Interplay between Skin Sympathetic Nerve Activity and Gut Microbiota, the Neurocardiac Axis in Acute Coronary Syndrome

Interplay between Skin Sympathetic Nerve Activity and Gut Microbiota, the Neurocardiac Axis in Acute Coronary Syndrome | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Interplay between Skin Sympathetic Nerve Activity and Gut Microbiota, the Neurocardiac Axis in Acute Coronary Syndrome View ORCID Profile Wei-Chung Tsai , Po Peng , Pei-Syuan Jhou , View ORCID Profile Yi-Chun Tsai , Wei-Wen Hung , View ORCID Profile Yi-Hsiung Lin , Shih-Jie Jhuo , View ORCID Profile Tien-Chi Huang , Li-Fang Chou , Tsung-Hsien Lin , Ho-Ming Su , Wen-Ter Lai , Chien-Hung Lee , Bin-Nan Wu , View ORCID Profile Shien-Fong Lin , Wei-Chun Hung doi: https://doi.org/10.1101/2025.05.06.652555 Wei-Chung Tsai 1 Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan 3 Faculty of Medicine, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan 4 Precision Sports Medicine and Health Promotion Center, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wei-Chung Tsai Po Peng 5 Welgene Biotech. Co., Ltd. Taipei Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pei-Syuan Jhou 5 Welgene Biotech. Co., Ltd. Taipei Taiwan 6 Department of Microbiology and Immunology, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yi-Chun Tsai 4 Precision Sports Medicine and Health Promotion Center, Kaohsiung Medical University , Kaohsiung, Taiwan 7 Division of General Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan 8 Division of Nephrology, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan 9 Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University , Kaohsiung, Taiwan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yi-Chun Tsai Wei-Wen Hung 4 Precision Sports Medicine and Health Promotion Center, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yi-Hsiung Lin 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yi-Hsiung Lin Shih-Jie Jhuo 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tien-Chi Huang 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tien-Chi Huang Li-Fang Chou 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tsung-Hsien Lin 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ho-Ming Su 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wen-Ter Lai 2 Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chien-Hung Lee 11 Department of Public Health College of Health Science, Kaohsiung Medical University , Kaohsiung, Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bin-Nan Wu 12 Department of Pharmacology, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shien-Fong Lin 13 Institute of Biomedical Engineering, National Yang Ming Chiao Tung University , Hsinchu, Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shien-Fong Lin Wei-Chun Hung 4 Precision Sports Medicine and Health Promotion Center, Kaohsiung Medical University , Kaohsiung, Taiwan 6 Department of Microbiology and Immunology, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan 10 Division of Endocrinology and Metabolism Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University , Kaohsiung, Taiwan PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: wchung{at}kmu.edu.tw Abstract Full Text Info/History Metrics Preview PDF ABSTRACT BACKGROUND Previous studies have linked gut dysbiosis to the acute coronary syndrome (ACS) and sympathetic nervous system disturbances. However, the relationship between skin sympathetic nerve activity (SKNA) and gut microbiota in ACS patients remains largely unexplored. We hypothesize that elevated SKNA may interact with specific microbial profiles in ACS. METHODS AND RESULTS Fifteen ACS patients with age- and sex-matched controls were enrolled in the cross-sectional study. Subjects with type 2 diabetes or on antibiotics, proton pump inhibitors, or metformin were excluded. Demographic and clinical data were recorded, and SKNA was measured by neuECG. Gut microbial profiles were analyzed by sequencing the V3-V4 region of the 16S rRNA gene. Significant differences in alpha diversity, including Chao 1 index and Faith’s phylogenetic diversity, were observed between the ACS and control groups ( P <0.001). Distinct beta diversity between the two groups was also identified using unweighted normalized UniFrac analysis ( P =0.001). Redundancy analysis and Mantel test revealed a significant relationship between SKNA and microbial profiles. Several butyrate-producing bacteria were decreased in the ACS group, while the genus Hungatella , a trimethylamine producer, showed a significant increase and was positively correlated with SKNA. Additionally, predicted microbial functional analysis indicated an elevation in branched-chain amino acid biosynthesis in the ACS group. CONCLUSIONS This study is the first to establish an association between SKNA and gut microbial profiles in ACS patients. SKNA can serve as a biomarker for ACS, offering a simple and non-invasive tool to explore the interaction between the nervous system and gut microbiota in cardiovascular diseases. WHAT IS NEW? The Skin sympathetic nerve activity (SKNA) is a novel and non-invasive recording of the sympathetic nervous system from the skin of patients. The NEW findings in our study from acute coronary syndrome (ACS) patients are (1) to establish the correlation between SKNA, heart rate variability and microbiota, (2) the Hungatella is correlated with SKNA and standard deviation of NN intervals (SDNN). WHAT ARE THE CLINICAL IMPLICATIONS? Since microbiota and sympathetic nervous system (SNS) might be essential factors of ACS, it is important to assess the abundance of microbiota and SNS activity for accurate management of ACS. The Hungatella , one of the microbiota involved in atherosclerotic cardiovascular disease, heart failure, inflammatory process, and anxiety disorder, might be the link between SNS and ACS. In our study, we prove the positive association between Hungatella and SKNA. Therefore, by checking the SKNA of patients with ACS, we might discover the SNS activity and also the underlying microbiota etiology of ACS. Then, it is critical and accurate to target Hungatella in ACS patients with high SKNA to reduce the risk of ACS. Introduction Acute coronary syndrome (ACS) is one of the leading causes of mortality worldwide. 1 Hemodynamically significant ventricular arrhythmias (VA) during ACS occur in approximately 8% of patients and lead to mortality. 2 The sympathetic hyperinnervation caused by ACS can lead to the development of VA. Sympathetic drive leads to increased automaticity, triggered activity, and re-entry mechanisms in the heart, all of which can contribute to VA. 3 , 4 Sympathetic overdrive after ACS could provoke further myocardial ischemia, leading to a vicious cycle of VA and sudden cardiac death. Heart rate variability (HRV), heart rate turbulence, and abnormal T wave alternans can be used to assess the risk of death or VA after MI. 5 , 6 neuECG is a well-validated non-invasive method of simultaneously recording electrocardiogram and skin sympathetic nerve activity (SKNA). 7 Increased SKNA is known to precede spontaneous VA in canine models and humans. 8 – 11 We also found that SKNA increased in ACS patients and correlated with VA in a clinical study. 12 Accumulating evidence has demonstrated the critical roles of gut dysbiosis and the imbalance of their metabolites in the development of ACS. A decrease in butyrate-producing bacteria, along with a corresponding reduction in butyrate concentrations, is correlated with elevated inflammation, cardiac damage, and increased mortality. 13 Trimethylamine N-oxide (TMAO), which is formed from trimethylamine (TMA) generated by the action of gut microbiota, has been reported as a novel prognostic marker for ACS. 14 Also, the relationships between gut microbiota and circulating metabolites demonstrated by Liu et al. provide a better understanding of the interplay between host and gut microbiota in atherosclerotic pathogenesis. 15 A canine study shows that TMAO can activate the cardiac sympathetic nervous system (SNS) and worsen ischemic VA via the left stellate ganglion (SG) pathway or central autonomic activation. 16 Accumulation of branched-chain amino acids (BCAA) is an alternative biomarker of cardiometabolic disease. 17 – 19 Pedersen et al. further suggest that elevated serum BCAA levels were attributed to gut microbiome rather than dietary intake. 20 As mentioned above, gut microbiota have been reported to play a role in ACS and could potentially modulate sympathetic neurons. However, the interplay between the neuro-cardiac axis and the specific microtome has not been clearly understood in clinical ACS patients. Considering all the above evidence, we conducted the study to test the hypothesis that SKNA is increased in ACS patients compared to controls and that the increased SKNA is associated with specific microtomes that may play an important role in linking microbiota, SNS, and ACS. METHODS Participants This single-center, cross-sectional study was approved by the ethics committee of Kaohsiung Medical University Hospital, and all study subjects provided written informed consent. This study is registered with ClinicalTrials.gov, identifier: NCT03243448 . The enrollment period of the study participants was from 2019 to 2021 at Kaohsiung Medical University and Kaohsiung Medical University Hospital. Inclusion criteria: ACS patients and control participants who were 1:1 matched in terms of age and gender. Each group consisted of 15 individuals, all 20 years or older. ACS patients were defined as those with myocardial infarction and unstable angina admitted to the coronary care unit after coronary angiography and/or percutaneous coronary intervention as standard of care. The controls enrolled were healthy adults who joined the health management program at Kaohsiung Medical University. Exclusion criteria for this study included the use of antibiotics, metformin, or proton pump inhibitors, and diagnoses of diabetes or colon cancer, as all these could interfere with the results of the microbiota study. Indeed, the stringent exclusion criteria can make enrolling ACS patients in the study challenging. Demographic and clinical data such as age, gender, body mass index (BMI), clinical factors (hypertension, diabetes, dyslipidemia, mean arterial pressure) and laboratory data (estimated glomerular filtration rate, serum level of potassium, triglyceride, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, hemoglobin, hemoglobin A1C were collected in both ACS and control groups. neuECG, SKNA, and HRV Measurements The detailed methods of neuECG recording have been reported elsewhere. 7 In brief, the neuECG used conventional lead I ECG electrodes and equipment with a high sampling rate (10,000 Hz) and wide bandwidth (1-2000 Hz) version of the ME6000 Biomonitor System (Mega Electronics Ltd, Finland) to record the electrical signal from the skin of the research subjects. The signals were then band-pass filtered at 500-1000 Hz to display SKNA and 1-150 Hz to display ECG. The neuECG was recorded during baseline, stress 21 , and recovery (5 minutes for each phase). The stress consisted of mental arithmetic stress induced by arithmetic involving the subtraction of serial 13’s from a 4-digit number for 5 minutes. Data were analyzed using customized software to determine the average SKNA (aSKNA [µV]) per digitized sample over the monitoring period. 7 The research subjects were asked to lie down and rest for 10 minutes prior to SKNA recording to reduce bias due to motion artifacts and SNS interference.. Short-term HRV analysis was conducted for 5-minute intervals during each phase of SKNA recording, in order to facilitate comparison. The R peak of the QRS complex in each 5-minute window of the neuECG signal was automatically detected by the modified Pan Tompkins algorithm 22 and the RR interval was obtained beat by beat. We analyzed the time and frequency domain of HRV using MATLAB (Mathworks, Inc., USA). 22 , 23 Fecal Sample Collections The fecal samples were frozen immediately after collection and delivered to the laboratory in a cooler bag within 24 hours. Subsequently, the samples were stored at -80 °C for up to 3 days before processing. Fecal DNA Extraction and 16S rRNA Sequencing Fecal DNA was extracted using a stool DNA extraction kit (Topgen Biotechnology Co., Ltd., Kaohsiung, Taiwan) as previously described. 24 The DNA concentration and quality were assessed using the Colibri Microvolume spectrophotometer (Titertek Berthold, Pforzheim, Germany) before frozen at −20 °C to preserve its integrity. The V3-V4 regions of 16S rRNA gene in each sample were amplified using the primer pairs 341F (5’- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG -3’) and 805R (5’- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTA ATCC -3’). Library preparation was conducted using the Illumina MiSeq platform, generating 2 x 300 bp reads. Raw sequence data was imported into QIIME2, 25 where paired-end reads were merged and denoised into amplicon sequence variants (ASVs) using the DADA2 plugin, 26 as previously described. 24 Statistical Analysis Disease status (ACS vs. health control) was our primary comparison variable, with the baseline SKNA value as a continuous outcome. A priori sample size calculation, assuming a two-sided type I error of 5%, a standard deviation of 0.25 for SKNA, and a 1:1 case/control ratio, estimated a total of 28 participants (14 ACS and 14 controls) to achieve 80% statistical power for detecting an effect size of 0.28. The significance of differences in anthropometric data, clinical factors, laboratory results, SKNA parameters, and HRV parameters between the ACS and control groups was assessed using SPSS version 18.0 for Windows (SPSS Inc., Chicago, Illinois). The P values were calculated using the Mann-Whitney U test for continuous data and the Chi-square test for categorical data. A P value of <0.05 was considered statistically significant. Bioinformatics We employed the Chao 1 index and Faith’s phylogenetic diversity to assess alpha diversity, comparing differences between the ACS and control groups using Kruskal-Wallis tests. For beta diversity, we performed analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. These analyses were assessed using principal coordinate analyses (PCoA) based on unweighted UniFrac measurements. 27 Additionally, redundancy analysis was conducted to examine the relationship between microbial beta diversity and clinically relevant factors. ASV taxonomy assignment was conducted using SciKit Learn-based approach, 28 querying the SILVA reference database (release v138, trimmed to the V3-V4 region, L7 taxonomy). 29 Significant different taxa between the ACS and control groups were identified using linear discriminant analysis effect size (LEfSe) with P < 0.05 (factorial Kruskal-Wallis test). 30 To enhance the stringency, the logarithmic linear discriminant analysis (LDA) score was set to 3. Moreover, random forest analysis was employed to identify the discriminant genera between the two groups. The fixed-effects models explored the relationships between the targeted genera and SKNA and the standard deviation of NN intervals (SDNN). Generalized linear mixed-effects models (GLMMs) with sequencing depth as weights were fitted using the lme4 package in R. Age and gender were included as random effects. Phylogenetic investigation of communities by reconstruction of observed states 2 (PICRUSt2) was utilized to predict functional differences between the microbial communities of the ACS and control groups, based on the MetaCyc database. 31 RESULTS Clinical Characteristics A total of 15 patients with ACS were included in this study, along with the matched control subjects. The clinical characteristics are shown in Table 1 . The two groups had no statistically significant differences in anthropometric data and clinical factors. For the laboratory data, the ACS group exhibited significantly elevated fasting glucose and HbA1c levels and markedly reduced HDL levels compared to the control group. The SKNA parameters during baseline and recovery phases (SKNAb and SKNAr) were significantly higher in the ACS group. Additionally, the ACS group demonstrated significantly diminished SDNN values during the stress phase (SDNNs) in the HRV parameters. View this table: View inline View popup Table 1. Characteristics of the ACS and control groups The ACS Patients Displayed Altered Gut Microbiota Composition Compared to Healthy Controls The composition of gut microbiota in the ACS and control groups was determined using the 16S rRNA gene sequencing. As depicted in Figure 1 , a notable reduction in alpha diversity was observed in the ACS group, evident from the Chao 1 index ( P value < 0.001) and Faith’s phylogenetic diversity ( P value < 0.001). Distinct beta diversity clustering of gut microbiota between the ACS and control groups was observed using unweighted UniFrac (ANOSIM: R =0.702, P value = 0.001; PERMANOVA: pseudo-F = 9.573, P value = 0.001) or weighted unnormalized UniFrac measurements (ANOSIM: R = 0.123, P value = 0.008; PERMANOVA: pseudo-F = 2.860, P value = 0.011), as illustrated in Figure 2 . Download figure Open in new tab Figure 1. Comparison of the alpha diversity between the ACS and control groups. A , the median of Chao1 index was 309.00 (25th-75th percentile: 257.15-383.27) in the ACS group and 760.38 (671.15-783.42) in the control group. B , the median of Faith’s phylogenetic diversity was 16.39 (25th-75th percentile: 14.81-18.20) in the ACS group and 32.46 (23.73-37.83) in the control group. Download figure Open in new tab Figure 2. Beta diversity clustering of gut microbiota between the ACS and control groups. A , the unweighted UniFrac measurements. B , the weighted unnormalized UniFrac measurements. Blue circle indicates the ACS group, and orange triangle indicates the control group. Association between clinically relevant variables and microbial beta diversity Clinically relevant variables were further investigated for their association with microbial beta diversity by RDA. Fasting glucose was excluded from further analysis due to stress hyperglycemia in the ACS subjects despite significant differences between the ACS and control groups. To mitigate the potential accumulation of signals, the SKNA data were segregated into three distinct phases: baseline, stress, and recovery ( Figures 3A to 3C ). The SKNAb, SKNAr, SDNNs, and HDL had the most potent influences (the longest arrow) on microbial beta diversity. The SKNAb and SKNAr showed positive correlations with the ACS group during the baseline and recovery phases, respectively. Conversely, SDNNs negatively correlated with the ACS group in the stress phase. The HDL levels positively correlated with the control group in all three phases. Download figure Open in new tab Figure 3. Clinical relevant factors influencing the beta microbial beta diversity between the ACS and control groups The clinical confounding factors of BMI (body mass index), Cr (creatinine), Hb (hemoglobin), HbA1c, HDL (high-density lipoprotein), K (potassium), LDL (low-density lipoprotein), LF/HF (low frequency power/high frequency power), MBP (mean blood pressure), SDNN (standard deviation of normal to normal R wave) and SKNA (skin sympathetic nerve activity) were examined by redundancy analysis (RDA) based on the unweighted UniFrac measurements. A , the baseline phase. B , the stress phase. C , the recovery phase. Green spot indicates the subjects belonging to the ACS group, and orange spot indicates the subjects belonging to the control group. Mantel test further confirmed that the SKNAb ( r = 0.151, P value = 0.023), SDNNs ( r = 0.249, P value = 0.002), and SKNAr ( r = 0.209, P value = 0.007) were also significantly related to gut microbiota profiles during the baseline, stress, and recovery phases, respectively ( Table 2 ). The HDL levels were significantly associated with the dissimilarity of gut microbiota in all three phases. No other clinical parameters showed a significant relationship with microbial beta diversity. View this table: View inline View popup Download powerpoint Table 2. Evaluation of the relationship between clinical relevant factors and gut microbial profiles by Mantel test HDL indicates high-density lipoprotein; SDNN, standard deviation of normal to normal R wave interval; SKNA, skin sympathetic nerve activity. Relative abundances of fecal microbiota between the ACS and control groups Relative differences in the abundances of fecal microbiota between the ACS and control groups were estimated by LEfSe, employing a logarithmic LDA score threshold of > 3. Within the ACS group was an enrichment of the phylum Desulfobacterota, the class Desulfovibrionia, as well as 3 orders, 6 families, 15 genera, and 19 species (Figure S1). Within the control group, enrichment was observed in the phylum Bacteroidota, the classes Actinobacteria and Bacteroidia, as well as 3 orders, 7 families, 29 genera, and 45 species. Remarkably, Bifidobacterium bifidum , classified as a lactic acid bacterium, exhibited enrichment extending throughout its taxonomic hierarchy up to the class level in the control group. Random forest analysis was employed to enhance the stringency of identifying discriminant taxa, and 22 genera were found to be discriminant by both the LEfSe and random forest analysis ( Figure 4A ). Eisenbergiella , Hungatella , Ligilactobacillus , and Ruminococcaceae UBA1819 were enriched in the ACS group. At the same time, 18 genera were decreased, including four genera belonging to Prevotellaceae ( Paraprevotella , Prevotella , Prevotella _9, and Prevotellaceae_NK3B31_group) and several butyrate producers (including Anaerostipes , Butyricicoccus , Catenibacterium , Eubacterium ruminantiu m_group, Fusicatenibacter , Lachnospira , and Roseburia ). Additionally, the ACS group observed an unexpected decrease in opportunistic microbes Haemophilus and Klebsiella . Download figure Open in new tab Figure 4. The 22 genera identified by intersection of LEfSe and random forest analysis. A , the relative abundances. B , the correlation with the SKNAb, SKNAr, SDNNs and HDL. Links between the gut microbiome and the clinical characteristics of ACS Because HDL-C, SKNAb, SDNNs, and SKNAr demonstrated significant associations with the dissimilarity of gut microbiota between the ACS and control groups, we proceeded to explore the correlations among the microbial relative abundances of the 22 genera that were identified as discriminant taxa through the LEfSe and random forest analysis. As shown in Figure 4B , Hungatella exhibited a negative correlation with SDNNs ( r = -0.392, P value = 0.032) and a positive correlation with SKNAr ( r = 0.441, P value = 0.015). Notably, it also showed a marginally significant correlation with SKNAb ( r = 0.358, P value = 0.052). Lachnospira was positively correlated with SDNNs ( r = 0.393, P value = 0.032), while the Rikenellaceae RC9 gut group positively correlated with HDL-C ( r = 0.392, P value = 0.032). None of the other discriminant taxa showed significant correlations with the four clinical parameters. Based on the results of LEfSe and random forest, we further analyzed the fixed effect to determine the correlation between SKNAb, SKNAr, and SDNNs and the abundance of gut microbiota after controlling for age and gender ( Figure 5A and B ). The fixed effects of SKNAb and SKNAr on Hungatella were 0.66 ( P <0.001) and 5.757 ( P <0.001), respectively. The fixed effect of SDNNs on Lachnospira was 0.057 ( P <0.001). Download figure Open in new tab Figure 5. The fixed effect after controlling age and gender to determine the correlation between microbiota and autonomic survey. A , the correlation between Hungatella with SKNA. B , the correlation between Lachnospira with SDNN. Prediction of microbial functions PICRUSt2 analysis showed that 49 and 41 MetaCyc pathways were enriched in the ACS and control groups, respectively ( Figure 6 ). In the ACS group, 12 and 10 pathways were significantly enriched under amino acid biosynthesis and nucleoside and nucleotide biosynthesis. Among them, 7 pathways related to branched-chain amino acids (BCAA) biosynthesis were noted. Regarding the control group, 21 pathways were enriched under cofactor, prosthetic group, electron carrier, and vitamin biosynthesis, and 4 pathways under aromatic compound degradation. Download figure Open in new tab Figure 6. Comparison of Picrust2 predicted MetaCyc pathways between the ACS and control groups. A , The pathways enriched in the ACS group. B , The pathways enriched in the control group. DISCUSSION While Meng et al. highlighted the important role of gut microbes-derived metabolite TMAO in the development of VA through the activation of cardiac SNS, 16 our study is the first to demonstrate the interplay between skin SNS and gut dysbiosis in ACS patients. Previously, we have shown that SKNA was associated with VA in ACS, establishing it as a novel biomarker for ACS risk stratification. 12 The current study further highlights the significant association of SKNA in ACS patients, specifically identifying a link with gut microbiota profiles. The value of SKNA was positively correlated to the relative abundance of Hungatella , a TMA-producer ( Figure 4 and Figure 5A ). Hungatella , combined with Eggerthella lenta , was specifically required to convert L-carnitine to Trimethylamine (TMA). 32 TMA is the diet-derived precursor to TMAO, the major metabolite of the microbiome correlated with the outcomes of ACS. 14 Altered Hungatella abundance was linked to an increased incidence of unruptured intracranial aneurysms, an atherosclerotic cardiovascular disease. 33 In two independent cross-sectional cohorts of patients with systolic heart failure (HF), increased Hungatella was associated with HF. 34 Hungatella was also negatively correlated with HDL-C in stroke 35 and positively correlated with IL-10 levels. 36 Based on the above evidence, Hungatella is logically associated with cardiovascular disease and ACS. In addition, SGLT2i can reduce Hungatella in type 2 diabetes 37 and polyphenol-rich extracts can lower plasma TMAO in overweight and obese adults in association with Hungatella modulation 38 , demonstrating the cardiovascular benefits of reducing Hungatella in subjects at risk of cardiovascular disease. Other than cardiovascular disease, Hungatella was also found to be more significant enrichment in patients with generalized anxiety disorder. 39 As anxiety was thought to correlate with high SNS status, the association between Hungatella and SNS is reasonable. Type 2 diabetes, often linked with gut dysbiosis, is prevalent among patients with ACS and is recognized as a risk factor for the development of ACS. However, Emoto et al. compared the composition of gut microbiota among coronary artery disease (CAD) patients, non-CAD controls but with coronary risk factors such as type 2 diabetes, and healthy volunteers to demonstrate that a change in gut microbiota in the CAD group was not associated with type 2 diabetes. 40 A key aspect of our study was the exclusion of type 2 diabetes in the enrolled subjects to clearly define the changes in gut microbiota profiles specific to ACS. Compared to the matched controls, ACS patients exhibited a decrease in butyrate-producing bacteria and an increase in TMA-producing bacteria. These findings align with previous studies that included patients with type 2 diabetes, 13 , 15 further highlighting the significant roles of these microbiota in the development of ACS. Elevated production of BCAAs, including leucine, isoleucine, and valine, was observed in the predicted microbial functional analysis using PICRUSt2 ( Figure 6 ). Among the 12 pathways related to amino acid biosynthesis that were elevated in ACS subjects, five were associated with isoleucine biosynthesis, one with valine biosynthesis, and one with overall BCAA biosynthesis. Previous studies have shown that increased plasma BCAA concentrations are found in patients with heart failure or coronary artery disease and can predict adverse outcomes. 17 – 19 Beyond impaired cardiac BCAA catabolism, growing evidence suggested that abnormal BCAA accumulation may originate from the gut microbiota. 20 Additionally, BCAA accumulation has been shown to exacerbate microglia-induced neuroinflammation in a rat model of ischemia/reperfusion. 41 These findings further support the association between microbiota and SKNA in ACS, as addressed in this study. This study has several limitations. First, the sample size was relatively small due to the rigorous selection process, particularly the exclusion of clinical factors such as type 2 diabetes and use of proton pump inhibitors or metformin, which are common in ACS patients. Although the stringent selection process gives the strength of getting rid of the influence of the clinical factor on microbiota data, it also reduce the external validity since the clinical factor is common in ACS patients. Another limitation was using the V3-V4 region of 16S rRNA gene sequencing, which limited the identification of some bacteria to the genus level only. Additionally, we did not measure TMAO or BCAA levels in the subjects, hindering us from evaluating their relationships with gut microbiota and SKNA. Finally, other factors beyond SKNAb, SKNAr, and SDNNs may have contributed to microbial diversity in our study, as indicated by the relatively low correlation coefficients (r = 0.151, 0.209, and 0.249, respectively, in Table 2 ). In conclusion, this study is the first to demonstrate an association between SKNA and gut microbial profiles in ACS patients. By applying stringent selection criteria to exclude type 2 diabetes, we identified specific patterns in ACS patients, including alterations in butyrate-producing bacteria, TMA-producing bacteria, and gut microbial BCAA biosynthesis. Together with our previous research, these findings suggest that SKNA could serve as a potential biomarker for ACS. Nonstandard Abbreviations and Acronyms ACS Acute coronary syndrome ANOSIM Analysis of similarities aSKNA Average skin sympathetic nerve activity ASVs Amplicon sequence variants BCAA Branched-chain amino acids CAD Coronary artery disease HRV Heart rate variability LAD Linear discriminant analysis LEfSe Linear discriminant analysis effect size PCoA Principal coordinate analyses PERMANOVA Permutational multivariate analysis of variance PICRUSt2 Phylogenetic investigation of communities by reconstruction of observed states 2 SDNN Standard deviation of NN intervals SDNNs SDNN during stress phase SG Stellate ganglion SKNA Skin sympathetic nerve activity SKNAb SKNA parameters during baseline phase SKNAr SKNA parameters during recovery phase SNS Sympathetic nervous system TMA Trimethylamine TMAO Trimethylamine N-oxide VA Ventricular arrhythmias SOURCES OF FUNDING This study was supported in part by grants from the Ministry of Science and Technology, R.O.C “MOST 110-2314-B-037-111” and ”MOST 113-2314-B-037 -054 -MY3”, Kaohsiung Medical University Hospital “KMUH111-1T10”, “SI11001”, “SI11101”, “SI11201”, “110KMUOR01”, “NK111P24”, “NSYSU-KMU-112-P13” and “KMUH111-1T10”. 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Share Interplay between Skin Sympathetic Nerve Activity and Gut Microbiota, the Neurocardiac Axis in Acute Coronary Syndrome Wei-Chung Tsai , Po Peng , Pei-Syuan Jhou , Yi-Chun Tsai , Wei-Wen Hung , Yi-Hsiung Lin , Shih-Jie Jhuo , Tien-Chi Huang , Li-Fang Chou , Tsung-Hsien Lin , Ho-Ming Su , Wen-Ter Lai , Chien-Hung Lee , Bin-Nan Wu , Shien-Fong Lin , Wei-Chun Hung bioRxiv 2025.05.06.652555; doi: https://doi.org/10.1101/2025.05.06.652555 Share This Article: Copy Citation Tools Interplay between Skin Sympathetic Nerve Activity and Gut Microbiota, the Neurocardiac Axis in Acute Coronary Syndrome Wei-Chung Tsai , Po Peng , Pei-Syuan Jhou , Yi-Chun Tsai , Wei-Wen Hung , Yi-Hsiung Lin , Shih-Jie Jhuo , Tien-Chi Huang , Li-Fang Chou , Tsung-Hsien Lin , Ho-Ming Su , Wen-Ter Lai , Chien-Hung Lee , Bin-Nan Wu , Shien-Fong Lin , Wei-Chun Hung bioRxiv 2025.05.06.652555; doi: https://doi.org/10.1101/2025.05.06.652555 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)