Fecal level of butyric acid, a microbiome-derived metabolite, is increased in patients with severe carotid atherosclerosis

Patients and control subjects

Between August 2017 and June 2019, 60 adult patients with severe atherosclerosis; defined as moderate (50–69%) or severe (≥ 70%) carotid stenosis; were consecutively recruited at Oslo University Hospital. Patients with known immunodeficiency or cancer were excluded. For comparison, 44 healthy control subjects were recruited from the same area of Norway as the patients, 12 of these were the patients’ spouses. The controls were healthy individuals with normal findings on carotid ultrasound as well as CRP < 5. A total of 81 participants provided fresh frozen fecal samples available for analysis; 43 patients and 38 control subjects, respectively (Fig. 3).

Figure 3
figure 3

Flow-chart showing inclusion process for participants available for SCFA analysis in feces. Box in lower right corner showing participants available for additional analysis (i.e. plasma markers of gut barrier damage and inflammation activation, 16S rRNA in feces and plasma SCFAs).

The study was approved by the Norwegian Regional Committees for Medical and Health Research Ethics (ID REC 2017/2202 A) and was performed in accordance with the Declaration of Helsinki. All the participants gave written informed consent before inclusion.

Our study was registered in ClinicalTrials.gov (NCT04803838). We aimed to show that patients with severe carotid atherosclerosis would have lower level of fecal butyric acid and less butyric acid producing bacteria, evidence of gut dysbiosis and increased inflammation compared to control subjects.

Carotid ultrasound

Color duplex ultrasound was performed on all participants with a Philips Epiq 5 (Philips, USA), using a Linear probe (3–12 MHz) on both carotid arteries. The degree of internal carotid artery stenosis was determined according to consensus criteria41.

General health and diet

Information regarding previous medical history, risk factors for stroke and medications was collected from a questionnaire and/or medical journals. A validated dietary questionnaire42 was completed, waist- and hip circumferences were measured, and weight and height measures submitted by the participants.

Blood sampling protocol and analysis

Venipuncture of a forearm vein was performed in fasting participants. Blood was drawn into pyrogen-free tubes without any additives and allowed to clot at room temperature (within 1 h) before centrifugation (2500g for 20 min). For the gut barrier damage markers, 4 ml Vacutettes with EDTA as anticoagulant were used. All blood samples were stored at − 80 °C until further analysis. Analysis for leukocytes, thrombocytes, total cholesterol HDL, LDL triglycerides, creatinine, GFR, HbA1c and CRP were performed.

Analysis of plasma SCFAs

Metabolomic profiling was perfomed by Metabolon, Inc. (Durham, NC, USA) using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), as described by Evans43. Forty-eight samples, from respectively 33 patients and 15 control subjects were analyzed on Metabolon’s global HD4 platform and the complex lipids panel (CLP). Lipids were extracted from the biofluid in the presence of deuterated internal standards using an automated BUME extraction44. The lipids and fatty acids were detected as described by the provider using ammonium acetate dichloromethane:methanol (50:50), followed by infusion-MS analysis, performed on a Shimazdu LC with nano PEEK tubing and the Sciex SelexIon-5500 QTRAP. Individual lipid species were quantified by taking the peak area ratios of target compounds and their assigned internal standards, then multiplying by the concentration of internal standard added to the sample45. Lipid species concentrations were background-subtracted using the concentrations detected in process blanks (water extracts). The resulting background-subtracted, run-day normalized lipid species concentrations were then used to calculate the lipid class and fatty acid total concentrations, as well as the mol% composition values for lipid species, lipid classes, and fatty acids.

Analysis of markers of gut barrier damage and inflammasome activation

Plasma levels of IFABP, CCL25, IL-18, and LBP were measured in duplicate by enzyme immunoassays (EIA) using commercially available antibodies (R&D Systems, Minneapolis, MN, USA) in a 384 format using a combination of a SELMA (Jena, Germany) pipetting robot and a BioTek (Winooski, VT, USA) dispenser/washer. Absorption was read at 450 nm with wavelength correction set to 540 nm using an EIA plate reader (Bio-Rad, Hercules, CA, USA). All samples for a marker were run on the same 384 plate and intrassay coefficient of variation was < 10%.

Fecal sample collection and storage

Study participants sampled feces at home by defecating into a clean device and then transferring into provided clean tubes without additives. Samples were frozen immediately at − 20 °C, brought to the hospital in a cooling device, and subsequently frozen at − 80 °C. Participants were instructed to freeze their samples immediately. Storage time at − 20 °C ranged between 1 and 14 days for both patients and controls. Some patients submitted samples during the hospital stay, and samples were stored at a maximum of 4 h in a standard refrigerator before frozen at − 80 °C. The participants evaluated their stools according to the Bristol stool scale46.

Analysis of fecal SCFAs

Fecal samples (0.5 g) and distilled water containing 3 mmol/L of 2-ethylbutyric acid (as internal standard) and 0.5 mmol/L of H2SO4 were homogenized. 2.5 mL of the homogenate was vacuum distilled according to the method of Zijlstra et al.47 and modified by Høverstad et al.48. The distillate was analyzed with gas chromatography (Agilent 6850; Agilent, CA, USA) using a capillary column (serial no. USE400311H, Agilent J&W GC columns; Agilent, CA, USA) and quantified while using internal standardization. Flame ionization detection was employed. The total amount of all SCFAs and the amount of acetic, propionic, butyric, isobutyric, valeric, isovaleric, caproic, and isocaproic acids expressed in mmol/Kg wet weight were measured.

Analysis of gut microbiota composition and function

Fecal DNA was extracted using the commercial ZymoBIOMICS™ DNA Miniprep Kit (ZR, Zymo Research, Irvine, CA, USA), according to manufacturer’s instructions, with slight modifications.

Libraries for 16S rRNA amplicon sequencing were prepared as previously described49. Briefly, the hypervariable regions V3 and V4 of the 16S rRNA gene were amplified using dual-indexed universal primers (319F:ACTCCTACGGGAGGCAGCAG and 806R:GGACTACHVGGGTWTCTAAT) and Phusion High-Fidelity PCR Master mix m/HF buffer (Thermo Fisher Scientific, USA). The PCR products were cleaned and normalized using the SequalPrep Normalization Plate Kit (Thermo Fisher Scientific, USA). Quality control and quantification of the pooled libraries were performed using an Agilent Bioanalyzer (Agilent Technologies, USA) and Kapa Library Quantification Kit (Kapa Biosystems, London, UK). Sequencing was performed at the Norwegian Sequencing Centre (Oslo, Norway), using the Illumina MiSeq platform and v3 kit (Illumina, San Diego, CA, USA), set at 300 base pair paired-end reads.

Paired-end reads were filtered for Illumina Universal Adapters and PhiX, demultiplexed, quality trimmed and merged using BBDuk 38.9050, Cutadapt 3.351 and BBMerge 38.9052. Denoising reads to Amplicon Sequence Variants (ASVs), taxonomic classification, filtering of contaminants and rare ASVs and building of a phylogenetic tree was done with QIIME2 version 2021.253. To reduce the effect of uneven sequencing depths, we rarefied all samples to a common level of 10,600 counts. We calculated diversity metrics in QIIME2 and tested for differential abundance of genera using this rarefied dataset (Mann–Whitney U test). Before differential abundance testing we applied a taxa prevalence filter of 20% on the rarefied dataset. To assess the functional characteristics of the gut microbiota, we performed an analysis with PICRUSt254 in QIIME2 to predict the metagenomic content in each sample and tested for differentially abundant pathways with aldex2 (with prevalence filter 20%).


Descriptive statistics are given as number and proportion (%), mean with standard deviations or median (min–max). Mann–Whitney U test was used to compare the non-parametric categorical variables with continuous variables. The Spearman’s rank correlation test was used to evaluate relationships between variables.

P-values are two-sided and considered significant when < 0.05. IBM SPSS Statistics for Windows, statistical software version 25.0 (IBM Corp., Armonk, NY, USA) was used for data analyses.