The post-marketing safety of venlafaxine: a real-world two-decade pharmacovigilance study using the FAERS database

Objective: Venlafaxine, a serotonin-norepinephrine reuptake inhibitor widely prescribed for major depressive disorder and related conditions, remains insufficiently characterized regarding its adverse event profile. This study aimed to comprehensively evaluate the post-marketing safety of venlafaxine using data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods: The data were extracted from the FAERS database from the first quarter (Q1) 2004 to Q2 2025. Adverse drug events were analyzed using the Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), Proportional Reporting Ratio (PRR), and Multi-Item Gamma Poisson Shrinker (MGPS) methods.

Results: A total of 47,325 venlafaxine-related adverse event reports were analyzed. Disproportionality analysis revealed significant signals for drug ineffectiveness (n = 2,108, ROR = 1.6, PRR = 1.6, χ² = 483.4, IC = 0.7, EBGM = 1.6), drug hypersensitivity (n = 1,014; ROR = 4.9, PRR = 4.8, χ² = 2998.7, IC = 2.2, EBGM = 4.7), and anxiety (n = 198, ROR = 4.3, PRR = 4.2, χ² = 2202.0, IC = 2.0, EBGM = 4.1). Strongest signals (EBGM = 9.9) were observed for suicide attempt, agitated depression, and renal dysplasia. Tachycardia (EBGM = 8.3) was the most frequently reported adverse event among patients under 18 years, while emotional disorder (EBGM = 9.5) predominated in those aged 65 years and older. Most adverse events (39.3%) occurred within the first 30 days of venlafaxine therapy initiation.

Conclusion: This pharmacovigilance analysis systematically identified significant safety signals associated with venlafaxine. The findings provide important evidence to support safer clinical use of venlafaxine and may assist in optimizing individualized therapeutic decisions in practice.

1 Introduction

Major depressive disorder (MDD) is a highly prevalent and often debilitating mental disorder associated with low mood, anhedonia, alterations in behavior, and emotional processing (American Psychiatric Association, 2022; Cao et al., 2019; Kaiser et al., 2015), and major impairments in social and occupational functioning (Fervaha et al., 2016; Stewart et al., 2003). An estimated 4% of the population encounters depression, including 5.7% of adults (4.6% among men and 6.9% among women), and 5.9% of adults aged 70 years and older. Approximately 332 million people in the world have depression (Global Burden of Disease GBD, 2021, 2024). Depression is about 1.5 times more common among women than among men. Worldwide, more than 10% of pregnant women and women who have just given birth experience depression (Woody et al., 2017). In 2021, an estimated 727,000 people lost their lives to suicide. Suicide is the third leading cause of death among 15–29-year-olds. According to a cross-national study, more than one in five people will experience at least one depression episode at some point in their lifetime (McGrath et al., 2020). In high-income countries, only about one-third of people with depression receive mental health treatment (Evans-Lacko et al., 2018).

Medication remains a central pillar in the management of MDD. Among the available antidepressants, venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), represents one of the most extensively studied and prescribed drugs. In the United States alone, venlafaxine ranked 51st among all prescribed medications in 2023, with over 13.2 million prescriptions dispensed to nearly 3 million patients. This significant prescribing volume underscores the clinical importance of understanding its safety profile. Venlafaxine has received regulatory approval for the treatment of MDD in the United States, the United Kingdom, and across member states of the European Union (USFDA, 2009; European Medicines Age ncy, 2008; National Health Service, 2018). While typically recommended as a second-line intervention, venlafaxine is widely utilized, particularly among patients who exhibit inadequate response to first-line antidepressants (Singh and Saadabadi, 2024; Haddad et al., 2015; Kaplan, 2002). Its dose-dependent noradrenergic effects improve outcomes in treatment-resistant depression with faster onset and higher remission rates than SSRIs in nonresponders.

As an SNRI, venlafaxine works as a bicyclic drug, and it inhibits the reuptake of serotonin and norepinephrine, influencing central monoaminergic transmission (Figure 1) (Holliday and Benfield, 1995; Kent, 2000). However, despite decades of clinical use, no definitive dose escalation trials have reliably described an optimal dose response relationship that balances efficacy and tolerability (Kent, 2000; Hieronymus, 2019). This lack of rigorous dose-response characterization underscores the importance of large-scale real-world surveillance data, such as the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS), to capture the spectrum of adverse events across diverse conditions.

Figure 1

Figure 1. Mode of action of venlafaxine.

Venlafaxine is associated with several well-documented safety concerns outlined in its prescribing information. The U.S product labeling carries a boxed warning noting that antidepressants increase the risk of suicidal thoughts and behavior in children, adolescents, and young adults (U.S. Food and Drug Administration, 2017). This is supported by a meta-analysis demonstrating an increased risk of suicidality in patients younger than 25 years treated with antidepressants (Stone et al., 2009; Hammad et al., 2006). Additional concerns in the labeling highlight risks of persistent hypertension, serotonin syndrome, and withdrawal symptoms upon discontinuation (Baldwin et al., 2007; Sternbach, 1991). Beyond these, case reports of overdose or intoxication have described severe complications, including persistent glucose-resistant hyperglycemia (Bekka and Eyer, 2022; Kobylianskii and Wu, 2021; Özdemir, 2021), acute cardiovascular toxicities such as myocardial injury and arrhythmias (Vinetti et al., 2011; Howell et al., 2007), and neurologic effects, including seizures and impaired consciousness (Khalifa et al., 1999).

Despite these known risks, most safety evidence for venlafaxine is derived from randomized controlled trials, which typically have limited durations, exclude many real-world populations, and many underdetect rare or delayed AEs. Moreover, real-world data regarding their incidence, severity, and temporal distribution remain limited. To bridge this gap, the present study utilizes the FAERS database, an extensive post-marketing surveillance resource that aggregates AE reports from diverse sources, including healthcare providers, patients, and pharmaceutical manufacturers, and regulatory stakeholders. Detailed parameters such as patient demographic, drug administration characteristics, timing, and nature of reported AEs, causality assessments, and clinical outcomes were systematically extracted from the analysis. To identify potential safety concerns, disproportionality analysis was employed to statistically evaluate the association between venlafaxine and specific AEs, comparing each observed drug-event pair to all others within the database. Heightened disproportionality metrics indicate that specific venlafaxine-AE combinations are occurring more frequently than expected by chance, suggesting a potential safety signal for further investigation. Through this approach, the current study aims to clarify the real-world safety profile of venlafaxine, detect novel risk signals, and provide actionable evidence to support clinicians and pharmacists in optimizing therapeutic decisions and ensuring patient safety.

2 Materials and methods

2.1 Data source and study design

This retrospective pharmacovigilance study analyzed adverse event reports from the FAERS database. FAERS is a publicly accessible, spontaneous reporting database collecting post-marketing safety data submitted by healthcare professionals, consumers, and pharmaceutical manufacturers. Data spanning from the first quarter of 2004 to the second quarter of 2025 were downloaded in ASCII format from the FDA website. The investigation focused on venlafaxine, and cases were identified using the generic drug name “Venlafaxine,” “Venlafaxine Hcl,” “Venlafaxine Hydrochloride,” and the brand names “Effexor,” “Effexor xr,” “Venbysi xr”. All adverse events were codified according to the Medical Dictionary for Regulatory Activities (MedDRA) Version 28.1, which provides a standardized hierarchical classification of adverse events from detailed Preferred Terms (PTs) to aggregate System Organ Classes (SOCs).

2.2 Data preprocessing and quality control

Data cleaning was performed using SAS software version 9.4 to ensure the reliability of analyses. The FAERS database often contains duplicate case reports and inconsistent or missing data due to its voluntary reporting nature. Deduplication was conducted following FDA guidelines by sorting reports based on unique identifiers, including CASEID, PRIMARYID, and FDA_DT (report date). When multiple reports shared the same CASEID, only the latest report (determined by the highest FDA_DT) was retained. In cases where the CASEID and FDA_DT were identical, the submission with the highest PRIMARYID was kept, preserving the most current information. After deduplication, the cleaned demographic (DEMO), drug (DRUG), and adverse reaction (REAC) datasets were merged. Analysis was restricted to cases where venlafaxine was declared the primary suspect drug to reduce confounding effects. Time-to-onset calculations were performed by subtracting the start date of venlafaxine therapy from the adverse event occurrence date. Inconsistent cases, such as those with AE dates preceding drug start or missing dates, were excluded from analysis. The data processing workflow is shown in Figure 2.

Figure 2

Figure 2. The flow diagram of selecting and analyzing venlafaxine-related ADEs from FAERS database.

2.3 Disproportionality signal detection methods

To detect potential associations between venlafaxine and reported adverse events, disproportionality analysis, a cornerstone method in pharmacovigilance, was employed using four complementary algorithms, such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS). The two-by-two table for understanding these methods is provided in Supplementary Table S1. These methods evaluate whether a drug-event pair occurs more frequently than expected compared to the full FAERS database, indicating potential safety signals. Frequentist methods (ROR and PRR) provide sensitivity in detecting commonly reported events, while Bayesian approaches (BCPNN and MGPS) better adjust for low counts and reduce false positives. The combination of these diverse methods enhances the robustness and reliability of signal detection.

2.4 Subgroup analysis

To elucidate variations in AE patterns across patient populations, subgroup analyses were conducted according to key demographic and clinical variables. Patients were categorized by age (<18 years, 18–60 years, and >60 years), sex (male and female), and geographic region to assess differences in AE distributions among these subgroups. This stratified analysis facilitated the identification of demographic trends and potential vulnerability profiles associated with Venlafaxine exposure. In addition, a time-to-onset analysis was performed to characterize the temporal relationship between the initiation of venlafaxine therapy and the manifestation of AEs, providing insights into latency periods and the progression of drug-related safety signals.

2.5 Signal criteria and statistical analysis

Signals were defined according to established international thresholds as described in Supplementary Table S2. For ROR and PRR, signals required a minimum of three case reports, with the lower bound of the 95% confidence interval exceeding 1. PRR further required a minimum ratio value of 2 to consider a signal noteworthy. The BCPNN method flagged signals when the lower limit of the Information Component’s 95% credibility interval (IC025) surpassed zero, consistent with World Health Organization guidelines. The MGPS criterion required the lower bound of the Empirical Bayes Geometric Mean’s 95% confidence interval (EBGM05) to exceed 2, providing a conservative measure for rare events. These criteria were applied using SAS and SQL to process and analyze large-scale datasets efficiently. This multipronged approach allowed for balanced sensitivity and specificity in identifying venlafaxine-related adverse event signals.

Because disproportionality analyses evaluate thousands of drug–event combinations, multiplicity is an inherent concern. Classical statistical corrections are not routinely applied in pharmacovigilance signal detection; instead, shrinkage-based methods (MGPS, BCPNN) and minimum case-count thresholds help reduce random fluctuation. Despite these built-in protections, residual false-positive signals may still occur and should be interpreted cautiously.

3 Results

3.1 Basic information on AERs

A comprehensive analysis of venlafaxine-related adverse event reports (AERs) identified 47,325 cases in the study period, which is shown in Figure 2. Demographic analysis revealed a significant gender disparity, with female patients accounting for 63.8% of reports compared to 25.0% in males. The age distribution demonstrated the following pattern: <18 years (1.3%), 18–44 years (26.8%), 45–64 years (24.0%), >64 years (12.1%), with 35.8% of reports containing unspecified age data. The mean age of reported cases was 47.4 years. Temporal analysis indicated 2013 as the peak reporting year (11.2% of total reports). Primary reporters consisted predominantly of consumers (53.1%), followed by physicians (26.3%) and other healthcare professionals (12.3%). Geographically, the United States contributed the majority of reports (57.8%), followed by the United Kingdom (15.2%). Clinical outcomes analysis revealed hospitalization (initial or prolonged) as the most frequent serious outcome (52.7%), with deaths reported in 16.7% of cases. Temporal pattern analysis showed 39.3% of adverse events occurred within 30 days of treatment initiation, though the mean time to event onset was 493.9 days. The mean patient weight across reported cases was 79.4 kg, and all these demographic results are shown in Table 1.

Table 1

Table 1. Demographic and clinical characteristics of adverse event reports for venlafaxine in the FAERS database (2004–2025).

3.2 Onset time of adverse events

The onset time of adverse events related to venlafaxine was evaluated to understand the temporal distribution of reported reactions following drug administration (Table 1; Figure 3).

Figure 3

Figure 3. Onset time distribution of venlafaxine-related adverse events (AEs).

The mean onset time was 493.9 days (SD = 1153.2), while the median onset was 72.5 days (interquartile range: 10–277 days). The shortest observed onset was 1 day, and the longest was 10,743 days, indicating considerable variability in the timing of adverse event occurrence.

The distribution of onset times, as visualized in Figure 3, revealed a bimodal pattern. The majority of adverse events (39.3%) occurred within the first 30 days after venlafaxine initiation, as seen by the prominent initial bar in the graph. Event frequency then declined with increasing duration. The 8.0% of events were reported between 31 and 60 days, 3.8% between 61 and 90 days, and approximately 2.8% each for both 91–120 days and 121–150 days, with a low of 1.8% for 151–180 days. Notably, a substantial proportion of events also occurred at later intervals, such as 20.3% between 181 and 360 days and 21.2% beyond 360 days of drug exposure.

3.3 Distribution of the AERs at the preferred terms (PT) level

As presented in Table 2, the top 30 Preferred Terms (PTs) ranked by signal frequency revealed drug ineffective (n = 2,108; ROR = 1.6, PRR = 1.6, IC = 0.7, EBGM = 1.6) as the most frequently reported event. The subsequent highest-frequency signals included drug hypersensitivity (n = 1,014; ROR = 4.9, PRR = 4.8, IC = 2.2, EBGM = 4.7), anxiety (n = 918; ROR = 4.3, PRR = 4.2, IC = 2.0, EBGM = 4.1), dizziness (n = 824; ROR = 2.6, PRR = 2.5, IC = 1.3, EBGM = 2.5), and completed suicide (n = 786; ROR = 11.0, PRR = 10.7, IC = 3.4, EBGM = 10.4). Except for drug ineffectiveness, all high-frequency PTs correspond with known adverse reactions documented in venlafaxine’s prescribing information.

Table 2

Table 2. The top 30 PTs ranked by the frequency of positive signals.

As detailed in Table 3, highest signal intensity was identified for suicide attempt (n = 262; ROR = 10.3, PRR = 10.2, IC = 3.3, EBGM = 9.9), agitated depression (n = 7; ROR = 30.7, PRR = 32.6, IC = 4.8, EBGM = 9.9), renal dysplasia (n = 6; ROR = 43.4, PRR = 46.5, IC = 5.2, EBGM = 9.9), bipolar I disorder (n = 18; ROR = 13.7, PRR = 14.0, IC = 3.7, EBGM = 9.8), and parotid gland enlargement (n = 7; ROR = 29.2, PRR = 31.1, IC = 4.7, EBGM = 9.8). Despite lower case counts, these PTs demonstrated substantially higher signal intensities, suggesting potential novel adverse reaction associations warranting further clinical investigation.

Table 3

Table 3. Top 30 PTs ranked by the intensity of positive signals.

3.4 Sex-based subgroup analysis

A sex-based subgroup analysis was conducted to explore differences in adverse event reporting associated with venlafaxine. PTs meeting all four statistical detection criteria were identified separately for males and females, as summarized in Table 4.

Table 4

Table 4. Signal strength of reports of venlafaxine at the preferred term level in the FAERS database grouped by sex.

Among female patients, PTs with more than 50 reported cases included nightmare, disturbance in attention, suicide attempt, intentional overdose, and foetal exposure during pregnancy, agitation, anger, suicidal ideation, irritability, depressed mood, coma, intentional self-injury, drug abuse, drug interaction, and electrocardiogram QT prolongation, impaired activities of daily living, feelings of worthlessness, feeling guilty, nervousness, and cardiac arrest. The highest EBGM values were observed for feelings of worthlessness (EBGM = 65.8) and feeling guilty (EBGM = 61.9), indicating strong signal strength within the female subgroup (Table 4).

Among male patients, PTs with more than 50 reported cases included foetal exposure during pregnancy, completed suicide, suicide attempt, erectile dysfunction, disturbance in attention, suicidal ideation, depressed mood, intentional overdose, irritability, anger, agitation, coma, toxicity to various agents, drug withdrawal syndrome, maternal exposure during pregnancy, drug interaction, anxiety, drug abuse, drug hypersensitivity, and aggression. The highest EBGM values were identified for maternal exposure during pregnancy (EBGM = 30.9) and foetal exposure during pregnancy (EBGM = 8.9), showing significant signal intensity among male patients (Table 4).

3.5 Age-based subgroup analysis in different PT groups

An age-stratified subgroup analysis was conducted to identify variations in venlafaxine-associated adverse event reporting across different age categories (<18, 18–44, 45–64, and >64 years). PTs demonstrating statistical significance across all four signal detection methods within each age group are summarized in Table 5.

Table 5

Table 5. Signal strength of venlafaxine at the preferred term class level in the FAERS database grouped by age.

In patients under 18 years of age, significant safety signals were detected for tachycardia (EBGM = 8.3, EBGM05 = 5.3) and somnolence (EBGM = 7.9, EBGM05 = 5.5). Both events demonstrated high ROR and PRR values with narrow confidence intervals, suggesting robust associations in this age group.

Among patients aged 18–44 years, a strong cluster of psychiatric and metabolic adverse events was observed, including feeling guilty, drug withdrawal syndrome, restless legs syndrome, depersonalization/derealisation disorder, hypoglycemia, psychiatric symptoms, decreased interest, galactorrhoea, apathy, and gestational diabetes. The most prominent signals in this group were feeling guilty (EBGM = 9.7, EBGM05 = 6.2) and drug withdrawal syndrome (EBGM = 9.5, EBGM05 = 8.1), with high χ² values (>800), indicating strong drug-event associations.

For the 45–64-year age group, notable events included shock hemorrhagic, bruxism, dependence, colitis microscopic, mydriasis, generalized tonic-clonic seizure, sudden death, and thinking abnormal. The highest signal intensity was found for shock hemorrhagic (EBGM = 9.9, EBGM05 = 6.3), followed closely by bruxism (EBGM = 9.4, EBGM05 = 6.0), both showing high ROR (15) and IC values (>3.8), denoting strong statistical significance.

In patients over 64 years of age, prominent PTs included emotional disorder, anger, status epilepticus, suicidal ideation, and apathy. The strongest associations were identified for emotional disorder (EBGM = 9.5, EBGM05 = 6.0), anger (EBGM = 9.4, EBGM05 = 6.4), and status epilepticus (EBGM = 9.3, EBGM05 = 5.7). All of these events presented elevated ROR and PRR (>12) and IC values greater than 3.5, reflecting consistent signal patterns in elderly patients.

3.6 Demographic characteristics of death and non-death cases

Table 6 presents the demographic characteristics of venlafaxine-related adverse event reports, comparing cases resulting in death with those that did not. A total of 2,660 death cases and 44,552 non-death cases were identified. Among death cases, female patients accounted for 1,569 (3.32%), representing a larger proportion compared to males (1,076, 2.28%), with the difference being statistically significant (P < 0.001).

Table 6

Table 6. Characteristics between death outcomes and non-death outcomes.

With respect to age distribution, death cases were predominantly reported in the 18–44 years (1,121 cases, 2.13%) and 45–64 years (885 cases, 1.68%) age groups. The >64 years group accounted for 414 cases (0.79%), while patients under 18 years represented only 25 cases (0.05%). A significant difference in age group distribution between death and non-death cases was observed (P = 0.0028). Overall, these results indicate that fatalities associated with venlafaxine were more frequently observed among adults aged 18–64 years, with a higher proportion occurring in female patients.

3.7 Functional enrichment analyses of venlafaxine-associated genes

To contextualize the detected safety signals biologically and pharmacologically, functional enrichment analyses were conducted using a curated set of venlafaxine-associated genes derived from established pharmacological and safety literature (SLC6A4, SLC6A2, HTR2A, HTR2C, MAOA, MAOB, TPH2, BDNF, FKBP5, CYP2D6, CYP2C19, and ABCB1). These genes encompassed primary therapeutic targets, downstream components of serotonergic signaling, ion channel–related safety mechanisms, and key enzymes involved in drug metabolism, thereby capturing both pharmacodynamic and pharmacokinetic determinants of venlafaxine response.

Disease Ontology enrichment demonstrated significant associations with mood disorder, depressive disorder, and anxiety disorders, providing clinical relevance to the strongest venlafaxine-related safety signals (Figure 4A). These disease-level annotations align with venlafaxine’s approved indications as a serotonin–norepinephrine reuptake inhibitor used in major depressive and anxiety disorders, supporting the interpretability of the FAERS-derived risk profile. Gene Ontology biological process analysis showed significant enrichment for monoamine transport, serotonin secretion, serotonin receptor signaling pathway, and dopamine response. These enriched processes are consistent with venlafaxine’s primary actions on monoaminergic neurotransmission and support the biological plausibility of neuropsychiatric adverse events identified in the FAERS analysis (Figure 4B).

Figure 4

Figure 4. Functional analysis of venlafaxine targeted genes. Functional enrichment analyses of venlafaxine-associated genes. (A) Disease ontology enrichment analysis. (B) Gene ontology biological process analysis. (C) KEGG pathway analysis. (D) Reactome pathway analysis.

KEGG pathway analysis indicated significant enrichment in serotonergic synapse signaling, neuroactive ligand–receptor interaction, synaptic vesicle cycle, and dopaminergic synapse pathways (Figure 4C). These findings suggest that venlafaxine-associated targets are functionally integrated within central nervous system neurotransmission and autonomic regulatory circuits, providing a mechanistic framework for both therapeutic effects and off-target safety signals. Reactome pathway analysis further revealed enrichment of neuronal system signaling pathways, serotonin receptor signaling, and cytochrome P450–mediated drug metabolism, highlighting convergent central pharmacodynamic effects and hepatic pharmacokinetic processes relevant to interindividual variability in adverse event susceptibility (Figure 4D). Collectively, these functional annotations support that the highest-intensity venlafaxine-associated adverse events, including suicide attempts and agitated depression, are underpinned by coherent pharmacological and biological mechanisms rather than isolated statistical artifacts.

4 Discussion

In this large-scale FAERS analysis of 47,325 venlafaxine-related adverse event reports from 2004 to 2025, several well-established safety concerns were confirmed. The strong signals for suicide attempts and completed suicide align with the FDA boxed warning on increased suicidal ideation in children, adolescents, and young adults (Coupland et al., 2015; Rynn et al., 2017; Kamp et al., 2024). While short-term RCTs have shown uncertain but potentially elevated risks of suicidality, our real-world data provide complementary evidence over a broader population and longer timeframe. Commonly documented adverse effects, including nausea, dizziness, somnolence, dry mouth, insomnia, and sexual dysfunction, were consistent with RCT and post-marketing literature (Parinda Parikh et al., 2025). Similarly, venlafaxine’s labeled risks for hypertension, serotonin syndrome, and withdrawal syndrome were supported, with signal detection emphasizing the need for monitoring, particularly in adults with comorbidities (Kıvrak et al., 2014; Calvi et al., 2021). These findings underscore the importance of confirming known safety issues in a large, heterogeneous population beyond the controlled trial environment.

Beyond known risks, our study revealed several less-characterized safety signals. “Drug ineffective” was the most frequently reported PT, emphasizing the clinical issue of treatment non-response, particularly in second-line or augmentation therapy. Although ‘drug ineffective’ was the most frequently reported PT, this term represents therapeutic non-response and does not constitute a biological adverse event. Its prominence underscores real-world treatment challenges but should not be interpreted alongside mechanistic safety signals. While RCTs assess efficacy, spontaneous reports rarely focus on non-response, suggesting that real-world pharmacovigilance can capture treatment gaps and guide early dose adjustments or switching strategies (Moncrieff et al., 2025). Additionally, rare but high-intensity signals such as renal dysplasia, agitated depression, and parotid gland enlargement may represent idiosyncratic reactions or off-target toxicities. While causality cannot be inferred, these findings suggest avenues for case-series reviews and mechanistic studies. Renal dysplasia is particularly notable given venlafaxine’s reduced clearance in renal impairment (Troy et al., 1994), warranting additional monitoring in this subgroup.

Age-stratified analyses revealed distinct safety patterns. In the <18-year cohort, tachycardia and somnolence were prominent, reflecting cardiovascular and central nervous system sensitivity in younger patients. Previous pediatric studies (45 patients) documented new agitation and suicidal ideation in approximately 6%–7% of cases, supporting our findings (Rynn et al., 2007; Simas et al., 2024). Among older adults (>64 years), emotional disorder, anger, and status epilepticus were notable, highlighting neurologic and psychiatric vulnerability in this population, consistent with prior reports of reduced tolerability versus other antidepressants in frail elderly patients (Oslin et al., 2003). These results emphasize the need for age-tailored monitoring strategies, particularly for neuropsychiatric and adverse cardiovascular events.

The bimodal time-to-onset distribution, with 39% of events occurring within 30 days and 21% beyond 360 days, indicates both early and late risk windows. Early events align with known tolerability issues such as nausea, somnolence, and anxiety. In contrast, late-onset events underscore the limitations of short-term RCTs (≤12 weeks) in capturing delayed adverse effects (Kamp et al., 2024). Hospitalization occurred in 52.7% of reports and deaths in 16.7%, highlighting the clinical significance of post-marketing events. Deaths were most frequent among adults aged 18–64 and slightly more common in females, emphasizing the need for careful risk–benefit assessment across demographic subgroups.

Female predominance in reports (63.8%) likely reflects both prescribing patterns and sex-specific susceptibility or reporting behavior. Strong signals for feelings of worthlessness and guilt in females suggest heightened vulnerability to mood-domain adverse events. Among males, high EBGM values for maternal and fetal exposure may reflect reporting nuances or misclassification but warrant further investigation. These sex-based differences highlight the need for individualized patient counseling and monitoring.

These enrichment results support a mechanistically grounded interpretation of the venlafaxine safety signals observed in FAERS, indicating that the most prominent adverse events arise within the same neurobiological and pharmacokinetic framework as the drug’s therapeutic actions. The convergence of Disease Ontology, Gene Ontology, KEGG, and Reactome findings highlighting mood and anxiety disorders, monoaminergic transport and signaling, serotonergic and dopaminergic synaptic pathways, autonomic regulation, and cytochrome P450–mediated metabolism suggests that signals such as suicide attempt and agitated depression are embedded in coherent biological networks rather than representing isolated statistical artifacts.

Taken together, this study extends current knowledge by confirming the consistency of known label warnings while identifying new or rarely described risks from two decades of real-world evidence. By integrating multiple disproportionality models across demographic subgroups, it contributes to a detailed understanding of venlafaxine’s safety landscape. Future research should prioritize mechanistic exploration of high-signal, low-frequency events and incorporate pharmacoepidemiological validation using linked electronic health record systems to overcome the limitations of spontaneous reporting data, such as underreporting and incomplete exposure detail.

Future validation studies could leverage large EHR platforms such as the FDA Sentinel Initiative, OMOP/OHDSI databases, MarketScan, Optum, or CPRD. These resources allow for verification of key safety signals such as suicidality, hypertensive crises, neonatal complications, and withdrawal phenomena using robust confounder adjustment methods, including active-comparator designs, propensity score weighting, and high-dimensional propensity scores.

Although congenital and neonatal signals (renal dysplasia, neonatal cerebral hemorrhage) were based on few reports, they represent clinically important findings that require follow-up in maternal–infant linked EHR datasets to differentiate signal from artifact.

In conclusion, these findings highlight the breadth of venlafaxine-associated safety signals in clinical practice, emphasizing both expected adverse effects and emerging risks across different age and sex groups. Long-term pharmacovigilance, coupled with personalized treatment strategies and proactive monitoring, remains essential to optimize the risk–benefit balance of venlafaxine therapy in major depressive disorder.

5 Limitations

The imitations include the underreporting of adverse events, inconsistencies in report accuracy, missing or incomplete information, various reporting biases, confounding by indication, and the inability to determine causality between drug exposure and the reported outcomes. Nevertheless, spontaneous reporting systems still offer meaningful real-world safety evidence, supplementing clinical trial data that are often constrained by small sample sizes, brief study durations, and limited representation of high-risk or diverse patient groups, including older adults, children, and individuals with multiple comorbid conditions. Because FAERS lacks consistent information on concomitant therapy, dosage, and time on treatment, the observed AE patterns cannot be interpreted in a dose-dependent or indication-specific manner. These missing exposure characteristics are inherent limitations of spontaneous reporting systems and restrict causal inference. Some implausible PTs, such as maternal or foetal exposure reported in males, likely arise from data-entry or linkage errors. These artifacts highlight the importance of cautious interpretation of subgroup signals.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author contributions

SC: Writing – original draft. HY: Writing – review and editing. AA-M: Writing – review and editing. YZ: Formal Analysis, Visualization, Writing – review and editing. BN: Conceptualization, Investigation, Writing – review and editing. BY: Conceptualization, Investigation, Supervision, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The authors extend their appreciation to the ongoing research funding program (ORF-2025-1219), King Saud University, Riyadh, Saudi Arabia.

Acknowledgements

We thank Rajeswari Chappa, Senior SAS Programmer, for her invaluable support during data analysis and manuscript preparation.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2026.1737113/full#supplementary-material

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