An increasing body of evidence suggests that the gut microbiota has a profound impact on human health. While the microbiome of a healthy individual is relatively stable, gut microbial dynamics can be influenced by host lifestyle and dietary choices [1
]. An acute change in dietary pattern from animal-based to plant-based diet alters gut microbial populations within 24 h and then reverts to baseline within 48 h of returning to the baseline dietary pattern [2
]. Studies that involve intake of a specific dietary component demonstrate how certain microbiota tend to respond to nutrient-specific challenges. Protein, fats, digestible and non-digestible carbohydrates, probiotics, and dietary polyphenols all induce shifts in the microbiome with secondary effects on host immunological and metabolic markers [1
]. An emerging and rapidly growing scientific literature is implicating the microbiome in a number of conditions and disorders including inflammatory bowel disease, obesity, type 2 diabetes mellitus, cardiovascular disease, cancer, autism, mood and neurodegenerative disorders [4
The consumption of polyphenol-rich foods, including fruits and vegetables, has been reported to reduce pathogenic Clostridia
and to enrich beneficial bacteria such as Bifidobacterium
species in human studies [9
]. In conjunction with these changes, reductions in plasma triglycerides and C-reactive protein have been noted [9
]. Dietary polyphenols have been shown to help maintain intestinal health by preserving the gut microbial balance through the stimulation of the growth of beneficial bacteria and the inhibition of pathogenic bacteria, exerting prebiotic-like effects.
We have previously demonstrated the effect of water extract of culinary spices, including cinnamon, Mediterranean oregano, ginger, rosemary, black and cayenne pepper, on the growth of 33 Bifidobaterium
spp., and its antimicrobial activity against 88 intestinal, pathogenic, and toxigenic bacterial strains in an in vitro model. These spices promoted the growth of Bifidobaterium
spp. Cinnamon, ginger, oregano, black pepper, and cayenne pepper showed activity against several pathogenic Fusobaterium
spp. and selected Clostridium difficile
]. The present pilot study was designed to investigate the effects of mixed spices (cinnamon, oregano, ginger, black pepper, and cayenne pepper) at culinary doses consumed over 2 weeks in a standardized 5 g capsule on the production of gut microbiota and short-chain fatty acids (SCFAs) in healthy subjects compared to a placebo maltodextrin capsule in a parallel randomized controlled clinical trial.
2. Materials and Methods
2.1. Study Design and Spice Intervention
The pilot study was conducted in accordance with the guidelines of the Office of the Human Research Protection Programs (OHRPP) of the University of California, Los Angeles. The clinical protocol was approved by the UCLA Internal Review Board (IRB) and the study was registered at the NIH Clinical Trial Registry (NCT03676803). A total of 66 healthy women and men aged 18 to 65 were screened in 2017 through local advertisement. Participants with a history of gastrointestinal surgery, diabetes mellitus, or other serious medical conditions such as chronic hepatic or renal disease, bleeding disorder, congestive heart disease, chronic diarrhea, myocardial infarction, coronary artery bypass graft, angioplasty within 6 months prior to screening, current diagnosis of uncontrolled hypertension or chronic gastrointestinal disorders, bulimia, anorexia, laxative abuse, or endocrine disorders were excluded. Participants who were consuming a high-fiber/polyphenol diet; taking any medication or dietary supplement interfering with the absorption of polyphenols; pregnant or breastfeeding; frequently using prebiotics, probiotics, yogurt, or fiber supplements; taking antibiotics or laxatives within the previous 3 months; or currently using tobacco products were also excluded. Thirty-one subjects meeting enrollment criteria were recruited and provided written informed consent before the study began. A randomized, placebo-controlled, double-blind pilot study was conducted. Subjects were randomized according to an algorithm modified from a previous publication [12
]. The study was divided into two periods: an initial run-in period of 1 week and an intervention period of 2 weeks. After the run-in period, 31 subjects were randomly allocated to consume either 5 g capsules of spice mixture containing 1 g (20%) cinnamon, 1.5 g (30%) oregano, 1.5 g (30%) ginger, 0.85 g (17%) black pepper, and 0.15 g (3%) cayenne pepper, or 5 g capsules containing maltodextrin daily for 2 weeks. The individual spices were purchased from local grocery stores.
At the start of study, participants received a diet instructional handout and were counseled by a registered dietitian on compliance. During the run-in and intervention period, all participants consumed a beige diet (low fiber < 10 g and low polyphenols < 3 servings of polyphenol rich fruit/vegetables per day). The beige diet handout advised participants to eat foods beige in color and rich in simple carbohydrates like white breads/bagels, crackers, granola bars, rice, macaroni/pasta, yogurt, dairy, poultry, cereal, and bananas and to avoid foods high in polyphenols and/or fiber. Participants were also provided with weekly checklists to track their fruit and vegetable intake throughout the study with a limit of three servings per day. This checklist included a list of higher fiber/polyphenol foods to avoid as well as a list of lower fiber/polyphenol fruits and vegetables they could consume as a part of their three servings per day, with a serving size estimation tool included. At the baseline and final visits, participants also completed and returned 3-day food records that were evaluated by the dietitian for compliance with the beige diet.
At baseline and at the end of 2 weeks of intervention period, body weight, body mass index (BMI) and composition were determined. Body composition was measured using the Tanita-BC418 bioelectrical impedance analyzer (Tanita Corp., Tokyo, Japan). Height was measured without shoes using a stadiometer (Detecto-Medic; Detecto-Scales; Brooklyn, NY, USA) and recorded to the nearest 0.1 cm. In the meantime, subjects reported on their overall well-being at baseline and at the end of two weeks by completing the SF-36 general wellness questionnaire.
2.2. Sample Collection
Fasting blood, 24 h urine, and stool samples were collected from each participant at baseline and at week 2 follow-up visit. Blood in EDTA was centrifuged 1500× g for 10 min at 10 °C and plasma stored at −80 °C until analysis. Stool collection utilized Stool Collection Kit (Thermo Fisher Scientific, Waltham, MA, USA), as per instructions, and specimen was stored on ice pack in a freezer box for up to 24 h before delivery on ice packs to UCLA. Subjects’ self-reported stool type numbers from The Bristol Stool Chart were recorded for assessment of stool consistency, and subjects also completed the UCLA Digestive Disease Center Symptom Questionnaire for the assessment of gastrointestinal symptoms (gas, bloating, diarrhea, etc.). At UCLA, the specimen was processed immediately and the aliquots stored at −20 °C.
2.3. Determination of Fecal SCFA and Urinary Rosmarinic Acid
Aliquot of stool samples was diluted, acidified, and filtered, and SCFAs (acetic, propionic, butyric, and valeric acid) were quantified by gas chromatography flame ionization detection, as previously described [13
]. SCFA standard mix was purchased from Sigma-Aldrich (St Louis, MO, USA). Urinary free rosmarinic acid as a biochemical marker of adherence to spice supplementation was measured by high-performance liquid chromatography according to published methods [11
2.4. DNA Extraction, 16S rRNA Sequencing, and Taxonomic Assignment
DNA from stool was extracted using the MoBio power soil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA). The quality and quantity of the DNA was confirmed using a Nanodrop 1000 (Thermo Fisher Scientific. Sequencing was performed at MR DNA (www.mrdnalab.com
, Shallowater, TX, USA) on a MiSeq (Illumina, San Diego, CA, USA), following the manufacturer’s guidelines. The minimum DNA concentration was 10 ng/μL. Negative controls were analyzed with the samples. The phiX ratio was a minimum of 20%. The 16S rRNA gene V4 variable region PCR primers 515/R806 with barcode on the forward primer were used in a 28 cycle PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, Germantown, MD, USA) under the following conditions: 94 °C for 3 min, followed by 28 cycles of 94 °C for 30 s, 53 °C for 40 s, and 72 °C for 1 min, after which a final elongation step at 72 °C for 5 min was performed. After amplification, PCR products were checked in 2% agarose gel to determine the success of amplification and the relative intensity of bands. Sequence data were processed using MR DNA analysis pipeline (MR DNA). In brief, sequences were joined and depleted of barcodes, then sequences <150 bp and sequences with ambiguous base calls were removed. Sequences were then denoised, operational taxonomic units (OTUs) generated, and chimeras removed. OTUs were defined by clustering at 3% divergence (97% similarity). Final OTUs were taxonomically classified using BLASTn against a curated database derived from Greengenes 12_10 [15
2.5. Identification of Changes in OTU Abundance Associated with Intervention
To explore the changes of microbial composition during intervention, all analyses were conducted in R (version 3.5.2) (The R Foundation, Vienna, Austria) [16
] with applied packages, including “phyloseq” [17
], “ggplot2” [18
], “vegan” [19
], and “DESeq2” [20
]. An OTU count table, taxonomy classification table, related clinical and demographic data, and the OTU phylogenetic tree were imported and analyzed as a “phyloseq” object. Measures of beta-diversity were computed using the weighted and unweighted UniFrac distance metric using phyloseq in R. Differences in whole communities across groups were determined by Permutational Multivariate Analysis of Variance (PERMANOVA) using the Adonis command provided by Vegan in R. The results were visualized via Principal Coordinate Analysis (PCoA) ordination (ggplot2).
OTUs differentially abundant between follow up and baseline visits in subjects receiving spice and placebo interventions were identified using DESeq2 [20
]. This algorithm performs normalization using size factors estimated by the median-of-ratios method, employs an empirical Bayesian approach to shrink dispersion, and fits the data to negative binomial models [21
]. Differential abundance was determined using the Wald test with automatic filtering of low abundance OTUs and automatic calculation of adjusted p
-values (Bonferroni correction), and the enriched OTUs were visualized using the ggplot2 package in R. As Bonferroni correction is often considered overly conservative, an extended set of OTUs significant at p
< 0.2 was listed.
Differentially abundant OTUs between spice and placebo groups were also detected by DESeq2. Null and test models were constructed for each OTU using DESeq. Each model includes variables “time” (baseline and follow up) and “intervention” (placebo or spice). Differentiating the two models was an interaction between “time” and “intervention”, which was only present in the test model. To identify OTUs with abundance changes that occurred specifically with spice but not placebo intervention, we used a likelihood ratio test (LRT) to test for a significant additional contribution of the interaction between time and spice vs. placebo exposure on OTU abundance in the test model when compared to the null model.
2.6. Statistical Analysis
Descriptive statistics, such as mean ± SEM, were used to summarize subjects’ demographic characteristics and biochemical results. Regression was calculated using LINEST function in Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA, USA). Statistical difference of alpha diversity (Chao1 and Shannon), fecal SCFAs, and bacterial abundance at phylum levels were evaluated between interventions and overtime using a mixed model ANOVA, as there was a mixture of between groups (spice and placebo) and repeated measures (baseline and follow-up) variables using IBM SPSS Statistics version 23 (IBM, Armonk, NY, USA). Statistical significance was accepted at p ≤ 0.05.
Here, we report that daily intake of 5 g of mixed spices for 2 weeks in healthy subjects resulted in a significant reduction in the relative abundance of the phylum Firmicutes (p
= 0.033), and a trend of increasing in phylum Bacteroidetes (p
= 0.097) as compared with a matched control group. Most healthy adult microbiota are dominated by these two bacterial phyla, which together make up about 95% of gut microbiota [22
]. A significantly higher abundance of Firmicutes and a higher Firmicutes/Bacteroidetes ratio has been frequently demonstrated in obese individuals [6
], whose gut microbiome is characterized by an increased capacity for energy harvest, inflammation, and gut barrier disruption [24
In addition to the shifts observed in Firmicutes and Bacteroidetes abundance, a number of OTUs in the family of Bifidobateriaceae
, as well as the family of Streptococcaceae
were significantly altered between groups (Table 2
). Spice intervention significantly enhanced two Bifidobacterium
OTUs of the Bifidobacteriaceae family—B. animalis
and B. pseudolongum
—as well as one Lactobacillus
OTU compared with control group. Bifidobacterium
has been shown to associate with the production of a number of potentially health promoting metabolites, including short chain fatty acids, conjugated linoleic acid, and bacteriocins [26
]. The abundance of B. animalis
was reported to negatively associate with the body mass index [27
]. Results from the present study are consistent with published reports in that consuming polyphenol rich foods increases the relative abundance of Bifidobacterium
and reduces pathogenic Clostridium species
are considered “healthy bacteria”, and members of the Bifidobacterium
, as well as Streptococcus,
are frequently used as probiotic strains with evident health benefits such as immune-modulation, cancer prevention, inflammation management, and the control of diverse bacterial consortia infections [3
We noted that the abundance of all three Ruminococcus
spp. were increased. In humans, Ruminococcus
spp. were found as abundant members of a “core gut microbiome” in a majority of humans [32
]. Some Ruminococcus
spp. in our gut microbiomes play an important role in helping us degrade and convert complex polysaccharides into a variety of nutrients for their hosts [33
]. The slow digestion of these special carbohydrates has been associated with numerous health benefits such as reversing infectious diarrhea and reducing the risk of diabetes and colon cancer [34
]. Interestingly, the Bacteroides
spp., such as B. fragilis
, also have the ability to recognize and metabolize plant- and host-derived polysaccharides, and to produce polysaccharides as well [35
]. Polysaccharide A, e.g., synthesized by B. fragilis,
can promote immunological tolerance to pathogenic species such as Helicobacter hepaticus
and protect the host from inflammation and associated colorectal cancer [35
SCFAs constitute approximately 10% of the energy source in healthy people. These microbial-derived products are utilized by the host and exert a range of health-promoting functions. Butyrate is used preferentially as an energy source by the gut mucosa, is anti-inflammatory, and protects against colorectal cancer [39
], whereas propionate is largely taken up by the liver and is a good precursor for gluconeogenesis, and promotes satiety and reduction in cholesterol liponeogenesis and protein synthesis [40
]. Acetate is the most predominant gut-produced SCFA in peripheral blood and plays a role in prevention of weight gain through an anorectic effect, inflammation, and metabolic dysregulation [42
]. Valerate is present in substantially low amount, and research on this SCFA is very limited. A recent study, however, showed that valerate significantly inhibited the growth of C. difficile
in vitro and in vivo, suggesting valerate can potentially be used as a safe, microorganism-free method to treat C. difficile
]. In the present work, the level of all four SCFAs was trending higher after spice intervention but the difference did not attain statistical significance due perhaps to the large interpersonal variations and small size of the study population. Nonetheless, our results indicated that spice supplementation can change SCFA production.
Human studies investigating the effects of spice supplementation on gut microbiota composition are very limited. Kang et al., reported that intake of capsaicin powder in healthy subjects increased the Firmicutes/Bacteroidetes ratio, accompanied with increased plasma levels of glucagon-like peptide 1 and gastric inhibitory polypeptide and decreased plasma ghrelin level [46
]. In animal models, Van Hul et al., reported that cinnamon bark extract lowered fat mass gain and adipose tissue inflammation in mice fed a high-fat diet leading to reduced liver steatosis and lower plasma nonesterified fatty acid levels that were associated with change on the microbial composition [47
]. Spices have been shown to alter serum biochemical parameters related to inflammation or low-grade inflammation induced by high-fat diet [47
] and have protective effects against chronic diseases [50
]. This evidence indicates that spices may play important role in modulating the growth of gut microbiota and promoting human health.
The observed lack of significant difference in microbial richness is largely attributed to the brevity of the intervention, which is also believed to account to for the lack of change in species evenness as well as of overall microbial composition. Another limitation of the study is that to avoid the sustained effects [52
] of spices on microbiota, our study design was a randomized placebo-controlled and not a crossover clinical trial, therefore, there are large interpersonal variations in gut microbiota composition and metabolite (SCFA) formation. Lastly, since this is not a controlled feeding study, dietary recall data analyzed by a dietitian was the only method used to assess participants’ compliance with the beige diet. Analysis of dietary data may have an impact on interpretation of the results.