673.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4063.02 \u0026plusmn; 1458.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3010.48 \u0026plusmn; 1522.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSerum IL6 (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.13 \u0026plusmn; 1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e24.5 \u0026plusmn; 18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat IL6, Morning (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.87 \u0026plusmn; 2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.99 \u0026plusmn; 3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e7.05 \u0026plusmn; 4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat IL6, Evening (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.33 \u0026plusmn; 1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.72 \u0026plusmn; 3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e5.08 \u0026plusmn; 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat IL6, Night (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.77 \u0026plusmn; 2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10.16 \u0026plusmn; 5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e7.17 \u0026plusmn; 3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSerum TNF\u0026alpha; (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.22 \u0026plusmn; 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.02 \u0026plusmn; 8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat TNF\u0026alpha;, Morning (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.92 \u0026plusmn; 1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.01 \u0026plusmn; 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.38 \u0026plusmn; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat TNF\u0026alpha;, Evening (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.82 \u0026plusmn; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.00 \u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.27 \u0026plusmn; 1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eSweat TNF\u0026alpha;, Night (mean, pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.71 \u0026plusmn; 1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.64 \u0026plusmn; 1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.38 \u0026plusmn; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll inpatients were admitted for cirrhosis-related complications with a mean length of stay 5.5 \u0026plusmn; 0.81 days. Sixteen of the subjects had alcohol-related liver disease, nine with viral hepatitis, and seven with metabolic-dysfunction as the primary etiology of cirrhosis. Fourteen IP were given antibiotics and seven had documented infections: three urinary tract infections, one bacteremia, one Helicobacter pylori, one \u003cem\u003eS. aureus\u003c/em\u003e hand wound infection, and one Coronavirus disease-2019 infection. Four inpatients had overt HE on admission. SIP was administered to 9 of 10 outpatients and 15 of 22 inpatients\u0026mdash;SIP total, physical, and psychosocial scores were all higher in the inpatient cohort. Liver function, as measured by MELD-Na, was significantly worse on each day of the study in the inpatient group. The transplant-free survival rate after one year was 0.56 and significantly lower in the inpatient group (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Inpatients with cirrhosis have worse liver function and functional status than outpatients. Data are presented as means\u0026plusmn;SD unless otherwise noted. The SIP scores are a quality-of-life summary assessments and higher scores indicated lower quality of life. The MELD-Na score ranges from 6-40 with higher scores associated with higher risks of short-term mortality. Abbreviations: HE: hepatic encephalopathy; SIP: sickness impact profile; MELD-Na: Model for End Stage Liver Disease Sodium.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutpatient Cirrhosis (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInpatient Cirrhosis (n=22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eTotal SIP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e14.8\u0026plusmn;8.3 (n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e32.2\u0026plusmn;16.6 (n=15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003ePhysical SIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e12.6\u0026plusmn;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e34.5\u0026plusmn;20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003ePsychosocial SIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e13.4\u0026plusmn;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e23.3\u0026plusmn;21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eDay 0 MELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e9.4\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e22.1\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eDay 1 MELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e9.7\u0026plusmn;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e23.4\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eDay 2 MELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e9.1\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e26.6\u0026plusmn;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eTransplant-free survival (1 year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e9 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e9 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) of sweat and serum biomarkers was examined across different cirrhosis groups (Figure 2A-F). Subjects in the inpatient group exhibited higher levels of each biomarker, regardless of the fluid source. The overall R\u0026sup2; for CRP was 0.392 (Figure 2A), while the R\u0026sup2; for CRP for outpatient group improved to 0.666 (Figure 2D). Subjects in the outpatient group had serum CRP expression levels below 15 mg/mL, while subjects in the IP group had serum CRP expression levels up to 100 mg/mL. Higher serum CRP values did not correspond to a significant increase in sweat CRP levels. A similar pattern of differences in the coefficient of determination between serum and sweat levels was observed with TNF-a (Figure 2B and 2E) and IL6 (Figure 2C and 2F) biomarkers. These differences in the coefficient of determination between serum and sweat levels could be attributed to the optimization of the dynamic range of the sweat sensor assay performance for each of the biomarkers respectively. The Sweat AWARE device is designed for enabling patient centered clinical decision support system and optimized for use as a remote patient monitoring system. The current dynamic range of the sweat sensor assay performance matching with the inflammation levels of outpatient subjects would make the device optimal for primarily for use in a remote outpatient monitoring setup.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eClinical Outcomes and Sweat Analysis\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen sweat biomarker averages were compared among clinical status and controls, all sweat CRP, IL6, and TNF\u0026alpha; control levels were lower compared to outpatient and inpatient group. (Figure 3A-C). The measurements of CRP, IL6, and TNF\u0026alpha; via the Sweat AWARE device were assessed to determine its utility in classifying inpatient and outpatient individuals versus control. The Sweat AWARE device effectively distinguished the healthy control group from outpatient cirrhosis based on the measured sweat biomarkers.\u003c/p\u003e\n\u003cp\u003eCorrelation heatmap analysis of sweat and serum biomarker levels was examined on these groups (Figure 3D-F). Sweat levels were averaged by time of the day, in three parts as, Morning sweat levels between 6 am \u0026ndash; 2 pm; Evening sweat levels between 2 pm \u0026ndash; 10 pm; and Night sweat levels between 10 pm \u0026ndash; 6 am. Sickness Impact Profile (SIP) scores showed both positive and negative correlations with inflammatory markers (CRP, IL6, TNF\u0026alpha;) in sweat based on the time of the day. Moderately positive correlations were found for inflammatory marker levels in serum to sweat inflammatory levels for CRP and IL6 during morning time periods between 6 am \u0026ndash; 2 pm, while a slight negative correlation was found for serum to sweat TNF\u0026alpha; levels. In contrast, moderately negative correlations were found between serum and sweat inflammatory levels for all 3 biomarkers for the Evening time periods between 2 pm \u0026ndash; 10 pm and for the Nighttime periods between 10 pm \u0026ndash; 6 am. It should be noted that blood sampling in the study and SIP scores were all done during the morning time period.\u003c/p\u003e\n\u003cp\u003eCorrelation heatmap analysis suggested a positive association with morning time period sweat CRP and IL6 and SIP-physical quality of life. Serum CRP had moderate correlations with SIP physical (0.51), serum IL6 (0.56), and serum TNF\u0026alpha; (0.47). Morning time period sweat CRP had moderate correlations with SIP physical (0.32), serum IL6 (0.58), serum CRP (0.35), and serum TNF\u0026alpha; (0.28). Serum IL6 had moderate to strong correlations with SIP physical (0.56), serum CRP (0.55), and serum TNF\u0026alpha; (0.65). Morning time period sweat IL6 had moderate correlations with SIP physical (0.52), serum CRP (0.22) and serum IL6 (0.44). Serum TNF\u0026alpha; had moderate correlations with SIP physical (0.47), serum CRP (0.51), and serum IL6 (0.65). Morning time period sweat TNF\u0026alpha; showed very low correlations with all the other variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, Inflammatory markers (CRP, IL-6, TNF\u0026alpha;) in serum generally show moderate correlations with the SIP physical subscale, suggesting a potential link between inflammation and physical symptoms. Inflammatory markers CRP and IL-6 in morning sweat had generally moderate correlations with other variables, indicating that changes in these markers may reflect the SIP measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOutpatients with cirrhosis when analyzed independently were consistently found to have sweat inflammation levels starting to elevate during the evening periods and peaking towards the early night periods. The sweat inflammation levels start to fall much later in the night periods and early morning periods. Moderately positive correlations were found for inflammatory marker levels in sweat to SIP scores for the evening time periods between 2 pm \u0026ndash; 10 pm and moderately negative correlations for the morning time periods between 6 am \u0026ndash; 2 pm. The correlations were mixed in the night periods between 10 pm \u0026ndash; 6 am. These trends can be clearly seen in the temporal plots of the sweat inflammatory biomarkers (Figure 4A-D).\u003c/p\u003e\n\u003cp\u003eAmong compensated versus decompensated cirrhosis, the average decompensated sweat biomarker values measured during the study period were a slightly higher in both CRP and IL6 (Figures 5A-D). In subjects who received antibiotics, the sweat CRP and IL6 levels were lower than subjects who did not receive antibiotics, where the area of curve of antibiotic of CRP is 4384945 and non-antibiotic is 4557502 (Figure 6A-D). In subjects who died or received a liver transplant, the admission sweat CRP and IL-6 levels were elevated compared with subjects who survived (Figure 7A-D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCircadian characteristics of sweat biomarker expression:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny physiological or behavioral characteristic exhibit circadian rhythm and quantifying this rhythm has inherent value.\u003csup\u003e36\u003c/sup\u003e Inflammation is elevated due to chronic conditions and the relative changes in the inflammation levels over time are due to the illness states and the physiological state of the subject. Diurnal refers to a characteristic elevation of biomarker expression mainly in the day time while nocturnal refers to a characteristic elevation of biomarker expression mainly during night time.\u003csup\u003e37\u003c/sup\u003e Hedge\u0026rsquo;s G was used to determine the circadian expression of the sweat inflammatory biomarker levels over time. The Hedge\u0026apos;s\u003c/p\u003e\n\u003cp\u003eG statistic expresses the difference of the means in units of the pooled standard deviation. It is typically used in the context where one of the samples is a control sample. That is, we are interested in the effect size of the sample being analyzed relative to a control sample. A Hedge\u0026rsquo;s G value of 0.2 or lower (small effect); 0.2 \u0026ndash; 0.5 (medium effect); and 0.8 or greater (large effect). In this case, we use this Hedge\u0026rsquo;s G statistic to compare the means of the sweat inflammatory biomarker levels averaged by time of the day, in three parts as, Morning sweat levels between 6 am \u0026ndash; 2 pm; Evening sweat levels between 2 pm \u0026ndash; 10 pm; and Night sweat levels between 10 pm \u0026ndash; 6 am and comparing between subjects with cirrhosis i.e., inpatients and outpatients who are inflamed due to cirrhosis and healthy control subjects with no inflammation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Hedge\u0026rsquo;s G for sweat CRP and IL6 in Figure 8A and 8C shows that the patients with cirrhosis have higher Hedge\u0026rsquo;s G value at night when compared to morning or evening periods hence, the significance of the biomarker is higher in the night. For TNFa the Hedge\u0026rsquo;s G values are all below 0.5 indicating that the effect size is low to medium as compared to CRP and IL6 which had Hedge\u0026rsquo;s G values above 0.8 indicating a larger effects size in CRP and IL6 expressions in sweat. From the Hedge\u0026rsquo;s G values for the three sweat biomarkers, it can be concluded that CRP and IL6 show a significant effect size between evening and night periods in cirrhosis subjects compared to morning periods, while in the same significance in effect size is not observed in control subjects.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated a novel sweat sensor device can safely be applied to outpatients and inpatients with cirrhosis and generate discriminatory data when compared to healthy controls. We showed that serum and sweat inflammatory markers are correlated in patients with cirrhosis. We found that decompensated patients had dampened diurnal variation of these cytokine levels compared to compensated patients and healthy controls. Sweat-based inflammatory biomarkers on average were higher in infected patients and in those who had a poor transplant-free survival.\u003c/p\u003e \u003cp\u003eA key attribute of this study is the demonstration that continuous sweat monitoring illustrates important clinical manifestations of a chronic inflammatory disease state, such as cirrhosis. For instance, sleep cycle disturbances were first described in decompensated cirrhosis in the 1950s and complaints such as difficulty falling asleep, insomnia, and excessive daytime sleepiness are quite common in the cirrhosis population.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e However, the pathophysiology is complex and incompletely understood with various internal and external factors playing a role. For instance, the release of melatonin is disrupted in cirrhosis, which leads to higher rates of insomnia and excessive daytime sleepiness and naps.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Furthermore, co-morbid conditions such as sleep apnea also play a role in cirrhosis patients\u0026rsquo; sleep disruption.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Emerging data show circadian disorders are linked with chronic inflammation.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Our study provides intriguing evidence that continuous noninvasive monitoring of inflammatory biomarkers may predict outcomes \u0026ndash; as patients with cirrhosis had higher evening and nocturnal levels of sweat CRP and IL6 when compared with healthy controls.\u003c/p\u003e \u003cp\u003eSerum IL-6 is associated with poor outcomes in patients with cirrhosis using blood levels in prior studies.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, those studies have largely focused on blood levels at discrete timepoints without focusing on variations in levels over time. Cirrhosis is a disease of chronic inflammation requiring frequent hospitalizations and numerous studies have looked at serum biomarkers as predictors of inflammation and decompensation\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. We chose sweat CRP, IL6, and TNFα as the target cytokines in this study based on previous research of serum studies linking these biomarkers to important clinical outcomes. Perdigoto et al\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e showed that serum CRP can predict infections in a prospective cohort of inpatients with cirrhosis. A recent meta-analysis documented serum IL6 may discriminant bacterial infections and predict hepatic encephalopathy in subjects with cirrhosis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e TNFα also is elevated in decompensated cirrhosis and correlates with severity of ACLF, although a clinical trial of anti-TNFα therapy did not show a benefit in patients with ACLF due to increased secondary infections.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e However, the unique patterns of cytokines throughout the day in decompensated and more advanced patients compared to healthy people and outpatients could be important in monitoring patients over time than one-time blood draws. This follows the circadian rhythms in these cytokine levels and extends them into compensated and decompensated cirrhosis patients\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to survival and infections, investigating quality-of-life impairment and inflammation is critical in cirrhosis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. SIP inquires about QOL over the last 24 hours and was administered in the morning, as was the blood draw. As expected more advanced patients (inpatients) had a worse QOL compared to outpatients. Interestingly, while there was a moderate correlation with morning blood inflammatory markers and SIP, this pattern was distinct across time periods in the sweat. Morning sweat inflammatory markers were more positively correlated with SIP but not the evening and night-time levels. This circadian variation is important in case these questionnaires are administered during different times of the day.\u003c/p\u003e \u003cp\u003ePrevious sweat sensor studies used healthy controls and to identify potential chronic inflammatory markers and showed correlation with serum and sweat CRP and IL-6 as distinguishing factors in patients with inflammatory bowel disease (IBD)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The Sweat AWARE device showed direct correlation of serum and sweat markers among inpatients with IBD-related complications. Early data also suggest a pattern in sweat biomarkers among active IBD versus healthy controls.\u003c/p\u003e \u003cp\u003eThis study had several limitations. The sample size was small as this was a pilot study, thus larger and more heterogenous cohorts are needed to further define sweat inflammatory patterns. We were unable to analyze sweat data between compensated and decompensated outpatients based on the small sample size. Longer clinical outcome data are also needed, although we did show the SIP scores were worse among inpatients and correlated with sweat cytokines and elevated IL6 was associated with reduced TFS. The sweat sensor data were collected for a short interval (up to 3 days) and longer monitoring may identify distinct patterns in the natural history of hospitalized patients. While the sweat sensor was noninvasive and easy to apply, further modification of the sensor to generate point-of-care results (i.e., wider dynamic range) would be useful to obtain clinical applicability as patients with cirrhosis express high levels of inflammation as observed by the serum levels analyzed in this study. Given the large amount of data produced by the AWARE sensor, future studies should apply deep-learning models for analysis. Finally, serum data were drawn daily as opposed to continuous monitoring with the sweat sensor so the correlations may be over or underestimated depending on timing of the lab draw. We attempted to correct this discrepancy by trying to average sweat data over 2 hours based on the half-life of each biomarker.\u003c/p\u003e \u003cp\u003eIn conclusion, a novel, noninvasive device detects inflammatory cytokines in the sweat of patients with cirrhosis. The sweat biomarkers correlated with serum values and distinct sweat patterns are seen in outpatients and inpatients with cirrhosis. Sweat Inflammation biomarker levels are elevated in those with cirrhosis and follow a circadian behavior and there as differences in expression between outpatients and inpatients. Such an analysis would not have been possible to assess with serum collections. However, this is a limited sample size and these need to be validated in a larger cohort. Specifically, sweat cytokines are elevated in hospitalized patients who are at higher risk for infections. Elevated sweat IL-6 may be an important predictor of mortality and larger studies are indicated to further characterize inflammatory sweat biomarkers in cirrhosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eSM and SP reports a significant interest in EnLiSense LLC, a companythat may have a commercial interest in the results of this research and technology, no other COI for other authors\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e Partly supported by VA Merit Review 2I01CX001076 and I01CX002472 to JSB and a Richmond Institute for Veterans Research Pilot Award to BCD.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJSB conceptualized the study with SP, JSB, SS, SP, AR, SR, BCD and AF were involved in study conduct, BCD and JSB obtained funding, KL and SM provided technical support and all authors were involved in analysis and drafting of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to IRB restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsrani, S.K., Devarbhavi, H., Eaton, J. \u0026amp; Kamath, P.S. Burden of liver diseases in the world. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 151-171 (2019).\u003c/li\u003e\n\u003cli\u003eTerrault, N.A. et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 1560-1599 (2018).\u003c/li\u003e\n\u003cli\u003eBhattacharya, D., Aronsohn, A., Price, J., Lo Re, V. \u0026amp; Panel, A.-I.H.G. Hepatitis C Guidance 2023 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection. \u003cem\u003eClin Infect Dis\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eCrabb, D.W., Im, G.Y., Szabo, G., Mellinger, J.L. \u0026amp; Lucey, M.R. Diagnosis and Treatment of Alcohol-Associated Liver Diseases: 2019 Practice Guidance From the American Association for the Study of Liver Diseases. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 306-333 (2020).\u003c/li\u003e\n\u003cli\u003eRinella, M.E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1966-1986 (2023).\u003c/li\u003e\n\u003cli\u003eBajaj, J.S., Kamath, P.S. \u0026amp; Reddy, K.R. The Evolving Challenge of Infections in Cirrhosis. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e384\u003c/strong\u003e, 2317-2330 (2021).\u003c/li\u003e\n\u003cli\u003eVilstrup, H. et al. Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 715-735 (2014).\u003c/li\u003e\n\u003cli\u003eBiggins, S.W. et al. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 1014-1048 (2021).\u003c/li\u003e\n\u003cli\u003eShawcross, D.L., Davies, N.A., Williams, R. \u0026amp; Jalan, R. Systemic inflammatory response exacerbates the neuropsychological effects of induced hyperammonemia in cirrhosis. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 247-254 (2004).\u003c/li\u003e\n\u003cli\u003eShawcross, D.L. et al. Infection and systemic inflammation, not ammonia, are associated with Grade 3/4 hepatic encephalopathy, but not mortality in cirrhosis. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 640-649 (2011).\u003c/li\u003e\n\u003cli\u003eArroyo, V. et al. The systemic inflammation hypothesis: Towards a new paradigm of acute decompensation and multiorgan failure in cirrhosis. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 670-685 (2021).\u003c/li\u003e\n\u003cli\u003eBernardi, M., Moreau, R., Angeli, P., Schnabl, B. \u0026amp; Arroyo, V. Mechanisms of decompensation and organ failure in cirrhosis: From peripheral arterial vasodilation to systemic inflammation hypothesis. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1272-1284 (2015).\u003c/li\u003e\n\u003cli\u003eMoreau, R. et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e, 1426-1437, 1437 e1421-1429 (2013).\u003c/li\u003e\n\u003cli\u003eTrebicka, J. et al. The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 842-854 (2020).\u003c/li\u003e\n\u003cli\u003eTandon, P. \u0026amp; Garcia-Tsao, G. Bacterial infections, sepsis, and multiorgan failure in cirrhosis. \u003cem\u003eSemin Liver Dis\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 26-42 (2008).\u003c/li\u003e\n\u003cli\u003eSriskandan, S. \u0026amp; Altmann, D.M. The immunology of sepsis. \u003cem\u003eJ Pathol\u003c/em\u003e \u003cstrong\u003e214\u003c/strong\u003e, 211-223 (2008).\u003c/li\u003e\n\u003cli\u003eTilg, H. et al. Serum levels of cytokines in chronic liver diseases. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 264-274 (1992).\u003c/li\u003e\n\u003cli\u003eAlbillos, A. et al. Tumour necrosis factor-alpha expression by activated monocytes and altered T-cell homeostasis in ascitic alcoholic cirrhosis: amelioration with norfloxacin. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 624-631 (2004).\u003c/li\u003e\n\u003cli\u003eLee, F.Y. et al. Plasma interleukin-6 levels in patients with cirrhosis. Relationship to endotoxemia, tumor necrosis factor-alpha, and hyperdynamic circulation. \u003cem\u003eScand J Gastroenterol\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 500-505 (1996).\u003c/li\u003e\n\u003cli\u003eZhang, W., Yue, B., Wang, G.Q. \u0026amp; Lu, S.L. Serum and ascites levels of macrophage migration inhibitory factor, TNF-alpha and IL-6 in patients with chronic virus hepatitis B and hepatitis cirrhosis. \u003cem\u003eHepatobiliary Pancreat Dis Int\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 577-580 (2002).\u003c/li\u003e\n\u003cli\u003eClaria, J. et al. Systemic inflammation in decompensated cirrhosis: Characterization and role in acute-on-chronic liver failure. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 1249-1264 (2016).\u003c/li\u003e\n\u003cli\u003eAlbillos, A. et al. Cirrhosis-associated immune dysfunction. \u003cem\u003eNat Rev Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 112-134 (2022).\u003c/li\u003e\n\u003cli\u003eButterworth, R.F. The liver-brain axis in liver failure: neuroinflammation and encephalopathy. \u003cem\u003eNat Rev Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 522-528 (2013).\u003c/li\u003e\n\u003cli\u003eCordoba, J. et al. High prevalence of sleep disturbance in cirrhosis. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 339-345 (1998).\u003c/li\u003e\n\u003cli\u003eIrwin, M.R., Olmstead, R. \u0026amp; Carroll, J.E. Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. \u003cem\u003eBiol Psychiatry\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 40-52 (2016).\u003c/li\u003e\n\u003cli\u003eMontagnese, S., Middleton, B., Skene, D.J. \u0026amp; Morgan, M.Y. Night-time sleep disturbance does not correlate with neuropsychiatric impairment in patients with cirrhosis. \u003cem\u003eLiver Int\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1372-1382 (2009).\u003c/li\u003e\n\u003cli\u003eBajaj, J.S. et al. Disruption of sleep architecture in minimal hepatic encephalopathy and ghrelin secretion. \u003cem\u003eAliment Pharmacol Ther\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 103-105 (2011).\u003c/li\u003e\n\u003cli\u003eMontagnese, S. et al. Sleep-wake abnormalities in patients with cirrhosis. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 705-712 (2014).\u003c/li\u003e\n\u003cli\u003eBajaj, J.S. et al. Acute-on-Chronic Liver Failure Clinical Guidelines. \u003cem\u003eAm J Gastroenterol\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 225-252 (2022).\u003c/li\u003e\n\u003cli\u003eMunje, R.D., Muthukumar, S., Jagannath, B. \u0026amp; Prasad, S. A new paradigm in sweat based wearable diagnostics biosensors using Room Temperature Ionic Liquids (RTILs). \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1950 (2017).\u003c/li\u003e\n\u003cli\u003eJagannath, B. et al. A Sweat-based Wearable Enabling Technology for Real-time Monitoring of IL-1beta and CRP as Potential Markers for Inflammatory Bowel Disease. \u003cem\u003eInflamm Bowel Dis\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 1533-1542 (2020).\u003c/li\u003e\n\u003cli\u003eLiu, C. et al. Cytokines: From Clinical Significance to Quantification. \u003cem\u003eAdv Sci (Weinh)\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e2004433 (2021).\u003c/li\u003e\n\u003cli\u003ePepys, M.B. \u0026amp; Hirschfield, G.M. C-reactive protein: a critical update. \u003cem\u003eJ Clin Invest\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 1805-1812 (2003).\u003c/li\u003e\n\u003cli\u003eBergner, M., Bobbitt, R.A., Carter, W.B. \u0026amp; Gilson, B.S. The Sickness Impact Profile: development and final revision of a health status measure. \u003cem\u003eMed Care\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 787-805 (1981).\u003c/li\u003e\n\u003cli\u003e, Vol. 2024 (\u003c/li\u003e\n\u003cli\u003eDijk, D.J. \u0026amp; Duffy, J.F. Novel Approaches for Assessing Circadian Rhythmicity in Humans: A Review. \u003cem\u003eJ Biol Rhythms\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 421-438 (2020).\u003c/li\u003e\n\u003cli\u003eWang, C., Lutes, L.K., Barnoud, C. \u0026amp; Scheiermann, C. The circadian immune system. \u003cem\u003eSci Immunol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, eabm2465 (2022).\u003c/li\u003e\n\u003cli\u003eSherlock, S., Summerskill, W.H., White, L.P. \u0026amp; Phear, E.A. Portal-systemic encephalopathy; neurological complications of liver disease. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e267\u003c/strong\u003e, 454-457 (1954).\u003c/li\u003e\n\u003cli\u003eMostacci, B. et al. Sleep disturbance and daytime sleepiness in patients with cirrhosis: a case control study. \u003cem\u003eNeurol Sci\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 237-240 (2008).\u003c/li\u003e\n\u003cli\u003eSteindl, P.E. et al. Disruption of the diurnal rhythm of plasma melatonin in cirrhosis. \u003cem\u003eAnn Intern Med\u003c/em\u003e \u003cstrong\u003e123\u003c/strong\u003e, 274-277 (1995).\u003c/li\u003e\n\u003cli\u003eMontagnese, S., Middleton, B., Mani, A.R., Skene, D.J. \u0026amp; Morgan, M.Y. On the origin and the consequences of circadian abnormalities in patients with cirrhosis. \u003cem\u003eAm J Gastroenterol\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 1773-1781 (2010).\u003c/li\u003e\n\u003cli\u003eBajaj, J.S. et al. Effects of obstructive sleep apnea on sleep quality, cognition, and driving performance in patients with cirrhosis. \u003cem\u003eClin Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 390-397 e391 (2015).\u003c/li\u003e\n\u003cli\u003eKronsten, V.T. \u0026amp; Shawcross, D.L. Clinical implications of inflammation in patients with cirrhosis. \u003cem\u003eAm J Gastroenterol\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003ePerdigoto, D.N., Figueiredo, P.N. \u0026amp; Tome, L.F. Clarifying the role of C-reactive protein as a bacterial infection predictor in decompensated cirrhosis. \u003cem\u003eEur J Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 645-651 (2018).\u003c/li\u003e\n\u003cli\u003eWu, Y., Wang, M., Zhu, Y. \u0026amp; Lin, S. Serum interleukin-6 in the diagnosis of bacterial infection in cirrhotic patients: A meta-analysis. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, e5127 (2016).\u003c/li\u003e\n\u003cli\u003eLabenz, C. et al. Raised serum Interleukin-6 identifies patients with liver cirrhosis at high risk for overt hepatic encephalopathy. \u003cem\u003eAliment Pharmacol Ther\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 1112-1119 (2019).\u003c/li\u003e\n\u003cli\u003eNaveau, S. et al. A double-blind randomized controlled trial of infliximab associated with prednisolone in acute alcoholic hepatitis. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1390-1397 (2004).\u003c/li\u003e\n\u003cli\u003eNakao, A. Temporal regulation of cytokines by the circadian clock. \u003cem\u003eJ Immunol Res\u003c/em\u003e \u003cstrong\u003e2014\u003c/strong\u003e, 614529 (2014).\u003c/li\u003e\n\u003cli\u003eMontagnese, S. \u0026amp; Bajaj, J.S. Impact of Hepatic Encephalopathy in Cirrhosis on Quality-of-Life Issues. \u003cem\u003eDrugs\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 11-16 (2019).\u003c/li\u003e\n\u003cli\u003eRobert P. Hirten, K.-C.L., Jessica Whang, Sarah Shahub, Nathan K.M. Churcher, Drew Helmus, Sriram Muthukumar, Bruce Sands, Shalini Prasad Longitudinal monitoring of IL-6 and CRP in inflammatory bowel disease using IBD-AWARE. \u003cem\u003eBiosensors and Bioelectronics: X\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5146199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5146199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCirrhosis is the end stage result of chronic liver injury and clinical events are driven by worsening of complex inflammatory pathways leading to frequent hospitalizations and death or need for liver transplantation. Inflammatory biomarkers such as c-reactive protein (CRP), tumor necrosis factor alpha (TNFα) and interleukin-6 (IL6) are typically elevated in serum of cirrhosis patients and associated with worse outcomes. These markers are not routinely checked due to the invasive nature of blood draws and difficulty in interpretation of a single measurement. Therefore, we designed a study to measure these biomarkers using a continuous monitor of passively expressed sweat in a well characterized cohort of subjects with cirrhosis. We enrolled 32 patients with cirrhosis and 12 controls. The AWARE sweat sensor was placed on each subject with the sensor staying in continuous contact with the skin and exchanged daily for 3 days. Serum lab draws to measure CRP, TNFα, IL6, and liver function were performed along with quality-of-life surveys and hepatic encephalopathy testing. We found that CRP, TNFα, and IL6 were correlated in sweat and serum among cirrhosis and controls. All three biomarkers in sweat and serum were elevated in inpatients compared with outpatients or controls. IL6, whether measured in sweat or serum, was associated with lower transplant-free survival. Continuous monitoring of sweat showed nocturnal elevations of CRP and IL6 when compared to healthy controls. Outpatients with cirrhosis were consistently found to have inflammation levels starting to elevate during the evening periods and peaking towards the early night periods. The levels start to fall much later in the night periods and early morning periods. These data suggest that further investigation of continuous measurement of sweat biomarkers in patients with cirrhosis is warranted.\u003c/p\u003e","manuscriptTitle":"A Novel Sweat Sensor Detects Specific Inflammatory Circadian Patterns in Inpatients and Outpatients with Cirrhosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 19:05:40","doi":"10.21203/rs.3.rs-5146199/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-15T18:53:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-13T14:13:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-08T18:56:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-07T09:17:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-02T12:53:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320041894612941959242132137703468912875","date":"2024-10-02T10:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183945785630273974360718098191888344284","date":"2024-10-02T09:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245758201519480891409393272035334701379","date":"2024-09-30T02:21:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256603995885452522724728997155832026319","date":"2024-09-29T07:07:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-27T08:35:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-26T00:26:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-25T11:28:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2024-09-24T14:59:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b21133db-9a9c-4d5d-890a-22ccfeedb54a","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38989215,"name":"Health sciences/Medical research/Translational research"},{"id":38989216,"name":"Health sciences/Diseases/Gastrointestinal diseases/Liver diseases/Liver cirrhosis"}],"tags":[],"updatedAt":"2024-12-30T16:00:17+00:00","versionOfRecord":{"articleIdentity":"rs-5146199","link":"https://doi.org/10.1038/s41746-024-01404-1","journal":{"identity":"npj-digital-medicine","isVorOnly":false,"title":"npj Digital Medicine"},"publishedOn":"2024-12-28 15:57:19","publishedOnDateReadable":"December 28th, 2024"},"versionCreatedAt":"2024-11-19 19:05:40","video":"","vorDoi":"10.1038/s41746-024-01404-1","vorDoiUrl":"https://doi.org/10.1038/s41746-024-01404-1","workflowStages":[]},"version":"v1","identity":"rs-5146199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5146199","identity":"rs-5146199","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}