Next Article in Journal
Role of Ginseng and L-Carnitine in Modulating Exercise Endurance and Oxidative Stress in Rats
Previous Article in Journal
Preoperative Nutrition in Bariatric Surgery: A Narrative Review on Enhancing Surgical Success and Patient Outcomes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 17
Issue 3
10.3390/nu17030567
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dairy Consumption and the Colonic Mucosa-Associated Gut Microbiota in Humans—A Preliminary Investigation

by
Ellie Chen
1,
Nadim J. Ajami
2,
Donna L. White
1,3,4,5,
Yanhong Liu
1,5,
Shawn Gurwara
1,
Kristi Hoffman
2,
David Y. Graham
1,4,6,
Hashem B. El-Serag
1,3,4,5,6,
Joseph F. Petrosino
2,4,5 and
Li Jiao
1,*
1
Department of Medicine, Baylor College of Medicine (BCM), Houston, TX 77030, USA
2
The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine (BCM), Houston, TX 77030, USA
3
Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center (MEDVAMC), Houston, TX 77030, USA
4
Texas Medical Center Digestive Disease Center, Houston, TX 77030, USA
5
Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine (BCM), Houston, TX 77030, USA
6
Section of Gastroenterology, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center (MEDVAMC), Houston, TX 77030, USA
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(3), 567; https://doi.org/10.3390/nu17030567
Submission received: 1 January 2025 / Revised: 29 January 2025 / Accepted: 30 January 2025 / Published: 2 February 2025
(This article belongs to the Section Nutritional Epidemiology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: Dairy consumption has been associated with various health outcomes that may be mediated by changes in gut microbiota. Methods: This cross-sectional study investigated the association between the colonic mucosa-associated gut microbiota and the self-reported intake of total dairy, milk, cheese, and yogurt. A total of 97 colonic mucosal biopsies collected from 34 polyp-free individuals were analyzed. Dairy consumption in the past year was assessed using a food frequency questionnaire. The 16S rRNA gene V4 region was amplified and sequenced. Operational taxonomic unit (OTU) classification was performed using the UPARSE and SILVA databases. OTU diversity and relative abundance were compared between lower vs. higher dairy consumption groups. Multivariable negative binomial regression models for panel data were used to estimate the incidence rate ratio and 95% confidence interval for bacterial counts and dairy consumption. False discovery rate-adjusted p values (q value) < 0.05 indicated statistical significance. Results: Higher total dairy and milk consumption and lower cheese consumption were associated with higher alpha microbial diversity (adjusted p values < 0.05). Higher total dairy and milk consumption was also associated with higher relative abundance of Faecalibacterium. Higher milk consumption was associated with higher relative abundance of Akkermansia. Higher total dairy and cheese consumption was associated with lower relative abundance of Bacteroides. Conclusions: Dairy consumption may influence host health by modulating the structure and composition of the colonic adherent gut microbiota.

1. Introduction

Dairy products provide a rich source of nutrients, including protein, fats, carbohydrates, calcium, magnesium, selenium, phosphorous, potassium, zinc, and vitamins B2, B6, B12, D, and K [1,2]. However, the association between dairy consumption and the risk of various diseases, including cancer, remains inconclusive [3]. Fermented dairy products, in particular, have been shown to have many health benefits, such as improving bone health [4], building muscle mass, lowering blood pressure [5], and reducing the risk of cardiovascular disease and diabetes [6,7,8,9]. Additionally, moderate dairy consumption has been linked to improved cognitive function in later life [10]. On the other hand, higher consumption of dairy and milk has been associated with an increased risk of stroke, cardiovascular mortality, and all-cause mortality [11]. Given that dairy products are a heterogeneous food group comprising milk, cheese, yogurt, and butter, it is recommended to investigate the health effects of each individual food group separately.
Research has also examined the relationship between dairy consumption and molecular biomarkers in humans. A systematic review of intervention studies concluded that dairy and milk consumption did not exhibit pro-inflammatory effects in either healthy adults or adults with metabolic abnormalities, with long-term dairy supplementation showing a weak anti-inflammatory effect [12]. Another systematic review reported that higher dairy consumption, compared to lower or no consumption, was associated with reduced levels of pro-inflammatory biomarkers [13]. In postmenopausal women, higher dairy consumption, excluding butter, was associated with favorable profiles of lipids, insulin response, and inflammatory biomarkers [14]. Overall, biomarker-based studies do not indicate adverse effects of dairy consumption on inflammatory biomarkers [15]. Additionally, the previous findings suggested that specific dairy foods may uniquely influence these biomarkers [14]. Further research into the impact of dairy consumption on other biological functions could enhance our understanding of its potential health benefits or risks.
In humans, the gut microbiota is a dynamic physiological system that plays a critical role in various host functions, including nutrient metabolism, xenobiotic and drug metabolism, antimicrobial defense, immunomodulation, and maintaining the structural integrity of the gut mucosal barrier [16]. It is plausible that dairy consumption influences gut microbiota composition and diversity, particularly due to the probiotic effects of fermented dairy products such as yogurt and kefir [17,18]. A systematic review of eight studies concluded that milk, yogurt, and kefir may modulate the fecal microbiota composition in favor of the host [19]. In one randomized crossover study of 46 healthy overweight individuals, a high-dairy diet significantly altered fecal microbiota [20]. A study in men found no overall association between total dairy consumption and bacterial composition, though milk consumption was linked to higher relative abundance of Ruminococcaceae and Bifidobacterium [21]. A previous study found that consuming A2 milk increased Bifidobacterium spp. compared to consuming regular milk in humans [22]. These previous findings highlight the importance of investigating individual dairy foods in association with the gut microbiota. However, to date, the impact of dairy products on the mucosa-associated gut microbiota has not been examined.
Therefore, we investigated the relationship between the consumption of total dairy products, milk, cheese, and yogurt and the composition and structure of the colonic mucosa-associated gut microbiota in individuals with grossly normal colonoscopic findings. We hypothesized that dairy consumption affects the composition and structure of the gut microbiota.

2. Materials and Methods

2.1. Study Population

The research design, eligibility criteria, and data collection protocol were described previously [23]. This was a cross-sectional study of prospectively and consecutively enrolled veterans who had a scheduled colonoscopy between August 2013 and April 2017 at the Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC) in Houston, Texas. All participants provided written informed consent for study activities. The study protocol was approved by the Institutional Review Boards of Baylor College of Medicine (BCM) and MEDVAMC.
We excluded individuals with the following criteria: (1) a history of familial or hereditary colon diseases or inflammatory bowel disease; (2) invasive cancer, except for nonmelanoma skin cancer; (3) colorectal polyps within the past 3 years; (4) end-stage renal disease requiring dialysis; (5) severe mental disabilities; (6) surgery or hospitalization within the past year; (7) oral or systemic use of antibiotics within the past 3 months; (8) infection with hepatitis B virus, hepatitis C virus, HIV, or methicillin-resistant Staphylococcus aureus; or (9) bleeding disorders or use of anticoagulants. Individuals who had changed their dietary habits in the past 3 months were also excluded. Participants were instructed to discontinue routinely used medications 7 days prior to the procedure and antidiabetic medications 1 day prior.
Eligible participants were included if they had grossly normal-appearing colon mucosa on colonoscopy. During the study period, we enrolled 612 participants, 562 of whom completed the colonoscopy procedure; 172 were found to have a grossly normal colon, of whom 133 had colonic biopsies obtained for this study (1–6 biopsies from cecum to rectum). We sent mucosal samples from 69 participants for sequencing. Among them, 40 participants completed the Food Frequency Questionnaire (FFQ). Five were excluded because their energy intake was <800 or >5000 kcal per day. Thus, we included 99 mucosal samples from 35 participants who had sequencing data. Two samples from one individual were further excluded due to the poor sequencing quality. Therefore, we included a total of 97 colonic biopsies from 34 participants in the final analysis.

2.2. Data Collection

After obtaining informed consent, we collected participants’ lifestyle data using an interviewer-administered questionnaire. We also obtained the anthropocentric parameters from each participant. Dietary data were collected using a self-administered, 110-item validated Block FFQ -2005, which has been validated as a tool to assess diet–disease risk [24]. NutritionQuest calculated the average daily servings of nutrients and foods over the past year, adjusting for caloric intake using the density method [25]. We computed the healthy eating index (HEI) to assess diet quality [26].

2.3. Library Construction and 16S rRNA Gene Sequencing

The microbiome profiling was performed at the Alkek Center for Metagenomics and Microbiome Research (CMMR) at BCM as described previously [27,28]. The bacteria were examined by amplifying the V4 region of the highly conserved small-sub-unit ribosomal gene (16S rRNA) and sequencing on the MiSeq platform (Illumina, SD, CA, USA) using the 2 × 250 bp paired-end protocol. The V4 region was amplified by PCR using primers 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT). Each resulting amplicon set was barcoded with a unique 12 mer tag.
The read pairs were demultiplexed based on the unique molecular barcodes tagged during amplification, and reads were merged using USEARCH v7.0.1090. Chimeras or low-quality sequences were removed using USEARCH v7.0.1090 and UCHIME v4.2. The 16S rRNA gene sequences were clustered into the Operational Taxonomic Units (OTUs) at a similarity cutoff value of 97% using the UPARSE algorithm. The OTUs were mapped to the SILVA v128 database to determine taxonomies using UPARSE. The abundances were recovered by mapping the demultiplexed reads to the UPARSE OTUs. The downstream analyses were performed using the Agile Toolkit for Incisive Microbial Analyses (ATIMA) (Houston, TX, USA). The samples were rarefied to 1648 reads per sample, resulting in the loss of all negative controls and two mucosal samples from one individual. Therefore, we included a total of 97 colonic biopsies from 34 participants in the final analysis.

2.4. Data Analysis

To examine the distribution of demographic and lifestyle factors by higher vs. lower total dairy intake, Student’s t-test was used to analyze differences in continuous variables with a normal distribution. Fisher’s exact test was used for categorical variables because frequencies in at least one cell were lower than 5. The median value derived from 34 participants served as the cutoff to categorize intake as lower vs. higher. The ATIMA was used to identify trends in taxa abundance and diversity according to higher vs. lower total dairy, milk, cheese, and yogurt intake, respectively. We examined all taxa with relative abundance > 0.05%. The unclassified OTUs were included in the analyses. We used principal coordinates analysis plots (PCoA) with the Monte Carlo permutation test to estimate p values for beta-diversity and used the Weighted UniFrac as the distance matrix. The Mann–Whitney test was used to compare the relative abundance of bacterial taxonomic levels by the intake level. Multivariable negative binomial regression analysis was used to estimate the incidence rate ratio (IRR) and its 95% confidence interval (CI) of having the bacterial count in the higher intake group compared to the lower intake group, adjusting for age, race (non-Hispanic White, African American, and Hispanic), body mass index (BMI), smoking status (never, former, and current), alcohol use (never, former, and current), diabetes, hypertension, HEI score, and biopsy segment. We additionally included the individual nutrient variables listed in Table 1 in the model. To account for the repeated colonic biopsy sampling from the same individuals, we ran a negative binomial regression for panel data (random effect). The beta-diversity and the relative abundance of the bacterial taxa did not differ by colon segment (p values = 0.99). Nevertheless, we repeated the analysis using a single segment (sigmoid) in the sensitivity analysis.
All tests were two-sided. A p value < 0.05 in the general analysis or a false discovery rate (FDR)-adjusted p-value (q value) < 0.05 in the microbiota analysis indicated statistical significance, respectively. STATA 16.0 (STATA Corporation, College Station, TX, USA) was used for data analysis.

3. Results

The study participants had a mean age of 62.7 years (range: 55–71 years), with only one woman participant. The median calorie-adjusted daily intake included 0.57 servings of total dairy, 0.24 cups of milk, 0.27 milk-equivalent servings of cheese, and 0.002 cups of yogurt. Two participants consumed low-fat cheese, while the rest consumed regular cheese. The distribution of age, race, BMI, smoking status, and disease history was comparable between participants with higher vs. lower total dairy consumption. Those with higher total dairy intake had significantly greater consumption of calcium, vitamin D, riboflavin, cobalamin, saturated fat, and lactose (p values < 0.05). However, there were no differences in diet quality and vitamin B6 intake between the groups (Table 1). None of the participants reported a history of autoimmune disorders.
Higher total dairy and milk intake and lower cheese intake were associated with a higher alpha-diversity (q values < 0.05). Yogurt intake was not associated with alpha-diversity (Figure 1A–D).
The bacterial community composition, which was indicated by the beta-diversity, differed significantly based on higher vs. lower intake of total dairy, milk, cheese, and yogurt (yes vs. no) (Figure 2A–D).
Figure 3 and Supplemental Table S1 show the differences in the relative abundance of major phyla by dairy consumption. Participants with higher consumption of total dairy products, cheese, and yogurt had a lower relative abundance of Bacteroidetes (q value < 0.05). In contrast, those with higher intake of total dairy products, milk, and cheese exhibited a higher relative abundance of Verrucomicrobia (q value < 0.05). Additionally, the relative abundance of Fusobacteria was greater among individuals with higher consumption of total dairy products and cheese compared to their counterparts (q values < 0.05).
Figure 4 and Supplemental Table S2 show significant differences in the relative abundance of major bacterial genera (>0.05% relative abundance) by dairy consumption. Compared to their counterparts, individuals with higher consumption of total dairy and milk had a greater relative abundance of Faecalibacterium and Haemophilus. Those consuming more total dairy and cheese exhibited a higher relative abundance of Bacteroides and Fusobacterium. A higher intake of milk and cheese was associated with an increased relative abundance of Akkermansia. While Parabacteroides was more abundant among those consuming more milk, it was less abundant in those consuming more cheese. Additionally, individuals with higher cheese consumption had an increased relative abundance of Escherichia and a decreased relative abundance of Subdoligranulum. Finally, a higher intake of total dairy products was linked to a greater relative abundance of Bifidobacterium (all q values < 0.05) (Supplemental Table S2).
Multivariable negative binomial regression for panel data analysis confirmed a statistically significant positive association between Faecalibacterium and the intake of total dairy and milk, as well as between Akkermansia and milk intake. However, these associations were attenuated after adjusting for lactose intake in the models. The analysis also confirmed a statistically significant inverse association between Bacteroides and Subdoligranulum and cheese intake. Adjusting for known nutrients did not impact the incidence rate ratio (IRR) estimates. Additionally, total dairy intake was inversely associated with Bacteroides (Table 2).
Supplemental Table S3 shows the genera that significantly differed in relative abundance based on the sigmoid sample. We observed the increased Faecalibacterium in those with higher total dairy and milk intake (q values < 0.05) and increased Bifidobacterium with higher total dairy intake and increased Akkermansia with higher milk intake.

4. Discussion

We characterized the gut microbiota using colonic mucosa specimens. Higher intake of dairy and milk, along with lower cheese intake, was associated with a greater bacterial alpha-diversity. Beta-diversity analyses showed differences in bacterial composition between higher vs. lower intake of all types of dairy products. Higher total dairy and milk consumption was associated with a greater relative abundance of Faecalibacterium, while milk consumption was associated with a higher relative abundance of Akkermansia. Conversely, higher total dairy and cheese consumption was associated with a lower relative abundance of Bacteroides. Higher cheese intake was also associated with a lower relative abundance of Subdoligranulum. Our study indicated that dairy and milk consumption promote the colonization of beneficial gut bacteria in the colonic mucosa in humans. The study findings were in agreement with those reported by a recent review [29].
Our findings showed that bacterial community composition differed between higher vs. lower dairy intake. Similarly, a prospective study of a Chinese population reported significant differences in fecal gut microbial community structure (beta-diversity) when comparing the highest with the lowest intake categories for total dairy, milk, and yogurt. Dairy consumption was also associated with a higher alpha-diversity [30]. However, a cross-sectional study of Australian men found no difference in the alpha-diversity of fecal microbiota across the dairy intake group. However, differences in beta-diversity were observed between higher vs. lower milk and yogurt consumption [21]. Overall, our study, which analyzed mucosal samples, aligns with other studies which showed that the consumption of milk, cheese, and yogurt would influence bacterial composition in feces.
Participants with higher dairy and milk intake had a greater relative abundance of Faecalibacterium, a Gram-positive anaerobic bacterium from the Firmicutes phylum and Ruminococcaceae family [31]. Interestingly, diverse strains of Faecalibacterium have been detected in dairy cow milk, highlighting its potential probiotic property [32]. Faecalibacterium produces butyrate and other short-chain fatty acids through the fermentation of dietary fiber and its anti-inflammatory property has been well recognized [31]. It is one of the most abundant human intestinal microbiota, with Faecalibacterium prausnitzii being the most abundant species [33]. Experimental research has shown that F. prausnitzii can ameliorate colorectal tumorigenesis [34]. In addition, F. prausnitzii has been found to be downregulated in individuals with coronary artery disease and anxiety [35]. The depletion of Faecalibacterium has been associated with an increased risk of inflammatory bowel disease [36]. Our observations may help explain the reduced risk of various diseases associated with higher consumption of non-fermented milk [37,38]. However, it is noted that one crossover study in overweight individuals reported a reduction in fecal F. prausnitzii with a high-dairy diet [20]. Future research should evaluate the impact of individual dairy foods on this key commensal bacterium and the respective physiological consequences.
Participants with higher milk intake had a greater relative abundance of Verrucomicrobia and its genus Akkermansia in their colonic mucosal samples. This finding is consistent with a previous crossover study in hyperinsulinemic individuals, which showed that higher dairy intake was associated with an increase in the butyrate-producing bacteria of the Firmicutes and the Verrucomicrobia phyla compared to adequate dairy intake, with both negatively correlated with insulin resistance [39]. In addition, a study in infants found that mare’s milk consumption led to a higher relative abundance of Akkermansia in fecal samples [40]. Akkermansia is a mucin-degrading, Gram-negative, obligate anaerobic bacterium [41] that has been associated with a healthier metabolic profile through improved glucose and lipid metabolism [42]. It has also been associated with enhanced gut barrier function, reduced age-related mucosal thinning, and decreased inflammation, contributing to improved colon health [43,44]. However, in fiber-deprived conditions, Akkermansia has been shown to promote colonic inflammation [45]. Further study is needed to explore optimal strategies for enhancing the health-promoting roles of Akkermansia across diverse populations.
Faecalibacterium and Akkermansia are two abundant commensal bacteria in the human gut that play crucial roles in immune-related diseases [46]. Our study showed that the abundances of these two bacteria are influenced by dairy or milk products. It is noted that adjusting for lactose attenuated the association between total dairy and milk intake and the relative abundance of Akkermansia and Faecalibacterium, suggesting that nutrients in dairy products may possess prebiotic properties.
Previous research has highlighted the potential health benefits of yogurt consumption [47], including the inverse association with the risk of cardiovascular diseases and type 2 diabetes [48]. In a study of 130 healthy individuals, natural yogurt consumption was associated with higher fecal levels of Akkermansia compared to non-consumers, while sweetened yogurt intake was associated with lower levels of Bacteroides [49]. However, several other human studies did not observe changes in Akkermansia abundance associated with yogurt consumption [50,51,52]. Due to the low yogurt consumption in our study population, we could not draw a meaningful conclusion. Bifidobacterium, a common yogurt starter known for its probiotic function [53], had a low relative abundance in our study samples, as did Lactobacillus and Streptococcus, two other widely used yogurt culture starters [54]. These bacteria have been shown to be transiently increased in the gut microbiome in yogurt consumers [50,55].
Higher cheese intake was associated with a lower relative abundance of Subdoligranulum, a spore-forming, butyrate-producing bacterium from the Firmicutes phylum and Ruminococcaceae family [56]. One study found that Subdoligranulum was more common in the healthy mucosa compared to colon polyps [57], while other research reported its enrichment in patients with gastrointestinal neoplasms [58]. Additionally, Subdoligranulum has been linked to chronic inflammation and poor metabolic control in children with irritable bowel syndrome [59].
Higher total dairy and cheese intake were also associated with a lower relative abundance of Bacteroides, the most abundant genus in our study population. Bacteroides produce toxins that can induce tumorigenesis and have been linked to colon cancer development [60,61]. Conversely, lower levels of Bacteroides in the gut have been associated with inflammatory bowel disease [62]. Taken together, these findings provide conflicting evidence regarding whether Subdoligranulum and Bacteroides are beneficial or detrimental to the host. Similarly, epidemiologic studies on the association between cheese intake and colorectal cancer (CRC) have yielded inconsistent results. Some research has reported an inverse association between cheese intake and CRC risk [63], while others have found no significant association [37,64]. A deeper understanding of the functional roles of Subdoligranulum and Bacteroides is essential for interpreting these findings and their implications for health.
The differing impact of milk and cheese consumption on gut bacterial composition may be explained by differences in their production and nutrient content. Cheese production involves fermentation, during which enzymes or bacteria break down lactose and produce lactic acid [65]. Milk contains a higher amount of lactose [66], which can act as a prebiotic that promotes the growth of a cancer-protective colonic microbiota [67,68]. In contrast, hard cheese contains very little lactose. Therefore, turning milk into cheese may alter its nutrient composition, potentially modifying its prebiotic or probiotic effects. However, in our study samples, the relative abundances of lactic-acid bacteria, including Streptococcus, Veillonella, and Bifidobacterium, were all below 0.5%.
Our study had multiple strengths. First, we examined the association between colonic adherent bacteria and dairy consumption. Mucosa-associated bacteria are thought to have a stronger connection to the host’s immune function compared to luminal bacteria [69]. Second, we implemented stringent eligibility criteria aiming at minimizing potential confounding factors such as antibiotics use and other diseases. Third, we accounted for multiple covariates when analyzing the association between bacteria and dairy consumption. Lastly, data collection and 16S rRNA sequencing were conducted and analyzed consistently, reducing the potential for systemic bias.
This study also had several limitations. First, we could not infer causality in the association between dairy consumption and alterations in gut microbiota given our cross-sectional design. Second, our participants were mostly veteran men over the age of 50, limiting the generalizability of the findings to women and other populations. For example, our participants reported lower average dairy consumption compared to older men in the National Health and Nutrition Examination Survey (NHANES), with average daily intake of total dairy, milk, and cheese being 1.80, 1.00, and 1.70 servings, respectively [70], compared to 0.56, 0.22, and 0.26 servings in our study. Future studies are warranted that can investigate these associations in populations with higher dairy consumption. According to MyPlate by the United States Department of Agriculture, adult men are recommended to consume three cups of dairy products daily. Third, potential information bias in the FFQ data cannot be ruled out. Fourth, although all participants had normal colonoscopy results, we were unable to assess other functional disorders that might have influenced the colonic gut microbiota [71]. Lastly, our final sample size was smaller than feces-based studies due to the use of mucosal samples. Comparisons between our mucosa-based findings and previous feces-based findings may not be valid. Our study findings should be confirmed in other large studies using mucosal samples.

5. Conclusions

Variations in dairy consumption were associated with distinct community composition and structure of gut microbiota attached to the colon. The potential beneficial effects of dairy and milk consumption on the gut microbiota community composition and structure aligned with the observed inverse association between dairy intake and the risk of various diseases. It is noted that milk and cheese intake were associated with the gut microbiota differentially. Further research is needed to explore the roles of less abundant bacteria in dairy consumption and their impact on health outcomes. Genomic, metagenomic, and metabolomic approaches are needed to identify bacterial species and functions influenced by dairy consumption and to identify the populations that could benefit from dairy consumption through personalized nutritional intervention [72]. In addition, targeted fecal microbiota transplantation using the optimized strains could be a promising preventive and treatment tool for various diseases [73].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17030567/s1, Table S1: Relative abundance (%) of the major bacterial phyla by dairy product intake. Table S2: Relative abundance (%) of the major bacterial genera by dairy product intake. Table S3: Relative abundance of bacterial genera by dairy consumption in sigmoid samples.

Author Contributions

Conceptualization, L.J. and J.F.P.; methodology, N.J.A., K.H., J.F.P. and L.J.; software, N.J.A., K.H. and J.F.P.; validation, N.J.A., K.H. and J.F.P.; formal analysis, L.J. and K.H.; investigation, L.J., D.L.W., Y.L., H.B.E.-S., S.G., D.Y.G. and J.F.P.; resources, D.Y.G. and H.B.E.-S.; data curation, L.J.; writing—original draft preparation, E.C., S.G. and L.J.; writing—review and editing, N.J.A., D.L.W., Y.L., J.F.P., L.J., D.Y.G. and H.B.E.-S.; visualization, L.J.; supervision, L.J. and H.B.E.-S.; project administration, L.J.; funding acquisition, L.J. and J.F.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Cancer Prevention and Research Institute of Texas (RP#140767, PI: Jiao, L; Petrosino, J), Gillson Longenbaugh Foundation (#001, PI: Jiao, L), Golfers against Cancer organization (#001, PI: Jiao, L), the Houston Veterans Affairs Health Services Research Center of Innovations (CIN13-413, IQuESt COIN), and the National Institute of Diabetes and Digestive and Kidney Diseases (P30 DK56338, PI: El-Serag, HB). White received research salary support from the US Department of Veterans Affairs (CX001430. PI: White, DL). Liu received support from the National Cancer Institute (K07CA181480. PI: Liu, YH). The opinions expressed reflect those of the authors and not necessarily those of the Department of Veterans Affairs, the US government, or Baylor College of Medicine. The funding sources had no role in the study design, collection, analysis, data interpretation, and writing.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Baylor College of Medicine (BCM) and Michael E. DeBakey VA Medical Center (MEDVAMC) (protocol code H30941, originally approved on 3 May 2013, and then 1 April 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data available on request due to local policy on privacy.

Acknowledgments

We thank David Ramsey at Baylor College of Medicine for data management. We thank Jocelyn Uriostegui, Sarah Plew, Ashley Johnson, Ava Smith, Preksha Shah, Kathryn Royse, Liang Chen, Zhengsheng Wang, and Mahmoud Al-Saadi for collecting and processing samples.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gorska-Warsewicz, H.; Rejman, K.; Laskowski, W.; Czeczotko, M. Milk and Dairy Products and Their Nutritional Contribution to the Average Polish Diet. Nutrients 2019, 11, 1771. [Google Scholar] [CrossRef] [PubMed]
  2. Gaucheron, F. Milk and dairy products: A unique micronutrient combination. J. Am. Coll. Nutr. 2011, 30, 400S–409S. [Google Scholar] [CrossRef] [PubMed]
  3. Scialo, T.E.; Pace, C.M.; Abrams, D.I. The Dairy and Cancer Controversy: Milking the Evidence. Curr. Oncol. Rep. 2024, 26, 191–199. [Google Scholar] [CrossRef] [PubMed]
  4. Iuliano, S.; Poon, S.; Robbins, J.; Bui, M.; Wang, X.; De Groot, L.; Van Loan, M.; Zadeh, A.G.; Nguyen, T.; Seeman, E. Effect of dietary sources of calcium and protein on hip fractures and falls in older adults in residential care: Cluster randomised controlled trial. BMJ 2021, 375, n2364. [Google Scholar] [CrossRef] [PubMed]
  5. Sandby, K.; Magkos, F.; Chabanova, E.; Petersen, E.T.; Krarup, T.; Bertram, H.C.; Kristiansen, K.; Geiker, N.R.W. The effect of dairy products on liver fat and metabolic risk markers in males with abdominal obesity—A four-arm randomized controlled trial. Clin. Nutr. 2024, 43, 534–542. [Google Scholar] [CrossRef]
  6. Lovegrove, J.A.; Hobbs, D.A. New perspectives on dairy and cardiovascular health. Proc. Nutr. Soc. 2016, 75, 247–258. [Google Scholar] [CrossRef] [PubMed]
  7. Nakano, A.; Ueno, H.M.; Kawata, D.; Tatara, Y.; Tamada, Y.; Mikami, T.; Murashita, K.; Nakaji, S.; Itoh, K. Dairy consumption, bone turnover biomarkers, and osteo sono assessment index in Japanese adults: A cross-sectional analysis of data from the Iwaki Health Promotion Project. Bone Rep. 2024, 21, 101770. [Google Scholar] [CrossRef]
  8. Tunick, M.H.; Van Hekken, D.L. Dairy Products and Health: Recent Insights. J. Agric. Food Chem. 2015, 63, 9381–9388. [Google Scholar] [CrossRef]
  9. Unger, A.L.; Astrup, A.; Feeney, E.L.; Holscher, H.D.; Gerstein, D.E.; Torres-Gonzalez, M.; Brown, K. Harnessing the Magic of the Dairy Matrix for Next-Level Health Solutions: A Summary of a Symposium Presented at Nutrition 2022. Curr. Dev. Nutr. 2023, 7, 100105. [Google Scholar] [CrossRef]
  10. Anderson, R.C.; Alpass, F.M. Effectiveness of dairy products to protect against cognitive decline in later life: A narrative review. Front. Nutr. 2024, 11, 1366949. [Google Scholar] [CrossRef] [PubMed]
  11. Van Parys, A.; Saele, J.; Puaschitz, N.G.; Anfinsen, A.M.; Karlsson, T.; Olsen, T.; Haugsgjerd, T.R.; Vinknes, K.J.; Holven, K.B.; Dierkes, J.; et al. The association between dairy intake and risk of cardiovascular disease and mortality in patients with stable angina pectoris. Eur. J. Prev. Cardiol. 2023, 30, 219–229. [Google Scholar] [CrossRef] [PubMed]
  12. Ulven, S.M.; Holven, K.B.; Gil, A.; Rangel-Huerta, O.D. Milk and Dairy Product Consumption and Inflammatory Biomarkers: An Updated Systematic Review of Randomized Clinical Trials. Adv. Nutr. 2019, 10, S239–S250. [Google Scholar] [CrossRef]
  13. Moosavian, S.P.; Rahimlou, M.; Saneei, P.; Esmaillzadeh, A. Effects of dairy products consumption on inflammatory biomarkers among adults: A systematic review and meta-analysis of randomized controlled trials. Nutr. Metab. Cardiovasc. Dis. NMCD 2020, 30, 872–888. [Google Scholar] [CrossRef]
  14. Shi, N.; Olivo-Marston, S.; Jin, Q.; Aroke, D.; Joseph, J.J.; Clinton, S.K.; Manson, J.E.; Rexrode, K.M.; Mossavar-Rahmani, Y.; Fels Tinker, L.; et al. Associations of Dairy Intake with Circulating Biomarkers of Inflammation, Insulin Response, and Dyslipidemia among Postmenopausal Women. J. Acad. Nutr. Diet. 2021, 121, 1984–2002. [Google Scholar] [CrossRef]
  15. Nieman, K.M.; Anderson, B.D.; Cifelli, C.J. The Effects of Dairy Product and Dairy Protein Intake on Inflammation: A Systematic Review of the Literature. J. Am. Coll. Nutr. 2021, 40, 571–582. [Google Scholar] [CrossRef] [PubMed]
  16. Jandhyala, S.M.; Talukdar, R.; Subramanyam, C.; Vuyyuru, H.; Sasikala, M.; Nageshwar Reddy, D. Role of the normal gut microbiota. World J. Gastroenterol. 2015, 21, 8787–8803. [Google Scholar] [CrossRef] [PubMed]
  17. Barengolts, E.; Smith, E.D.; Reutrakul, S.; Tonucci, L.; Anothaisintawee, T. The Effect of Probiotic Yogurt on Glycemic Control in Type 2 Diabetes or Obesity: A Meta-Analysis of Nine Randomized Controlled Trials. Nutrients 2019, 11, 671. [Google Scholar] [CrossRef] [PubMed]
  18. Sharifi, M.; Moridnia, A.; Mortazavi, D.; Salehi, M.; Bagheri, M.; Sheikhi, A. Kefir: A powerful probiotics with anticancer properties. Med. Oncol. 2017, 34, 183. [Google Scholar] [CrossRef] [PubMed]
  19. Aslam, H.; Marx, W.; Rocks, T.; Loughman, A.; Chandrasekaran, V.; Ruusunen, A.; Dawson, S.L.; West, M.; Mullarkey, E.; Pasco, J.A.; et al. The effects of dairy and dairy derivatives on the gut microbiota: A systematic literature review. Gut Microbes 2020, 12, 1799533. [Google Scholar] [CrossRef]
  20. Swarte, J.C.; Eelderink, C.; Douwes, R.M.; Said, M.Y.; Hu, S.; Post, A.; Westerhuis, R.; Bakker, S.J.L.; Harmsen, H.J.M. Effect of High versus Low Dairy Consumption on the Gut Microbiome: Results of a Randomized, Cross-Over Study. Nutrients 2020, 12, 2129. [Google Scholar] [CrossRef]
  21. Aslam, H.; Collier, F.; Davis, J.A.; Quinn, T.P.; O’Hely, M.; Pasco, J.A.; Jacka, F.N.; Loughman, A. Gut Microbiome Diversity and Composition Are Associated with Habitual Dairy Intakes: A Cross-Sectional Study in Men. J. Nutr. 2021, 151, 3400–3412. [Google Scholar] [CrossRef]
  22. Fernandez-Rico, S.; Mondragon, A.D.C.; Lopez-Santamarina, A.; Cardelle-Cobas, A.; Regal, P.; Lamas, A.; Ibarra, I.S.; Cepeda, A.; Miranda, J.M. A2 Milk: New Perspectives for Food Technology and Human Health. Foods 2022, 11, 2387. [Google Scholar] [CrossRef] [PubMed]
  23. Liu, Y.; Ajami, N.J.; El-Serag, H.B.; Hair, C.; Graham, D.Y.; White, D.L.; Chen, L.; Wang, Z.; Plew, S.; Kramer, J.; et al. Dietary quality and the colonic mucosa-associated gut microbiome in humans. Am. J. Clin. Nutr. 2019, 110, 701–712. [Google Scholar] [CrossRef] [PubMed]
  24. Molag, M.L.; de Vries, J.H.; Ocké, M.C.; Dagnelie, P.C.; van den Brandt, P.A.; Jansen, M.C.; van Staveren, W.A.; van’t Veer, P. Design Characteristics of Food Frequency Questionnaires in Relation to Their Validity. Am. J. Epidemiol. 2007, 166, 1468–1478. [Google Scholar] [CrossRef] [PubMed]
  25. Willett, W.C.; Howe, G.R.; Kushi, L.H. Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr. 1997, 65, 1220S–1228S; discussion 1229S–1231S. [Google Scholar] [CrossRef]
  26. Reedy, J.; Lerman, J.L.; Krebs-Smith, S.M.; Kirkpatrick, S.I.; Pannucci, T.E.; Wilson, M.M.; Subar, A.F.; Kahle, L.L.; Tooze, J.A. Evaluation of the Healthy Eating Index-2015. J. Acad. Nutr. Diet. 2018, 118, 1622–1633. [Google Scholar] [CrossRef]
  27. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Huntley, J.; Fierer, N.; Owens, S.M.; Betley, J.; Fraser, L.; Bauer, M.; et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012, 6, 1621–1624. [Google Scholar] [CrossRef] [PubMed]
  28. Vatanen, T.; Franzosa, E.A.; Schwager, R.; Tripathi, S.; Arthur, T.D.; Vehik, K.; Lernmark, A.; Hagopian, W.A.; Rewers, M.J.; She, J.X.; et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 2018, 562, 589–594. [Google Scholar] [CrossRef] [PubMed]
  29. Mondragon Portocarrero, A.D.C.; Lopez-Santamarina, A.; Lopez, P.R.; Ortega, I.S.I.; Duman, H.; Karav, S.; Miranda, J.M. Substitutive Effects of Milk vs. Vegetable Milk on the Human Gut Microbiota and Implications for Human Health. Nutrients 2024, 16, 3108. [Google Scholar] [CrossRef]
  30. Shuai, M.; Zuo, L.S.; Miao, Z.; Gou, W.; Xu, F.; Jiang, Z.; Ling, C.W.; Fu, Y.; Xiong, F.; Chen, Y.M.; et al. Multi-omics analyses reveal relationships among dairy consumption, gut microbiota and cardiometabolic health. EBioMedicine 2021, 66, 103284. [Google Scholar] [CrossRef]
  31. Sokol, H.; Pigneur, B.; Watterlot, L.; Lakhdari, O.; Bermudez-Humaran, L.G.; Gratadoux, J.J.; Blugeon, S.; Bridonneau, C.; Furet, J.P.; Corthier, G.; et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl. Acad. Sci. USA 2008, 105, 16731–16736. [Google Scholar] [CrossRef] [PubMed]
  32. Savin, K.W.; Zawadzki, J.; Auldist, M.J.; Wang, J.; Ram, D.; Rochfort, S.; Cocks, B.G. Faecalibacterium diversity in dairy cow milk. PLoS ONE 2019, 14, e0221055. [Google Scholar] [CrossRef]
  33. Miquel, S.; Martin, R.; Rossi, O.; Bermudez-Humaran, L.G.; Chatel, J.M.; Sokol, H.; Thomas, M.; Wells, J.M.; Langella, P. Faecalibacterium prausnitzii and human intestinal health. Curr. Opin. Microbiol. 2013, 16, 255–261. [Google Scholar] [CrossRef]
  34. Dikeocha, I.J.; Al-Kabsi, A.M.; Chiu, H.T.; Alshawsh, M.A. Faecalibacterium prausnitzii Ameliorates Colorectal Tumorigenesis and Suppresses Proliferation of HCT116 Colorectal Cancer Cells. Biomedicines 2022, 10, 1128. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, H.; Zhang, L.; Li, Y.; Meng, X.; Chi, Y.; Liu, M. Gut Microbiota and Its Metabolites: The Emerging Bridge Between Coronary Artery Disease and Anxiety and Depression? Aging Dis. 2024. [Google Scholar] [CrossRef]
  36. Lopez-Siles, M.; Martinez-Medina, M.; Suris-Valls, R.; Aldeguer, X.; Sabat-Mir, M.; Duncan, S.H.; Flint, H.J.; Garcia-Gil, L.J. Changes in the Abundance of Faecalibacterium prausnitzii Phylogroups I and II in the Intestinal Mucosa of Inflammatory Bowel Disease and Patients with Colorectal Cancer. Inflamm. Bowel Dis. 2016, 22, 28–41. [Google Scholar] [CrossRef] [PubMed]
  37. Aune, D.; Lau, R.; Chan, D.S.M.; Vieira, R.; Greenwood, D.C.; Kampman, E.; Norat, T. Dairy products and colorectal cancer risk: A systematic review and meta-analysis of cohort studies. Ann. Oncol. 2012, 23, 37–45. [Google Scholar] [CrossRef]
  38. Ralston, R.A.; Truby, H.; Palermo, C.E.; Walker, K.Z. Colorectal cancer and nonfermented milk, solid cheese, and fermented milk consumption: A systematic review and meta-analysis of prospective studies. Crit. Rev. Food Sci. Nutr. 2014, 54, 1167–1179. [Google Scholar] [CrossRef] [PubMed]
  39. Khorraminezhad, L.; Leclercq, M.; O’Connor, S.; Julien, P.; Weisnagel, S.J.; Gagnon, C.; Droit, A.; Rudkowska, I. Dairy Product Intake Modifies Gut Microbiota Composition Among Hyperinsulinemic Individuals. Eur. J. Nutr. 2021, 60, 159–167. [Google Scholar] [CrossRef]
  40. Li, N.; Li, B.; Guan, J.; Shi, J.; Evivie, S.E.; Zhao, L.; Huo, G.; Wang, S. Distinct Effects of Milks From Various Animal Types on Infant Fecal Microbiota Through in vitro Fermentations. Front. Microbiol. 2020, 11, 580931. [Google Scholar] [CrossRef] [PubMed]
  41. Naito, Y.; Uchiyama, K.; Takagi, T. A next-generation beneficial microbe: Akkermansia muciniphila. J. Clin. Biochem. Nutr. 2018, 63, 33–35. [Google Scholar] [CrossRef] [PubMed]
  42. Dao, M.C.; Everard, A.; Aron-Wisnewsky, J.; Sokolovska, N.; Prifti, E.; Verger, E.O.; Kayser, B.D.; Levenez, F.; Chilloux, J.; Hoyles, L.; et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: Relationship with gut microbiome richness and ecology. Gut 2014, 65, 426–436. [Google Scholar] [CrossRef]
  43. van der Lugt, B.; van Beek, A.A.; Aalvink, S.; Meijer, B.; Sovran, B.; Vermeij, W.P.; Brandt, R.M.C.; de Vos, W.M.; Savelkoul, H.F.J.; Steegenga, W.T.; et al. Akkermansia muciniphila ameliorates the age-related decline in colonic mucus thickness and attenuates immune activation in accelerated aging Ercc1 -/Δ7 mice. Immun. Ageing 2019, 16, 6. [Google Scholar] [CrossRef] [PubMed]
  44. Ottman, N.; Reunanen, J.; Meijerink, M.; Pietilä, T.E.; Kainulainen, V.; Klievink, J.; Huuskonen, L.; Aalvink, S.; Skurnik, M.; Boeren, S.; et al. Pili-like proteins of Akkermansia muciniphila modulate host immune responses and gut barrier function. PLoS ONE 2017, 12, e0173004. [Google Scholar] [CrossRef] [PubMed]
  45. Desai, M.S.; Seekatz, A.M.; Koropatkin, N.M.; Kamada, N.; Hickey, C.A.; Wolter, M.; Pudlo, N.A.; Kitamoto, S.; Terrapon, N.; Muller, A.; et al. A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen Susceptibility. Cell 2016, 167, 1339–1353. [Google Scholar] [CrossRef]
  46. Effendi, R.; Anshory, M.; Kalim, H.; Dwiyana, R.F.; Suwarsa, O.; Pardo, L.M.; Nijsten, T.E.C.; Thio, H.B. Akkermansia muciniphila and Faecalibacterium prausnitzii in Immune-Related Diseases. Microorganisms 2022, 10, 2382. [Google Scholar] [CrossRef]
  47. Kok, C.R.; Hutkins, R. Yogurt and other fermented foods as sources of health-promoting bacteria. Nutr. Rev. 2018, 76, 4–15. [Google Scholar] [CrossRef] [PubMed]
  48. Nestel, P.J.; Mori, T.A. Dairy Foods: Beneficial Effects of Fermented Products on Cardiometabolic Health. Curr. Nutr. Rep. 2023, 12, 478–485. [Google Scholar] [CrossRef] [PubMed]
  49. Gonzalez, S.; Fernandez-Navarro, T.; Arboleya, S.; de Los Reyes-Gavilan, C.G.; Salazar, N.; Gueimonde, M. Fermented Dairy Foods: Impact on Intestinal Microbiota and Health-Linked Biomarkers. Front. Microbiol. 2019, 10, 1046. [Google Scholar] [CrossRef] [PubMed]
  50. Le Roy, C.I.; Kurilshikov, A.; Leeming, E.R.; Visconti, A.; Bowyer, R.C.E.; Menni, C.; Falchi, M.; Koutnikova, H.; Veiga, P.; Zhernakova, A.; et al. Yoghurt consumption is associated with changes in the composition of the human gut microbiome and metabolome. BMC Microbiol. 2022, 22, 39. [Google Scholar] [CrossRef]
  51. Suzuki, Y.; Ikeda, K.; Sakuma, K.; Kawai, S.; Sawaki, K.; Asahara, T.; Takahashi, T.; Tsuji, H.; Nomoto, K.; Nagpal, R.; et al. Association between Yogurt Consumption and Intestinal Microbiota in Healthy Young Adults Differs by Host Gender. Front. Microbiol. 2017, 8, 847. [Google Scholar] [CrossRef] [PubMed]
  52. Uyeno, Y.; Sekiguchi, Y.; Kamagata, Y. Impact of consumption of probiotic lactobacilli-containing yogurt on microbial composition in human feces. Int. J. Food Microbiol. 2008, 122, 16–22. [Google Scholar] [CrossRef]
  53. Sibanda, T.; Marole, T.A.; Thomashoff, U.L.; Thantsha, M.S.; Buys, E.M. Bifidobacterium species viability in dairy-based probiotic foods: Challenges and innovative approaches for accurate viability determination and monitoring of probiotic functionality. Front. Microbiol. 2024, 15, 1327010. [Google Scholar] [CrossRef] [PubMed]
  54. Wasilewska, E.; Zlotkowska, D.; Wroblewska, B. Yogurt starter cultures of Streptococcus thermophilus and Lactobacillus bulgaricus ameliorate symptoms and modulate the immune response in a mouse model of dextran sulfate sodium-induced colitis. J. Dairy Sci. 2019, 102, 37–53. [Google Scholar] [CrossRef]
  55. Chai, W.; Maskarinec, G.; Lim, U.; Boushey, C.J.; Wilkens, L.R.; Setiawan, V.W.; Le Marchand, L.; Randolph, T.W.; Jenkins, I.C.; Lampe, J.W.; et al. Association of Habitual Intake of Probiotic Supplements and Yogurt with Characteristics of the Gut Microbiome in the Multiethnic Cohort Adiposity Phenotype Study. Gut Microbiome 2023, 4, e14. [Google Scholar] [CrossRef]
  56. Eeckhaut, V.; Van Immerseel, F.; Croubels, S.; De Baere, S.; Haesebrouck, F.; Ducatelle, R.; Louis, P.; Vandamme, P. Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum. Microb. Biotechnol. 2011, 4, 503–512. [Google Scholar] [CrossRef]
  57. Brim, H.; Yooseph, S.; Zoetendal, E.G.; Lee, E.; Torralbo, M.; Laiyemo, A.O.; Shokrani, B.; Nelson, K.; Ashktorab, H. Microbiome analysis of stool samples from African Americans with colon polyps. PLoS ONE 2013, 8, e81352. [Google Scholar] [CrossRef] [PubMed]
  58. Youssef, O.; Lahti, L.; Kokkola, A.; Karla, T.; Tikkanen, M.; Ehsan, H.; Carpelan-Holmström, M.; Koskensalo, S.; Böhling, T.; Rautelin, H.; et al. Stool Microbiota Composition Differs in Patients with Stomach, Colon, and Rectal Neoplasms. Dig. Dis. Sci. 2018, 63, 2950–2958. [Google Scholar] [CrossRef] [PubMed]
  59. Chumpitazi, B.P.; Hollister, E.B.; Oezguen, N.; Tsai, C.M.; McMeans, A.R.; Luna, R.A.; Savidge, T.C.; Versalovic, J.; Shulman, R.J. Gut Microbiota Influences Low Fermentable Substrate Diet Efficacy in Children With Irritable Bowel Syndrome. Gut Microbes 2014, 5, 165–175. [Google Scholar] [CrossRef]
  60. Chung, L.; Orberg, E.T.; Geis, A.L.; Chan, J.L.; Fu, K.; Shields, C.E.D.; Dejea, C.M.; Fathi, P.; Chen, J.; Finard, B.B.; et al. Bacteroides fragilis Toxin Coordinates a Pro-carcinogenic Inflammatory Cascade via Targeting of Colonic Epithelial Cells. Cell Host Microbe 2018, 23, 203–214. [Google Scholar] [CrossRef] [PubMed]
  61. Purcell, R.V.; Pearson, J.; Aitchison, A.; Dixon, L.; Frizelle, F.A.; Keenan, J.I. Colonization with enterotoxigenic Bacteroides fragilis is associated with early-stage colorectal neoplasia. PLoS ONE 2017, 12, e0171602. [Google Scholar] [CrossRef] [PubMed]
  62. Zhou, Y.; Zhi, F. Lower Level of Bacteroides in the Gut Microbiota Is Associated with Inflammatory Bowel Disease: A Meta-Analysis. BioMed Res. Int. 2016, 2016, 5828959. [Google Scholar] [CrossRef] [PubMed]
  63. Barrubés, L.; Babio, N.; Becerra-Tomás, N.; Rosique-Esteban, N.; Salas-Salvadó, J. Association Between Dairy Product Consumption and Colorectal Cancer Risk in Adults: A Systematic Review and Meta-Analysis of Epidemiologic Studies. Adv. Nutr. 2019, 10, S190–S211. [Google Scholar] [CrossRef] [PubMed]
  64. Papier, K.; Bradbury, K.E.; Balkwill, A.; Barnes, I.; Smith-Byrne, K.; Gunter, M.J.; Berndt, S.I.; Le Marchand, L.; Wu, A.H.; Peters, U.; et al. Diet-wide analyses for risk of colorectal cancer: Prospective study of 12,251 incident cases among 542,778 women in the UK. Nat. Commun. 2025, 16, 375. [Google Scholar] [CrossRef]
  65. Sharpe, M.E. Lactic acid bacteria in the dairy industry. Int. J. Dairy Technol. 1979, 32, 9–18. [Google Scholar] [CrossRef]
  66. Scrimshaw, N.S.; Murray, E.B. The acceptability of milk and milk products in populations with a high prevalence of lactose intolerance. Am. J. Clin. Nutr. 1988, 48, 1142–1159. [Google Scholar] [CrossRef] [PubMed]
  67. Szilagyi, A. Redefining lactose as a conditional prebiotic. Can. J. Gastroenterol. = J. Can. Gastroenterol. 2004, 18, 163–167. [Google Scholar] [CrossRef] [PubMed]
  68. Szilagyi, A.; Nathwani, U.; Vinokuroff, C.; Correa, J.A.; Shrier, I. The effect of lactose maldigestion on the relationship between dairy food intake and colorectal cancer: A systematic review. Nutr. Cancer 2006, 55, 141–150. [Google Scholar] [CrossRef]
  69. Van den Abbeele, P.; Van de Wiele, T.; Verstraete, W.; Possemiers, S. The host selects mucosal and luminal associations of coevolved gut microorganisms: A novel concept. FEMS Microbiol. Rev. 2011, 35, 681–704. [Google Scholar] [CrossRef] [PubMed]
  70. Quann, E.E.; Fulgoni, V.L.; Auestad, N. Consuming the Daily Recommended Amounts of Dairy Products Would Reduce the Prevalence of Inadequate Micronutrient Intakes in the United States: Diet Modeling Study Based on NHANES 2007–2010. Nutr. J. 2015, 14, 90. [Google Scholar] [CrossRef] [PubMed]
  71. Wei, L.; Singh, R.; Ro, S.; Ghoshal, U.C. Gut microbiota dysbiosis in functional gastrointestinal disorders: Underpinning the symptoms and pathophysiology. JGH Open Open Access J. Gastroenterol. Hepatol. 2021, 5, 976–987. [Google Scholar] [CrossRef] [PubMed]
  72. Chen, E.; Jiao, L. Dairy intake and mucosa-associated gut microbiome in healthy individuals. Am. J. Gastroenterol. 2018, 113, S137. [Google Scholar] [CrossRef]
  73. Brusnic, O.; Onisor, D.; Boicean, A.; Hasegan, A.; Ichim, C.; Guzun, A.; Chicea, R.; Todor, S.B.; Vintila, B.I.; Anderco, P.; et al. Fecal Microbiota Transplantation: Insights into Colon Carcinogenesis and Immune Regulation. J. Clin. Med. 2024, 13, 6578. [Google Scholar] [CrossRef] [PubMed]
Nutrients 17 00567 g001
Figure 1. The alpha-diversity of the gut bacteria by higher vs. lower intake of total dairy (A), milk (B), cheese (C), and yogurt (no vs. yes) (D). Comparisons of bacterial Shannon index (Y-axis) were performed using the Mann–Whitney test between higher versus lower intake of each dairy product (X-axis). Adjusted p value indicates the significance of the difference.
Figure 1. The alpha-diversity of the gut bacteria by higher vs. lower intake of total dairy (A), milk (B), cheese (C), and yogurt (no vs. yes) (D). Comparisons of bacterial Shannon index (Y-axis) were performed using the Mann–Whitney test between higher versus lower intake of each dairy product (X-axis). Adjusted p value indicates the significance of the difference.
Nutrients 17 00567 g001
Nutrients 17 00567 g002
Figure 2. The beta-diversity of the gut bacteria by higher vs. lower intake of total dairy (A), milk (B), cheese (C), and yogurt (no vs. yes) (D). The principal coordinate plot used the Weighted UniFrac as the distance matrix. The Monte Carlo permutation test was used to estimate p values. Each symbol represents a sample. The lower intake group (black) was separated from the higher intake group (gray). The proportion of variance explained by the first two principal coordinates was denoted in the corresponding axis label. For all the panels, the X-axis is PC1 (principal component 1, explaining 33.2% of the variation); the Y-axis is PC2 (principal component 2, explaining 25.9% of the variation).
Figure 2. The beta-diversity of the gut bacteria by higher vs. lower intake of total dairy (A), milk (B), cheese (C), and yogurt (no vs. yes) (D). The principal coordinate plot used the Weighted UniFrac as the distance matrix. The Monte Carlo permutation test was used to estimate p values. Each symbol represents a sample. The lower intake group (black) was separated from the higher intake group (gray). The proportion of variance explained by the first two principal coordinates was denoted in the corresponding axis label. For all the panels, the X-axis is PC1 (principal component 1, explaining 33.2% of the variation); the Y-axis is PC2 (principal component 2, explaining 25.9% of the variation).
Nutrients 17 00567 g002
Nutrients 17 00567 g003
Figure 3. Relative abundance (%) of the major bacterial phyla by total dairy (A), milk (B), cheese (C), and yogurt (D).
Figure 3. Relative abundance (%) of the major bacterial phyla by total dairy (A), milk (B), cheese (C), and yogurt (D).
Nutrients 17 00567 g003
Nutrients 17 00567 g004
Figure 4. Relative abundance (%) of major bacterial genera by total dairy (A), milk (B), cheese (C), and yogurt (D). (Only the major genera (>0.5% relative abundance) with statistically significant differences (q values < 0.05) are shown in the figure).
Figure 4. Relative abundance (%) of major bacterial genera by total dairy (A), milk (B), cheese (C), and yogurt (D). (Only the major genera (>0.5% relative abundance) with statistically significant differences (q values < 0.05) are shown in the figure).
Nutrients 17 00567 g004
Table 1. Characteristics of the study participants by total dairy intake.
Table 1. Characteristics of the study participants by total dairy intake.
Characteristics
Mean ± Standard Deviation or n (%)		Lower Dairy Intake
n = 17		Higher Dairy Intake
n = 17		p Value
Age (years)62.7 ± 6.061.3 ± 5.10.50
Male, n (%)16 (94%)17 (100%)0.31
Racial group
   White/Caucasian, n (%)13 (76.5)11 (64.7)0.86
   African American, n (%)2 (11.8)4 (23.5)
   Hispanic, n (%)2 (11.8)2 (11.8)
Body mass index (kg/m2)33.2 ±6.734.6 ±6.30.54
Smoking Status
   Never, n (%)6 (35.3)7 (41.2)1.00
   Former, n (%)7 (41.2)7 (41.2)
   Current, n (%)4 (23.5)3 (17.6)
Alcohol Status 0.22
   Never drinker, n (%)4 (23.5)5 (29.4)
   Former drinker, n (%)3 (17.6)7 (41.2)
   Current drinker, n (%)10 (58.9)5 (29.4)0.46
Hypertension, yes (%)13 (76.4)12 (70.6)0.71
Type 2 diabetes, yes (%)9 (52.7)8 (47.1)1.00
Total HEI score60.6 ± 9.6661.2 ± 8.500.84
Calcium (mg/day/1000 kcal)316 ± 69.6460 ± 90.7< 0.0001
Vitamin D (IU/day/1000 kcal)44.5 ± 24.996.9 ± 68.40.006
Riboflavin (mg/day/1000 kcal)0.92 ± 0.261.24 ± 0.29 0.002
Vitamin B6 (mg/day/1000 kcal)0.87 ± 0.280.96 ± 0.240.31
Cobalamin (µg/day/1000 kcal)2.00 ± 0.77 2.95 ± 0.660.0005
Saturated fat (g/day/1000 kcal)11.3 ± 1.61 14.9 ± 3.230.0002
Lactose (g/day/1000 kcal)2.52 ± 1.607.24 ± 5.350.001
Milk intake (cup/Day)0.14 ± 0.110.49 ± 0.420.002
Cheese (serving/Day)0.22 ± 0.030.35 ± 0.050.03
Yogurt intake (cup/Day)0.003 ± 0.0010.049± 0.080.031
HEI, healthy eating index; p value for Student’s t test for continuous variables or Fisher’s exact test for categorical variables.
Table 2. The incidence rate ratio and 95% confidence interval of having bacteria count in mucosal samples by higher vs. lower intake of dairy products.
Table 2. The incidence rate ratio and 95% confidence interval of having bacteria count in mucosal samples by higher vs. lower intake of dairy products.
Prevalence, %Median CountIRR (95% CI) 1IRR (95% CI) 2IRR (95% CI) 3
HigherLowerHigherLower
Total dairyn = 40n = 57n = 40n = 57
Akkermansia64.950.017.00.57.37 (1.79–30.3)8.09 (2.15–30.3)1.69 (0.31–9.20)
Faecalibacterium10085.0159454.70 (2.52–8.75)4.88 (2.56–9.30)5.00 (2.10–11.9)
Bifidobacterium10.026.3007.81 (1.17–52.2)7.98 (1.32–48.0)5.32 (0.76–37.2)
Bacteroides1001005954040.61 (0.39–0.96)0.61 (0.39–0.96)0.43 (0.24–0.79)
Milk intaken = 54n = 43n = 54n = 43
Akkermansia81.530.223.5028.6 (7.72–106)25.4 (5.83–110)7.18 (1.26–40.9)
Faecalibacterium10086.0156664.70 (2.24–9.86)4.16 (1.97–8.78)2.89 (1.21–6.90)
Cheese intaken = 46n = 51n = 46n = 51
Bacteroides1001003385540.52 (0.31–0.85)0.50 (0.30–0.82)-
Subdoligranulum89.11007380.46 (0.29–0.74)0.54 (0.34–0.87)-
CI: Confidence interval. IRR: incidence rate ratio. 1 Negative binomial regression model for panel data used the bacterial count as the dependent variable. The food intake was modeled as higher vs. lower. The lower intake was the reference group. The model was adjusted for age, race (non-Hispanic White, African American and Hispanic), body mass index, smoking status (never, former, and current), alcohol use (never, former, and current), diabetes, hypertension, HEI score, and biopsy segment. The incidence rate of Faecalibacterium in those who had a higher milk intake was 4.16 times that of those who had a lower milk intake. 2 For milk or dairy intake, the model was further adjusted for cobalamin. For cheese intake, adjustment for nutrients did not further attenuate the IRR estimates. 3 For milk or dairy intake, the model was further adjusted for lactose based on model 1.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, E.; Ajami, N.J.; White, D.L.; Liu, Y.; Gurwara, S.; Hoffman, K.; Graham, D.Y.; El-Serag, H.B.; Petrosino, J.F.; Jiao, L. Dairy Consumption and the Colonic Mucosa-Associated Gut Microbiota in Humans—A Preliminary Investigation. Nutrients 2025, 17, 567. https://doi.org/10.3390/nu17030567

AMA Style

Chen E, Ajami NJ, White DL, Liu Y, Gurwara S, Hoffman K, Graham DY, El-Serag HB, Petrosino JF, Jiao L. Dairy Consumption and the Colonic Mucosa-Associated Gut Microbiota in Humans—A Preliminary Investigation. Nutrients. 2025; 17(3):567. https://doi.org/10.3390/nu17030567

Chicago/Turabian Style

Chen, Ellie, Nadim J. Ajami, Donna L. White, Yanhong Liu, Shawn Gurwara, Kristi Hoffman, David Y. Graham, Hashem B. El-Serag, Joseph F. Petrosino, and Li Jiao. 2025. "Dairy Consumption and the Colonic Mucosa-Associated Gut Microbiota in Humans—A Preliminary Investigation" Nutrients 17, no. 3: 567. https://doi.org/10.3390/nu17030567

APA Style

Chen, E., Ajami, N. J., White, D. L., Liu, Y., Gurwara, S., Hoffman, K., Graham, D. Y., El-Serag, H. B., Petrosino, J. F., & Jiao, L. (2025). Dairy Consumption and the Colonic Mucosa-Associated Gut Microbiota in Humans—A Preliminary Investigation. Nutrients, 17(3), 567. https://doi.org/10.3390/nu17030567

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop