Clinical study on the relationship between gut microbiota dysbiosis and iron metabolism and septic cardiomyopathy

doi:10.21203/rs.3.rs-3076065/v1

Background: Septic cardiomyopathy is a common complication of sepsis and is characterized by ventricular systolic and/or diastolic dysfunction and reduced ejection fraction. Studies have demonstrated the role of gut microbiota and iron metabolism in sepsis and cardiovascular disease, but few studies have reported on the changes and role of gut microbiota and iron metabolism in septic cardiomyopathy. The aim of this study was to explore the changes and correlation of gut microbiota and iron metabolism in septic cardiomyopathy and to provide new directions for early diagnosis of septic cardiomyopathy.

Methods: This study was a Single-center, prospective, observational study. Patients with sepsis who were admitted to the critical care medicine department of Subei People's Hospital between February 2022 and September 2022 were selected. Echocardiography was performed within 72 hours of the patient's admission to the Intensive care unit. Patients were divided into septic cardiomyopathy group and non-septic cardiomyopathy group according to the grouping criteria. Blood and stool specimens were collected from patients included in the study on days 1, 3 and 7 of enrollment. The blood specimens for testing of iron metabolism levels. The stool specimens were for 16S rDNA sequencing to detect intestinal microbiota diversity. The basic vital signs and clinical data of the patient were recorded. To compare the gut microbiota diversity, iron metabolism level, 28-day morbidity and mortality rate, length of ICU stay, and total length of stay in the two groups.

Results: A total of 48 patients were enrolled during the study period, including 23 patients in the septic cardiomyopathy group and 25 patients in the non-septic cardiomyopathy group. Analysis of iron metabolism levels in the two groups showed that there was a statistical difference in serum ferritin levels between the two groups on day 1 and day 3 of enrollment (P < 0.05), and that ferritin levels were higher in the septic cardiomyopathy group than in the non-septic cardiomyopathy group. Other iron metabolism levels including serum iron, serum transferring, transferrin saturation, and total iron binding capacity on days 1, 3, and 7 were not statistically significant (P > 0.05). Analysis of the richness and diversity of the gut microbiota in the two groups showed that the ACE index and Chao1 index were statistically different between the two groups (P < 0.01), while the Shannon index and Simpson index were not statistically different (P > 0.05). Beta diversity of gut microbiota was analyzed in both groups and PCoA analysis showed a significant difference (P < 0.01).

We compared the composition of the gut microbiota at different taxonomic levels in the two groups of patients, and at the phylum level, the abundance of Actinobacteria (P=0.018) and unidentified_Bacteria (P=0.024) was lower in the septic cardiomyopathy group. At the family level, the abundance of Aeromonadaceae was lower in the septic cardiomyopathy group (P=0.023). At the genus level, Citrobacter was more abundant in septic cardiomyopathy (P=0.007). At the species level, Bacteroides_nordii (P=0.037) and [Clostridium]_celerecrescens (P=0.026) were more abundant in septic cardiomyopathy. By Linear discriminant analysis Effect size (LEfSe) analysis, we identified Enterobacter and Klebsiella_quasipneumoniae as possible gut microbe specific for septic cardiomyopathy (LDA score=4.2747, P=0.003). Using Spearman's rank correlation analysis of clinical indicators and gut microbiota, we found that Bacteroides_thetaiotaomicron was positively correlated with B-type natriuretic peptide, serum iron, and transferrin saturation (P < 0.05). Bacteroides_fragilis was negatively correlated with cardiac Troponin I, transferrin, total iron binding capacity were negatively correlated (P < 0.05). Prevotella_disiens and Prevotella_timonensis were negatively correlated with ferritin (P < 0.05).

Conclusion: Our study suggests that ferritin may have predictive value for early identification of septic cardiomyopathy, while Enterobacteriaceae may be the gut microbiota specific to septic cardiomyopathy. Furthermore, alterations in gut microbiota diversity may influence changes in iron metabolism and ultimately induce the development of septic cardiomyopathy, and larger studies are needed to validate this in the future.

Trial registration: The trial completed registration at the China Clinical Trials Registry (registration number ChiCTR2200056572) on 8 February 2022, and the study was conducted in strict accordance with the registration information.

sepsis

septic cardiomyopathy

iron metabolism

gut microbiota

Sepsis is a life-threatening syndrome of organ dysfunction caused by a dysregulated host response to infection, with a very high incidence of morbidity and mortality^[1]. Although current medical care is improving, the death rate from sepsis has not decreased significantly and sepsis remains a common cause of death in intensive care units worldwide^[2]. In recent years, studies have found that patients with sepsis combined myocardial injury have a higher risk of inducing multi-organ dysfunction and failure and a worse prognosis^{[3, 4]}. Through epidemiological surveys, it has been found that sepsis patients with combined myocardial insufficiency account for about 40%-50% of the total number of patients, and their morbidity and mortality rates are as high as 70%-90%^[5], and septic cardiomyopathy has become one of the common causes of death in intensive care units. Therefore, effective prevention, early diagnosis and intervention of septic cardiomyopathy can significantly reduce sepsis morbidity and mortality and improve sepsis prognosis.

Septic cardiomyopathy, also known as sepsis-induced myocardial dysfunction, was first described in 1984 as a reversible myocardial depression with an initial decrease in left ventricular ejection fraction accompanied by a decrease in cardiac index^[6]. However, with the continuous updating of relevant studies, we found that not only left ventricular systolic dysfunction exists in septic cardiomyopathy, but also left ventricular diastolic dysfunction and right ventricular systolic dysfunction^[7]. Although septic cardiomyopathy has not been clearly defined, most scholars agree that septic cardiomyopathy is a reversible myocardial depression characterized by left ventricular dilatation and biventricular systolic and/or diastolic dysfunction, and the recovery period is 7–10 days^[8]. However, there are no clear guidelines for the diagnosis, treatment, and management of septic cardiomyopathy, and the independent risk factors associated with its prognosis are not yet known.

Iron is a trace element that is essential in many basic processes in humans and bacteria^[9]. Numerous studies have been conducted to find an association between alterations of iron metabolism and sepsis. By analyzing serum iron levels in patients with sepsis, found that serum iron levels were reduced in patients with sepsis compared to healthy controls^[10]. In parallel, a significant reduction in serum iron and transferrin saturation levels was also observed in established animal models of sepsis^[11]. In addition, it was found that ferritin levels were elevated in sepsis patients and higher in the mortality group^[12], while transferrin levels were reduced, and that low levels of transferrin led to increased morbidity and mortality in sepsis patients^[13]. It is important to note that in recent years, studies have found that changes in iron metabolism are associated with cardiovascular disease. It has been suggested that decreased serum iron and transferrin levels may be involved in the development of heart failure, and it has also been found that iron deficiency is associated with a poor prognosis in patients with heart failure, and that the symptoms of patients with heart failure improve with appropriate intravenous iron supplementation^{[14, 15]}. It has also been demonstrated that elevated serum iron levels are associated with the mechanism of atherosclerosis^[16]. However, the relationship between alterations of iron metabolism and septic cardiomyopathy is currently unclear. A recent study in an animal model of septic cardiomyopathy found elevated ferritin levels and decreased transferrin and transferrin receptor levels in the septic cardiomyopathy group^[17]. However, another experimental animal study found significantly lower ferritin levels in the septic cardiomyopathy group^[18]. This illustrates the complexity of iron metabolism in septic cardiomyopathy and suggests that iron metabolism may serve as a potential biological marker for the diagnosis and prediction of prognosis in septic cardiomyopathy.

The intestinal tract is the largest reservoir of bacteria in the body and holds a large number of different microorganisms known as the gut microbiota^{[19, 20]}. Through continuous evolution, the gut microbiota is inextricably linked to the host and is an important micro-ecosystem^[21]. In healthy humans, the gut microbiota plays an important role in maintaining human immunity and physiology^[22]. It has been shown that a balanced and diverse gut microbiota enhances immunity, while dysbiosis of the gut microbiota can increase susceptibility to sepsis by increasing pathogenic bacteria and decreasing beneficial bacteria, and that sepsis itself and antibiotic therapy can further worsen the gut microbiota^[23]. A recent study found that a significant reduction in gut microbiota diversity occurred in ICU patients with sepsis and was accompanied by a significant increase in the number of inflammation-associated gut microbiota. At the same time, the abundance of pathogenic bacteria such as Enterococcus was significantly increased in non-surviving sepsis patients, suggesting that the gut microbiota is expected to be a potential marker for predicting the prognosis of sepsis patients^[24]. In addition, targeted treatment of microbiota such as probiotics, prebiotics and fecal transplants can restore the structure of the gut microbiota, reduce the inflammatory response and improve the prognosis of patients with sepsis^[25]. Interestingly, recent studies suggested that changes in the gut microbiota may be involved in the pathogenesis of cardiovascular diseases, such as coronary atherosclerosis and heart failure^{[26, 27]}. However, few studies have now reported the changes and role of gut microbiota in septic cardiomyopathy.

In addition, a correlation between the gut microbiota and iron metabolism has been demonstrated. An experimental animal study on fatty liver disease found that changes in dietary iron content significantly reshaped the composition of the gut microbiota, and that the gut microbiota also led to altered iron metabolism^[28]. It has been suggested that the intestinal microbiota may regulate iron absorption through short-chain fatty acids, mainly propionic and acetic acids^[29]. Therefore, we postulate that gut microbial changes in patients with septic cardiomyopathy may have a correlation with iron metabolism.

Our study aims to assess the differences in iron metabolism and gut microbiota between septic cardiomyopathy and non-septic cardiomyopathy, and to explore the correlation between iron metabolism and gut microbiota and their role in the development of septic cardiomyopathy, providing new ideas for the diagnosis and treatment of septic cardiomyopathy. In addition, we hope to explore potential biological markers for early diagnosis of septic cardiomyopathy.

Study design and patients

This study was a single-center, prospective, observational study. The study was approved by the Medical Ethics Committee of Subei People's Hospital (Grant No. 2022ky110), and informed consent was signed with patients or their families. The trial completed registration at the China Clinical Trials Registry (registration number ChiCTR2200056572), and the study was conducted in strict accordance with the registration information. Adult patients with sepsis and septic shock admitted to the intensive care medicine department of Subei People's Hospital between February 2022 and September 2022 were selected.

The study was approved by the Ethics Committee of Subei People's Hospital, and all participants had their informed consent signed by themselves or their families. Patients included in the study were divided into a septic cardiomyopathy group and a non-septic cardiomyopathy group according to the grouping criteria. Inclusion criteria: (1) adult patients > 18 years old; (2) meeting the criteria for sepsis-3^[1];(3) diagnostic criteria for septic cardiomyopathy (meeting one of them)^{[30, 31]}: ①Left Ventricular Ejection Fraction (LVEF) < 50%; ②Early mitral valve peak diastolic velocity/early mitral annular peak diastolic velocity (E/e') > 11; ③Early mitral valve peak diastolic velocity/late mitral valve peak diastolic velocity (E/ A) < 1 or > 2; ④ Tricuspid annular plane systolic excusion (TAPSE) < 1.6 cm. Exclusion criteria: (1) Previous history of chronic cardiac insufficiency;(2) Severe arrhythmias that may affect ultrasound measurements༛(3) Moderate to severe mitral and/or aortic valve disease༛(4) Patients who have undergone colostomy or ileostomy༛(5) Diagnosis of sepsis more than 24 hours prior to enrollment༛(6) Echocardiogram not performed within 72 hours or unclear echocardiogram images.

Blood specimens and stool specimens were collected on day 1, day 3 and day 7 of patient enrollment. We abbreviated the stool specimens for the septic cardiomyopathy group as SCM and for the non-septic cardiomyopathy group as SM. We recorded the patient's diagnosis of septic cardiomyopathy on day 1 and enrolled as SCM1, followed by SCM2 and SCM3 on days 3 and 7, respectively. we collected stool specimens on days 1, 3, and 7 of enrollment. The first sample of the enrolled patients was collected before the administration of nutritional support and after the administration of antibiotics.

Methods

Echocardiographic measurement

We diagnose septic cardiomyopathy based on echocardiography^{[30, 31]}, and transthoracic echocardiographic measurements were performed by a senior sonographer who was unaware of the experimental design. Measurements were performed using a Chinese Philips (Sparq) echocardiography M5s probe (frequency 1.7–3.4 MHz). All parameters were averaged over 3 to 5 cardiac cycles. In the M mode, the left ventricular long-axis view places the sampling line perpendicular to the interventricular septum and left ventricular posterior wall, measures the left ventricular end-diastolic internal diameter and left ventricular end-systolic internal diameter, and automatically calculates LVEF by means of a cardiac examination software package. In the apical four-chamber cardiac section, the PW or CW sampling interval is placed on the left ventricular chamber side of the mitral valve orifice, and the image is frozen after obtaining the mitral valve orifice blood flow spectrum, and the peak flow velocity at peak E (early diastole) and peak flow velocity at peak A (end diastole) are measured separately by the cardiac examination software package, and the results of E/A can be calculated directly. In the apical four-chamber cardiac section, the PW sampling interval was placed at the septal mitral annulus, and TDI (tissue Doppler) was turned on to obtain the mitral annulus tissue Doppler spectrum after freezing the image; the peak flow velocity of the e' peak (early diastolic reversal peak) was measured by the cardiac examination software package, and E/e' could be calculated directly based on the peak flow velocity of the E peak. And measurements were made in apical four-chamber cardiac view in M mode, with the sampling line aligned to the lateral tricuspid annulus, and then TAPSE was measured on time-to-motion images.

Extraction, amplification and sequencing of genomic DNA

The genomic DNA of the samples was extracted by CTAB or SDS method, and then the purity and concentration of DNA was detected by agarose gel electrophoresis, and then an appropriate amount of sample DNA was taken in a centrifuge tube and the sample was diluted to 1 ng/µl using sterile water. Using diluted genomic DNA as a template, PCR is performed using specific primers with Barcode (Phusion® High-Fidelity PCR Master Mix with GC Buffer, New England Biolabs) and efficient high-fidelity enzymes according to the selection of sequencing regions to ensure amplification efficiency and accuracy. The PCR products were detected by electrophoresis using 2% concentration of agarose gel; the PCR products that passed the test were purified by magnetic beads, quantified by enzyme labeling, mixed in equal amounts according to the concentration of PCR products, mixed thoroughly and then detected by electrophoresis using 2% agarose gel, and the products were recovered for the target bands using the gel recovery kit provided by qiagen. The library was constructed using TruSeq® DNA PCR-Free Sample Preparation Kit. The constructed library was quantified by Qubit and Q-PCR, and after the library was qualified, the library was sequenced using NovaSeq6000.

16S rDNA data analysis

The data of each sample was split from the downstream data according to Barcode sequence and PCR amplification primer sequence, and the reads of each sample were spliced by FLASH^[32](V1.2.7, http://ccb.jhu.edu/software/FLASH/)after truncating Barcode and primer sequence, and then valid data were obtained by filtering process and chimera removal process^{[33, 34]}. The entire valid data of all samples are clustered using the Uparse algorithm (Uparse v7.0.1001)^[35], which by default clusters the sequences into Operational Taxonomic Units (OTUs) with 97% consistency. Fast multiple sequence comparison was performed using MUSCLE^[36] (Version 3.8.31) software, and then homogenized with the least amount of data in the sample. Alpha diversity and Beta diversity analyses were performed using Qiime software (Version 1.9.1) and R software (Version 2.15.3). Parametric and non-parametric tests were performed separately for the analysis of variance, and the T-test and wilcox test were chosen if there were only two groups, and the Tukey test and the wilcox test of the agricolae package were chosen if there were more than two groups. Linear discriminant analysis of effect sizes was performed using LEfSe software, and the screening value for linear discriminant analysis was 4.

Data collection

Demographic characteristics and underlying medical conditions were systematically recorded for each case. The clinical parameters, including site of infection and chronic coexisting diseases, were recorded. The central venous pressure (CVP), APACHE II score, and SOFA score after admission were recorded. Laboratory indices were recorded, including lactate (Lac), cardiac Troponin I (cTnI), B-type natriuretic peptide (BNP), procalcitonin (PCT), serum iron (SI), serum ferritin (SF), serum transferring (SF), transferrin saturation (TSAT), total iron binding capacity (TIBC), and soluble transferrin receptor (sTfR). Main observation: differences in iron metabolism parameters and gut microbiota diversity between the two groups of patients. Secondary observables: 28-day morbidity and mortality rate, length of ICU stay, total length of stay, mechanical ventilation rate, continuous renal replacement therapy (CRRT) use rate, vasopressor dosing intensity (VDI), and fluid use within 24 hours of admission.

Statistical analysis

All statistical data were processed using IBM SPSS 27.0.1 software. Categorical variables were expressed as frequencies and percentages (n, %), and comparisons between groups were made using the χ2 test or Fisher's exact test. Normally distributed continuous variables were expressed as mean ± standard deviation (M ± SD), and two independent samples t-test was used for comparison between groups. Non-normally distributed measurements were expressed as median and interquartile range (M(IQR)), and the rank sum test was used for comparison between groups. The area under the subject work characteristic (ROC) curve was used to evaluate the predictive ability for septic cardiomyopathy. p < 0.05 was considered a statistically significant difference.

Admission Process

A total of 86 patients diagnosed with sepsis and septic shock during the study period were included in the screening according to the sepsis-3 diagnostic criteria. Thirty-eight patients were excluded based on exclusion criteria: 20 patients admitted to the ICU after enterostomy, 10 patients with previous cardiac insufficiency, 6 patients with severe arrhythmias or severe regurgitation of valves, and 2 patients whose patients refused to participate in the study. Finally, 48 patients were enrolled in the study, with 25 patients in the non-septic cardiomyopathy group and 23 patients in the septic cardiomyopathy group according to the grouping basis. The enrollment process is detailed in Figure1.

Baseline data characteristics and comparison of each indicator

Of the 48 patients included in the analysis, the mean age was 66.46 years, with 68.8% of the patients being male. Pulmonary infections were the main infection source

in the patients, with a total of 22 cases (45.8%). Hypertension was the most common among the combined underlying conditions, with a total of 21 cases (43.8%). Mechanical ventilation and CRRT were used after admission in 32 and 19 patients each, accounting for 66.7% and 39.6%, respectively. The patient 28-day morbidity and mortality rate was 31.3% (15/48), with a median ICU length of stay of 12 days (IQR 6-21) and a median total length of stay of 19 days (IQR 12-26). There was no statistical significance between the septic cardiomyopathy and non-septic cardiomyopathy groups including age, BMI, SOFA score, and APACHE II score (P > 0.05). There were no statistically significant laboratory indices including cTnI, BNP, PCT, and IL-6 between the two groups (P > 0.05). However, there were statistically significant differences in the percentage of men, number of smokers, number of alcohol drinkers, CVP and Lac levels between the two groups (P < 0.05), and all of them were higher in the septic cardiomyopathy group than in the non-septic cardiomyopathy group (Table1).

Table1. of baseline characteristics of enrolled patients

BMI: body mass index; SOFA score: sequential organ failure score; APACHE II score: acute physiological and chronic health score; CVP: central venous pressure; Lac: lactate; cTnI: troponin I; BNP: B-type natriuretic peptide; PCT: calcitoninogen; IL-6: interleukin-6; ICU: intensive care unit

Iron metabolism levels in patients with septic cardiomyopathy at different time periods

As shown in Figure2, there was a statistically significant decrease in serum ferritin levels between the two groups from day 1 to day 3 after enrollment, and the ferritin levels were higher in both septic cardiomyopathy groups than in the non-septic cardiomyopathy group. In both groups, the day 1 ferritin levels were 1189 ng/ml (IQR 560-5991) and 643 ng/ml (IQR 329-1175), respectively (P=0.021), and the day 3 ferritin levels were 791 ng/ml (IQR 533.75-1317.25) and 456 ng/ml (IQR 329- 967) (P=0.05). After day 7 of enrollment, ferritin levels were 659 ng/ml (IQR 459-1316) in the septic cardiomyopathy group and 428 ng/ml (IQR 296- 705.50) in the non-septic cardiomyopathy group, with no statistical difference between the two groups (P=0.056). In addition to this, soluble transferrin receptors on day 7 of enrollment were also statistically different (P=0.040) between the two groups of patients (2.25±0.94 mg/l in the septic cardiomyopathy group and 3.25±1.35 mg/l in the non-septic cardiomyopathy group) (Table2). Other iron metabolism levels including serum iron, transferrin, transferrin saturation, and total iron binding capacity on days 1, 3, and 7 were not statistically significant between the two groups (P > 0.05).

Value of iron metabolism levels for early diagnosis of septic cardiomyopathy

We performed ROC analysis of iron metabolism at different time periods in the septic cardiomyopathy group of patients and showed that ferritin levels on days 1 and 3 had predictive value for early identification of septic cardiomyopathy, with an area under the ROC curve of 0.694 (P=0.021) and 0.689 (P=0.050), respectively (Table3, Figure3). We also combined ferritin levels on days 1 and 3 to predict the risk of disease, and the results showed an area under the ROC curve of 0.673 (P =0.073). The sensitivity of predicting the risk of septic cardiomyopathy was 47.83% and the specificity was 84% according to the Jorden index, using day 1 ferritin 1413 ng/ml as the cut-off point.

Table2. Comparison of iron metabolism levels between the two groups of patients at different time points

Indicator		Sepsis cardiomyopathy group		Non-septic cardiomyopathy group		P-value
	n=23		n=25
D1 SI (umol/l)	7.74(3.60,17.67）		5.43(4.15,7.97)		0.231
D1 SF (ng/ml)	1189(560,5991）		643(329,1175)		0.021
D1 TRF (mg/L）	3.72(2.93,4.27)		3.61(2.99,4.55)		0.992
D1 TSAT	0.26(0.14,0.62）		0.24(0.12,0.42)		0.464
D1 TIBC (umol/l)	29.70(23.37,34.11)		28.82(23.84,36.32)		0.992
D1 sTfR (mg/l)	2.47±1.34		3.15±1.35		0.084
	n=18		n=19
D3 SI (umol/l)	8.04(5.64,10.47）		8.71（5.40,12.65）		0.715
D3 SF (ng/ml)	791.00(533.75,1317.25)		456(321,967）		0.05
D3 TRF (mg/L）	3.62(3.19,3.90)		3.90(3.37,4.72)		0.197
D3 TSAT	0.27(0.21,0.32)		0.24(0.15,0.50)		0.843
D3 TIBC (umol/l)	28.89(25.45,31.14)		31.16(26.93,37.64)		0.197
D3 sTfR (mg/l)	2.39±0.98		2.54±1.12		0.678
	n=13		n=12
D7 SI (umol/l)	7.73(5.65,10.85)		7.66(4.89,9.65)		0.744
D7 SF (ng/ml)	659.00(459.00,1316.00)		428.00(296.00,705.50)		0.056
D7 TRF (mg/L）	3.88±0.79		3.54±1.3		0.436
D7 TSAT	0.28(0.20,0.33)		0.27(0.15,0.44)		0.744
D7 TIBC (umol/l)	30.97±6.28		28.28±10.38		0.436
D7 sTfR (mg/l)	2.25±0.94		3.25±1.35		0.04

SI: serum iron; SF: serum ferritin; TRF: transferrin; TSAT: transferrin saturation; TIBC: total iron binding capacity; sTfR: soluble transferrin receptor

Table3. The value of iron metabolism at different time periods for the diagnosis of septic cardiomyopathy

Indicator	AUC	95%CI	Cut-off value	Sensitivity (%)	Specificity (%)
D1 SF	0.694	0.545 - 0.843	1413	47.83	84
D3 SF	0.689	0.516 - 0.861	475.5	88.89	52.63
D1+D3 SF	0.673	0.496 - 0.849	0.347	94.44	42.11

SF: serum ferritin; AUC: area under the subject's working characteristic curve; 95% CI: 95% confidence interval

Gut microbiota

Gut microbiota richness and Alpha diversity

By sequencing the stool samples of all patients included in the analysis, a total of 3422 OUTs were observed in all samples. 303 unique OUTs were found in the septic cardiomyopathy group, while 2298 OUTs were found in the non-septic cardiomyopathy group, with 821 OTUs common to both groups. Venn diagram results indicated that patients in the non-septic cardiomyopathy group had a higher gut microbiota were more abundant (Figure4).

We analyzed and compared the richness indices of the intestinal microbiota of the two groups of patients, including the ACE index (Figure5A) and Chao1 index (Figure5B), and the results showed that there was a significant difference in the richness of the intestinal microbiota between the two groups (P < 0.01). The abundance was significantly higher in the non-septic cardiomyopathy group than in the septic cardiomyopathy group in both groups, indicating that the gut microbiota species species

were significantly reduced in the septic cardiomyopathy group. We also analyzed and compared the Alpha diversity indices of the gut microbiota between the two groups of patients, including Shannon's index (Figure5C) and Simpson's index (Figure5D), where the Simpson's index presentation form we used Simpson's Index of Diversity (1 - D), and the results suggested that the Alpha diversity was not statistically different between the two groups of patients (P > 0.05). We further assessed the time-related changes in gut microbiota Alpha diversity in both groups. As shown in Figure5D, there was no significant change in the Simpson index of gut microbiota on day 1 compared with day 3 in the septic cardiomyopathy group, but the Simpson index of gut microbiota on day 7 in the septic cardiomyopathy group showed a decrease and was statistically different (P < 0.05). It illustrates that the gut microbiota Alpha diversity decreased with time in patients in the septic cardiomyopathy group. However, the gut microbiota Alpha diversity index of patients in the non-septic cardiomyopathy group did not change significantly with increasing time, and the results suggest that the diversity of the gut microbiota of patients in the non-septic cardiomyopathy group was at a standstill during this 7-day period.

Beta diversity of gut microbiota

We performed PCoA analysis based on Unweighted Unifrac distance and selected the combination of principal coordinates with the highest contribution for graphing to compare Beta diversity between patients in the septic cardiomyopathy and non-septic cardiomyopathy groups. As shown in Figure6, there was a significant difference in Beta diversity between the two groups of patients (P < 0.001), indicating that the gut microbiota structure in the septic cardiomyopathy group was significantly different from that in the non-septic cardiomyopathy group.

Microbial composition and characteristics

We compared the distribution of gut microbiota at different taxonomic levels between the two groups and showed significant differences in the distribution of gut microbiota at different levels between the two groups (Figure 7A-D). At the phylum level, the relative abundance of both Actinobacteria (P = 0.018) and an unknown_Bacteria (P = 0.024) was lower in the septic cardiomyopathy group than in the non-septic cardiomyopathy group (Figure7E). At the family level, the proportion of Aeromonadaceae was significantly lower in the septic cardiomyopathy group than in the non-septic cardiomyopathy group (P = 0.023) (Figure7F). At the genus level, patients in the septic cardiomyopathy group had significantly higher relative abundance of Citrobacter (P = 0.007) and lower relative abundance of Parasutterella (P = 0.043) compared with the non-septic cardiomyopathy group (Figure 7G). At the species level, the relative abundance of Corynebacterium_tuberculostearicum was significantly lower in the septic cardiomyopathy group compared with patients in the non-septic cardiomyopathy group (P = 0.014), whereas Bacteroides nordii (P = 0.037) and [Clostridium]_celerecrescens (P = 0.026) had significantly higher microbial abundance (Figure7H).

Dysbiosis of the gut microbiota

We studied the composition of the gut microbiota in both groups by LEfSe analysis and identified specific gut microbiota in the septic cardiomyopathy group (LDA score > 4, P < 0.05). As shown in Figure8B, the top two gut microbiota in the septic cardiomyopathy group were Enterobacter (LDA score = 4.2747, P = 0.003) and Klebsiella_quasipneumoniae (LDA score = 4.2747, P = 0.003). Thus, Enterobacter and Klebsiella_quasipneumoniae may be the specific intestinal microorganisms of septic cardiomyopathy.

Functional changes in microbiota between groups

We analyzed the different functional pathways between the septic cardiomyopathy and non-septic cardiomyopathy groups. 16S rDNA sequencing data were analyzed at level 3 by applying the Tax4Fun prediction function. Compared to stool samples from the non-septic cardiomyopathy group, changes in the abundance of intestinal microbiota in patients with septic cardiomyopathy were associated with pathways such as porphyrin and chlorophyll metabolism, lipopolysaccharide_biosynthesis_proteins and biotin_metabolism (Figure9).

Relationship between species-level abundance of gut microbiota and clinical indicators

We analyzed the relationship between the abundance of gut microbiota species levels and clinical indicators in patients with septic cardiomyopathy (Figure10). Through Spearman correlation analysis, we found that changes in the gut microbiota may be a potential cause of changes in clinical indicators.

Bacteroides_thetaiotaomicron was positively correlated with BNP, day 1 serum iron, and day 1 transferrin saturation (P < 0.05). Bacteroides_fragilis was negatively correlated with cTnI (P < 0.05). Prevotella_disiens and Prevotella_timonensis were negatively correlated with day 1 serum ferritin (P < 0.05). Bacteroides_vulgatus, Bacteroides_fragilis, and Prevotella_buccalis were negatively correlated with day 1 transferrin, day 1 total iron binding (P < 0.05).

Secondary observation indicators

In terms of clinical outcomes, as shown in Table4, the 28-day morbidity and mortality rate was 43.5% (10/23) for patients in the septic cardiomyopathy group and 20% (5/25) for patients in the non-septic cardiomyopathy group, but there was no statistically significant difference between the two groups (P = 0.08). The median length of ICU stay and total length of stay were 13 days (IQR 6-26) and 15 days (IQR 10-27) in the septic cardiomyopathy group and 12 days (IQR 6-15) and 19 days (IQR 12-24) in the non-septic cardiomyopathy group, respectively, with a median difference of 1 day and 4 days between the two groups, but not statistically significant (P = 0.642 and P = 0.844). The number of patients on mechanical ventilation and CRRT was 15 (65.2%) and 11 (47.8%) in the septic cardiomyopathy group and 17 (68%) and 8 (32%) in the non-septic cardiomyopathy group, respectively, with no statistically significant differences between the two groups (P = 0.838 and P = 0.263). The total 24-hour post-admission crystalloid volume was 3295.43±1327.35 ml in the septic cardiomyopathy group and 2649.8±1292.79 ml in the non-septic cardiomyopathy group, with no statistically significant difference between the two groups (P = 0.095), and the total 24-hour post-admission colloid volume was 250 ml in the septic cardiomyopathy group (IQR 0-1100) and 250 ml in the non- septic cardiomyopathy group was 150 ml (IQR 0-325), with no statistically significant difference between the two groups (P = 0.080).

24-hour vasoactive drug utilization

Analysis of vasoactive drug use between the two groups of patients showed no statistically significant differences in the use of norepinephrine, dopamine, epinephrine, or vasopressin between the two groups (P > 0.05). Further analysis of the intensity of vasoactive drugs between the two groups of patients revealed that there was also no statistically significant difference between the two groups (P = 0.236) (Table5).

In the present study, we analyzed the level of iron metabolism and the composition of the gut microbiota in ICU patients with septic cardiomyopathy and non-septic cardiomyopathy patients. At the same time, we investigated the relationship between iron metabolism levels and septic cardiomyopathy at different time periods, and we also investigated the relationship between the gut microbiota and clinical indicators related to septic cardiomyopathy. The main objective of our present study was to understand whether intestinal flora induces septic cardiomyopathy by affecting iron metabolism and also to find potential biological markers for early identification of septic cardiomyopathy. With this analysis, we found statistical differences in iron metabolism levels between the two groups of patients, especially differences in ferritin levels. We also found differences in the taxonomic composition and microbial diversity of the gut microbiota between the two groups of patients, as well as differences in the distribution of the gut microbiota at different levels.

Our study found no statistically significant differences in gut microbiota Alpha diversity between the septic cardiomyopathy and non-septic cardiomyopathy groups. However, there were significant differences in the abundance and Beta diversity of the gut microbiota between the two groups of patients. This suggests that the structure of the gut microbiota in patients with septic cardiomyopathy differs from that of patients with non-septic cardiomyopathy. In addition to this, we found that the composition of the gut microbiota was significantly different between the septic cardiomyopathy and non-septic cardiomyopathy groups. The proportion of Actinobacteria was significantly reduced in patients with septic cardiomyopathy compared to those with non-septic cardiomyopathy. In contrast, a previous study by Chen Y et al^[37] showed that patients with septic cardiomyopathy were characterized by an increase in Proteobacteria and a decrease in Bacteroidota at the portal level, which is inconsistent with our findings. Combining multiple relevant factors that affect the gut microbiota^[38], including geography, diet, nutritional support, and antibiotic use, I must interpret our findings cautiously here. Our study was based on the use of antibiotics after and may not be generalizable given the geographical and dietary differences. In addition to differences at the phylum level, other microorganisms at different levels, such as Aeromonadaceae, Citrobacter, Bacteroides_nordii, and [Clostridium]_celerecrescens, were also statistically different between the two groups. Citrobacter is part of the Enterobacteriaceae family^[39] and is a member of the normal flora of the intestine and is a conditional pathogenic bacterium. Previous studies have shown that it may lead to a variety of infections, such as urinary tract infections, abdominal infections, and sepsis^[40], commonly in critically ill patients with multiple comorbidities^[41]. Citrobacter has been reported to be multi-drug resistant^[42] and associated with high mortality, which may be due to its promotion of systemic inflammatory responses and sepsis^[43]. This hints at the complexity of antibiotic selection in patients with septic cardiomyopathy and the refractory nature of septic cardiomyopathy. In a study of the distribution of intestinal microbiota species, it was found that the abundance of intestinal Citrobacter was significantly higher in patients with septic cardiomyopathy, and LEfSe analysis also indicated that Enterobacter and Klebsiella_quasipneumoniae of the Enterobacteriaceae family were the dominant intestinal bacteria in patients with septic cardiomyopathy, suggesting that changes in the abundance of Enterobacteriaceae could be a potential biological marker for septic cardiomyopathy, but the exact mechanism is not well understood, and whether it affects cardiomyocyte activity through some metabolite still needs to be confirmed by further studies.

In the present study, we also found statistical differences in ferritin levels between the septic cardiomyopathy and non-septic cardiomyopathy groups, with ferritin levels exceeding the normal range in both groups and higher in the septic cardiomyopathy group than in the non-septic cardiomyopathy group. The potential role of ferritin in promoting lipopolysaccharide-induced septic cardiomyopathy has been previously investigated^[17]. This suggests that ferritin is either a key biological marker for the early diagnosis of septic cardiomyopathy. Our study also analyzed this conjecture and found that ferritin levels on days 1 and 3 were valuable for the diagnosis of septic cardiomyopathy, but unfortunately, their sensitivity and specificity were low and the area under the ROC curve was below 70%. Therefore, we combined day 1 and day 3 ferritin to diagnose septic cardiomyopathy, and the results showed no statistical significance. We believe that the reason for this result may be related to our small sample size, but it is undeniable that our study also suggests the potential value of ferritin for the early diagnosis of septic cardiomyopathy, which we need to validate in future by conducting larger studies.

We also performed Spearman rank correlation analysis of the top 30 gut microbiota species abundance in the septic cardiomyopathy group with clinical indicators as well as iron metabolism parameters. The results showed a significant correlation between the gut microbiota and clinical indicators and iron metabolism parameters in patients with septic cardiomyopathy. Cardiac Troponin I and B-type natriuretic peptide are potential indicators of myocardial dysfunction, with cardiac Troponin I representing ischemic necrosis of myocytes^[44] and B-type natriuretic peptide reflecting myocardial loading status^[45]. Interestingly, the two Bacteroides member bacteria showed opposite trends, which may be due to the influence of other confounding factors, such as sex, age, and severity of disease. We hypothesize that Bacteroides may be associated with the progression of septic cardiomyopathy, and future larger multicenter studies are needed to test this hypothesis. Notably, some of these gut microbes were found to be associated with several iron metabolism parameters, such as Bacteroides thetaiotaomicron which was positively associated with serum iron and transferrin saturation. There are also multiple intestinal microorganisms associated with the same iron metabolism index, for example, Prevotella_disiens and Prevotella_timonensis were negatively correlated with ferritin, and Bacteroides_vulgatus, Bacteroides_fragilis, and Prevotella_buccalis were negatively correlated with transferrin and total iron binding. Interestingly, both Bacteroides and Prevotella belong to Bacteroidota^[46], and in agreement with the findings between them, Bacteroidota is involved in the regulation of iron homeostasis^[47]. In recent years, it has been found that changes in gut microbiota may be associated with iron metabolism and that gut microbes themselves can lead to changes in iron metabolism^[28]. Sang-Hyun Cho et al^[48]. found that flora can bind transferrin through iron-regulated proteins, leading to increased intracellular iron concentration and decreased serum iron concentration. According to the study, excess intracellular iron induces lipid peroxidation, which leads to ferroptosis, and ferroptosis is one of the key mechanisms of sepsis-induced myocardial dysfunction^[18]. Therefore, we hypothesize that sepsis can induce changes in iron metabolism through gut microbial dysbiosis and subsequently lead to myocardial injury, but the exact mechanism needs to be further investigated.

By functional prediction, we found differences in some KEGG pathways in the two groups of patients. Patients with septic cardiomyopathy exhibited a significant increase in certain KEGG pathways (e.g., porphyrin and chlorophyll metabolism, lipopolysaccharide_biosynthesis_protein and biotin_metabolism) compared to patients with non-septic cardiomyopathy. One study found that plasma lipopolysaccharide levels are associated with an increased risk of cardiovascular disease^[49], and the source of most plasma lipopolysaccharides is the intestine^[50]. Although there are few metabolomic studies related to septic cardiomyopathy, we can speculate that dysbiosis of the gut microbiota in septic patients may lead to elevated plasma lipopolysaccharide levels and induce the development of septic cardiomyopathy. At the same time, changes in these functional pathways may have a protective or detrimental effect on patients with septic cardiomyopathy, which may be a target for future treatment or prevention of septic cardiomyopathy.

In addition, our findings suggest that the presence of myocardial damage in patients with sepsis is associated with their poor prognosis. Our study found that patients with septic cardiomyopathy had increased intensity of vasopressor dosing intensity as well as increased length of ICU stay and increased 28-day morbidity and mortality compared with patients with non-septic cardiomyopathy, although these differences were not significant and may be related to the small sample size we included in our analysis. The study by Kim et al^[51]. also found that myocardial dysfunction exacerbated the severity of sepsis patients and worsened their prognosis, which is consistent with our findings. Therefore, we need to pay further attention to sepsis-induced myocardial dysfunction, and early identification, early diagnosis, and early intervention in the treatment of myocardial dysfunction may be effective in improving the prognosis of patients with sepsis.

Of course, there are some limitations to our study. First, our study was an observational study and there was not a rigorous process for treatment protocols by the supervising physicians, which may have led to some degree of bias in our study. Second, the present study is a single-center study, the number of cases included in the study is small, and there are many factors affecting changes in gut microbial diversity, and our findings may lack generalizability. Then, our study found statistically significant differences in the proportion of smoking and alcohol consumption between the two groups of patients, but we did not further analyze the differences in gut microbiota and changes in iron metabolism levels between the two groups in a stratified manner. Finally, our study observed differences in gut microbiota between septic cardiomyopathy and non-septic cardiomyopathy and also found correlations between gut microbiota and iron metabolism, unfortunately we did not analyze the correlation between differential flora and iron metabolism in the two groups of patients. Therefore, we need to further improve the study protocol in future studies.

Our study suggests that ferritin may have predictive value for early identification of septic cardiomyopathy, while Enterobacteriaceae may be the gut microbiota specific to septic cardiomyopathy. In addition, alterations in gut microbiota diversity may affect changes in iron metabolism and ultimately induce the development of septic cardiomyopathy, which needs to be verified in larger studies in the future.

Abbreviations	Full Name
APACHE	Acute Physiology and Chronic Health Evaluation
BMI	Body Mass Index
BNP	B-type Natriuretic Peptide
CVP	Central Venous Pressure
CRRT	Continuous renal replacement therapy
cTnI	cardiac Troponin I
CI	Confidence Interva
ICU	Intensive care unit
IQR	Inter Quartile Range
LVEF	Left Ventricular Ejection Fraction
Lac	Lactate
OTUs	Operational Taxonomic Units
PCT	procalcitonin
ROC	Receiver Operating Characterist
SI	serum iron
SF	serum ferritin
sTfR	soluble transferrin receptor
SOFA	Sequential Organ Failure Assessment
TRF	serum transferring
TAPSE	Tricuspid annular plane systolic excusion
TIBC	total iron binding capacity
TSAT	transferrin saturation
ECG	Echocardiography

Ethics approval and consent to participate

The protocol was registered with the Chinese Clinical Trial Registry (No: ChiCTR2200056572) on 8 February 2022, and approved by the Ethical Committee of the Northern Jiangsu People’s Hospital (2022ky110). All patients gave informed consent.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they do not have any competing interests.

Funding

This work was supported by Jiangsu Province “333 Projects” (BRA2020183), Northern Jiangsu People’s Hospital Support Project (fcjs202024).

Authors' contributions

Jiangquan Yu developed the research protocol and conducted the initial research search. Xiang-Hui Li drafted the manuscript. Mengfei Zhang and Jiayan Yang included patients and collected data. Jing Wang and Lin Song completed the statistical analysis. Ruiqiang Zheng performed a critical review of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank all the reviewers who participated in the review. We would like to express our sincere gratitude to Dr. Shuyue Kong and Dr. Yutao Hu for their help in collecting samples.

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No competing interests reported.

Clinical study on the relationship between gut microbiota dysbiosis and iron metabolism and septic cardiomyopathy

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Study design and patients

Methods

Echocardiographic measurement

Extraction, amplification and sequencing of genomic DNA

16S rDNA data analysis

Data collection

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1