Diet Quality and the Gut Microbiota in Women Living in Alabama

Abstract

Introduction:

The gut microbiota is associated with obesity and modulated by individual dietary components. However, the relationships between diet quality and the gut microbiota and their potential interactions with weight status in diverse populations are not well-understood. This study examined the associations between overall diet quality, weight status, and the gut microbiota in a racially balanced sample of adult females.

Methods:

Female participants (n=71) residing in Birmingham, AL provided demographics, anthropometrics, biospecimens, and dietary data in this observational study in March 2014 to August 2014 and data analysis was conducted in August 2017 to March 2019. Weight status was defined as a BMI (kg)/height (m²) <30 kg/m² for non-obese and ≥30 kg/m² for obese. Dietary data collected included an Automated Self-Administered 24-Hour (ASA24) recall and Healthy Eating Index-2010 (HEI-2010) score. Diet quality was defined as having high HEI (≥median) or low HEI (<median). The fecal microbiota was collected and the 16SrRNA gene was amplified to profile the microbiota composition. Differences in diet quality based on weight status were assessed using 2-sample t-tests. The associations between diet quality, the gut microbiota, and weight status were analyzed using negative binomial models.

Results:

Participants (43 Black, 28 White) aged 40.39 ± 13.86 years who were non-obese (56%) and obese (44%) were studied. Greater alpha diversity was observed among those with higher HEI scores (p=0.037), but did not differ by weight status. Higher abundances of Bacteroidetes (p=0.006) and Firmicutes (p=0.042) were associated with higher HEI. Higher Bacteroidetes levels was observed among non-obese (p=0.006).

Conclusions:

Diet quality measured by the HEI was associated with alpha diversity of the gut microbiota among adult females. Abundances of phyla that have been linked with weight status (Bacteroidetes and Firmicutes) were positively associated with diet quality.

INTRODUCTION

The abundance of certain bacterial groups are hallmarks of lower risk of several metabolic and gastrointestinal diseases.¹ Diet contributes nutrients that the human gut microbiota need to survive,¹ and a growing number of studies have considered the effect of diet quality on the gut microbiome.^2-4 Few studies have evaluated the interaction between weight status and dietary quality with characteristics of the gut microbiota,⁴ and even fewer focus on individuals living in the Southeastern region of the U.S.

Undigested dietary substrates are metabolized by the human gut microbiota, thus dietary intake can affect the microbe abundances. Namely, fiber survives the human digestive system and is a major fuel source for the microbiota. While dietary fiber may not increase microbial diversity, it does selectively increase the abundance of bacterial groups associated with health.¹ Over the last 2 decades, the gut microbiota associated with distinct diets worldwide have been described.^5,6 Overall, plant-based rural and hunter-gatherer diets are characteristically higher in dietary fiber leading to a higher abundance of phylum Bacteroidetes and genus Prevotella than urban and Westernized diets.⁶ Westernized diets are typically higher in fat, animal proteins, and refined carbohydrates while lower in fiber, however variability exists within diets of Westernized countries.⁵ The amounts and types of carbohydrates, dietary fibers, fats, and proteins consumed can alter both harmful and beneficial bacteria in the gut.^1,5,7

The observed variance in both the diet and microbiota has sparked interest in the impact of the overall diet quality on the microbiota.^2-4,8 Conformance to dietary patterns or guidelines can be quantified by diet indices. The Healthy Eating Index (HEI), based on the USDA Dietary Guidelines for Americans (DGA),⁹ along with other dietary indices are beginning to be explored in relation to the microbiota. Diet quality is linked to the abundance of some bacterial groups and some studies,^2,4,8 but not all.³ Further, some studies show that alpha diversity, which is defined as a measure of microbiome diversity within a sample, increases with diet quality.^2,4,8 In one of the largest ethnically diverse microbiota and diet studies, the Adiposity Phenotype Study (APS) cohort of the Multiethnic Cohort study, both alpha and beta diversity were positively associated with diet quality, with fruit and vegetable intake displaying the greatest impact on microbial diversity.⁴ Here beta diversity is defined as a measure of similarity or dissimilarity of microbial composition between samples. However, there is a knowledge gap in the association between the gut microbiome and regional diets in the U.S.

The association between gut microbiota and obesity¹⁰ have been documented. Results of prior studies weakens the previous assumption that lean and obese microbiota are characterized by differences in phyla Firmicutes, Bacteroidetes, and F:B ratio. Instead, lifestyle factors may better explain the observed variation in Firmicutes and Bacteroidetes abundances.¹¹ In a study of Mediterranean Diet adherence on the microbiota, Garcia-Mantrana et al. observed phylum and genus level differences between the microbiota of normal weight and overweight,³ however the interaction of diet and weight status was not considered. Given the complex interactions among the gut microbiota, metabolism, and lifestyle of its host,¹⁰ a better understanding of the interplay between the microbiota and potentially obesogenic lifestyle of its host is needed.

Adherence to the DGA is not optimal in the Southern region of the U.S.^12-15 and may contribute to the disproportionate burden of obesity¹⁶ and other chronic conditions observed in this region.^17,18 The studies linking weight status, diet quality, and the gut microbiota are limited, particularly in the Southeastern region of the U.S. Previously, significant differences in overall diet quality and individual dietary components when comparing obese and non-obese females¹⁹ were reported from this observational study. The objectives of this study were (1) to test for differences in the gut microbiota by weight status among a racially balanced sample of females, (2) to compare the gut microbiota by diet quality as measured by the HEI, and (3) to determine whether an interaction between weight status and HEI is associated with characteristics of the gut microbiota. While this study does not evaluate differences in the gut microbiota by race, findings of this work give insight into differences among a racially balanced sample of Black and White participants residing in the Southeastern region of the U.S.

METHODS

This study is a secondary analysis of data derived from a cross-sectional study of healthy female volunteers from the Birmingham, Alabama Metropolitan area. For the original study, participants provided demographic, anthropometric, survey, and dietary data to examine for associations with the gut bacteria using collected fecal samples. Methods and primary outcomes of this study²⁰ and a more thorough description of the diet quality of the sample¹⁹ are reported elsewhere. For this secondary analysis, comparisons of characteristics of the gut microbiota by diet quality measured by HEI are the outcomes of interest.

Study Population

A total of 106 female participants enrolled in the original study between March 2014 and August 2014. Inclusion criteria included being either a self-reported non-Hispanic Black or non-Hispanic White female aged 19 years or older. Individuals were excluded if they were pregnant or smokers at the time of screening, unable to read or write, had a previous cancer diagnosis, or had taken medications known to alter the gut (e.g., proton-pump inhibitors) or antibiotics in the previous 90 days. Two did not provide fecal samples and 22 were excluded after reporting medications that are known to alter the microbiome. The UAB IRB approved the study and all participants provided written informed consent.

Measures

Collected demographic data included race, ethnicity, age, income, education, and marital status. Race and ethnicity were self-identified as non-Hispanic Black or non-Hispanic White. Demographics and anthropometrics were collected at the first visit and diet and fecal samples were collected at the second visit, approximately 1 week later.

The gut microbiota was analyzed via fecal samples. Details of collection and processing are published elsewhere.²⁰ In short, samples were collected with a wipe and frozen until delivered to the microbiome lab. A Fecal DNA isolation kit from Zymo Research was used to isolate the microbial genomic DNA. PCR was then used on the prepared DNA samples with unique bar coded primers to amplify the variable region 4 (V4) region of the 16S rDNA gene to create an amplicon library from individual samples. The PCR product was ~255 bases from the V4 segment of the 16S rDNA gene, and 251 base paired-end reads were sequenced using Illumina MiSeq. Analysis of the sequence data utilized the QIIME-based QWRAP pipeline²¹ to produce sample operational taxonomic unit (OTU) tables. Analysis included quality control, merging of paired-end reads, OTU picking, and taxonomic assignment.

Trained research personnel measured participants’ weight and height using a standardized protocol described in a previous paper²⁰ published from this observational study. Weight and height were measured using a calibrated 2-in-1 measuring station (Seca 284 measuring station, Hanover, MD) in light-weight clothing without shoes. BMI was calculated as weight (kg)/height (m²).

One 24-hour diet recall was collected by the National Cancer Institute (NCI) Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) version 2014 was used to collect in person with the assistance of a trained data collector. ASA24 is administered as a multiple-pass standardized interview and provides a series of prompts with multi-level food probes to assess food types and amounts. The program computes total intake, macronutrient composition, nutrient and food group estimates. Because this study included one 24-hr recall, a more conservative approach was used to avoid days of more extreme intake. Participants who did not report calories within 600–4,400 kcal, outliers determined by NHANES data,²² were excluded from the current analysis (n=2). After reviewing triaged records that did not meet NHANES guidelines for portion and nutrient outliers (grams of protein and fat, Vitamin C, and Beta-carotene), an additional n=9 participants with extreme diets or believed errors in reported food were excluded from the current analysis.

The Healthy Eating Index-2010 (HEI-2010) scoring system was used to assess diet quality. HEI-2010 is an a priori scoring system based on the 2010 DGA, the most current recommendations at the time of the study. The DGA promote whole grain consumption, fruit and vegetable intake, attaining protein through a variety of sources (i.e., plants and seafood), and limiting empty calories, sodium, and refined grains, while allowing flexibility in eating patterns for individual preferences, cultural and ethnic influences, and vegetarianism.^9,23

The HEI-2010 separates food intake into 12 components: 9 adequacy components (Total Fruit, Whole Fruit, Total Vegetables, Greens and Beans, Whole Grains, Dairy, Total Protein Foods, Seafood and Plant Proteins, and Fatty Acid ratio) score high by reaching recommended intake and 3 moderation components (Refined Grains, Sodium, and Empty Calories) receive high scores for maintaining moderation. Intake evaluation is density based (food component intake per 1,000 kcal or percent of calories) and is scored proportionally when between minimum and maximum standards. Age and sex alter daily requirements, therefore, HEI uses the least restrictive recommendation to award the optimal score.⁹ The maximum Total Score is 100 points with a higher score signifying closer compliance with 2010 DGA recommendations⁹ and a score of ≥80 is ideal.¹² Base code available online²⁴ was used to convert the data generated by ASA24 to individual HEI-2010 component scores and total score. HEI scores were used to categorize participants into 2 groups (low diet quality and high diet quality) in order to assess differences in gut microbiota based on diet quality.

Statistical Analysis

Descriptive statistics were calculated for all participants and stratified by 2 weight status groups: non-obese (BMI <30 kg/m²) or obese (BMI ≥30 kg/m²). Means and SDs were calculated for continuous variables. Frequencies and percentages were calculated for all categorical variables. Mean differences in anthropometric and dietary measures were assessed based on weight status using 2-sample t-tests. Associations between categorical variables were evaluated using Chi-square tests. Statistical significance accepted when p<0.05 (2-tailed).

Analysis of alpha and beta diversities were performed using R Packages phyloseq.²⁵ Descriptive statistics for alpha diversity were assessed for 2 types of stratified groups: weight status (non-obese with BMI <30 vs obese with BMI ≥30) and HEI status (HEI > median vs HEI ≤ median). The median, first quartile and third quartile were calculated for 5 alpha diversity indexes: Observed, Chao1, ACE, Shannon, and Simpson.²⁰ Shapiro-Wilk tests were used to evaluate normality and appropriate statistical tests for evaluating between-group differences. Two sample t-tests were used when normality assumptions were met and Mann Whitney U tests as the nonparametric test. Statistical significance accepted with p<0.05. Beta diversity distance metrics were evaluated using non-metric multidimensional scaling (NMDS). Differences in microbiota based on 2 types of stratified groups: weight status or HEI status were analyzed. Three beta diversity metrics were used: Bray Curtis, Weighted UniFrac, and Unweighted UniFrac.²⁰

All analyses were performed using OTU count data and the R Package phyloseq, Negative binomial models in R Package BhGLM were used to assess the associations between diet variables and OTUs at the Phylum and Genus levels.^26,27 Models were adjusted for age, race, weight status, BMI, and waist circumference. First, all OTUs available were analyzed at the Phylum and Genus levels. False discovery rate (FDR) was used and statistical significance accepted with FDR p<0.05 level.

All plots and figures were created using R Packages “ggplot2” and “vegan.”

All statistical analyses were performed using R (Version 3.4.4).

RESULTS

The present study included 71 female participants with a mean age of 40.4 years (SD 13.9) and BMI of 30.6 kg/ m² (SD 8.9) (Table 1). The average BMI of non-obese and obese participants was 24.7 kg/ m² (SD 2.7) and 38.3 kg/ m² (SD 8.3), respectively. Over half reported some college education and a total household income of $40,000 or more. A greater proportion of participants with obesity were Black compared to White (75% vs 25%; p=0.01).

Table 1.

Participant Characteristics With Statistical Significance (p<0.05)

Characteristics	All (N=71)	Non-obese (N=40)	Obese (N=31)	p- value
Age, mean (SD)	40.4 (13.9)	39.7 (14.8)	41.3 (12.8)	0.65
BMI, mean (SD)	30.6 (8.9)	24.7 (2.7)	38.3 (8.3)	<0.01
Education, n (%)				0.24
≤High school	7 (9.9)	2 (28.6)	5 (71.4)
Some college or degree	47 (66.2)	29 (61.7)	18 (38.3)
Post graduate	17 (23.9)	9 (52.9)	8 (47.1)
Household income, n (%)				0.26
<$10,000	14 (19.7)	7 (50)	7 (50)
$10,000–$19,999	5 (7.0)	3 (60)	2 (40)
$20,000–$29,999	3 (4.2)	1 (33.3)	2 (66.7)
$30,000–$39,999	12 (16.9)	5 (41.7)	7 (58.3)
$40,000–$49,999	15 (21.1)	7 (46.7)	8 (53.3)
≥$50,000	22 (31.0)	17 (77.3)	5 (22.7)
Black, n (%)	43 (60.6)	19 (44.2)	24 (55.8)	0.02
Calories, kcal (SD)	1,998.8 (1,032.7)	2,041.3 (1,046.9)	1,943.9 (1,028.7)	0.70
Carbohydrate, g (SD)	241.9 (160.5)	251.4 (170.5)	229.7 (148.5)	0.58
Protein, g (SD)	76.4 (34.6)	80.5 (33.9)	71.1 (35.3)	0.26
Fat, g (SD)	83.8 (45.4)	83.3 (45.5)	84.3 (46.1)	0.93
Dietary fiber, g (SD)	17.6 (9.9)	17.9 (10.5)	17.2 (9.1)	0.77
Total grains, oz eq (SD)	5.5 (3.8)	5.0 (2.7)	6.2 (4.9)	0.22
Non-whole grain, oz eq (SD)	4.8 (3.8)	4.3 (2.7)	5.5 (4.9)	0.22
Whole grains, oz eq (SD)	0.7 (1.1)	0.7 (1.0)	0.7 (1.2)	0.96
Total vegetables, cup eq (SD)	1.9 (1.4)	1.9 (1.6)	1.9 (1.1)	0.92
Total fruit, cup eq (SD)	1.1 (1.2)	1.5 (1.4)	0.7 (0.8)	0.01
Saturated fatty acids, g (SD)	27.2 (16.8)	27.9 (17.7)	26.2 (15.9)	0.67
Monounsaturated fatty acids, g (SD)	30.9 (18.7)	29.8 (18.4)	32.3 (19.4)	0.58
Polyunsaturated fatty acids, g (SD)	18.7 (11.4)	18.7 (11.6)	18.7 (11.3)	0.99
Total unsaturated fat, g (SD)	49.6 (28.6)	48.5 (28.2)	51.0 (29.5)	0.71
HEI-2010 scores [maximum points], mean (SD)
Vegetable Intake [5]	3.7 (1.5)	3.5 (1.7)	3.9 (1.1)	0.19
Dark Green Vegetable and Bean Intake [5]	2.3 (2.3)	2.2 (2.3)	2.4 (2.4)	0.68
Fruit Intake [5]	2.6 (2.2)	3.1 (2.2)	2.0 (2.0)	0.04
Whole Fruit Intake [5]	2.4 (2.4)	2.9 (2.4)	1.7 (2.3)	0.03
Whole Grain Intake [10]	2.4 (3.2)	2.4 (3.2)	2.3 (3.3)	0.85
Total Dairy Intake [10]	5.0 (3.5)	5.2 (3.4)	4.8 (3.7)	0.62
Total Protein Intake [5]	4.1 (1.4)	4.3 (1.2)	3.9 (1.6)	0.19
Seafood and Plant Protein Intake [5]	1.9 (2.3)	2.0 (2.4)	1.6 (2.1)	0.46
Type of Fat Intake [10]	5.0 (3.9)	4.4 (3.9)	5.7 (3.7)	0.18
Sodium Intake [10]	3.1 (3.4)	3.5 (3.6)	2.5 (3.1)	0.22
Refined Grain Intake [10]	6.5 (3.6)	6.8 (3.6)	6.1 (3.6)	0.40
Empty Calories [20]	12.2 (6.1)	12.8 (6.5)	11.4 (5.4)	0.34
HEI-2010 Total Score [0-100]	51.0 (16.2)	53.2 (17.4)	48.3 (14.3)	0.21

Note: Boldface indicates statistical significance (p<0.05).

kcal, kilocalories; g, grams; oz eq, ounce equivalents; cup eq, cup equivalents; HEI, Healthy Eating Index.

Mean intake was 1,998.8 kcal (SD 1,032.7), 76.4 g protein (SD 34.6), 83.8 g fat (SD 45.4), 241.9 g carbohydrate (SD 160.5), and 17.6 g fiber (SD 9.9). There were no significant differences in calorie, macronutrient, or fiber intake based on weight status (Table 1). The mean HEI total score was 51.0 (SD 16.2) and did not differ by weight status. Fruit intake was greater in non-obese than obese both by number of servings (1.5 cup eq vs 0.7 cup eq; p=0.01) and HEI scoring (3.1 vs 2.0; p=0.04). Whole fruit intake was higher in non-obese than obese (HEI score: 2.9 vs 1.7; p=0.03).

The low HEI group had a higher BMI (33.4 vs 28.0; p=0.02) and higher caloric intake (2,257.8 vs 1,746.9; p=0.04). Fat, protein, and fiber intake did not differ, but the low HEI group reported a higher intake of carbohydrates (p=0.02). The high HEI group reported greater intake of vegetables (p=0.02), green vegetables and beans (p<0.001), total fruit (p<0.001), whole fruit (p<0.001), whole grains (p=0.04), total protein (p=0.02), protein from seafood and plant sources (p<0.001), a better monounsaturated to saturated fat ratio (p<0.01), and fewer refined grains (p<0.001) and empty calories (p<0.001).

From fecal samples, 13 phyla, 123 genera, and 718 OTUs were identified. At the phylum level, the abundance of Bacteroidetes (65.06%) was greatest followed by Firmicutes (24.02%) and Actinobacteria (10.92%) (Figure 1A). The most dominant genera were Lactobacillus (28.49%), Bacteroides (24.02%), Blautia (19.85%), Bifidobacterium (10.92%), Faecalibacterium (5.17%), Roseburia (4.46%), and other unclassified genera (7.09%) (Figure 1D).

Figure 1.

Microbial abundance comparisons of the most dominant groups. Microbial abundance at the phylum level is captured for (A) the general profile, (B) according to HEI status, and (C) according to weight status. At the genus level, the microbial abundance is shown for (D) the general profile, (E) according to HEI status, and F) according to weight status.

HEI, Healthy Eating Index

When comparing non-obese and obese, measures of alpha diversity indicated similar within sample gut microbial diversity (Table 2). There was no evidence of differences in the overall microbial distribution across groups, or beta diversity, as shown by the lack of clustering by group in Figure 2B and the Bray Curtis (p=0.577), Unweighted Unifrac (p=0.461), and Weighted Unifrac (p=0.788) methods (Table 3; Figure 2B).

Table 2.

Measures of Alpha Diversity by Weight Status With p<0.05 Considered Statistically Significant

Measures	Weight status		p-value
	Non-obese (N=40) Median (Q1, Q3)	Obese (N=31) Median (Q1, Q3)
Observed	275 (259.5, 307.5)	266 (247.5, 290.2)	0.248
Chao1	335 (298.2, 361.4)	315 (280.6, 346.6)	0.143
ACE	327 (311.4, 358.9)	307 (286.0, 343.0)	0.077
Shannon	4 (3.2, 3.8)	4 (3.3, 3.8)	0.471
Simpson	0.916 (0.903, 0.957)	0.944 (0.898, 0.956)	0.553

ACE, Abundance-based Coverage Estimator.

Figure 2. Illustration of a non-metric multidimensional scaling (NMDS) plot. Results are shown for (A) HEI status and (B) weight status.

Notes: The NMDS analysis provides a visualization of the level of similarity of each observation in the dataset based on the distance matrix. Then each observation is assigned a location in a low-dimensional space. Ellipses represent 95% CI around the centroid.

HEI, Healthy Eating Index.

Table 3.

Measures of Beta Diversity by Weight Status and HEI Status With p<0.05 Considered Statistically Significant

Measures	Weight status p-value	HEI status p-value
Bray Curtis	0.577	0.297
Unweighted UniFrac	0.461	0.280
Weighted UniFrac	0.788	0.071

HEI, Healthy Eating Index.

Alpha diversity assessed by the Chao1 (p=0.030) and ACE (p=0.037) method displayed significant differences by HEI groups (Table 4). Beta diversity did not differ when comparing HEI groups using the Bray Curtis (p=0.297), Unweighted Unifrac (p=0.280), and Weighted Unifrac (p=0.071) methods (Table 3).

Table 4.

Measures of Alpha Diversity by HEI Status With p<0.05 Considered Statistically Significant

Measures	HEI status		p-value
	Low (N=35) Median (Q1, Q3)	High (N=36) Median (Q1, Q3)
Observed	263 (246.0, 291.0)	274 (260.0, 306.0)	0.136
Chao1	300 (280.9, 341.9)	333 (309.8, 361.4)	0.030
ACE	306 (282.8, 341.4)	331 (307.5, 364.2)	0.037
Shannon	3.59 (3.24, 3.95)	3.66 (3.29, 3.80)	0.818
Simpson	0.930 (0.900, 0.959)	0.939 (0.902, 0.953)	0.868

HEI, Healthy Eating Index; ACE, Abundance-based Coverage Estimator.

At the phylum level, better diet quality was inversely associated with Proteobacteria and TM7 (Figure 3A). No dietary components were significantly associated with the 2 most abundant phyla, Firmicutes and Bacteroidetes. Whole grain intake was inversely associated with Proteobacteria. Whole fruit intake was inversely associated with Verrucomicrobia. A higher ratio of unsaturated fats to saturated fats was inversely associated with a Proteobacteria. HEI for Fruit and Empty Calories was inversely associated with Proteobacteria and TM7. Intake of dark green vegetables and beans was inversely associated with Proteobacteria and Thermi. Diet interactions by weight status were significantly associated for the Proteobacteria phyla (Figure 3B). The relationship between Proteobacteria and whole fruit intake were positively associated with weight status. Finally, total HEI score was significantly and positively associated with weight status and Firmicutes.

Figure 3. Associations between diet and all OTUs at the phylum level: (A) main effect of diet and (B) interaction between diet and weight status.

Notes: A “+” sign indicates a positive main effect (A) or positive interaction (B).

HEI, Healthy Eating Index; OTU, Operational Taxonomic Unit.

The associations between OTUs, diet, and weight status were explored at the Genus level. Significant associations between diet quality and microbiota groups were found after assessing fruit and fiber intake. HEI fruit score was inversely associated with Akkermansia (p<0.05) and Gernella (p<0.01). Bifidobacterium was positively associated with HEI fruit score (p<0.01) and demonstrated a negative interaction between weight status and fruit intake (p<0.01). Total and HEI score for whole grain was positively associated with Dorea (p<0.01), but an interaction between weight status and intake did not significantly relate to Dorea abundance. The genus Prevotella was, however, negatively associated with whole grain intake that differed based on weight status group. HEI vegetable score inversely associated with Lactococcus (p<0.05). Dietary fat intake was strongly associated with several species in the gut microbiota. Fat intake was inversely associated with Prevotella, Escherichia, and Lachnospira (p<0.01). HEI fat ratio, total unsaturated fats (mono- and polyunsaturated fat total) and polyunsaturated fats inversely associated with Lactobacillus (p<0.01). Further, total fat, mono and poly fats, and monounsaturated fats were positively associated with both Haemophilus (p<0.001) and Odoribacter (p<0.01). Lastly, Devosia was negatively associated with vegetable intake that differed based on weight status group (p<0.01).

DISCUSSION

The present study assessed differences in the gut microbiota by diet quality and weight status among a racially balanced sample of 71 females living in Birmingham, AL. Greater alpha diversity was associated with a higher diet quality score but was not related to weight status. Beta diversity did not differ based on weight status nor diet quality. Several gut bacterial groups were associated with individual dietary components. At the phylum level, Bacteriodetes and Firmicutes were abundant, but were not found to be significantly associated with any specific dietary intake in this sample. However, the phylum Proteobacteria, was found to be inversely related to whole grain, fat, dark green vegetables and beans, fruit, and a better adherence to limited empty calorie intake. Higher HEI scores were associated with lower abundances of Proteobacteria and TM7. At the genus level, Bifidobacteria was positively related to fruit intake and was inversely linked weight status. This finding was consistent with another study which found Bifidobacteria was associated with a plant-based diet.³ Several fats such as, total fat, unsaturated fats (mono and poly fat), and monounsaturated fats were positively associated with Lactobacillus, Haemophilus, and Odoribacter. Notably, Lactobacillus has been studied in other contexts to examine irritable bowel syndrome (IBS), and thus this bacteria has been documented in nutrition and health literature. The HEI score for fat intake was inversely related to Prevotella, Escherichia, and Lachnospira. The present study did not find differences between beta diversity based on diet quality or weight status. However, the present findings are consistent with other reports that higher diet quality assessed by HEI is associated with greater alpha diversity.^2,4 Like Maskarinec et al., the present study did not observe any significant diet associations with Firmicutes.⁴ In contrast to Maskarinec et al. who observed an inverse association between Actinobacteria and diet quality,⁴ this study only noted a non-significant trend.

Previously, fruit and vegetable intake has been found to be most beneficial to alpha diversity.⁴ In this sample, fruit or vegetable intake was associated with increased Bifidobacterium, and inversely associated with Proteobacteria, Akkermansia, and Verrucomicrobia. Polysaccacharides such as starches and fiber found in fruit are associated with higher Bifidobacterium.³ The present finding that Proteobacteria was associated with consumption of dietary fats is consistent with another study which found higher abundance of Proteobacteria in children with obesity or a high fat intake.²⁸ A positive association was observed between total fat and healthy unsaturated fats with Haemophilus. In contrast, Garcia-Mantrana et al. observed less healthy saturated fats were associated with Haemophilus.³ Reasons for this contradictory finding may be a result of using different metrics for assessing diet quality given that Garcia-Mantrana et al. examined the Mediterranean Diet and this study used the HEI-2010.

Limitations

This study has a few limitations. The small sample limits the strength to detect potentially significant relationships between diet, weight status and the gut microbiota. Hence, a larger sample would strengthen the ability to detect potential differences in gut microbiota, diet and weight status. Self-reported dietary intake may also allow for recall bias or inaccurate reporting by participants, thus the decision to exclude participants with implausible dietary recalls. This study also has significant strengths that outweigh the limitations. The present sample has an equivalent proportion of obese and non-obese, and non-Hispanic White and Black females representing the Southeastern region of the U.S. which allows us to explore the gut microbial differences in a diverse, understudied population with a regional-specific diet. With this sample, these findings provide valuable insight into chronic disease risk in a potentially high-risk, underrepresented, and vulnerable population since 20% of the participants reported less than a $10,000 household income.

CONCLUSIONS

These findings suggest strong correlations between diet quality and the gut microbiota in a racially balanced sample population. Specifically, whole grain, fiber, fruit, vegetable, and fat intake were strongly linked to bacterial groups related to health outcomes. As more information is discovered about signatures for an optimal health-promoting gut microbiota, these findings will help to inform strategies to target for modification of the gut microbiota. This research will offer insight into behavioral and potential physiological pathways for chronic disease prevention and progression.

Figure 4. Associations between OTUs and Diet with main effect of diet (A) and interaction between diet and weight status (B) at the Genus Level.

Notes: “+” signs indicate a positive main effect (A) or positive interaction (B).

HEI, Healthy Eating Index; OTU, Operational Taxonomic Unit.