Dietary inflammatory potential in relation to the gut microbiome: results from a cross-sectional study

Abstract

Diet has direct and indirect effects on health through inflammation and the gut microbiome. We investigated total dietary inflammatory potential via the literature-derived index (DII®) with gut microbiota diversity, composition, and function. In cancer-free patient volunteers initially approached at colonoscopy and healthy volunteers recruited from the medical center community, we assessed 16S rDNA in all subjects who provided dietary assessments and stool samples (n=101) and the gut metagenome in a subset of patients with residual fasting blood samples (n=34). Associations of energy-adjusted DII scores with microbial diversity and composition were examined using linear regression, permutational multivariate analysis of variance, and linear discriminant analysis. Spearman correlation was used to evaluate associations of species and pathways with DII and circulating inflammatory markers. Alpha- and beta-diversity did not significantly differ across DII levels; however, Ruminococcus torques, Eubacterium nodatum, Acidaminococcus intestini, and Clostridium leptum were more abundant in the most pro-inflammatory diet group, while Akkermansia muciniphila was enriched in the most anti-inflammatory diet group. With adjustment for age and BMI, R. torques, E. nodatum, A. intestini remained significantly associated with a more pro-inflammatory diet. In the metagenomic and fasting blood subset, A. intestini was correlated with circulating PAI-1, a pro-inflammatory marker (rho=0.40), but no associations remained significant upon correction for multiple testing. An index reflecting overall inflammatory potential of the diet was associated with specific microbes, but not overall diversity of the gut microbiome in our study. Findings from this preliminary study warrant further research in larger samples and prospective cohorts.

INTRODUCTION

Diet is one of the most influential and accessible modulators of the gut microbiome, the human intestine’s vast and diverse microbial ecosystem increasingly recognized as a key player in the development of obesity, type 2 diabetes, cardiovascular disease and cancer ^1–6. The composition and collective function of the gut community affects how and what the human host is able to extract from the diet by providing the machinery that converts dietary content into biological signals with profound systemic effects on host health ⁷

Not unlike the complex and interactive nature of the microbiome, dietary habits are multidimensional with many interrelated components. Recently, Bowyer et al. and Maskarinec et al. demonstrated the utility of a priori dietary patterns or indices for capturing and controlling for variation in the gut microbiome due to the effects of participants’ diets ^8–12. The Dietary Inflammatory Index (DII®) assesses the balance of pro- and anti-inflammatory dietary factors based on literature-derived associations between various dietary components and inflammatory biomarkers. This index, with its specific focus on inflammation, inherently differs from other index-based scores assessed in previous studies measuring adherence to established dietary guidelines or healthy eating patterns [e.g., the Healthy Eating Index (HEI) or Mediterranean Diet Score (MDS)] ¹³.

Diet-microbiome interactions may be one of the most promising targets to reduce chronic inflammation, a key pathophysiological mechanism underlying diet’s influence on multiple chronic diseases through the common mechanism of the NF-κB pathway and a complex interaction of cellular, molecular and metabolic factors ^14–22. Given that diet can modulate inflammation through both pro-inflammatory and anti-inflammatory mechanisms, we hypothesized that the inflammatory potential of usual dietary habits would be associated with the overall composition and functional capacity of the gut microbiome. In this study, we investigated total dietary inflammatory potential, as assessed by the literature-derived DII, in relation to gut microbiota diversity and composition. In a subset of individuals with fasting blood samples and metagenomic data, we further explored microbes and microbial gene pathways associated with DII score and circulating inflammatory markers.

METHODS

Study population

The study population is composed of 101 cancer-free individuals, including patient volunteers initially approached in the colonoscopy clinic of MD Anderson Cancer Center (N=36) and community volunteers recruited from the medical center community (N=65). Detailed study recruitment and eligibility criteria are provided in Supplemental Figure 1. Initially, from 2013 to 2016, a total of 132 patient volunteers with no history of cancer and 449 community volunteers expressed an interest in participating. Both subgroups were screened and interviewed by a clinical provider and/or trained study coordinator with regard to their medical history and medication use and asked to provide stool samples using the same collection protocol. Study ineligibility criteria included current smoker, antibiotic use within the past month, incident or prevalent cancer other than non-melanoma skin cancer, one or more chronic conditions that restricts dietary intake (e.g., Celiac disease), major intestinal surgery (e.g., gastric bypass), currently pregnant or lactating. BMI was calculated from measured weight (kg)/height (m)² and categorized based on WHO criteria ²³. Study subjects were included in the current analysis if they completed the dietary questionnaires, provided a stool sample, and passed microbiome data quality filtering criteria. All procedures were reviewed and approved by The University of Texas MD Anderson Institutional Review Board. Informed consent was obtained from all research participants; and all methods were performed in accordance with relevant guidelines and regulations.

Dietary assessment

Participants completed dietary histories via one of two versions of the National Cancer Institute (NCI)-developed food frequency questionnaire (FFQ). For consistency with historic recruitment of patients in prior and ongoing MD Anderson studies, patient volunteers completed a modified version of the NCI Health Habits and History Questionnaire (NCI-HHHQ) ^24,25, which queries the frequency of intake and portion size of 165 food and beverage items, including ethnic foods commonly consumed in the Texas region. Daily nutrient consumption was estimated using the US Department of Agriculture Food and Nutrient Database for Dietary Studies ²⁶. Community volunteers completed the NCI Dietary History Questionnaire II (DHQ II), a more recent web-based adaptation of the NCI-HHHQ ²⁶, which queries 134 food items. DHQ II responses were processed using Diet*Calc software ²⁷.

E-DII score calculation

Food and nutrient intake derived from responses to FFQs were linked to the corresponding inflammatory effect scores designated in the DII to calculate the energy adjusted (E-DII) score for each participant ¹³. The DII is a literature-derived, population-based dietary index designed to quantify the overall inflammatory potential of an individual’s entire diet. The details of the development and scoring algorithm are described elsewhere ¹³. In short, approximately 2,000 primary research articles published through 2010 which investigated the effects of 45 different food parameters (mostly macronutrients, micronutrients, some bioactive components and individual food items such as garlic and tea) on six inflammatory markers [i.e., IL-1β, IL-4, IL-6, IL-10, TNF-α and C-reactive protein (CRP)] were identified and scored to derive the component-specific inflammatory effect scores ¹³. Thirty-one DII components (plus garlic in the NCI-HHHQ) were used to calculate the instrument-specific, energy-adjusted E-DII score for analysis. Both FFQs lacked information on some less commonly consumed spices (ginger, saffron, turmeric, pepper, oregano, rosemary) and phenols/flavonoids (eugenol, flavan-3-ol, flavones, flavonols, flavonones, anthocyanidins, isoflavones). Food and nutrient consumption was first energy-adjusted per 1000 kcal; and subsequently standardized for each component using mean and standard deviation data derived from a composite dietary database representing energy-adjusted intake from 11 populations around the world ¹³. The energy-adjusted and standardized dietary intakes were then converted to centered proportions to account for skewness, multiplied by the inflammatory effect score for each available DII parameter, and summed across all DII components to obtain the overall E-DII score ¹³. Higher (i.e., more positive) E-DII scores represent more pro-inflammatory diets while lower (i.e., more negative) E-DII scores indicate more anti-inflammatory diets. The E-DII score has been construct-validated and consistently associated with elevated inflammatory biomarker levels such as IL-6 ²⁸, TNF-α ²⁸ and CRP ²⁹.

Stool and blood sample collection

All participants were provided the same stool sample collection kit with detailed instructions. Following defecation into a plastic “toilet hat,” gloved participants used a sterile BBL culture swab collection and transport system (Becton, Dickinson France) to collect a small portion of their sample. Samples were either Express (overnight or same day) shipped or brought to their next scheduled visit. All fecal samples were received within less than 48 hours of collection, stored at −80°C, and processed within one year of collection ³⁰. Residual fasting blood samples drawn at the colonoscopy clinic visit were available in a subset of 34 patient volunteers with FFQ and stool samples.

Microbiome characterization

Total genomic DNA was extracted from fecal samples using the MoBio PowerSoil DNA Isolation Kit (Carlsbad, CA) following manufacturer’s instructions. The 16S rRNA gene was amplified using V4-targed primers (GGACTACHVGGGTWTCTAAT and GTGCCAGCMGCCGCGGTAA) ³¹, and amplicons were sequenced using the MiSeq platform (Illumina; CA, US). Raw FASTQ sequences were processed as previously described ³². Following quality filtering and chimera removal, sequence reads were clustered into operational taxonomic units (OTUs) using UPARSE ³³, with OTUs subsequently mapped to a V4-optimized version of the SILVA database (v.123) at 97% similarity level. The remaining samples were rarefied to 2,742 reads/sample and relative abundances were calculated (Supplemental Table 1). Basic Local Alignment Search Tool (BLAST) was used to identify the likely species represented by each OTU centroid sequence ³⁴.

Whole genome shotgun (WGS) sequencing was performed to comprehensively assess microbial genomic DNA of fecal samples in a subset of 34 patient volunteers who also had FFQ and residual fasting blood samples. WGS data provides species-level taxonomy of the gut microbiome, as well as the metabolic or functional gene content pathways represented within the microbial community. Individual libraries constructed from extracted total gDNA for each sample were pooled and sequenced via HiSeq 2000 (Illumina) using the 1×100 bp paired-end read protocol. Pooling resulted in a sequencing depth >3 Gb/sample. Quality filtering, trimming, demultiplexing, and read mapping were carried out by an in-house pipeline described previously ³⁵.To determine metabolic pathway content of the entire metagenome, reads aligning to known orthologues in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were tabulated and pathways constructed by calculating the minimum set through MinPath45 ³⁶.

Circulating inflammatory markers

Plasma leptin, TNF-α, IL-6, lipocalin 2 (LCN2), plasminogen activator inhibitor-1 (PAI-1), C-peptide, monocyte chemoattractant protein-1 (MCP-1) were assessed via multiplex assay (Millipore). All samples were run in duplicate with internal standards (pooled cancer case plasma), healthy control samples (normal plasma), and kit quality controls to assure plate to plate consistency.

Statistical analyses

The E-DII score was categorized according to instrument-specific tertiles. Characteristics of study participants were described with medians and standard errors of mean for continuous variables, and frequencies and percentages for categorical variables. We examined the difference of categorical variables across E-DII tertiles by Chi-square test and the difference of continuous variables by Kruskal-Wallis non-parametric test. We also examined the associations of these covariates with bacterial alpha and beta diversity to assess potential confounders for inclusion in the adjusted models of E-DII and microbiota associations.

Alpha-diversity was assessed by observed OTU, Shannon diversity index, Chao1 index, and Simpson’s diversity index. Differences in alpha-diversity across E-DII tertiles among all subjects and by study subgroup were assessed via the Kruskal-Wallis non-parametric test. We also examined associations of the continuous E-DII score with alpha-diversity in each subgroup using linear regression models. None of the potential confounders examined, including age (continuous), sex (males and females), BMI (continuous), medication use (yes/no), and study subgroup (patient vs. community volunteers) were associated with alpha-diversity or appreciably changed the model estimates; and thus, were not included in the final adjusted-model.

Similarly, Bray-Curtis dissimilarity, Weighted Unifrac, Unweighted Unifrac and Jaccard (beta-diversity metrics) between the highest versus the lowest E-DII tertile was assessed via permutational multivariate analysis of variance (PERMANOVA) among all the subjects and by study subgroup ³⁷. While none of the measured covariates were clearly associated with beta-diversity, we further examined whether BMI-status modified associations of beta diversity with E-DII in stratified analysis.

Differentially abundant bacterial taxa by E-DII tertiles were assessed using Linear Discriminant Analysis (LDA) Effect Size (LEfSe) under Galaxy environment ³⁸, applying the one-against-all strategy with a logarithmic LDA score threshold of 3 and α of 0.1 for factorial Kruskal-Wallis test among classes. Analysis was restricted to bacteria present in ≥20% of the study population. We further assessed the associations between differentially abundant candidate taxa identified from LEfSe and potential confounders as mentioned above. Given some taxa were associated with BMI, we subsequently evaluated associations of LEfSe-identified taxa in association with E-DII tertiles via a negative binomial regression model adjusted for age and BMI.

Due to the large number of low abundance species in metagenomic data, we employed the least absolute shrinkage and selection operator (LASSO) method to identify bacterial species and microbial gene content pathways associated with E-DII among 34 patient volunteers with residual fasting blood samples using the glmnet package in R ³⁹. Spearman’s rank correlation method was subsequently used to estimate the correlations of selected species and functional pathways based on LASSO and 16S LEfSe analyses with 7 circulating inflammatory biomarkers including leptin, TNF-α, IL-6, LCN2, PAI-1, C-peptide, and MCP-1. The Benjamini-Hochberg (B-H) method was used to adjust P values in the multiple correlation analyses while controlling for the expected false discovery rate at 0.05.

Sensitivity analysis was performed by rerunning all the analyses described above after removing one subject identified as an outlier based on his/her Shannon index value, defined as 1.5 interquartile range below the 25^th or above the 75^th percentile of the population’s value. All analyses were performed in SAS, R, Galaxy ³⁸, Agile Toolkit for Incisive Microbial Analyses (ATIMA) ⁴⁰. All tests were two-sided, with P values <0.05 considered statistically significant unless otherwise noted.

RESULTS

Characteristics of participants

The median E-DII score was 1.79 (range was from −5.15 to −3.08) for patient volunteers and −0.85 (range was from −4.04 to 2.83) for community volunteers. Subjects with higher E-DII scores (i.e., more pro-inflammatory diets) tended to have higher BMI (Table 1). However, there was no statistically significant difference across E-DII levels in the distribution of age, sex, history of precancerous colorectal polyps and medication use. When we examined these factors in relation to overall microbial diversity, none of the factors were strongly associated with alpha-diversity (all P>=0.15) or beta-diversity (all rho<=0.05; data not shown).

Table 1.

Characteristics of participants by E-DII score (n=101)

	Most anti-inflammatory diet		Most pro-inflammatory diet	P-valueb
	Tertile 1a	Tertile 2a	Tertile 3a
N	33	34	34
	Median (SE)	Median (SE)	Median (SE)
Age, years	36.0 (2.49)	45.0 (2.44)	41.5 (2.47)	0.78
BMI, kg/m2	23.10 (1.04)	28.6 (1.52)	30.2 (1.09)	0.001
Microbial alpha-diversity measures
Shannon Index	2.45 (0.12)	2.61 (0.10)	2.63 (0.11)	0.63
Observed OTUs	70.58 (4.18)	76.29 (4.24)	80.12 (4.83)	0.41
Chao1 Index	88.94 (5.58)	100.11 (6.60)	104.15 (6.61)	0.22
Simpson Index	0.83 (0.03)	0.87 (0.02)	0.85 (0.02)	0.65
	N (%)	N (%)	N (%)
Sex				0.33
Male	9 (27.27)	11 (32.35)	15 (44.12)
Females	24 (72.73)	23 (67.65)	19 (55.88)
Study subgroup				0.91
Community volunteers	22 (66.67)	21 (61.67)	22 (64.71)
Patient volunteers	11 (33.33)	13 (38.24)	12 (35.29)
History of precancerous colorectal polyps				0.37
Yes	8 (24.24)	11 (32.35)	6 (17.65)
No	25 (75.76)	23 (67.65)	28 (82.35)
Medication use for the following conditions
Hyperlipidemia				0.53
Yes	3 (9.09)	6 (17.65)	6 (17.65)
No	30 (90.91)	28 (82.35)	28 (82.35)
High blood sugar				0.60
Yes	1 (3.03)	0 (0)	1 (2.94)
No	32 (96.97)	34 (100)	33 (97.06)
Gastroesophageal reflux				0.67
Yes	2 (6.06)	4 (11.76)	4 (11.76)
No	31 (93.94)	30 (88.24)	30 (88.24)
Medication use for one or more of above conditions				0.56
Yes	6 (18.18)	8 (23.53)	10 (29.41)
No	27 (81.82)	26 (76.47)	24 (70.59)

^a.E-DII values across the three levels were calculated from FFQ-specific E-DII scores and categorized in to tertiles based on the distributions in each study subgroup.

^b.The difference of categorical variable across E-DII tertiles was tested by Chi-square test and difference of continuous variables was tested using Kruskal-Wallis non-parametric test as they are not normally distributed.

E-DII, energy-adjusted dietary inflammatory index

Associations of E-DII score with microbial diversity

Alpha diversity (within sample diversity), a measure of microbial richness and/or evenness, did not differ across E-DII levels among all participants in the crude analysis [Shannon index: P=0.63 (Figure 1a); other metrics (Table 1)]. Null results were similarly consistent within study subgroup (Figure 1b for Shannon Index; data not shown for other metrics). Null results also were observed when examining continuous associations of E-DII and alpha-diversity by subgroup (data not shown).

Figure 1.

Microbial alpha-diversity, as assessed by Shannon index, across E-DII tertiles among (a) all study subjects (n=101) and (b) by study subgroup (n=65 community volunteers, n=36 patient volunteers)

We examined several beta diversity (between sample diversity) metrics, representing different measures of dissimilarity or distance between groups by highest versus lowest E-DII level. We observed modest visual differences in overall community composition (Figure 2 and Supplemental Figure 2). Although the results of Unweighted Unifrac reached statistical significance, E-DII clusters, comparing the highest (pro-inflammatory) vs. lowest (anti-inflammatory) levels were not overly distinct (P=0.03, rho=0.02; Supplemental Figure 2). Similar results were observed when we examined E-DII and beta-diversity associations stratified by BMI status.

Figure 2.

Microbial community differences, as assessed by Bray-Curtis dissimilarity, between individuals with the most anti-inflammatory vs. pro-inflammatory diet among (a) all study subjects and (b) by study subgroup

Differentially abundant taxa by E-DII score

We identified several differentially abundant taxa in the LEfSe analysis across the three levels of E-DII score (Figure 3). Ruminococcus torques, Eubacterium nodatum, Acidaminococcus intestini, Clostridium leptum were more abundant in subjects consuming the most pro-inflammatory diets, while Akkermansia muciniphila was enriched in subjects with the most anti-inflammatory diets. With adjustment for age and BMI in the negative binomial model, E-DII associations with these 5 taxa did not appreciably change compared to crude model (Table 2). R. torques, E. nodatum, A. intestini remained significantly associated with a more pro-inflammatory diet.

Figure 3.

Differentially abundant taxa across E-DII tertiles using LEfSe approach among 101 subjects.

Table 2.

Crude and age- and BMI-adjusted associations of E-DII with 5 candidate taxa^a (n=101)

	Most anti-inflammatory diet Tertile 1 N=33	Tertile 2 N=34	Most pro-inflammatory diet Tertile 3 N=34	P-valuea
	eβ (95% CI)b	eβ (95% CI)b	eβ (95% CI)b
R. torques
Crude model	1.00 (ref)	1.17 (0.62–2.21)	3.16 (1.68–5.93)	0.001
Age- and BMI-adjusted model	1.00 (ref)	1.12 (0.55–2.28)	3.03 (1.48–6.20)	0.002
A. intestine
Crude model	1.00 (ref)	18.93 (2.23160.93)	257.21 (30.90–2140.72)	<.0001
Age- and BMI adjusted model	1.00 (ref)	7.59 (0.83–69.58)	127.96 (9.85–1661.54)	0.0005
E. nodatum
Crude model	1.00 (ref)	0.57 (0.19–1.70)	2.10 (0.86–5.16)	0.03
Age- and BMI-adjusted model	1.00 (ref)	0.43 (0.13–1.36)	1.74 (0.65–4.67)	0.018
C. Leptum
Crude model	1.00 (ref)	0.59 (0.17–2.04)	1.26 (0.38–4.20)	0.48
Age- and BMI-adjusted model	1.00 (ref)	0.33 (0.08–1.34)	1.24 (0.36–4.26)	0.13
A. muciniphila
Crude model	1.00 (ref)	0.74 (0.18–3.09)	0.23 (0.06–0.97)	0.16
Age- and BMI-adjusted model	1.00 (ref)	2.30 (0.45–11.65)	0.59 (0.10–3.72)	0.17

^a.Computed for the association between E-DII and 5 LEfSe selected taxa; the significant P-value indicates E-DII is significantly associated with abundance of the taxa

^b.Negative binomial model with log link was used to estimate e^β and 95% CI. Magnitude indicates how much more abundant the taxa (treated as count), e.g. 3-times more abundant in the highest, as compared to the lowest E-DII tertile.

Associations of E-DII with microbial species, pathways, and circulating inflammatory markers

Although the E-DII has been construct validated in several large studies^41–43, no significant associations were observed with inflammatory markers among the subset of subjects with fasting blood samples available, but all were in the expected direction (n=34; Supplemental Table 2). In the correlation analysis of microbial species and functional pathways with inflammatory biomarkers (n=34), Luteimonas mephitis, a low abundance species identified via LASSO based on its non-zero negative correlation with E-DII, was found to be inversely related to PAI-1 (rho= −0.40, P=0.02; B-H adjusted P=0.24). A. intestini, the taxa associated with the most pro-inflammatory diet in the overall (16S) analysis, was positively related to PAI-1 (rho=0.40, P=0.02; B-H adjusted P=0.24) in the WGS subset. Another LASSO-selected Lachnospiraceae bacterium strain associated with a more pro-inflammatory E-DII was also positively correlated with C-peptide (rho=0.41, P=0.02; B-H adjusted P=0.24). No other taxa selected from LEfSe in the larger 16S analysis, or LASSO in the WGS subset analysis were associated with circulating markers (all P values>0.05; Figure 4). Polyketide sugar unit biosynthesis was significantly positively correlated with E-DII (rho=0.32, P=0.03; B-H adjusted P=0.50). Secondary bile acid biosynthesis was positively correlated with IL-6 (rho=0.35, P=0.04; B-H adjusted P=0.50), but appeared to be modestly inversely correlated with E-DII scores favoring a more anti-inflammatory diet. Mammalian AMP-activated protein kinase (AMPK) signaling and carbon metabolism pathways, which were each modestly correlated with E-DII scores favoring a more anti-inflammatory diet, were inversely correlated with C-peptide (rho=−0.36 for AMPK and rho=−0.40 for carbon metabolism, respectively; both P values<0.05; B-H adjusted P>=0.50; Figure 5). Of note, none of these associations were statistically significant following correction for multiple testing.

Figure 4.

Correlation heatmap of E-DII associated species and circulating markers among 34 subjects with residual fasting blood samples and WGS sequencing of the gut microbiome. A. intestini, A. muciniphila, R. torques, H. biformis were selected as differentially abundant OTUs in the 16S analyses while other species were selected using the LASSO method based on non-zero estimates of correlation with E-DII. **Marks the statistically significant Spearman correlations with P<0.05 before correction for multiple testing (none of the correlations were significant after B-H adjustment).

Figure 5.

Correlation heatmap of E-DII associated pathways and circulating markers among 34 subjects with residual fasting blood samples and WGS sequencing of the gut microbiome. A total of 7 WGS characterized pathways were selected using the LASSO method based on non-zero estimates of correlation with E-DII. **Marks the statistically significant Spearman correlations with P<0.05 before correction for multiple testing (none of the correlations were significant after B-H adjustment).

DISCUSSION

Dietary inflammatory potential as measured by the E-DII score was associated with differential composition of specific microbes, but not overall diversity of the gut microbiome in this cross-sectional sample of 101 cancer-free individuals. In the overall analysis, R. torques, E. nodatum, A. intestini, C. leptum were more abundant in subjects consuming the most pro-inflammatory diets, while A. muciniphila was enriched in subjects with the most anti-inflammatory diets. In analysis adjusted for both age and BMI, R. torques, E. nodatum, A. intestini remained significantly associated with a more pro-inflammatory diet. In an exploratory subset of individuals with fasting blood samples and metagenomic characterization of the gut microbiome, pathways reflecting the functional capacity of the gut microbiome to support AMPK signaling, carbon metabolism, polyketide and secondary bile acid biosynthesis were associated with the inflammatory potential of the diet and/or systemic inflammation in the host.

We found several differentially abundant microbes by E-DII level. Four bacteria: R. torques, E. nodatum, A. intestini, C. leptum were associated with the most pro-inflammatory diet; and A. muciniphila was enriched in participants with the most anti-inflammatory diet. A. mucinphila and R. torques have previously been highlighted in a randomized cross-over intervention trial of the fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP) diet among 27 irritable bowel syndrome patients and 6 healthy subjects ⁴⁴. Results of this trial showed that low compared with moderate FODMAP intake was associated with a 5-fold reduction in A. mucinphila and a 1.5-fold increase in R. torques ⁴⁴. FODMAPs substrates are fermented by gut bacteria to short chain fatty acids (SCFA) with anti-carcinogenic and anti-inflammatory actions ¹⁷. A. muciniphila is a producer of SCFA, primarily acetate and propionate, through mucin degradation. Mouse studies have demonstrated that a high-fat diet (i.e., pro-inflammatory diet) decreases the abundance of this species and support a causative role for this species in lowering adipose tissue inflammation and improving insulin sensitivity and lipid metabolism ⁴⁵. However, the role of A. muciniphila in modulating inflammation in humans remains unclear. Two studies reported no association of A. muciniphila with systemic inflammatory markers, including high sensitivity-CRP, IL-6 and lipopolysaccharides ^46,47. In a recently published randomized, double-blind, placebo-controlled pilot study of 32 overweight/obese insulin-resistant volunteers, a 3-month administration of pasteurized A. muciniphila was found to significantly decrease two enzyme activities that are thought to be involved in modulating inflammation, i.e, dipeptidyl peptidase 4 (DPP4) and γ-glutamyltransferase (GGT) ⁴⁸. Previous human studies that investigated other a priori dietary patterns or indices (e.g., HEI, MDS and Healthy Food Diversity (HFD)-index) also showed that greater adherence to healthy diets was associated with increased abundance of other SCFA-producing microbes, such as Faecalibacterium prausnitzii ^8,10.

Less is known with regard to other potential inflammation-related taxa observed in our study and their interactions with the inflammatory potential of diet. E. nodatum is a periodontal pathogen⁴⁹ that has been linked to active rheumatoid arthritis and psoriatic arthritis characterized by chronic inflammation affecting joints and connective tissues⁵⁰. C. leptum is a carbohydrate-fermenting bacteria associated with Treg responses known to modulate intestinal and systemic inflammation in experimental models; and found to be lower in patients with inflammatory bowel disease^51–53. Although in our study higher C. leptum was observed in healthy individuals with a more pro-inflammatory diet, this cross-sectional association was attenuated with adjustment for age and BMI. A. intestini, previously recovered from various human clinical samples of hospitalized patients⁵⁴, appears to be responsive to supplementation with quercetin and partially hydrolyzed guar gum in human fecal samples^55,56; and is positively associated with LPS-stimulated TNFα production⁵⁷. This is generally consistent with our study finding of higher A. intestini in the most pro-inflammatory diet group; and its positive correlation with circulating PAI-1, a pro-inflammatory marker closely associated with obesity, type-2 diabetes, and CVD risk ⁵⁸. Conversely, L. mephitis, a species of Proteobacteria that reduces nitrite to nitrous oxide without production of nitrogen⁵⁹, was inversely correlated with E-DII and PAI-1 in our study.

Metagenomic pathways are expected to more closely reflect the functional nature of the gut microbiome as an interacting community of multiple microorganisms that supports (or hinders) the host. We found that the AMPK signaling pathway, a central regulator of cellular energy homeostasis and glucose and lipid metabolism ⁶⁰ was inversely associated with E-DII and C-peptide. In both human and animal studies, activation of AMPK in adipose tissue has been linked to several anti-inflammatory dietary factors, such as n-3 polyunsaturated fatty acids, polyphenolic compounds, and fiber ^61,62. However, there is little evidence to support our findings for microbial AMPK and carbon metabolism pathways with C-peptide, an insulin resistance marker ⁶³. We also identified a positive association of secondary bile acids biosynthesis with IL-6. Primary bile acids are converted to secondary bile acids through microbial modifications in the gut; and modulate signaling via the nuclear bile acid receptors, i.e, farnesoid X receptor (FXR) and G-protein-coupled bile acid receptor (TGR5). FXR and TGR5 signaling influence many different metabolic processes in the host, including energy homeostasis, glucose homeostasis, obesity, and inflammatory responses, involving IL-6 and TNF-α ^64–66. The positive association between E-DII and polyketide biosynthesis is also interesting, as bacterially-derived polyketides exhibit a number of bioactive properties that modulate antibacterial, antitumor, and antiviral activities ^67–69. The direction of this relationship and some others (e.g., the suggestive inverse association of DII with secondary bile acids biosynthesis) are somewhat unexpected and may be chance findings due to very limited sample size (n=34). While these pathways reflect the presence of genes needed to perform a particular metabolic function, they do not necessarily reflect the gut-derived metabolites that may or may not be produced from interactions between microbes and host diet or other exposures (e.g., medications). Additionally, some pathological pathways may not be activated given the study exclusion criteria and focus on healthy individuals. Larger prospective studies are warranted to confirm or refute our findings, as this analysis was exploratory in nature and none of our findings were significant following adjustment for multiple testing.

The biologic processes underlying diet-microbiome interactions that modulate inflammation are not fully known. With a diet enriched with more anti-inflammatory components such as fiber- and polyphenol-rich plant foods, saccharolytic fermentation of carbohydrates by gut microbes can produce SCFA (i.e., butyrate, acetate and propionate). SCFAs promote anti-inflammatory responses in the host through a series of mechanisms, including intestinal homeostasis, genetic/epigenetic regulation, and immunomodulatory signaling ¹⁷. Conversely, a pro-inflammatory diet (e.g., high in fat, simple carbohydrates and meat but low in fiber-rich plant foods) creates a pro-inflammatory milieu of protein catabolites and deconjugated bile acid residues, leading to increased inflammation-related phenotypes in the host ¹⁷. These include impairment of the mucosal barrier and altered gut permeability and immune responses ¹⁹. A local inflammatory environment further alters gut microbiota to affect systemic inflammation via adherence to the gut epithelium, passage through the gut barrier to enter systemic circulation, activation of an immune response through toll-like receptor binding and/or activation of regulatory T cells, and through the synthesis and secretion of cytotoxic biomolecules or metabolites^17,19,70. Additional human research is needed to elucidate the microbial pathways and metabolites that modulate diet-induced inflammation.

Interestingly, despite our findings for specific microbes and functional pathways, we did not observe significant or striking associations between gut microbial (alpha and beta) diversity and E-DII score in either the crude or adjusted analyses. While this could be attributed to insufficient variation, sample size, or residual confounding due to unmeasured factors, several studies comparing fecal microbiota diversity across participants following distinct diets (e.g., vegetarian, vegan and omnivore) also reported no, or only modest, differences in microbial diversity between diet groups ^71–75. Notably, the DII focuses on a balance of dietary components similar to other diet indices, but it may not be directly comparable to other studies due to its specific focus on inflammation. The HEI and MDS were significantly associated with various alpha diversity metrics in an assessment of three different dietary indices based on FFQ data from 2,070 members of the TwinsUK cohort ⁸. One ⁹ of three additional observational studies focused on increased adherence to the MDS or other diet quality indices reported a concurrent increase or difference in microbial alpha diversity ^76,77. Similarly, a dietary intervention among overweight and obese subjects, as well as a companion observational study focused on healthy vs. unhealthy dietary patterns, reported increased fecal microbial gene richness (total gene counts) among participants with healthier diets ^78,79.

Our study is novel in its specific focus on diet-related inflammation and its interaction with microbiome, a hypothesis rooted in biologically plausible relationships that are highly relevant to host health ^14,80. Using a construct-validated tool which converts major inflammation-related dietary factors commonly consumed by Americans to an overall interpretable diet score, the E-DII provides a comprehensive summary of the dietary inflammatory potential of participants’ entire diets ^41–43. Although not all of the DII components were queried in our study, majority of the missing dietary components, like spices, are typically consumed in very low amounts in the US. As previously reported, the range of DII scores may rely more on the amount of foods actually consumed rather than on the number of available DII components.⁸¹ Furthermore, misclassification in dietary inflammatory potential due to the missing dietary factors would likely be non-differential, thus attenuating our results toward the null. Other limitations of nutritional epidemiologic studies also apply here, as the FFQ is prone to response bias and measurement error. Importantly, other important factors or exposures that may affect the microbiota composition (e.g., medications) were carefully collected in personal interviews and medical charts. However, we are still learning the complexity and breadth of factors that affect the microbiome or mask observed diet-microbiome associations, and our sample size limited our ability to explore multiple factors simultaneously. At the time of recruitment, we excluded individuals who reported antibiotic use in the past month, however, this may not have been sufficient for some individual’s microbial communities to “return to normal”. Recent studies demonstrate that the recovery time may vary by the type, dose, and duration of antibiotic use ^82,83. Our inclusion of cancer-free patient and community volunteers was designed to maximize the variation needed to identify associations of diet with the microbiome. However, homogeneity due to exclusion criteria coupled with limitations in sample size could explain the low variation observed in alpha-diversity, beta-diversity, and other associations. Also, it may have been easier to detect differences had the range in DII scores gone towards their theoretical extremes (i.e., −9 to + 8). We were particularly limited in our subset analysis of patients with residual fasting blood samples, for which we also conducted WGS sequencing of the gut microbiome. To address this, we used LASSO, which is an efficient method to select species and pathways given a small number of subjects and the large number of zero-inflated species revealed in WGS sequencing. Taken overall, this study is of a preliminary and hypothesis-generating nature given the cross-sectional design with evaluation of diet, microbiome and blood markers at a single time point, as well as the lack of significant associations following adjustment for multiple comparisons, all of which prohibit causal and mechanistic inferences.

Dietary inflammatory potential was associated with differential composition of specific microbes but not overall gut microbiota diversity in this well-defined sample of 101 individuals. Our analysis highlighted several biologically plausible microbes potentially related to diet-induced inflammation. R. torques, E. nodatum, A. intestini, C leptum were more abundant in subjects consuming the most pro-inflammatory diets, while A. muciniphila was enriched in subjects with the most anti-inflammatory diets. Correlations between E-DII, microbes, functional gene content pathways, and inflammatory biomarkers in an exploratory subset further support the role of diet-microbiota interactions in modulating systemic inflammation in the host. Future prospective studies of dietary inflammatory potential and its interactions with the microbiome are warranted. It is increasingly important to understand how diet as a whole shapes the composition and function of the gut microbiome to modulate host inflammation, an important mechanism in the development of cancer and other major chronic diseases.