1. Introduction

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has revolutionized treatment paradigms for both type 2 diabetes mellitus (T2DM) and obesity. Marketed under distinct brand names—Ozempic® and Rybelsus® for diabetes, Wegovy® for weight management—the drug shares identical active moiety but differs in approved indications, dosing regimens, and target patient populations. Gastrointestinal adverse events, particularly nausea, vomiting, and diarrhoea, represent the most commonly reported side effects of GLP-1 receptor agonists. However, whether these risks vary systematically by treatment indication remains an open question with important clinical implications. Obesity-indicated patients may differ from T2DM patients in baseline metabolic profiles, concomitant medication use, and physiological responses to rapid weight loss—all factors potentially modifying adverse event susceptibility. Traditional pharmacovigilance approaches often examine aggregate safety signals without considering effect modification by clinical covariates. This study employs a data-driven subgroup discovery framework that autonomously explores multiple demographic and clinical variables, mathematically identifies the most divergent risk profiles, and prioritizes findings based on rigorous statistical criteria rather than preconceived hypotheses. Using the FDA Adverse Event Reporting System (FAERS) accessed via the openFDA API, we systematically investigated whether semaglutide-associated serious adverse events (SAEs) demonstrate differential reporting patterns across patient subgroups defined by sex, age, and treatment indication.

2. Methods

2.1 Data Source

We queried the openFDA Drug Adverse Events API (https://api.fda.gov/drug/event.json) to retrieve all FAERS reports listing semaglutide-containing products as suspect medications. Searches were conducted separately for each brand name (Ozempic, Wegovy, Rybelsus) using the patient.drug.medicinalproduct field to maximize capture of relevant reports. Only serious reports (defined by FDA criteria: death, life-threatening event, hospitalization, disability, or congenital anomaly) were included.

2.2 Data Extraction and Processing

For each report, we extracted:

• Demographics: Patient sex (coded 1=Male, 2=Female), age at event onset (with unit conversion to years)
• Drug information: Medicinal product name, drug indication (from drugindication field)
• Adverse events: Preferred terms from MedDRA dictionary (reactionmeddrapt)
• Seriousness criteria: Binary flags for each seriousness attribute Indications were algorithmically classified into mutually exclusive categories:
• Obesity: Reports containing keywords "obesity", "weight", "overweight", "body mass", "bmi", "diet", "slim", or "fat" WITHOUT concurrent diabetes keywords
• T2DM: Reports containing "diabetes", "type 2", "t2dm", "glycaemic", "glycemic", "hba1c", "blood glucose", or "hyperglycaemia"
• T2DM+Obesity: Reports containing both keyword sets
• Other: All remaining specified indications
• Unknown: Reports with no documented indication

2.3 Data Quality Assessment

Prior to subgroup analysis, we calculated missing data rates for each candidate covariate:

• Age: Percentage of records with null or unparseable age values
• Sex: Percentage coded as "Unknown" (code 0 or missing)
• Indication: Percentage classified as "Unknown" Per protocol, covariates exceeding 40% missingness were excluded from primary analyses to ensure statistical reliability.

2.4 Data-Driven Subgroup Discovery Algorithm

Our discovery pipeline operated as follows:

1. Top SAE Selection: Identify the 8 most frequently reported adverse events across the entire dataset.

2. Stratified Proportion Calculation: For each SAE × covariate combination, calculate the proportion of reports containing the SAE within each covariate stratum (e.g., Female vs. Male for sex; Obesity vs. T2DM vs. Other for indication).

3. Interaction Screening: For covariate-stratum pairs with ≥20 observations, perform two-proportion Z-tests:

   $$Z = \frac{p_1 - p_2}{\sqrt{p(1-p)\left(\frac{1}{n_1} + \frac{1}{n_2}\right)}}$$

   where $p_1, p_2$ are stratum-specific proportions, $n_1, n_2$ are stratum sample sizes, and $p$ is the pooled proportion.

4. Ranking Metric: Compute a composite score combining statistical significance and effect size:

   $$\text{Score} = -\log_{10}(p_{\text{value}}) \times |p_1 - p_2|$$

5. Selection: The SAE × covariate pair with the highest score advances to deep statistical analysis.

2.5 Deep Statistical Analysis

For the top-ranked finding, we constructed 2×2 contingency tables comparing the target stratum versus all other strata combined. Metrics computed included:

• Reporting Odds Ratio (ROR): $(a \times d) / (b \times c)$ with 95% confidence intervals via log-transformation
• Proportional Reporting Ratio (PRR): $[a/(a+b)] / [c/(c+d)]$
• Chi-square test: Yates-corrected $\chi^2$ statistic for independence
• Bayesian Information Component (IC): $\log_2[(a + 0.5) / (E + 0.5)]$ where $E$ is the expected count under independence, with lower 95% credibility bound ($IC_{025}$) All analyses were performed using Python 3.x with pandas, numpy, and scipy libraries.

3. Results

3.1 Dataset Characteristics

A total of 300 raw FAERS reports were retrieved (100 per brand), yielding 305 records after extraction and deduplication (some reports listed multiple brands). Distribution by brand:

• Ozempic: 104 records (34.1%)
• Wegovy: 100 records (32.8%)
• Rybelsus: 101 records (33.1%)

3.2 Data Quality Assessment

3.3 Demographic and Clinical Characteristics by Brand

3.4 Top Serious Adverse Events

The 15 most frequently reported SAEs across all brands:

3.5 Data-Driven Subgroup Discovery Results

Systematic screening of 8 top SAEs across 2 valid covariates yielded 16 SAE × covariate tests. Results ranked by composite score (−log₁₀(p) × effect size):

3.6 Discovery Selection Matrix

Rationale for prioritizing the Vomiting × Indication finding:

3.7 Deep Statistical Analysis: Vomiting by Indication

2×2 Contingency Table

Disproportionality Metrics

4. Discussion

4.1 Principal Findings

This data-driven pharmacovigilance analysis of semaglutide-associated SAEs in FAERS identified treatment indication as a critical effect modifier for vomiting risk. Patients prescribed semaglutide for obesity/weight management demonstrated substantially elevated vomiting reporting (33.3%) compared to those treated for T2DM or other indications (7.2%), corresponding to an absolute risk increase of 26.1 percentage points and a number-needed-to-harm of approximately 4. Secondary analyses revealed modest sex-based disparities, with females showing higher reporting rates of nausea (29.2% vs. 13.9%) and off-label use (9.9% vs. 2.8%) compared to males. These findings align with broader pharmacovigilance literature documenting sex differences in adverse drug reaction reporting.

4.2 Biological Plausibility

Several mechanisms may explain the striking indication-specific vomiting disparity:

1. Dose Escalation Dynamics: Wegovy (obesity indication) follows a more aggressive titration schedule reaching 2.4 mg weekly versus Ozempic's maximum 2.4 mg (typically 1.0-2.0 mg for T2DM). Higher maintenance doses correlate with increased GI adverse events in clinical trials.
2. Baseline Metabolic Differences: Obesity-indicated patients may exhibit altered gastric emptying, visceral adiposity-related mechanical factors, or gut microbiome compositions that potentiate GLP-1-mediated nausea/vomiting pathways.
3. Rapid Weight Loss Effects: Substantial weight reduction itself can induce transient GI disturbances, including ketosis-related nausea and altered bile acid metabolism affecting gastric motility.
4. Concomitant Medications: T2DM patients frequently take metformin, SGLT2 inhibitors, or insulin—agents that may mask or modify GI symptom perception compared to obesity patients taking fewer concurrent medications.
5. Reporting Bias: Obesity patients (predominantly female, younger) may demonstrate different healthcare-seeking behaviors or symptom reporting thresholds compared to older T2DM populations.

4.3 Comparison with Existing Literature

Our findings complement randomized controlled trial (RCT) data from the STEP (Semaglutide Treatment Effect in People with obesity) and SUSTAIN (Semaglutide Unabated Sustainability in Treatment of Type 2 Diabetes) programs. STEP trials reported vomiting in 24-31% of Wegovy recipients versus 7-11% in SUSTAIN trials for Ozempic—a magnitude of difference consistent with our real-world observations. However, RCTs typically employ strict inclusion criteria, structured follow-up, and standardized adverse event ascertainment. Our study extends these findings to heterogeneous real-world populations, capturing broader demographic diversity and clinical complexity absent from pivotal trials.

4.4 Strengths and Limitations

Strengths

• Unsupervised methodology: Avoids confirmation bias by letting data dictate which subgroup disparities warrant attention
• Rigorous statistical framework: Combines frequentist (Z-test, chi-square) and Bayesian (IC) approaches for robust signal validation
• Transparent data quality filters: Explicit handling of missing data prevents spurious conclusions from incomplete records
• Clinical actionability: Findings directly inform risk stratification and patient counseling strategies

Limitations

• Sample size constraints: Obesity subgroup (n=27) limits precision of effect estimates; wider confidence intervals reflect this uncertainty
• Spontaneous reporting biases: FAERS data subject to underreporting, stimulated reporting, and media effects that distort true incidence rates
• Missing age data: 42% age missingness precluded age-stratified analyses despite clinical relevance
• Indication misclassification: Keyword-based classification may miscategorize reports with ambiguous or non-standard terminology
• Cannot establish causality: Disproportionality analyses identify statistical associations, not causal relationships
• Temporal trends unaccounted: Analysis pools reports across time periods without adjusting for secular changes in prescribing patterns or awareness

4.5 Clinical Implications

Based on these findings, we propose the following clinical recommendations:

1. Enhanced Counseling for Obesity Patients: Prior to initiating semaglutide for weight management, explicitly discuss the substantially elevated vomiting risk (~33% in our cohort) and provide concrete management strategies (small frequent meals, antiemetic availability, dose hold criteria).
2. Gradual Dose Titration: Consider extending titration intervals beyond standard protocols for patients with prior GI sensitivity or low BMI thresholds.
3. Early Follow-up: Schedule check-ins within 2-4 weeks of initiation and after each dose escalation to assess tolerability and reinforce adherence strategies.
4. Shared Decision-Making: Present indication-specific risk profiles during treatment selection discussions, allowing patients to weigh benefits against personalized adverse event probabilities.
5. Pharmacovigilance Enhancement: Encourage comprehensive adverse event reporting for all semaglutide prescriptions, with particular attention to indication, dose, and temporal relationship to symptom onset.

5. Conclusion

This data-driven subgroup discovery analysis of FAERS reports reveals that treatment indication profoundly modifies vomiting risk in semaglutide-associated serious adverse events. Obesity-indicated patients experience approximately 4.6-fold higher vomiting reporting rates compared to T2DM/other patients—an effect that is statistically robust (p = 5.35×10⁻⁵), clinically substantial (26.1% absolute difference), and biologically plausible. These real-world evidence findings underscore the importance of moving beyond aggregate safety summaries toward nuanced, covariate-stratified risk characterization. Clinicians prescribing semaglutide for weight management should anticipate and proactively manage heightened GI toxicity, employing patient education, gradual titration, and close monitoring to optimize treatment persistence and outcomes. Future research directions include validating these signals in larger observational cohorts with complete covariate data, investigating pharmacogenomic modifiers of GI susceptibility, and developing predictive models to identify high-risk patients before treatment initiation.

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