Analysis of correlations between gut microbiota, stool short chain fatty acids, calprotectin and cardiometabolic risk factors in postmenopausal women with obesity: a cross-sectional study

Cardiometabolic risk factors (CMRFs), including obesity, abnormal lipid profile, hypertension, insulin resistance, and aberrant glycemic control, play a role in the pathogenesis of cardiovascular diseases (CVD), which is one of the leading causes of mortality. Gut microbiota has been recently acknowledged as pivotal for human metabolic health [1, 2]. Indeed, high richness has been linked to a favourable metabolic profile [3, 4]. On the contrary, low bacterial gene counts with metabolic risk factors as demonstrated in a group of postmenopausal women with obesity [5]. Historically, germ-free mice colonized with microbiota transplant from the obese donors elevated their weight and body fat significantly more than in case of lean donors [6]. Also, a dozen of microbiota-originated or microbially-modified molecules has been recently acknowledged as factors contributing to metabolic outcome [7]. Interestingly, the production of these is strictly dependent on obesity status thus the microecological niche as elegantly shown in an in vitro study [8]. Importantly, some of microbiota-related markers were shown to be correlated significantly with type-2 diabetes (T2D) to a more considerable extent than anthropometric [9] and human genome-originated ones [10].

Excessive storage of lipids and diminished sensitive toward insulin have also been linked to mitochondrial dysfunction [11]. Interestingly, the condition of this organelle echoes the condition of microbiota [12]. For instance, in obese and diabetic patients subjected to bariatric surgery faster sugar control correlated with changes in Krebs cycle, ketone and short chain fatty acid metabolism [13]. To add, the oxidation of SCFAs inhibits lipolysis and synthesis improving lipid profile [14]. As certain antioxidants were found to increase the synthesis of SCFA [15], their use in prevention of cardiometabolic malfunctions is advised. Consequently, microbial indices might serve as markers for the early identification of metabolic disturbances [4].

Short Chain Fatty Acids (SCFA)—the main microbiota metabolites, represented mainly by acetate (C2), propionate (C3), and butyrate (C4) [16], are linked to multiple favourable metabolic functions [17]. For example, C2 is a significant source of energy for colonocytes [18], C3 might be a substrate for gluconeogenesis [17], and C4 might enter lipogenesis and cholesterol metabolism and, importantly, control appetite via the gut-brain axis [19]. Furthermore, SCFA positively affect cardiometabolic health, among other, via improving gut-barrier integrity [20‐22], in particular elevating the expression of GAP and tight junction proteins [23] and, consequently, decreasing inflammation [24]. On the other hand, SCFA, however, can harm human metabolism. They are a source of about 5 -10% of the calories consumed daily, and their metabolites are involved in synthesizing lipids and glucose [25]. Moreover, faecal SCFA are positively associated with body weight [6, 26, 27], and in the obesity phenotype, there is a substantial upregulation of pathways related to SCFA production [28]. Additionally, de la Cuesta et al. [29] observed the association between SCFA excretion and gut dysbiosis, increased gut permeability, adiposity, and cardiometabolic risk factors.

Gut barrier dysfunction is increasingly recognized as a key factor in the pathogenesis of obesity, diabetes and metabolic disorders [30]. Calprotectin has the potential to be used as an indirect marker of gut permeability [31]. It is a 24 kDa dimer formed by the protein monomers S100A8 (10,835 Da) and S100A9 (13,242 Da) and makes up to 60% of the soluble proteins in the cytosol of human neutrophils [32, 33]. This marker is used mainly in older children and adults as a marker for inflammatory bowel diseases (IBD) [34].

The study of Brahe et al. of the Danish cohort of postmenopausal women with obesity showed that several gut bacterial species are linked to metabolic risk markers, also after adjustment for potential confounders [2]. They also observed that dietary fibre and fat could modify a negative correlation with insulin resistance biomarkers for B. longum and F. prausnitzii. However, the study by Brahe et al. was performed using a not very common technology and analytical pipeline. Metabolome and intestinal permeability parameters were also not analyzed. Moreover, the adjustment for differences in age, body fat percentage and diet caused the disappearance of many correlations. Therefore we decided to perform a similar analysis on the Polish population of obese postmenopausal women using anthropometric and biochemical metadata and assessing the cardiovascular function. In addition, we assessed the metabolic function of the bacteria by analyzing SCFA in the stool and the intestinal barrier status by employing faecal calprotectin. Thanks to the commonly used sequencing technology (Illumina) and bioinformatic pipelines, we were able to compare the observed results with data from a public database. We also used very demanding models of data adjustment. The study aimed to verify the following research hypotheses: (1) microbiota, SCFA and calprotectin are correlated with anthropometric, physiological and biochemical parameters in postmenopausal women suffering from obesity, and (2) stool microbiota composition is different between obese and non-obese postmenopausal women.

The study took place from 27 February 2016 to 31 December 2017 and analysed the faecal microbiota in a population of obese postmenopausal women. It was conducted at the Department of Education and Treatment of Obesity and Metabolic Disorders University of Medical Sciences in Poznań, Poland. The protocol was registered at the U.S. National Institute of Health (ClinicalTrials.gov; Identifier: NCT03100162). Ethical approval was obtained from the Bioethical Committee of Poznan University of Medical Sciences (No. 871/2015) and prior written informed consent was obtained from all participants. The informed consent allowed samples to be used for future analyses. The material obtained during this study was analysed in a multidirectional manner, and the results were presented in peer-reviewed scientific publications [35‐38].

The summary of demographic, clinical characteristics, and metabolic parameters are presented in Table 1. A summary of the gut microbiome data is shown in Fig. 1 and Additional file 1: Fig. S2. SCFA and calprotectin concentration were shown in Table 1 and in Fig. 2. The concentration of acetic acid (C2) in the group varied widely and ranged from 2.42 µM/ml to 15 µM/ ml. Almost in every case, the percentage of this acid was the highest among the tested acids and was most often in the range from 30 to 60% in comparison to the other tested short-chain fatty acids (SCFA). The concentration of propionic acid ranged from 0.661 µM/ml to 27 µM/ml, butyric acid from 0.7 µM/ml to 10 µM/mg, valeric acid (C5) from 0.0588 to 5.7 µM/ml and caproic acid (C6) from 0.00577 µM/ml to 1.28 µM/ml. The data distribution, IQR and median for C3, C4, C5 and C6 indicate that the data were accumulated at the lower end of the concentration ranges. The percentage of C3 acid was in the range of 12%—80%, C4 5%—33%, C5 0.1—19% and C6 0.01%—5%. The highest concentration of calprotectin in the group was 579 µg/ml however the data distribution shows that most of the results were below 100 µg/ml. Determination limit of calprotectin was 2.267 ng/ml.

This cross-sectional study revealed numerous correlations between the gut microbiota and risk factors for cardio-metabolic diseases. The microbiota of the surveyed women is characterized by high taxonomic diversity. We observed that alpha diversity was strongly and negatively correlated with LPS concentration and weakly correlated with other metabolic risk factors such as TG and visceral fat. Low bacterial richness was observed in metabolic disorders [4], inflammation [45] and obesity [28]. The gut microbiota plays an essential role in regulating the intestinal permeability of the intestinal mucosa, and a change in the microbiota community affects the mucosal barrier function of the gut [46, 47]. The elevated level of circulating insulin, typical for obesity phenotype, was found to increase the intestinal permeability, allowing bacterial toxins, such as LPS, to leak into the circulation, which, in turn, initiated a cascade of inflammatory reactions, thus, explaining the subclinical inflammation present in obese and insulin-resistant patients [48]. In our study, stool calprotectin was positively correlated with vascular stiffness, which may also indicate the role of increased intestinal permeability in the pathogenesis of cardiovascular diseases.

We observed a positive correlation between the phylum Actinobacteria and the lower taxonomic groups belonging to the class Coriobacteria with blood glucose levels. These observations confirm the previously described association of this group of bacteria with obesity and metabolic disorders [28, 49]. In contrast, the Tenericutes phylum was negatively correlated with blood glucose concentration in our study. On the contrary, in the study by Yan et al., this phylum occurred more frequently in obese rats but decreased after inducing diabetes and then increased largely after sitagliptin treatment as compared to the diabetic [50]. Bacteroides caccae, which is negatively correlated with waist circumference, is involved in the degradation of mucus, which helps reduce intestinal inflammation by decreasing bacterial epithelial adhesion [51]. Class Bacilli and order Lactobacillales were positively correlated with SBP. Adnan et al. observed a strong positive correlation between SBP and the lactate-producing genus Lactobacillus [52]. Lactobacillus can also synthesize neurotransmitters in the autonomic nervous system [53]. Furthermore, plasma lactate levels have been associated with increased blood pressure [54].

On the other hand, administration of Lactobacillus plantarum 299v [55] and Lactobacillus casei [56] can lower systolic blood pressure. However, strain-specific effects can differ from the effects of bacterial order. Proteobacteria abundance correlated with free fatty mass is associated with a reduction in mucus production and impairment of the gut barrier, and low-grade inflammation and is also associated with metabolic diseases such as obesity [57].

Brahe et al., in a study conducted on a similar group of women, have also observed a relationship between other groups of bacteria and cardiometabolic risk factors. However, the observed compounds are related to other taxonomic groups of bacteria [2]. It should be emphasized that the microbiota analysis carried out by Brahe et al. differed in the methodology of bacterial DNA sequencing, analytical pipeline and statistical methods used. Inconsistent microbiome research results make it difficult to understand the exact relationship between the human gut microbiome and the resulting disease [58]. This may be due to selection bias, geographic differences, unknown confounding factors, taxonomic differences, or the lack of standard sample collection, processing and analysis methods [59]. For this reason, it is necessary to both standardize the research protocol [60] as well as use open databases containing the results of microbiome studies in various populations [61].

To assess the universality of our observations, which is essential in microbiome research [62], we compared our data with those contained in the free and open Qiita database (https://​qiita.​ucsd.​edu). We related the microbiota composition observed in our study to results obtained in a similar cohort of women in the American Gut Project (The Microsetta Initiative—Researching Global Microbiomes, https://​microssetta.​ucsd.​edu), with both high and normal BMI. However, our results differed significantly at various taxonomic levels in both the high and normal BMI groups. Of particular note is the much lower percentage of Proteobacteria and the higher percentage of Actinobacteria observed in our study. Obesity is associated with various gut microbiota composition profiles [63]. These differences may have originated from the distinctness of the geographical locations of the investigated populations. The microbiome is not only distinct in Western and Eastern populations [64] but also differs between countries with similar lifestyles (e.g. USA and UK) and regions of the same country [42]. It should also be emphasized that we compared different variable regions of the 16S gene (V1- V2 vs V4), which may be a source of differences in the taxonomic composition of the bacteria [65]. This can introduce inaccuracies while assessing taxonomy [66] and further create problems when comparing the results of different studies, performing meta-analyses, and drawing generalized conclusions about the importance of microbiota in different diseases. Functional profile analysis seems more valuable than taxonomic analysis for this purpose [67]. The results are consistent with our previous observations in a Polish population [68] and in postmenopausal women [69].

However, in order to fully relate the information we obtained to a healthy population, a control group consisting of healthy and otherwise matched postmenopausal women of normal weight would be necessary.

An essential element of microbiota research is metabolomic analysis. In our study, we used both bioinformatics tools that can be utilized to assess the metabolic potential of the tested bacteria, as well as the SCFA analysis in the stool. We have shown that different metabolic pathways involved in gut microbiota can be correlated with cardiometabolic risk factors. These include pathways mainly related to lipid, bicarbonate, biotin and nucleotide metabolism. The strong positive correlation between biotin biosynthetic pathways and visceral fat may be of particular interest. This is in line with observations made by Wu et al. [70], who observed an association between the abundance of bacterial biotin biosynthesis pathways in individuals with different failures of glucose metabolism. Of note, intestinal bacteria produce biotin during insufficient supply [71]. However, this problem is not resolved, as Belda et al. [72] observed a reduced abundance of biotin-producing bacteria in severely obese individuals. In addition, they found reduced blood concentrations of this vitamin. Of note, Belda et al. used metagenomic analysis and employed counts of faecal microbial cell density. Our analysis was based on amplicons and determining the relative abundance of bacteria; moreover, women not only with severe obesity were enrolled in our study. These might stand for the discrepancies in these results. Despite the confirmation of biotin deficiency in T2D [73] and the beneficial effects of biotin supplementation on glucose metabolism [74‐76], further research concerning the role of gut bacteria in biotin metabolism and its association with metabolic disorders and obesity is necessary to search for markers of these disorders and therapeutic possibilities. Also, purine metabolism was found to be positively associated with visceral fat. Altered purine metabolism was observed by Concepcion et al. [77] in a cohort of youths with T2D. Of note, hyperuricemia has been linked to visceral fat accumulation, and uric acid serves as the primary metabolite of balanced purine dietary uptake, synthesis, and excretion [78]. We also found that alpha diversity and abundance of numerous bacteria are correlated with faecal SCFA. Alpha diversity is negatively correlated with faecal propionic acid and positively with valeric and hexanoic acid. The correlations of other bacterial groups apply to those considered SCFA producers as well as to others. Interestingly, the abundance of some bacteria considered to be SCFA producers negatively correlates with the faecal SCFA. Similar situations occur in other populations, for example, in people with Parkinson's disease [79], which can be explained by the disturbed composition of bacteria in pathological states. Another factor that may influence the nature of the correlation between bacterial abundance and SCFA is a significant limitation of faecal SCFA analysis. The content of SCFA in the stool results from the production of these compounds by the intestinal bacteria, their absorption and their expenditure in situ in the gastrointestinal tract [80]. Faecal SCFA excretion results may not correspond to those measured in blood, which may better reflect SCFA production and absorption. Vogt and Wolever [81] showed that the rates of acetate absorption and excretion are inversely correlated. In studies that measured SCFA in the circulation compared to the faeces, slightly different conclusions were drawn about the relationship of SCFA with obesity and cardiometabolic health. Boets et al. found that obese people have lower plasma concentrations of propionate and butyrate than lean people [82]. Moreover, Layden et al. found that serum acetate concentration was inversely related to fasting and 2-h insulin levels and visceral adipose tissue [83]. It follows that the most reliable information can be obtained by analyzing the concentration of SCFA to provide an insight into all the processes of their transformation in the body. It can be assumed that pathological processes can lead to disorders of SCFA metabolism or absorption, which makes these compounds a potential marker of various disease states.

We have not confirmed a correlation between SCFA and cardiometabolic risk factors. SCFA are considered an important metabolite of gut microbiota with a beneficial effect on health. On the other hand, as a source of energy, they may be associated with the occurrence of overweight or obesity. The association of SCFA with metabolic disorders and obesity is, therefore, ambiguous. SCFA play regulatory functions in the lipids, cholesterol and glucose metabolism, immune response and gut barrier integrity. Other studies showed that SCFA in faeces were negatively correlated with adiposity parameters such as BMI, VAT and waist circumference [84, 85]. SCFA have been suggested to mediate the activation of G protein-coupled receptors, such as GPR41 and GPR43, inhibit fat accumulation in adipose tissue and accelerate the metabolism of unincorporated lipids as well as glucose in other tissues, leading to a subsequent improvement in insulin sensitivity [86, 87]. The release of gut-derived satiety hormones like glucagon-like peptide-1 and peptide YY have also been implicated in this action [88, 89]. SCFA also play a role in the balance of fatty acid synthesis, fatty acid oxidation, and lipolysis in the body's tissues through peroxisome proliferator-activated receptors [90]. Dietary supplementation with acetate, propionate, butyrate or their mixture can significantly inhibit the body weight gain induced by high-fat diet feeding [91].

On the other hand, the study performed in monozygotic twin pairs confirmed the positive effects of SCFA on obesity [92]. In other studies, higher levels of SCFA in the stool were found in overweight or obese subjects compared to lean subjects [26, 27, 93‐95]. In addition, Turnbaugh et al., in an experimental study, showed a relationship between obesity, the gut microbiome and overproduction of SCFA [6]. However, the relationship between SCFA and metabolic disorders in postmenopausal women can be affected by decreased estrogen production, which causes intestinal dysbiosis [96]. The postmenopausal gut microbiota contains fewer SCFA-producing bacteria [69, 97‐99]. It is therefore difficult to establish a clear relationship between faecal SCFA and cardiometabolic risk factors. To shed light on causal mechanisms, we performed a mediation analysis, which shown that the effect of Lachnospiraceae, Firmicutes and Tenericutes on plasma glucose may be translated (partially or fully) by propionate. However, these results should be interpreted with caution, given certain limitations. First, mediation analysis provides only statistical support for a possible underlying causal mechanism. Thus, carefully designed experimental studies need to be conducted to establish such relationships.

The main limitation of this study is the lack of a control group consisting of normal weight healthy and otherwise matched postmenopausal women; however, taking into account the multifactorial nature of the analysis, this task was not easy. Moreover, the study aimed to analyze the correlation in a given cohort and not the differences between the cohorts, especially since the problem of appropriate matching of healthy control was difficult to solve in such studies. The main reasons for the reduced sample size were the strict inclusion and exclusion criteria and limited resources. Furthermore, mechanistic studies using a germ-free mouse model are required to confirm the results. Finally, it would be beneficial to compare our results with a similar cohort [100]; however, such head-to-head studies have not been conducted. Other limitations include 16S rRNA sequencing of V1–V2 regions (in the Polish population, the most suitable region is not defined), which provides limited insight into microbiota function [101], and further metabolome and immunome analysis are required.

We can conclude that microbiota composition and metabolic potential are associated with cardiometabolic risk factors and faecal SCFA concentration in obese postmenopausal women. However, there is no unequivocal relationship between faecal SCFA and the marker of intestinal barrier integrity and cardiometabolic risk factors. We also found that comparing results obtained in a study cohort with raw data contained in an extensive reference database was not informative regarding comparing stool microbiota between obese and non-obese patients, so appropriately matched control groups should be used in microbiota studies. Our study provides the translational significance. Future works might explain further whether the microbiota and its metabolic potential may be treated as markers of metabolic disorders in obese postmenopausal women. If these observations are confirmed, the creation of dysbiosis indices associated with the occurrence of these disorders could be used to develop appropriate dietary strategies and to monitor the health of women during this period of life. They may also serve as a basis for developing pre-, pro- and postbiotics that could reduce the risk of cardiovascular diseases.