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. 2024 Feb 5;7(4):991–1001. doi: 10.1021/acsptsci.3c00261

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management

Jane Shearer †,‡,§, Shrushti Shah ‡,§, Grace Shen-Tu ∥, Kristina Schlicht ⊥, Matthias Laudes ⊥,#, Chunlong Mu †,‡,*
PMCID: PMC11040554  PMID: 38665607

Abstract

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Human gut microbiota are recognized as critical players in both metabolic disease and drug metabolism. However, medication–microbiota interactions in cardiometabolic diseases are not well understood. To gain a comprehensive understanding of how medication intake impacts the gut microbiota, we investigated the association of microbial structure with the use of single or multiple medications in a cohort of 134 middle-aged adults diagnosed with cardiometabolic disease, recruited from Alberta’s Tomorrow Project. Predominant cardiometabolic prescription medication classes (12 total) were included in our analysis. Multivariate Association with Linear Model (MaAsLin2) was employed and results were corrected for age, BMI, sex, and diet to evaluate the relationship between microbial features and single- or multimedication use. Highly individualized microbiota profiles were observed across participants, and increasing medication use was negatively correlated with α-diversity. A total of 46 associations were identified between microbial composition and single medications, exemplified by the depletion of Akkermansia muciniphila by β-blockers and statins, and the enrichment of Escherichia/Shigella and depletion of Bacteroides xylanisolvens by metformin. Metagenomics prediction further indicated alterations in microbial functions associated with single medications such as the depletion of enzymes involved in energy metabolism encoded by Eggerthella lenta due to β-blocker use. Specific dual medication combinations also had profound impacts, including the depletion of Romboutsia and Butyriciocccus by statin plus metformin. Together, these results show reductions in bacterial diversity as well as species and microbial functional potential associated with both single- and multimedication use in cardiometabolic disease.

Keywords: gut microbiota, microbial structure, medications, cardiometabolic disease, pharmacology

Cardiometabolic disease (CMD) is a leading cause of death worldwide.1 Emerging evidence suggests that the gut microbiota may play a pivotal role in influencing the susceptibility and progression of CMD, although the precise mechanisms and causal connections are still the subject of ongoing research. Disruptions in the diversity of the gut microbiota, accompanied by a decline in its functional capacity, have been observed in various cardiometabolic conditions, including coronary artery disease, diabetic cardiomyopathy, and heart failure.2 Notably, aberrant gut microbiota may serve as a formidable catalyst in CMD, elevating risk by exacerbating underlying inflammation and oxidative stress.3 Causal links have been established between gut-derived bacterial metabolites and CMD, including trimethylamine N-oxide and phenylacetylglutamine.4

Research exploring the interactions between CMD medication and the gut microbiota is a relatively new and evolving field. Relevant to the present study, the gut microbiota is widely recognized as a key player in the metabolism of drugs.5 Extensive interactions between gut microbes and medications have been shown in vitro(6,7) with microbes possessing capacities to affect drug metabolism via gene, enzyme, and metabolite modification.8 For example, Eggerthella lenta produces glycoside reductase that inactivates the cardiac medication digoxin in the absence of arginine.9 At the same time, medication use can also affect the gut microbiota and its diversity in CMD.10

To address the complex nature of CMD, multiple medications are often used to manage various aspects of the disease, including hypertension, dyslipidemia, glucose intolerance, and heart failure. The impacts of single- and multimedication use on the gut microbiota are a topic of intense investigation, yet our understanding of the relationships between medications and the microbiota remains limited. Employing an established longitudinal cohort, the present study examined associations between gut microbiota and prescription medication use in CMD. These findings reveal the possible regulation of medication use on the gut microbiota from a population-based and clinical context.

Methods

Study Setting and Population

This study was approved by the Conjoint Health Research Ethics Board at the University of Calgary (REB17-1973). Written informed consent was received from all of the study participants prior to inclusion in the study. The trial complied with the protocols and clinical practice guidelines of the International Conference on Harmonization and the Declaration of Helsinki. Alberta’s Tomorrow Project is a population-based cohort started in 1999, with over 50,000 individuals enrolled to date. Details of the cohort have been published previously.11,12 A small sample subset of participants (∼1000) from the cohort were selected to be recontacted for the present study. Briefly, participants within the cohort were selected and contacted via phone or email in the Calgary area (Calgary, AB, Canada). A random digit dialing method mapped to Alberta Regional Health Authorities was initially employed to select households with eligible residents. Participants responding to the call and between 35–69 years of age at the time of enrollment were screened for eligibility. In total, 443 individuals (males: 28.2%; females: 71.8%) responded. Individuals were evaluated for suitability according to predetermined inclusion and exclusion criteria. Inclusion criteria for the current study included residence within the study area, the ability to complete the questionnaires (speak and read English), available medical records (including current medication use), provision of specimens (blood and feces) within a specified time frame, and the willingness to have samples analyzed for the measures of interest. Exclusion criteria included known confounders of the gut microbiota: antibiotic use in past 3 months, pregnant women and cancer patients. A study CONSORT flowchart is shown in Figure S1.

Sample size was not calculated prior to recruitment but was set to cover all participants with cardiometabolic diseases among the 443 recruits. The cardiovascular subcohort consisted of 134 patients from whom the diagnosis was confirmed by a physician. CMD was defined following recognized, international definitions of disease according to the American College of Cardiology and American Heart Association including hypertension,13 heart failure,14 atrial fibrillation,15 valvular heart disease,16 overweight and obesity,17 and myocardial infarction.18

Metadata Collection

Participants answered questionnaires relating to demographics, lifestyle, diet, eating/drinking behaviors, smoking, medication, and sleeping habits as previously described.19 Questionnaires were completed independently by the participants. Medication information was curated referring to DrugBank20 and Drugs.com.21 Medication was classified into categories based on the mechanism of action (Table 1). Cluster analysis of metadata was performed using Euclidean for distance-optimized data transformation22 as well as Hierarchical Ward’s linkage clustering.23

Table 1. Cardiometabolic Medication Use (n = 134).

  medication class number (%)
major medications angII_receptor_antagonist 41 (30.6)
  statin 41 (30.6)
  angiotensin-converting enzyme (ACE) inhibitors 33 (24.6)
  NSAID 31 (23.1)
  diuretic 28 (20.9)
  Ca_channel_blocker 24 (17.9)
  beta_blockers 17 (12.7)
  metformin 17 (12.7)
  proton pump inhibitors (PPIs) 15 (11.2)
  levothyroxine 15 (11.2)
  5-aminosalicylic acid (5-ASA) 12 (9.0)
  selective serotonin reuptake inhibitors (SSRIs) antidepressant 10 (7.5)
     
minor medications other_antidepressant 7 (5.2)
  anticonvulsant 6 (4.5)
  antihistamine 6 (4.5)
  serotonin and norepinephrine reuptake inhibitors 6 (4.5)
  tricyclic_antidepressant 6 (4.5)
  melatonin 5 (3.7)
  sodium-glucose cotransporter-2 (SGLT2) inhibitors 4 (3.0)
  beta2_adrenergic agonist 4 (3.0)
  estrogen 4 (3.0)
  non_opioid_analgesic 4 (3.0)
  opioid_analgesic 4 (3.0)
  anticoagulant 3 (2.2)
  non_statin_cholesterol_absorption_inhibitor 3 (2.2)
  sulfonylureas 3 (2.2)
  alpha_blocker 3 (2.2)
  cannabinoid 3 (2.2)
  corticosteroids 3 (2.2)
  progesterone 3 (2.2)
  antirheumatic 2 (1.5)
  thiazolidinedione 2 (1.5)
  dipeptidyl peptidase IV (DPP IV) inhibitors 2 (1.5)
  leukotriene receptor antagonists 2 (1.5)
  xanthine_oxidase_inhibitor 2 (1.5)
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Sample Collection, DNA Extraction, and Processing

Stool samples were self-collected using a Protocult stool collection device (Ability Building Center, Rochester, NY). Samples were frozen at −20 °C at home, transported to laboratory, aliquoted, and stored at −80 °C within 48 h of sample collection, until further analysis. Fecal genomic DNA was extracted using QIAamp Fast DNA stool mini kit (Qiagen, Germany) as previously described.24 Blank extraction controls were included for subsequent sequencing. Stool samples were collected 2 weeks following questionnaire administration, ensuring alignment of the sample with current medication use.

High-Throughput Sequencing and Analysis

The DNA library construction and pair-end sequencing were performed on an Illumina MiSeq platform with a MiSeq V3 600 cycle sequencing kit. Raw sequences were demultiplexed with 0 mismatches in the barcode sequences. Data processing was conducted using the DADA2 version 1.10 workflow, which gives rise to abundance tables of amplicon sequence variants (ASVs), which underwent chimera filtering. ASVs were taxonomically annotated using the Bayesian classifier provided in DADA225 and using the Ribosomal Database Project (RDP) classifier. To map ASV to taxonomically closest relatives, representative sequences per ASV were blasted using NCBI Nucleotide BLAST with reference to 16S rRNA databases. Data of the blank controls were subtracted from data obtained for sample composition.

Associations of Shannon Diversity Index with Bacterial Taxa and Medication Used

Shannon diversity index was calculated using the function diversity in the R package vegan (version 2.6–4). The correlation between Shannon diversity index and bacterial phylum was analyzed by using Spearman’s rho correlation analysis in GraphPad Prism version 9.3.1 (GraphPad Software, San Diego). P < 0.05 was considered to be significant correlation. Linear regression between the number of medications used (categorical data) and Shannon diversity index was evaluated using lm() function in packge stats (version 4.2.2) and results were visualized using ggplot2 (version 3.4.0).

Associations of Bray–Curtis Distance with Variations Explained by Medication Classes

Bray–Curtis distance was calculated using the function vegdist in the R package vegan (version 2.6–4). The variations of Bray–Curtis distance explained by each medication class (single- or multimedication) were evaluated using the function adonis2 from the R package vegan (version 2.6–4). Medication classes were used at this point and subsequent analyses if they were present in at least 10 participants. The P value was adjusted for multiple testing using the Benjamini and Hochberg method.26 FDR < 0.1 was considered significant.

Microbial Composition Associated with Medication Classes

The data of genus and ASVs were separately transformed into centered log-ratio before further analysis, considering the compositional property of microbiota data.27 To evaluate the relationship between microbial features and medication class, the associations of individual taxa to each medication class (single or dual medications) were assessed by Multivariate Association with Linear Model using MaAsLin2.28 For identifying dual medication combinations and avoiding overlap in medication pairs, medications were sorted in frequency-based descending order. Only medication pairs with >10 participants were included for analysis. The default settings in MaAsLin2 were used. The associations were corrected for age, sex, BMI, and diet. FDR < 0.25, as defaulted in MaAsLin2, was considered significant association with medication class.

Metagenomics Prediction of the Microbial Functional Profiles

The potential function of the gut microbiota was predicted from the ASV data using the Tax4Fun29 metagenomics prediction module available at the MicrobiomeAnalyst Pipeline,30 which resulted in the abundances of KEGG organisms’ functional profiles. The KEGG orthologues (KOs) were annotated and assigned to the KEGG pathways using the KEGG mapping tool. The associations of individual KOs to each medication were assessed with MaAsLin2, using centered log-ratio-transformed data and correcting for the impact of age, sex, BMI, and diet. FDR < 0.25, as defaulted in MaAsLin2, was considered significant association with medication class. Discriminant KOs were enriched into KEGG pathways through the Shotgun Data Profiling module implemented within MicrobiomeAnalyst. The presence of KOs in the representative bacterial strains associated with medications was mapped by searching against UniProt, NCBI Genome, and the BRENDA database. The coefficient heatmap of discriminant KOs with medication classes was mapped using GraphPad Prism version 9.3.1.

Data Availability

16S rRNA gene sequencing data has been deposited in NCBI’s Sequence Read Archive (SRA) database and are available through the accession number PRJNA 922681.

Results

Subject Characteristics and Metadata

The 134 participants included 50 males and 84 females in age ranging from 45.6 to 65.9 years (mean ± SD: 58.6 ± 4.9) and an average BMI of 28 ± 5.9 kg/m2 (mean ± SD). Predominant prescription medications analyzed were used by >10 participants of the cohort and included angiotensin II receptor blockers (ARBs) and statins (n = 41, 30.6%) followed by angiotensin-converting enzyme (ACE) inhibitors (n = 33, 24.6%), nonsteroidal anti-inflammatory medications (NSAID, n = 31, 23.1%), diuretics (n = 28, 20.9%), calcium channel blockers (n = 24, 17.9%), β-blockers (n = 17, 12.7%), metformin (n = 17, 12.7%), PPI (n = 15, 11.2%), and levothyroxine (n = 15, 11.2%) (Table 1). Although other medication use was reported (e.g., opioid analgesic, sulfonylureas, cannabinoid, DPP_4_inhibitors, and xanthine oxidase inhibitors), these medications were excluded from the downstream association analysis due to their low prevalence of use (<10 participants). Different frequencies of medication use were observed when participants were stratified by age, BMI, and sex (Figure 1A). For example, statins, ACE inhibitors, and diuretics were more frequently used by older individuals (>60 years) compared to those in midlife (40–59 years). Likewise, ARBs and NSAIDs were more frequently used by obese vs normal BMI and overweight participants. High blood pressure (HBP) was the most diagnosed condition (108/134) in the cohort with the highest number of prescribed medications. Clustering of metadata indicates medication-specific effects as well as different effects from age, BMI, sex, and diet (Figure 1B), which were included for statistical correction in the following analysis. For example, metadata cluster analysis showed that statins were used more by older individuals, but had a stronger correlation to total number of medications used (Figure 1B). Levothyroxine was also more used by older individuals (Figure 1A) and was clustered with age.

Figure 1.

Figure 1

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Number of medications used based on different categories (A) and cluster of variables based on individual metadata (B).

Individual Microbiota Variation

Microbiota composition was characterized by a highly individual variation, which was driven by the relative abundance of dominant phyla, particularly Firmicutes and Bacteroidetes (Figure S2A). The relative abundance of Firmicutes ranged from 24.1 to 87.6% (median of 50.8%), while Bacteroidetes varied from 3.7 to 71% (median 33.2%). The individual variation was further supported by the Shannon index measurement (ranging from 1.92 to 4.3 (median 3.66, Figure S2B). The relative abundances of Firmicutes and Actinobacteria were positively correlated with Shannon index (Spearman rho 0.4 and 0.36, respectively, P < 0.0001, Figure S2C,D). These results indicate the phylum-level variation as a major factor contributing to the interindividual variation. At the genus level, individual variation was also observed for the dominant taxa, such as Blautia, Dialister, and Bacteroides (Figure S3).

Gut Microbiota Composition Associations with Single Medications

To determine how individual medications related to the overall microbiota structure, we assessed the Bray–Curtis distance with variations explained by 12 medications that were taken in at least 10 participants. At a false discovery rate of <0.1, selective serotonin reuptake inhibitor (SSRI) antidepressants and ACE inhibitors were associated with microbiota composition as measured by Bray–Curtis distance (Figure 2A), while NSAIDs were significantly associated with Shannon diversity (Figure S4).

Figure 2.

Figure 2

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Multivariate association analyses between bacterial taxa and single medications. (A) The explained variance in Bray–Curtis distance by different types of medications. *FDR P < 0.05, # FDR 0.05 < P < 0.1 from adonis2 analysis. (B) Significant associations between medications and bacterial taxa at the phylum/genus level. (C) Significant associations between medications and bacterial taxa at the species level. The plus symbol represents significantly positive association, while the minus symbol represents significantly negative association. For (B),(C), significance was defined as FDR P < 0.25 by MaAsLin2.

We then performed multivariate association analyses between bacterial taxa and medications. At phylum and genus levels, a total of 32 associations were identified when corrected for age, sex, BMI, and diet (FDR < 0.25, Figures 2B and S5). ARB use was positively associated with Streptococcus and statin use was positively associated with Alistipes and Ruminococcus. Metformin use was positively associated with Esherichia/Shigella and negatively associated with Ruminococcus, Dorea, and Romboutsia. β-blocker use was negatively associated with Faecalibacterium and Collinsella. Levothyroxine use was negatively associated with Faecalibacterium and Bacteroides. PPI use was negatively associated with Eggerthella and Dorea.

To understand the relationship between bacterial species and medications, we further analyzed the association between ASVs and medications, which was combined with a blast to enable species-level resolutions. A total of 14 associations were identified when corrected for age, sex, BMI, and diet (FDR < 0.25, Figures 2C and S5). At the species level, metformin use was negatively associated with Bacteroides xylanisolvens and Dialister invisus. PPI use was positively associated with unclassified Lachnospiraceae and Alistipes. 5-Aminosalicylic acid (5-ASA) use was positively associated with Bacteroides thetaiotaomicron and Barnesiella intestinihominis, while negatively associated with unclassified Clostridium XIVa. β-Blocker use was negatively associated with Akkermansia muciniphila and E. lenta. SSRI antidepressant use was positively associated with Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii (Figure 2C). These results indicate the distinct associations between a single medication and gut microbiota composition.

Potential Microbial Functionalities Associated with Single Medications

To gain functional insights into the associations identified, a metagenomics function prediction was performed with the available ASV data from the present cohort. Tax4Fun found a total of 5293 KEGG Orthologies (KOs). Using the multivariate association algorithms in MaAsLin2, 745 KOs were found to be associated with β-blockers, levothyroxine, PPI, SSRI antidepressant, 5-ASA, and metformin (Figure 3A), characterized by the significant enrichment of K01997 (branched-chain amino acid transporter permease) by β-blockers. A total of 697 KOs were higher in medication use that were enriched in pathways including the biosynthesis of branched-chain amino acids, pyrimidine metabolism, peptidoglycan biosynthesis, and drug metabolism—other enzymes (Figure 3B).

Figure 3.

Figure 3

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Metagenomics functional prediction of KEGG orthologies (KOs). (A) Multivariate association analysis of KOs with different medications. All medications with significant associations (FDR < 0.25) are visualized. (B) Pathway enrichment analysis of the upregulated (e.g., positive association) or downregulated (e.g., negative association) KOs. (C) Associations between medications and encoded KOs.

Next, we were interested in determining whether the discriminant KOs might be present in the genomes of strains that exhibited strong positive/negative association with specific medications as shown in Figure 3C. Corresponding to the negative association between β-blockers and E. lenta, the enzymes that negatively associated with β-blockers were found in the representative strain E. lenta DFI.6.2, including formate-tetrahydrofolate ligase (EC:6.3.4.3), citrate synthase, aconitate hydratase (EC:4.2.1.3), and glutamate synthase (NADPH) large chain (EC:1.4.1.13) (Figure 3C). Similar alterations were found for 5-ASA, which were positively associated with N-acetylneuraminate synthase (EC:2.5.1.56) that was present in B. thetaiotaomicron strain ATCC 29148. Metformin use was negatively associated with the NitT/TauT family transport system substrate-binding protein that was present in B. xylanisolvens 2789STDY5608839 (Figure 3C). These results further implicate the functional association between specific microbes and medication use.

Multimedication Use Associations with Microbial Composition

To further understand the impact of multimedication use, we explored the frequency of different medication combinations. In the present cohort, dual medication combinations were most observed, with at least 10 participants having 6 specific pairs. A total of 15, 14, 14, 13, 13, and 10 participants were described as users of ARBs + NSAIDs, statin + ACE inhibitor, statin + ARBs, statin + metformin, statin + NSAIDs, and statin + PPI, respectively, as visualized in Figure 4A. To increase the statistical power using unbalanced replicates from medication users and nonusers, medication pairs present in at least 10 participants were employed for the following multimedication association analysis. Triple medication use was also observed, but was not included in the analysis due to the low frequency of participants in each group. However, as increasing medication use may affect the overall microbial composition,31 we assessed the relationship between microbial α-diversity and number of medications used. Linear regression analysis identified a significant negative correlation between the number of medications used and Shannon index (linear regression coefficient −0.05, r2 = 0.056, P = 0.005, Figure 4B), indicating that the increased use of medication changes the microbial diversity. When comparing the effects of medication combination on β-diversity, statin + ARB was significantly associated with microbiota composition, as measured by Bray–Curtis distance (Figure 5A).

Figure 4.

Figure 4

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Graphical representation of medication combinations and their association with the Shannon index. (A) Network visualization of dual medication combinations. Combinations of 3+ medications are not shown due to low frequency. Color intensity indicates frequency of specific, dual medication combinations. (B) Linear regression analysis of the relationship between number of medications used and Shannon index (linear regression coefficient −0.05, r2 = 0.056, P = 0.005).

Figure 5.

Figure 5

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Multivariate association analyses between bacterial taxa and dual medication use. (A) The explained variance in Bray–Curtis distance by different combinations of medications. *FDR P < 0.05 from adonis2 analysis. (B) Significant associations between multimedication and bacterial taxa. A plus symbol represents a significant positive association, while the minus symbol represents a significant negative association. Significance was defined as FDR P < 0.25 by MaAsLin2..

Next, we performed the multimedication association analysis with the microbial composition. A total of 20 associations were identified when corrected for age, sex, BMI, and diet (FDR < 0.25, Figures 5B and S5). Among the associations identified, ARB + NSAID was associated with seven features, including negative associations with Ruminococcus, Bacteroidetes, Alistipes, and B. intestinihominis. Statin + metformin was positively associated with Eshcherichia/Shigella, and Proteobacteria, and negatively associated with Butyricicoccus, Romboutsia, and D. invisus. Statin + NSAID were positively associated with A. muciniphila and negatively associated with Eggerthella. Overall, the combination of statin + metformin and ARB + NSAID imposed a more profound impact on the microbial composition compared to other dual medication combinations.

Discussion

Effective management of CMD often necessitates the utilization of multiple medications. For this reason, we examined the impact of both individual and multimedication use on gut microbiota structure, composition, and function in CMD. Employing multivariable association analysis strictly controlled for age, sex, BMI, and diet, we found many novel associations between bacterial taxa and medications.

To gain insight into differences in the gut microbial community structure with individual medications, the Bray–Curtis distances were calculated. Results showed variable impacts of medications, with the largest changes observed with SSRI antidepressants. This finding is in line with studies showing SSRI to exert antibiotic effects, disrupting both the integrity and stability of the gut microbiota.32,33 Although SSRIs are not commonly classified as medications for CMD, there exists a firmly established, two-way connection between depression and CMD with individuals with CMD being 1.5 times more likely to be diagnosed with depression compared to healthy controls.34,35

In the present study, many novel associations between medications and gut microbiota at both the genus and species levels were identified. Those of particular interest included the use of β-blockers and statins that were negatively correlated with A. muciniphila. A. muciniphila is a keystone mucin-degrading bacteria with metabolic and immunological benefits in the gut.36 Likewise, administration of pasteurized (but not live) A. muciniphila for 3 months improves insulin sensitivity and dyslipidemia in obese individuals with metabolic syndrome.37 Given these findings, it is worthwhile for future studies to test the protective effect of A. muciniphila supplementation when these medications are prescribed. Results also found a positive association between metformin and Escherichia/Shigella. This finding is consistent with reports of gastrointestinal side effects, reduced diversity, and elevated Escherichia species with metformin use.38,39 Nevertheless, in Type 2 Diabetes Mellitus, metformin may exhibit positive impact on the gut microbiome by enhancing the growth of short-chain fatty acid-producing bacteria, including Bacteroides, Butyricoccus, and Bifidobacterium.40 To this end, the impact of metformin needs to be evaluated in disease-specific and multimedication contexts.

Next, we used functional metagenomics prediction via KEGG orthologies to examine how microbial function was impacted by CMD medication use. As expected, medications resulted in the alteration of several microbial functions and pathways. For pathways, CMD as a collective group had the most profound impact on the biosynthesis of amino acids. Disturbances in branched-chain amino acids and amino acid profiles are known to be associated with CMD,41 and this finding prompts the question of whether medication-induced changes in microbiota also play a role. Examination of genes and proteins affected and their relation to specific microbes uncovered changes in the representative taxa E. lenta with β-blocker use. In particular, β-blockers selectively reduced the relative abundance of predicted enzymes, including citrate synthase, glutamate synthase, and aconitate hydratase, indicating that energy metabolism was likely affected. Of note, these functions were unaffected by other medications. E. lenta is an important microbe exerting diverse metabolic activities toward bile acids, medications,42 and short-chain fatty acids, particularly acetate for energy metabolism.43 Taken together, these results suggest that E. lenta survival and function may be compromised by β-blocker use.

As polypharmacy is common in CMD, we also examined the impact of multimedication use on the gut microbiota. Increasing medication use (up to 6) resulted in reductions in microbial α-diversity as assessed by the Shannon index. Similar findings have also been reported in patients with irritable bowel syndrome.31 Collectively, it is thought that increasing medication use alters the luminal microenvironment and favors the selective colonization of microbes with high adaptability. As high diversity is linked to greater microbial stability and resilience,44 future research could consider adding a multistrain probiotic to those taking multiple medications for CMD. There is emerging evidence to suggest that specific probiotics can improve symptomology related to CMD.45,46

Beyond the previously discussed medications, the current data set also presented an opportunity to investigate specific combinations of dual medications. This type of analysis has rarely been undertaken in the literature. Predictably, statins were the most prescribed medication in our cohort, and as such, most combinations involved statins with another medication. Results show that the combination of statins and ARBs was the most disruptive in terms of bacterial community structure as determined by the Bray–Curtis distance. Despite the observed gut microbiota disturbance, this combination is highly effective with protective effects on both cardiovascular mortality and morbidity.47−49

Examination of significant medication-microbe associations with dual medication use highlights the combination of statins and metformin as noteworthy. This combination displays several important relationships, including negative associations with Romboutsia and Butyriciocccus. Reduced Romboutsia abundance in hypertension has been previously demonstrated in the HELIUS study,50 but it is unclear whether underlying condition(s) and/or medication were to blame as the association existed with and without correction for antihypertensive medication use. This medication combination also reduced Butyriciocccus likely indicating reduced butyrate production, a microbe known to have several beneficial properties that are essential to maintaining gastrointestinal health.51

This study boasts several notable strengths, such as meticulous control over various confounding factors that are recognized for their influence on the gut microbiota. These encompass age, BMI, sex, and diet. Additionally, results explore the effects of single, dual, and multimedication use. Limitations include the correlational nature of the analysis and a lack of detailed information regarding the medication, specifically the prescription duration, dosage, and formulation. For instance, quick-release or extended-release formats could potentially exert a significant influence on how a drug affects the gut microbiota. Another limitation is the functional prediction of gut microbiota. In the future, fecal metabolomics profiling could be used to confirm functional changes including alterations to short-chain fatty acids, branched-chain fatty acids, and other metabolites.

In conclusion, the results reveal several novel medication–microbiota interactions as well as the influence of many common dual medication combinations. These discoveries enhance our understanding of the intricate interplay between pharmaceuticals and the gut microbiota in the context of CMD. Given the profound impact of the gut microbiota on overall health, finding a balance between medication usage and maintaining a healthy microbial structure could offer valuable insights for more effective disease management.

Acknowledgments

The authors thank the Alberta Tomorrow Project research staff at Richmond Diagnostics Center, Calgary, for all their help in participants’ recruitment and follow-ups, sample collection at the facility, data transfer, and coordinating with the research team at University of Calgary. Alberta’s Tomorrow Project is only possible because of the commitment of its research participants, its staff, and its funders: Alberta Health, Alberta Cancer Foundation, Canadian Partnership against Cancer and Health Canada, and substantial in-kind funding from Alberta Health Services. The views expressed herein represent the views of the author(s) and not of Alberta’s Tomorrow Project or any of its funders. This work was also supported by a grant of the ERA-HDHL Initiative: Gut metabotypes as Biomarkers for Nutrition and Health (ML: 2816ERA14E and 2816ERA13E and CIHR, Canada, FRN #150364). C.M. received postdoctoral funding from the Mitacs Accelerate Postdoctoral Fellowship.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsptsci.3c00261.

  • Additional information about CONSORT flowchart (Figure S1); individual microbiota variation (Figure S2); genus-level microbiota composition (Figure S3); the explained variance in Shannon diversity by single medication (Figure S4), and relative abundances of bacterial taxa with representative medications (Figure S5) (PDF)

Author Contributions

∇ Co-first author. J.S. and C.M. conceived and led the study. J.S. supervised the conduct of the study and data collection. S.S. conducted the experiments and collected and analyzed the data. C.M. analyzed the data. G.S. coordinated cohort recruitment, survey completion, and sample collection. S.S. provided assistance with metadata curation. K.S. and M.L. coordinated 16S rRNA sequencing and data transfer. C.M. and J.S. wrote the manuscript with input from all authors. All authors read and approved the final version of this manuscript.

The authors declare no competing financial interest.

Supplementary Material

pt3c00261_si_001.pdf (1.6MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pt3c00261_si_001.pdf (1.6MB, pdf)

Data Availability Statement

16S rRNA gene sequencing data has been deposited in NCBI’s Sequence Read Archive (SRA) database and are available through the accession number PRJNA 922681.

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