Diagnostic Evaluation of an Increased Risk of Developing Small Intestinal Bacterial Overgrowth Associated with Glucagon-like Peptide-1 (GLP-1) Receptor Agonists and Dual GLP-1/GIP Receptor Agonists: A Global Retrospective Multicenter Cohort Analysis
by
Yan Sun
1,†, 
Donovan Veccia
2,†,
Benjamin Douglas Xun Liu
1,
William Tse
3,
Ronnie Fass
2 and
Gengqing Song
2,*
1
Department of Medicine, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH 44109, USA
2
Division of Gastroenterology and Hepatology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH 44109, USA
3
Division of Hematology and Oncology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH 44109, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2025, 15(17), 2264; https://doi.org/10.3390/diagnostics15172264
Submission received: 18 July 2025
/
Revised: 29 August 2025
/
Accepted: 29 August 2025
/
Published: 7 September 2025
(This article belongs to the Special Issue Advances in Diagnosis of Digestive Diseases)
Abstract
Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) effectively manage type 2 diabetes mellitus (T2DM) but may impair gastrointestinal motility, increasing the risk of small intestinal bacterial overgrowth (SIBO). Diagnostic evaluation of SIBO commonly involves breath testing and clinical assessment. This study aimed to assess the association between GLP-1 RAs or dual GLP-1/glucose-dependent insulinotropic polypeptide (GIP) RAs are associated with incident SIBO. Methods: We conducted a retrospective cohort study using the TriNetX global database, identifying adult T2DM patients initiating GLP-1 RA or dual GLP-1/GIP RA therapy versus other second-line T2DM agents (OSLT2DM) from 1 January 2006 to 2 December 2024. Patients with major abdominal surgery, connective tissue disorders, gastroparesis, or other high-risk conditions for SIBO were excluded. 1:1 Propensity score matching was applied. Short-term (<1 year) and long-term (up to 5 years) risks were evaluated with Kaplan–Meier curves and univariable Cox models. Results: After matching, 216,173 patients per cohort were analyzed. Short-term analysis demonstrated a higher incidence of diagnostically confirmed SIBO in patients treated with GLP-1 RA/GIP (0.177 per 1000 patient-years) compared to OSLT2DM (0.083 per 1000 patient-years; HR 2.14, 95% CI 1.13–4.07; p = 0.0491). Long-term analysis indicated a non-significant trend toward increased risk in the GLP-1 RA/GIP group (HR 2.02, 95% CI 0.98–4.12), though Kaplan–Meier analysis revealed a sustained divergence (p = 0.017). Conclusions: GLP-1 RA and dual GLP-1/GIP RA therapy are associated with increased short-term SIBO risk. Symptom-driven SIBO breath-test evaluation may be warranted in patients initiating these agents.
1. Introduction
Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones naturally produced in the body that help regulate blood glucose levels after eating [1]. FDA-approved GLP-1 receptor agonists (GLP-1 RAs), including liraglutide, lixisenatide, exenatide, dulaglutide, and semaglutide, are widely prescribed in the United States for the treatment of type 2 diabetes mellitus (T2DM) and complications [2,3]. A newer dual glucose-dependent insulinotropic peptide (GIP) and GLP-1 receptor agonist, tirzepatide, is also approved for treating type 2 diabetes mellitus [2,4]. Their major benefits include effective glycemic control, weight loss, lowering of blood pressure and cholesterol, and improvement of renal function [5].
Despite their effectiveness, GLP-1 RAs have several negative gastrointestinal side effects, including nausea, vomiting, diarrhea, pancreatitis, bowel obstruction, and notably gastroparesis, which is associated with small intestinal bacterial overgrowth (SIBO) [6,7]. Recently, there was one case report that found semiglutide caused patient lactulose and gluten intolerance. 6 months after semiglutide discontinuation, the patient was diagnosed with SIBO [8]. Although not fatal by itself, SIBO can lead to serious complications as weight loss, malnutrition, absorption, diarrhea, bloating, and nausea, which significantly impact patients’ daily lives [9,10,11].
The primary goal of this study was to diagnostically assess the connection between GLP-1 RA usage and the incidence of SIBO, quantified through rates of clinically confirmed cases. Secondary objectives included determining the frequency of hydrogen breath tests used for diagnosis, the average duration of follow-up after diagnosis, and the proportion of patients initiating targeted treatment within one week following SIBO diagnosis.
2. Materials and Methods
2.1. Data Source
We conducted a retrospective cohort study using the TriNetX platform (TriNetX, 100 Cambridgepark Drive, Suite 501, Cambridge, MA, 02140, USA), a de-identified multicenter, global electronic health record (EHR) database [12]. TriNetX includes data on >300 million patients from participating healthcare organizations across 19 countries, and supports de-identified, line-level analyses within the platform, while ensuring complete patient anonymity. To protect privacy, occurrences involving 10 or fewer patients are uniformly suppressed and reported as “≤10”, and exact counts below this threshold are not disclosed. [12]. The MetroHealth Medical Center Institutional Review Board (IRB) determined this study to be exempt from IRB approval because it uses only de-identified and aggregated data, complying with HIPAA Privacy Rule (§164.514(a)). This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies; a completed checklist is provided in the Supplementary Material (Supplementary Table S2) [13].
2.2. Patient Selection
We identified adults (>18 years) with type 2 diabetes mellitus (T2DM) in the TriNetX database (Figure 1). The index event was the first observed prescription on or after 1 January 2006. Because the first GLP-1 receptor agonist (exenatide) received U.S. FDA approval in 2005, we began cohort accrual on 1 January 2006 to ensure capture of initiators in the post-approval period [6,14]. The prescription was either (a) a GLP -1 RAs (Exenatide, liraglutide, lixisenatide, dulaglutide, semaglutide) or the dual GLP-1/GIP RA tirzepatide (GLP RA/GIP cohort) or (b) another second-line antihyperglycemic agent (OSLT2DM cohort: sodium-glucose cotransporter-2 inhibitors (SGLT2), thiazolidinediones (TZD), dipeptidyl peptidase 4 inhibitors (DPP-4), and sulfonylureas, chosen for their prevalent use in similar populations without known associations with SIBO. Cohorts were mutually exclusive and assigned by the index agent. Query search criteria were based on standard diagnostic, procedure, or medication codes.
Exclusion criteria included patients younger than 18 years, any patient with a death date on or before the index date (deceased patients at baseline), individuals with pre-existing conditions known to impact gastrointestinal GI functions, e.g., major GI or abdominal surgeries, connective tissue disorders (e.g., amyloidosis, Ehlers-Danlos syndrome, Sjögren’s syndrome), inflammatory bowel diseases, and other major systemic or GI conditions (cystic fibrosis, intestinal failure, etc.) predisposing to SIBO. Patients with pre-existing gastroparesis prior to medication initiation were also excluded. Code lists are provided in Table 1.
Cohort definitions: We identified adult T2DM initiators of GLP-1 RA or dual GLP-1/GIP RA and OSLT2DM agents; cohort assignment was fixed by the index agent and not redefined thereafter. The same initiator cohorts were used for all analyses; “short-term” refers to evaluations at 3, 6, and 12 months, and “long-term” refers to evaluations after 3 months of initiation and observed for 60 months. Outcome definitions and censoring rules are described in Section 2.5. In the long-term cohort, we continuously monitored patients from initiation of prescription to 60 months.
2.3. Query Diagnostic Validation
We validated our database queries through an unmatched analysis of patients exposed to GLP-1 RAs, dual GLP-1/GIP RA, and OSLT2DM. We identified patients who received a new SIBO diagnosis after initiation and, within this SIBO subset, recorded the proportion who had a hydrogen breath test documented that those who received SIBO-direct antibiotics therapy within 7 days of the first SIBO code. Breath tests have moderate sensitivity and protocol-dependent performance; substrate and gas values were not uniformly available in TriNetX. Any resulting misclassification is likely non-differential between matched cohorts and would attenuate observed associations.
2.4. Covariates and Matching
We identified potential confounders related to SIBO risk and the selection of T2DM therapy, including age at index, sex, BMI, HbA1C, hypertension, hyperlipidemia, atherosclerotic cardiovascular disease, hypothyroidism, medication such as opioids, proton pump inhibitors (PPI), etc. (Table 2). We estimated the propensity to initiate GLP-1/GLP-GIP RAs versus OSLT2DM (Table 2). Dietary or probiotics intake data were not available in TriNetX; thus, diet could not be included as a covariate.
Continuous variables were summarized as mean (SD) and compared pre-match using Welchi’s t-test; categorical variables were summarized as n (n%) and compared using Pearson’s X (or Fisher’s exact when expected counts were <5). After matching, covariate balance was assessed using absolute standardized mean differences (SMD), with SMD < 0.10 indicating adequate balance. For cells with ≤observations, p-values were suppressed per data-use policy; exact tests were used for interference where applicable (Supplementary Table S1).
2.5. Propensity-Matched Study Outcomes
The outcome was diagnostic confirmation of SIBO: Using the propensity-matched initiator cohorts, we assessed incident SIBO beginning the day after the index prescription for 3, 6, or 12 months (short term). We also conducted a landmark analysis at 3 months: patients who were event-free and had continuous exposure ≥3 months were included; time zero was reset to the 3-month landmark, and outcomes were assessed from 3 to 60 months using KM and univariable Cox models.
2.6. Statistical Analysis
Statistical analyses were performed using the TriNetX platform or R software (version 4.4.2; RStudio). Descriptive statistics included means, standard deviations (SD), and proportions. Propensity score matching was conducted 1:1 using relevant covariates through greedy nearest neighbor algorithms, with a caliper width of 0.1, ensuring well-matched cohorts [15]. Characteristics with a standard mean difference between cohorts lower than 0.1 were described as well-matched [15]. Time-to-event analyses on the matched cohort used Kaplan–Meier estimates with log-rank tests [16]. Patients were observed until the diagnostic confirmation of SIBO, last clinical encounters, death, or the analysis horizon, as described above, whichever occurred first. Hazard ratios (HRs) were calculated using univariate Cox-proportional hazards models on the TriNetX platform, with statistical significance defined by a p-value of less than 0.05. Incidence rates were calculated per 1000 person-years; between-group comparisons of incidence used Poisson regression (log link) with an offset for log (person-time), reporting incidence rate ratios (IRR) with 95% CI and Wald p-value. Figures were generated in Microsoft Excel (Redmond, WA, USA, version 2410), R 4.4.2 (Vienna, Austria), and RStudio (version 2024.09.1-394, Boston, MA, USA).
3. Results
3.1. Study Population
From the source population of 163,617,518 patients [12], we identified 9,159,059 adult patients diagnosed with T2DM. Among these, 1,281,479 patients initiated therapy with either GLP-1 RA, dual GLP/GIP RA (GLP-1 RA/GIP), or OSLT2DM between 1 January 2006 and 2 December 2024. After applying exclusion criteria, the GLP-1 RA or dual GLP/GIP RAs cohort comprised 256,362 patients, while the control cohort consisted of 1,025,117 patients (Figure 1). After performing one-to-one propensity score matching, each matched cohort included 216,173 patients. Demographic features and comorbidities of the matched cohorts are detailed in Table 2. All baseline characteristics were well-balanced post-matching [15]. Figure 1 summarizes cohort selection for patients with extended GLP-1 RA/GIP or OSLT2DM treatment durations.
3.2. SIBO Incidence Rate in Short-Term and Long-Term Outcome
SIBO incidence is reported per 1000 person-years (PY); between-group differences are summarized as incidence rate ratios (IRR) with 95% CIs and two-sided p-values. For 3 months, GLP-1/dual GLP-1–GIP RAs: incidence 0.791/1000 PY vs. OSLT2DM: incidence 0.575/1000 PY; IRR 1.38 (95% CI 0.72–2.62), p = 0.332. For 6 months, GLP-1/dual GLP-1–GIP RAs: incidence 0.327/1000 PY vs. OSLT2DM: incidence 0.251/1000 PY; IRR 1.30 (95% CI 0.57–2.96), p = 0.533. For 12 months, GLP-1/dual GLP-1–GIP RAs: incidence 0.177/1000 PY vs. OSLT2DM: incidence 0.083/1000 PY; IRR 2.90 (95% CI 1.41–5.95), p = 0.0037. Followed 3 up to 60 months, GLP-1/dual GLP-1–GIP RAs: incidence 0.0758/1000 PY vs. OSLT2DM: incidence 0.0339/1000 PY; IRR 1.46 (95% CI 0.72–2.96), p = 0.292. In summary, in these persistence-restricted analyses, estimates at 3 and 6 months were attenuated and not statistically significant, whereas the 12-month subset showed a significant association.
3.3. Kaplan–Meier Analyses: Univariable Cox Proportional Hazards
Kaplan–Meier curves and univariable Cox models showed: 3 months: HR 1.786, 95% CI 0.942–3.386, log rank p = 0.072, which means higher hazard in GLP-1 RA/GIP, but not statistically significant. 6 months: HR 1.867, 95% CI 0.973–3.585, log rank p = 0.054, means borderline separation still not significant. 12 months: HR 2.140, 95% CI 1.13–4.07, log rank p = 0.049, the difference is significant. In the 3-month landmark analysis, Kaplan–Meier analyses demonstrated a statistically significant divergence beginning approximately 90 days post-treatment initiation and continuing over 60 months (822.44 ± 539.54 days, compared to 1256.15 ± 593.14 days, median days were 715 vs. 1514): HR 2.018, 95% CI 0.989–4.116, log rank p = 0.0173. Although the log rank indicated a significant between-group difference over 3–60 months, the Cox hazard ratio showed a non-significant (borderline) increased SIBO risk in the GLP-1 group (Figure 2).
4. Discussion
4.1. Epidemiology and Therapeutic Context
Approximately 30.3 million Americans have diabetes, 90–95% of whom have T2DM [17]. Since 2005 the first GLP-1RA was approved, around 679,265 individuals in the U.S. were treated with GLP-1 RAs as of 2023 [18]. Tirzepatide, the first dual GLP/GIP receptor agonist, was approved in 2022 [19]. Beyond glycemic control in T2DM, GLP-1 RA and tirzepatide are increasingly used in obesity [20,21]. Obstructive sleep apnea (OSA) [22], heart failure [23]. Concomitantly, multiple studies have reported gastrointestinal (GI) adverse events with GLP-1 RAs-including nausea, diarrhea, vomiting, constipation, abdaominal pain, and pancreatitis [24]. Recently, Amrutha S et al. used Facebook to investigate the adverse effects of GLP-1 RAs and tirzepatide. The results have also highlighted GI complaints with GLP-1 RAs. The GI symptoms, such as nausea, vomiting, pancreatitis, and diarrhea, were strongly associated together [25].
4.2. SIBO Background and Diagnostic Approch
SIBO (small intestinal bacterial overgrowth) is characterized by bacterial overgrowth or colonization of the small intestine, accompanied by GI symptoms such as bloating, diarrhea, nausea, malabsorption, and food intolerance [11]. Diagnostic options include a hydrogen breath test (non-invasive) and quantitative small bowel aspiration culture (invasive), with >103 colony-forming units (CFU)/mL commonly used as a threshold in duodenal aspiration. In this study, we used a hydrogen breath test to diagnose SIBO because it is practical and non-invasive [26]. Given the limitation of aspirating culture, a breath test is the most practical alternative [27].
4.3. Study Outcomes: Interpretation and Limitations
Direct evidence linking GLP-1 RAs and GLP-1/GIP RA to SIBO is limited; we therefore examined this association in a large, propensity-matched cohort. In the short-term windows, the hazard was higher at 3 and 6 months but did not reach statistical significance (few early events → wide CIs). By 12 months, the curves separated, and the association was significant, indicating a sustained difference within the first year. In contrast, the 3–60-month landmark window showed an attenuated/borderline association: excluding the early post-initiation period—when the difference is greatest—reduces the average contrast over the longer window. These patterns are expected for three reasons: (i) early windows have low event counts, yielding wide CIs and larger p-values; (ii) conditioning on persistence (patients who remain on therapy) preferentially keeps individuals who tolerated treatment and stayed event-free early (“depletion of susceptible”), which shrinks between-group differences; and (iii) starting follow-up at 3 months omits the initial high-risk period, so long-window averages are diluted. Taken together, the results are consistent with an early divergence that strengthens by 12 months but becomes less pronounced when early time is excluded.
4.4. Mechanisms Hypothesis
The mechanisms linking GLP-1 RA, GLP/GIP RA, and SIBO remain uncertain. Two hypotheses are most reasonable: delayed GI transit, increased bacterial proliferation. GLP-1 RA can reduce gut motility by slowing gastric emptying and altering the migration motor complex (MMC), increasing the risk of conditions like gastroparesis and bowel obstruction [28]. Parkman et al.’s [29] recent review noted that liraglutide (long-acting GLP-1 RA) is associated with slower gastric emptying and increasing fasting gastric volume [30]. Overall, 57% of patients treated with liaglutide developed delayed gastric emptying. Kalas et al. similarly reported increased gastroparesis in patients on long-term dulaglutide, liraglutide, and semaglutide therapy [31]. In a multi-database study, Beas et al. found that 41% of patients diagnosed with gastroparesis also had SIBO, highlighting the necessity for further diagnostic exploration [29]. Preclinically, a recent mouse study showed that liraglutide increased the cecal levels of caseinolytic protease B, a component of Escherichia coli, and of norepinephrine. This is the first study to investigate the unique underlying mechanisms related to the effects of GLP-1RA on changes in the gut bacterium [32]. In addition, impaired GI transit favors the proliferation of slow-growing bacterial species in a more stagnant intestinal environment [33]. Other clinical data showed microbiota shifts (fewer beneficial, more harmful bacteria) with constipation and delayed gastric emptying, underscoring the diagnostic significance of microbiota composition changes [34]. Together, these observations suggest that GLP-1-based therapy may predispose to SIBO via motility slowing and harmful bacterial proliferation. Mechanistic studies are still needed to define the specific microbial changes and their clinical implications. Better understanding these mechanisms would greatly enhance diagnostic precision and clinical management strategies.
4.5. Study Strength and Limitations
This study has several key strengths. First of all, this study utilizes a large, global real-world dataset (TriNetX; over 163 million patients), supporting broad generalizability and more stable estimates. Secondly, the use of propensity-score matching (1:1) effectively balanced confounders between study cohorts, increasing internal validity of the comparative results. Finally, Real-world databases capture outcomes as they occur in routine practice, offering a view beyond the selective conditions of randomized trials. This data reflects the complexity of everyday care and generally enhances the external validity and generalizability of findings to typical clinical settings.
Despite this strength, our study has several limitations. This retrospective electronic health record study relies on coded diagnoses and test records that vary across sites, so some misclassification and date imprecision are possible. TriNetX lacks structured dietary information; residual confounding by dietary patterns may persist despite propensity matching. TriNetX small-cell suppression (≤10) further limits granularity. Although propensity-score matching balanced measured covariates, unmeasured confounding may remain (e.g., diet, motility disorders, OTC PPIs, probiotics). On the other hand, breath-test substrate/protocols were heterogeneous and not uniformly captured, which may reduce sensitivity; any misclassification is likely non-differential. Finally, the KM module does not provide time-specific numbers at risk; we therefore report start-of-window risk sets. In addition, medication adherence/persistence cannot be fully verified across sites. These limitations temper—but do not negate the consistent direction of the initiator analyses.
4.6. Clinical Implicationn and Future Directions
A confirmed SIBO diagnosis is clinically meaningful: it explains bloating, abdominal pain, diarrhea/constipation, malabsorption, weight loss, and micronutrient deficiencies, and it is actionable (dietary measures and targeted antibiotics). Untreated SIBO can lead to repeated visits, labs/imaging, and empiric therapies. Given the low absolute incidence but higher relative hazard among GLP-1/dual users, routine universal screening is not warranted. A targeted, symptom-driven testing strategy (e.g., new/persistent GI symptoms after initiation, suspected delayed gastric emptying, or unexplained nutritional deficiencies) balances diagnostic yield with resource use. Our study did not evaluate cost-effectiveness; however, our recommendations emphasize case-finding rather than universal screening to minimize financial burden.
Moving forward, a prospective study that enrolls new initiators and follows them with standardized breath-testing would provide higher-quality data to confirm these findings. Designs should apply uniform breath-test protocols with centralized adjudication to limit misclassification. Clinically, for patients starting GLP-1 RA or dual GLP-1/GIP RA, symptom-driven SIBO screening by hydrogen breath test may be considered. Additionally, GI motility assessments with microbiome profiling are warranted to investigate the relationship of GLP-1-based therapy, delayed transit, and bacterial overgrowth, which will significantly enhance clinical management and prevention strategies.
In conclusion, to our knowledge, this is the first large-scale study to identify an association between GLP-1 RAs or GLP/GIP RA and an increased incidence of SIBO compared to other second-line diabetes medications. Given the widespread clinical application and proven effectiveness of GLP-1 RAs, GLP/GIP RA for managing T2DM and obesity, clinicians should remain aware of potential gastrointestinal side effects. Early, symptom-driven screening for SIBO with a hydrogen breath test may be warranted in patients initiating these agents. Finally, prospective studies with standardized diagnostic protocols are needed. Future research should focus on mechanisms linking GI motility change, gut microbiota, and SIBO to improve risk stratification and preventive care.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics15172264/s1, Table S1. Expanded Baseline Characteristics; Table S2: STROBE Statement—Checklist for Cohort Studies.
Author Contributions
Y.S. and D.V.: Validation, conceptualization, investigation, drafting manuscript. B.D.X.L.: investigation, data curation, data analysis, visualization. G.S., W.T. and R.F.: project supervision, editing manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The MetroHealth Medical Center Institutional Review Board (IRB) determined this study to be exempt from IRB approval because it uses only de-identified and aggregated data, complying with HIPAA Privacy Rule (§164.514(a)).
Informed Consent Statement
The study was conducted using de-identified retrospective data from the TriNetX global database. As such, informed consent was not required.
Data Availability Statement
All raw output data from TriNetX are available on reasonable requests by contacting the corresponding author.
Conflicts of Interest
The authors have no conflicts of interest to declare.
Abbreviations
The following abbreviations are used in this manuscript:
| SIBO | Small intestinal bacterial overgrowth |
| GLP-1 | Glucagon-like peptide-1 (GLP-1) |
| GIP | glucose-dependent insulinotropic polypeptide |
| OSLT2DM | other second-line diabetes medications |
| SGLT2 | sodium-glucose cotransporter-2 inhib |
| TZD | thiazolidinediones |
| DPP-4 | dipeptidyl peptidase 4 inhibitors |
| T2DM | type 2 diabetes |
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Figure 1.
Flow diagram of patient screening, exclusions, and cohort assembly for initiators of GLP-1 receptor agonists or dual GLP-1/GIP receptor agonists (GLP-1/GIP RAs) versus other second-line T2DM agents (OSLT2DM).
Figure 1.
Flow diagram of patient screening, exclusions, and cohort assembly for initiators of GLP-1 receptor agonists or dual GLP-1/GIP receptor agonists (GLP-1/GIP RAs) versus other second-line T2DM agents (OSLT2DM).
Figure 2.
Kaplan–Meier curves for time to small intestinal bacterial overgrowth (SIBO) in propensity-score-matched adults with type 2 diabetes, indicating GLP-1 receptor agonists or dual GLP-1/GIP receptor agonist (GLP-1 RA/GIP) versus other second-line diabetes medications (OSLT2DM). The figures show 3 months, 6 months, 12 months after initiation and 90-day landmark window at 3–60 months. Log rank p values and hazard ratios (HRs) with 95% confidence intervals are shown (from univariate Cox proportional hazards models).
Figure 2.
Kaplan–Meier curves for time to small intestinal bacterial overgrowth (SIBO) in propensity-score-matched adults with type 2 diabetes, indicating GLP-1 receptor agonists or dual GLP-1/GIP receptor agonist (GLP-1 RA/GIP) versus other second-line diabetes medications (OSLT2DM). The figures show 3 months, 6 months, 12 months after initiation and 90-day landmark window at 3–60 months. Log rank p values and hazard ratios (HRs) with 95% confidence intervals are shown (from univariate Cox proportional hazards models).
Table 1.
Diagnostic, procedure, and medication codes used for query, propensity matching, or outcomes.
Table 1.
Diagnostic, procedure, and medication codes used for query, propensity matching, or outcomes.
| Search Terms | Associated Code |
|---|---|
| Surgery on the Esophagus | CPT: 43279, 43280, 43283, 43284, 43285, 43286, 43287, 43288, 43289, 1007215, 1007219, 1007295 *, 1007298, 1007341, 1020218, 1007335 ICD-10: 0DV1, 0DV2, 0DV3, 0DV4, 0DV5, 0D11, 0D51, 0D52, 0D53, 0D54, 0D55, 0D84, 0DB1, 0DB2, 0DB3, 0DB4, 0DB5, 0DC1, 0DC2, 0DC3, 0DC4, 0DC5, 0DD1, 0DD2, 0DD3, 0DD4, 0DD55, 0DF5, 0DL1, 0DL2, 0DL3, 0DL4, 0DL5, 0DT1, 0DT2, 0DT3, 0DT4, 0DT5, 0D71, 0D72, 0D73, 0D74, 0D75 |
| Surgery on the Stomach | CPT: 1007344, 1007345, 1007352, 1007372, 1007392, 1007385 ICD-10: 0DV6, 0DV7, 0DB6, 0DB7, 0DC6, 0DC7, 0DD6, 0DD7, 0DF6, 0DL6, 0DL7, 0DT6, 0DT7, 0D16 |
| Dyskinesia of Esophagus | IDC-10: K22.4 |
| Scleroderma | IDC-10: M34 |
| Radiation Therapy | TNX Curated: 1001 |
| Lupus Erythematosus | ICD-10: L93 |
| Sjörgen Syndrome | ICD-10: M35.0 |
| Acquired Absence of Other Specified Parts of Digestive Tracts | ICD-10: Z90.49 |
| Other abdominal surgeries | CPT: 1007463, 1007422, 1007570, 1007695, 1007795, 1007908, 1007952, 1007846, 1007854, 1007892, 1018154, 47579, 1007887, 47700, 47701, 47570, 47715, 1007592, 1007596, 1007620, 1007663, 1007671, 1007687, 1007692 SNOMED: 4558008, 80146002, 11466000 ICD-9: 46.2 |
| Ehlers-Danlos Syndrome | ICD-10: Q79.6 |
| Intestinal Adhesions with Obstruction | ICD-10: K56.5 |
| Unspecified Diseases of Spinal Cord | ICD-10: G95 |
| Parkinson’s Disease | ICD-10: G20 |
| Multiple Sclerosis | ICD-10: G35 |
| Amyloidosis | ICD-10: E85 |
| Crohn’s Disease | ICD-10: K50 |
| Ulcerative Colitis | ICD-10: K51 |
| Other Specified Noninfective Gastroenteritis and Colitis | ICD-10: K52.8 |
| Noninfective Gastroenteritis and Colitis, Unspecified | ICD-10: K52.9 |
| Cystic Fibrosis | ICD-10: E84 |
| Other Unspecified Intestinal Obstruction | ICD-10: K56.6 |
| Fistula of Intestine | ICD-10: K63.2 |
| Intestinal Failure | ICD-10: K90.83 |
| Type 2 Diabetes Mellitus | ICD-10: E11 |
| Gastroparesis | ICD-10: K31.84 |
| Glucagon-like Protein-1 Receptor Agonists | ATC: A10BJ RxNorm: 1440051, 1551291, 1991302, 2601723, 475968, 60548 ** |
| Sulfonylureas | ATC: A10BB |
| Dipeptidyl Peptidase 4 Inhibitors | ATC: A10BH |
| Sodium-Glucose Co-Transporter 2 Inhibitors | ATC: A10BK |
| Thiazolidinediones | ATC: A10BG |
* Denotes codes that include all subcodes except codes that are denoted by’. ** Denotes a code that had a TriNetX filter attached to it to differentiate between its two formulations. ICD-10 = International classification of diseases, version 10, CPT = current procedural terminology, ATC = anatomical therapeutic chemical classification system, TNX = TriNetX curated codes, RxNorm = US normalized drug names.
Table 2.
Baseline characteristics of patients initiating GLP-1 receptor agonists or dual GLP-1/GIP receptor agonists (GLP-1/GIP RAs) versus other second-line type 2 diabetes medications (OSLT2DM), before and after propensity-score matching. Note: Per TriNetX privacy policy, *: cells with ≤10 patients are masked (displayed as “≤10” or 10); exact counts below 10 are not disclosed.
Table 2.
Baseline characteristics of patients initiating GLP-1 receptor agonists or dual GLP-1/GIP receptor agonists (GLP-1/GIP RAs) versus other second-line type 2 diabetes medications (OSLT2DM), before and after propensity-score matching. Note: Per TriNetX privacy policy, *: cells with ≤10 patients are masked (displayed as “≤10” or 10); exact counts below 10 are not disclosed.
| Before Matching | After Matching | |||||
|---|---|---|---|---|---|---|
| Cohort, No. (%) | Cohort, No. (%) | |||||
| Demographics | GLP-1 RA | Control | Std Diff. | GLP-1 RA | Control | Std Diff. |
| Total No. | 234,701 | 952,418 | 216,173 | 216,173 | ||
| Age, mean (SD), y | 55.6 ± 13.5 | 62.5 ± 12.6 | 0.5279 | 56.7 ± 13.0 | 56.7 ± 13.4 | 0.002 |
| Sex | ||||||
| Female | 130,070 (55.4%) | 394,335 (41.4%) | 0.2833 | 115,650 (53.5%) | 115,379 (53.4%) | 0.0025 |
| Male | 94,047 (40.1%) | 535,217 (56.2%) | 0.327 | 91,358 (42.3%) | 91,454 (42.3%) | 0.0009 |
| Ethnicity | ||||||
| Hispanic/LatinX | 19,273 (8.2%) | 86,609 (9.1%) | 0.0314 | 18,213 (8.4%) | 17,304 (8.0%) | 0.0153 |
| Not Hispanic/LatinX | 137,998 (58.8%) | 485,443 (51.0%) | 0.1578 | 125,595 (58.1%) | 131,526 (60.8%) | 0.0559 |
| Unknown | 77,430 (33.0%) | 380,366 (39.9%) | 0.1447 | 72,365 (33.5%) | 67,343 (31.2%) | 0.0497 |
| Race | ||||||
| Asian | 8366 (3.6%) | 79,874 (8.4%) | 0.2045 | 8231 (3.8%) | 7947 (3.7%) | 0.0069 |
| Black | 44,108 (18.8%) | 132,399 (13.9%) | 0.1326 | 39,336 (18.2%) | 39,746 (18.4%) | 0.0049 |
| White | 129,966 (55.4%) | 459,037 (48.2%) | 0.144 | 119,423 (55.2%) | 123,649 (57.2%) | 0.0394 |
| American Indian or Alaska Native | 792 (0.3%) | 2606 (0.3%) | 0.0116 | 741 (0.3%) | 577 (0.3%) | 0.0138 |
| Native Hawaiian or Other Pacific Islander | 1430 (0.6%) | 5888 (0.6%) | 0.0011 | 1332 (0.6%) | 1566 (0.7%) | 0.0133 |
| Unknown | 40,556 (17.3%) | 234,549 (24.6%) | 0.1813 | 38,337 (17.7%) | 34,059 (15.8%) | 0.053 |
| Diabetic Comorbidities | ||||||
| Primary hypertension | 159,587 (68.0%) | 640,293 (67.2%) | 0.0164 | 148,292 (68.6%) | 148,742 (68.8%) | 0.0045 |
| Hyperlipidemia | 109,315 (46.6%) | 401,625 (42.2%) | 0.0888 | 100,947 (46.7%) | 101,353 (46.9%) | 0.0038 |
| Atherosclerotic heart disease of native coronary artery | 31,441 (13.4%) | 205,487 (21.6%) | 0.2166 | 30,824 (14.3%) | 30,893 (14.3%) | 0.0009 |
| Other hypothyroidism | 35,475 (15.1%) | 91,180 (9.6%) | 0.1691 | 30,643 (14.2%) | 30,511 (14.1%) | 0.0018 |
| Type 2 diabetes mellitus with neurological complications | 28,515 (12.2%) | 95,272 (10.0% | 0.0684 | 26,598 (12.3%) | 26,714 (12.4%) | 0.0016 |
| Chronic kidney disease | 26,023 (11.1%) | 147,717 (15.5%) | 0.1305 | 25,181 (11.6%) | 25,195 (11.7%) | 0.0002 |
| Heart failure | 16,791 (7.2%) | 129,371 (13.6%) | 0.2121 | 16,593 (7.7%) | 17,064 (7.9%) | 0.0081 |
| Other functional intestinal disorders | 18,922 (8.1%) | 57,691 (6.1%) | 0.0783 | 16,232 (7.5%) | 15,598 (7.2%) | 0.0112 |
| Peripheral vascular disease, unspecified | 8504 (3.6%) | 42,087 (4.4%) | 0.0405 | 8128 (3.8%) | 7986 (3.7%) | 0.0035 |
| Atherosclerosis | 8079 (3.4%) | 40,400 (4.2%) | 0.0416 | 7757 (3.6%) | 7631 (3.5%) | 0.0031 |
| Cerebral infarction | 7801 (3.3%) | 50,448 (5.3%) | 0.0973 | 7609 (3.5%) | 7467 (3.5%) | 0.0036 |
| End-stage renal disease | 3604 (1.5%) | 17,977 (1.9%) | 0.0271 | 3414 (1.6%) | 3417 (1.6%) | 0.0001 |
| Fibrosis and cirrhosis of liver | 2979 (1.3%) | 13,675 (1.4%) | 0.0144 | 2824 (1.3%) | 2667 (1.2%) | 0.0065 |
| Other immunodeficiencies | 2369 (1.0%) | 4166 (0.4%) | 0.0675 | 1839 (0.9%) | 1984 (0.9%) | 0.0072 |
| Malnutrition | 1716 (0.7%) | 14,370 (1.5%) | 0.0739 | 1671 (0.8%) | 1505 (0.7%) | 0.009 |
| Opioid dependence | 1762 (0.8%) | 4262 (0.4%) | 0.0393 | 1515 (0.7%) | 1500 (0.7%) | 0.0008 |
| Human immunodeficiency virus (HIV) disease | 1376 (0.6%) | 3418 (0.4%) | 0.0332 | 1189 (0.6%) | 1271 (0.6%) | 0.005 |
| Drug-induced constipation | 1078 (0.5%) | 2148 (0.2%) | 0.04 | 894 (0.4%) | 875 (0.4%) | 0.0014 |
| Celiac disease | 614 (0.3%) | 658 (0.1%) | 0.0474 | 442 (0.2%) | 289 (0.1%) | 0.0172 |
| Other chronic pancreatitis | 408 (0.2%) | 3720 (0.4%) | 0.0409 | 404 (0.2%) | 467 (0.2%) | 0.0065 |
| Immunodeficiency with predominantly antibody defects | 361 (0.2%) | 812 (0.1%) | 0.0198 | 305 (0.1%) | 249 (0.1%) | 0.0072 |
| Diverticular disease of small intestine without perforation or abscess | 183 (0.1%) | 776 (0.1%) | 0.0012 | 172 (0.1%) | 154 (0.1%) | 0.003 |
| Common variable immunodeficiency | 100 (>0.1%) | 181 (>0.1%) | 0.0135 | 83 (>0.1%) | 65 (>0.1%) | 0.0045 |
| Acromegaly and pituitary gigantism | 92 (>0.1%) | 194 (>0.1%) | 0.0109 | 76 (>0.1%) | 67 (>0.1%) | 0.0023 |
| Alcohol-induced chronic pancreatitis | 42 (>0.1%) | 155 (>0.1%) | 0.0207 | 42 (>0.1%) | 92 (>0.1%) | 0.0131 |
| Achlorhydria | 39 (>0.1%) | 47 (>0.1%) | 0.0113 | 36 (>0.1%) | 17 (>0.1%) | 0.0079 |
| Myotonic muscular dystrophy | 25 (>0.1%) | 111 (>0.1%) | 0.0009 | 23 (>0.1%) | 28 (>0.1%) | 0.0021 |
| Combined immunodeficiencies | 21 (>0.1%) | 58 (>0.1%) | 0.0033 | 16 (>0.1%) | 15 (>0.1%) | 0.0005 |
| Tropical sprue | 10 * (>0.1%) | 12 (>0.1%) | 0.0057 | 10 (>0.1%) | 10 * (>0.1%) | 0.0001 |
| Gastroenteritis and colitis due to radiation | 11 (>0.1%) | 46 (>0.1%) | 0.0002 | 10 * (>0.1%) | 12 (>0.1%) | 0.0013 |
| Systemic sclerosis (scleroderma) | 0 (0.0%) | 0 (0.0%) | 0 | 0 (0.0%) | 0 (0.0%) | 0 |
| Amyloidosis | 0 (0.0%) | 0 (0.0%) | 0 | 0 (0.0%) | 0 (0.0%) | 0 |
| Gastroparesis | 0 (0.0%) | 0 (0.0%) | 0 | 0 (0.0%) | 0 (0.0%) | 0 |
| Labs | ||||||
| BMI | 37.2 ± 8.33 | 31.5 ± 7.52 | 0.7151 | 36.4 ± 7.9 | 35.9 ± 8.31 | 0.0595 |
| At least 25 kg/m2 | 168,304 (71.7%) | 492,218 (51.7%) | 0.421 | 150,141 (69.5%) | 150,743 (69.7%) | 0.0061 |
| At least 30 kg/m2 | 150,585 (64.2%) | 345,445 (36.3%) | 0.5809 | 132,446 (61.3%) | 132,557 (61.3) | 0.0011 |
| At least 35 kg/m2 | 111,793 (47.6%) | 188,694 (19.8%) | 0.6157 | 94,275 (43.6%) | 94,681 (43.8%) | 0.0038 |
| At least 40 kg/m2 | 70,047 (29.8%) | 95,351 (10.0%) | 0.5126 | 55,850 (25.8%) | 56,129 (26.0%) | 0.0029 |
| At least 45 kg/m2 | 38,925 (16.6%) | 44,772 (4.7%) | 0.3927 | 29,328 (13.6%) | 29,483 (13.6%) | 0.0021 |
| At most 50 kg/m2 | 164,607 (70.1%) | 554,881 (58.3%) | 0.2496 | 149,230 (69.0%) | 148,578 (68.7%) | 0.0065 |
| At most 45 kg/m2 | 155,179 (66.1%) | 542,021 (56.9%) | 0.1901 | 141,532 (65.5%) | 140,779 (65.1%) | 0.0073 |
| At most 40 kg/m2 | 135,445 (57.7%) | 508,701 (53.4%) | 0.0866 | 124,545 (57.6%) | 123,635 (57.2%) | 0.0085 |
| At most 35 kg/m2 | 100,515 (42.8%) | 439,347 (46.1%) | 0.0665 | 93,690 (43.3%) | 92,811 (42.9%) | 0.0082 |
| At most 30 kg/m2 | 52,960 (22.6%) | 298,935 (31.4%) | 0.1998 | 50,079 (23.2%) | 49,462 (22.9%) | 0.0068 |
| Hemoglobin—A1C | 7.79 ± 2.16 | 8.1 ± 2.18 | 0.1445 | 7.88 ± 2.17 | 7.95 ± 2.25 | 0.0313 |
| At least 7.0% | 105,322 (44.9%) | 357,521 (37.5%) | 0.1495 | 98,200 (45.4%) | 96,673 (44.7%) | 0.0142 |
| At least 7.5% | 87,847 (37.4%) | 298,785 (31.4%) | 0.1278 | 82,081 (40.0%) | 81,397 (37.7%) | 0.0065 |
| At least 8.0% | 75,221 (32.1%) | 248,228 (26.1%) | 0.1321 | 70,059 (32.4%) | 69,792 (32.3%) | 0.0026 |
| At least 8.5% | 64,362 (27.4%) | 203,207 (21.3%) | 0.1421 | 59,668 (27.6%) | 59,585 (27.6%) | 0.0009 |
| At least 9.0% | 56,002 (23.9%) | 170,295 (17.9%) | 0.1476 | 51,692 (23.9%) | 51,718 (23.9%) | 0.0003 |
| At least 9.5% | 47,943 (20.4%) | 141,157 (14.8%) | 0.1475 | 44,070 (20.4%) | 44,053 (20.4%) | 0.0002 |
| At least 10.0% | 41,338 (17.6%) | 118,895 (12.5%) | 0.1438 | 37,856 (17.5%) | 37,817 (17.5%) | 0.0005 |
| At most 11.0% | 150,745 (64.2%) | 432,191 (45.4%) | 0.3857 | 133,601 (61.8%) | 129,609 (60.0%) | 0.0378 |
| At most 10.5% | 148,443 (63.2%) | 422,823 (44.4%) | 0.3851 | 131,369 (60.8%) | 127,446 (59.0%) | 0.037 |
| At most 10.0% | 145,620 (62.0%) | 411,384 (43.2%) | 0.3845 | 128,625 (59.5%) | 124,702 (57.7%) | 0.0368 |
| At most 9.5% | 142,545 (60.7%) | 399,096 (41.9%) | 0.3836 | 125,658 (58.1%) | 121,680 (56.3%) | 0.0372 |
| At most 9.0% | 138,532 (59.0%) | 383,069 (40.2%) | 0.3829 | 121,816 (56.4%) | 117,808 (54.5%) | 0.0373 |
| At most 8.5% | 133,853 (57.0%) | 363,584 (38.2%) | 0.3845 | 117,348 (54.3%) | 113,434 (52.5%) | 0.0363 |
| At most 8.0% | 129,033 (55.0%) | 343,442 (36.1%) | 0.3869 | 112,735 (52.2%) | 108,934 (50.4%) | 0.0352 |
| Medications | ||||||
| Opiod analgesics | 108,932 (46.4%) | 381,176 (40.0%) | 0.1293 | 98,570 (45.6%) | 99,173 (45.9%) | 0.0056 |
| Proton pump inhibitors | 71,525 (30.5%) | 267,157 (28.1%) | 0.0533 | 64,820 (30.0%) | 65,383 (30.2%) | 0.0057 |
| Dicyclomine | 4813 (2.1%) | 9964 (1.0%) | 0.0814 | 3952 (1.8%) | 3864 (1.8%) | 0.0031 |
| Loperamide | 3068 (1.3%) | 11,347 (1.2%) | 0.0104 | 2749 (1.3%) | 2600 (1.2%) | 0.0062 |
| Diphenoxylate | 1123 (0.5%) | 2909 (0.3%) | 0.0277 | 945 (0.4%) | 940 (0.4%) | 0.0004 |
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Sun, Y.; Veccia, D.; Liu, B.D.X.; Tse, W.; Fass, R.; Song, G. Diagnostic Evaluation of an Increased Risk of Developing Small Intestinal Bacterial Overgrowth Associated with Glucagon-like Peptide-1 (GLP-1) Receptor Agonists and Dual GLP-1/GIP Receptor Agonists: A Global Retrospective Multicenter Cohort Analysis. Diagnostics 2025, 15, 2264. https://doi.org/10.3390/diagnostics15172264
AMA Style
Sun Y, Veccia D, Liu BDX, Tse W, Fass R, Song G. Diagnostic Evaluation of an Increased Risk of Developing Small Intestinal Bacterial Overgrowth Associated with Glucagon-like Peptide-1 (GLP-1) Receptor Agonists and Dual GLP-1/GIP Receptor Agonists: A Global Retrospective Multicenter Cohort Analysis. Diagnostics. 2025; 15(17):2264. https://doi.org/10.3390/diagnostics15172264
Chicago/Turabian StyleSun, Yan, Donovan Veccia, Benjamin Douglas Xun Liu, William Tse, Ronnie Fass, and Gengqing Song. 2025. "Diagnostic Evaluation of an Increased Risk of Developing Small Intestinal Bacterial Overgrowth Associated with Glucagon-like Peptide-1 (GLP-1) Receptor Agonists and Dual GLP-1/GIP Receptor Agonists: A Global Retrospective Multicenter Cohort Analysis" Diagnostics 15, no. 17: 2264. https://doi.org/10.3390/diagnostics15172264
APA StyleSun, Y., Veccia, D., Liu, B. D. X., Tse, W., Fass, R., & Song, G. (2025). Diagnostic Evaluation of an Increased Risk of Developing Small Intestinal Bacterial Overgrowth Associated with Glucagon-like Peptide-1 (GLP-1) Receptor Agonists and Dual GLP-1/GIP Receptor Agonists: A Global Retrospective Multicenter Cohort Analysis. Diagnostics, 15(17), 2264. https://doi.org/10.3390/diagnostics15172264
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