Drug-associated insomnia: A pharmacovigilance study based on FDA adverse event reporting system

Abstract

Insomnia has become an increasingly serious public health issue with complex causes, among which medications act as a significant factor. This study aims to systematically detect and evaluate drug adverse event signals associated with insomnia risk using the Food and Drug Administration Adverse Event Reporting System (FAERS) database. The analysis utilized data from the FAERS database covering January 2004 to December 2024. Disproportionality analysis was conducted using 4 algorithms: reporting odds ratio, proportional reporting ratio, information component (IC), and empirical Bayes geometric mean. Potential risk signals were deemed significant only when all 4 algorithms simultaneously met their thresholds. Subgroup analyses were further performed, stratified by age and sex, to assess the robustness of signals across different populations. From 2004 to 2024, there were 179,697 adverse event reports of insomnia in FAERS in which one or more medications were designated as the primary suspect. The top 30 medications with the strongest signal strength were predominantly nervous system medicines (18 types, 60%), followed by respiratory system medicines (3 types, 10%), and genitourinary system and sex hormones (3 types, 10%). The top 3 medications with the highest reporting frequency were mefloquine, viloxazine, and flibanserin. Subgroup analyses revealed distinct drug signal profiles across age groups and genders, with pediatric cases dominated by nervous system and anti-infective agents, adults and the elderly showing additional endocrine or hormonal signals, and sex specific signals such as finasteride in males and flibanserin in females. This pharmacovigilance study identifies insomnia risk signals across multiple drug classes, underscoring the need for clinical vigilance regarding drug-related sleep disturbances. Further prospective research is required to confirm these associations.

1. Introduction

Insomnia is a prevalent sleep disorder characterized by dissatisfaction with sleep quantity or quality. Its primary features include difficulty falling asleep, maintaining sleep, or waking up early despite having adequate opportunities for sleep.^[1] Insomnia is highly prevalent in clinical practice, affecting approximately 10 to 30% of adults in various populations and occurring in up to 50% of primary care patients, underscoring its significance as a major public health issue.^[2,3] The burden of insomnia extends beyond the immediate discomfort of poor sleep. It is associated with considerable health, social, and economic costs. Clinically, insomnia can lead to significant distress and impairment in various domains of functioning, including cognitive performance, emotional regulation, and daily activities.^[4,5] Individuals with insomnia often experience diminished attention, memory deficits, and reduced overall cognitive function, which can adversely affect work performance and daily responsibilities. From a health perspective, insomnia is linked to an increased risk of developing several chronic conditions. It has been associated with higher rates of mental health disorders, such as depression, anxiety, and substance abuse.^[6–9] Furthermore, insomnia can exacerbate existing health problems, including cardiovascular diseases, diabetes, and obesity.^[10–12] The disrupted sleep patterns characteristic of insomnia contribute to inflammatory responses, metabolic dysregulation, and hormonal imbalances, all of which can further complicate these conditions.^[13] The societal impact of insomnia is also profound. A research has found that the total costs of insomnia symptoms represents 1.9% of the overall burden of illness costs for 2021 in Canada.^[14] The disorder contributes to significant healthcare utilization, including frequent visits to healthcare providers and the use of medications and therapies. Indirect costs are considerable, encompassing lost productivity, absenteeism, and diminished quality of life. Hence, addressing insomnia is both urgent and essential due to its profound impact on individual health and its significant societal burden.

Drug-associated insomnia is a notable subset of insomnia where the disturbance in sleep is attributable to the use of medications. Sleep and waking function are closely connected in a 24-hour rhythm. Due to the pharmacological effects on the numerous receptors and neurotransmitters involved in sleep-wake regulation, many medications have the potential to disrupt sleep and wakefulness. Consequently, insomnia is a common side effect of both psychotropic and non-psychotropic drugs. Liguori et al conducted a review of previous studies and concluded that the use of antiepileptic drugs such as perampanel and lamotrigine is associated with an increased risk of insomnia.^[15] Serdarevic et al carried out a cross-sectional study in a community in Northeast Florida and found a significant association between the use of prescription opioid medications and insomnia.^[16] Research has suggested that stimulant tricyclic antidepressants, such as nortriptyline and protriptyline, may also exacerbate insomnia.^[17] Prior studies on drug-related insomnia are notably limited, often constrained by small sample sizes or issues with representativeness. Furthermore, there is a notable absence of comprehensive real-world studies that systematically investigate the pharmacovigilance signals related to insomnia. As one of the largest pharmacovigilance databases, the U.S. Food and Drug Administration's adverse event reporting system (FAERS) database has played a major role in the evaluation of drug safety. The objective of this study was to identify potential drug signals associated with insomnia from the FAERS database, aiming to generate hypotheses for clinical monitoring and epidemiological research.

2. Methods

2.1. Data collection

For this study, information from the adverse reaction reports was extracted, including demographic data, drug and therapy data, and adverse reaction data. and reporting sources were collected from the 2004 quarter 1 to 2024 quarter 4 in the publicly available FAERS database. Insomnia-related adverse events were defined using the following preferred terms (PTs) from the Medical Dictionary for Regulatory Activities (version 26.1): Insomnia (PT code: 10022437), Initial insomnia (PT code: 10022035), Middle insomnia (PT code: 10027590), Terminal insomnia (PT code: 10068932), Hyposomnia (PT code: 10067530), and Poor quality sleep (PT code: 10062519). There are 4 classifications to group each case according to the role of the medications administered in the adverse events: primary suspect drug (PS), secondary suspect drug (SS), concomitant (C), and interaction (I). We exclusively extracted and analyzed cases where the drug was designated as the PS. This study adhered to the FDA-recommended procedure for deduplication. Key fields, including PRIMARYID, CASEID, and FDA_DT, were extracted from the DEMO table. Subsequently, for all reports sharing the same CASEID, only the single report with the most recent FDA_DT was retained. In instances of identical FDA_DT, the report with the highest PRIMARYID was selected. To minimize confounding by indication, reports where the indication for drug use was any of the following conditions were excluded: insomnia, sleep disorders, sleep-wake disorders, malignant neoplasms, pain, rheumatoid arthritis, anxiety, psoriatic arthropathy, fibromyalgia, ankylosing spondylitis, neuralgia, arthritis, arthralgia, headache, hyperthyroidism, asthma, Crohn disease, ulcerative colitis, Rett syndrome, dermatitis, and urticaria. To enhance statistical stability and minimize false positives, only drugs with >20 reported cases were included in the following disproportionality analysis. Drug names were standardized to generic names via the RxNorm nomenclature system and then classified using the World Health Organization’s Anatomical Therapeutic Chemical Classification system (ATC). Since this study utilized de-identified, publicly available data from the FAERS database, the Ethics Committee of the Chongqing Mental Health Center has confirmed that no ethical approval was required.

2.2. Statistical analysis

Descriptive analysis was conducted to describe the clinical characteristics of insomnia cases, including gender, age, reporting country, and indications. The top 30 drugs related to insomnia were selected based on the number of reports. The 30 drugs were classified according to the ATC classification system.

Disproportionality analysis, which is widely used in pharmacovigilance studies to generate hypotheses on possible associations between drugs and AEs, was performed to identify potential signals in this study. In this study, the reporting odds ratio (ROR) was used as the primary analysis method. To improve the reliability of the results, disproportionate analyses were also performed using the proportional reporting ratio (PRR), information component (IC), and empirical Bayes geometric mean (EBGM) to detect the insomnia risk signal for each drug and conducted calculations using a 2-by-2 contingency table (Table S1, Supplemental Digital Content, https://links.lww.com/MD/R670). The equations and Criteria of the above 4 methods are detailed in Table S2, Supplemental Digital Content, https://links.lww.com/MD/R670. A positive signal was defined according to established criteria: ROR: a ≥ 3, ROR ≥ 3 and 95% CI lower > 1; PRR: a ≥ 3, PRR ≥ 2 and 95% CI lower > 1; IC: IC025 > 0; EBGM: EBGM05 > 2 (Table S2, Supplemental Digital Content, https://links.lww.com/MD/R670). In order to enhance robustness against the statistical instability inherent in individual methods, a safety signal was conservatively defined only when all 4 disproportionality metrics concurrently exceeded their respective thresholds.

Subgroup analyses were conducted to assess the heterogeneity of the potential safety signals for insomnia across different populations. Cases were stratified by age into children (<18 years), adults (18–64 years), and the elderly (≥65 years), and by sex into male and female groups. Disproportionality analysis was then performed separately within each subgroup using the same multi-method criteria. The above analyses were all conducted using R software (version 4.4.2).

3. Results

3.1. Characteristics of adverse event reports

A total of 179,697 adverse event reports (AERs) for insomnia, in which a drug was designated as the PS, were extracted from the FAERS database for the period spanning Q1 2004 to Q4 2024. Figure 1 shows that the annual number of insomnia-related AERs increased overall from 2004 to 2024, with a notable peak around 2015. Following this peak, AERs declined and stabilized in subsequent years. The clinical characteristics of these reports are listed in Table 1. The median age of the study population was 55 years (interquartile range 40–67). There was a higher proportion of reports among adults and females (40.79% and 57.98%, respectively), and the median time to onset was 2 days (interquartile range 0–43). The country with the highest number of reports was the United States (97,397, 54.2%), followed by the United Kingdom (9408, 5.24%), and Canada (8501, 4.73%). The top 3 indications for drug use were: depression (7732, 4.30%), hepatitis C (5000, 2.78%), and smoking cessation therapy (4455, 2.48%).

Figure 1.

Number of adverse event reports of insomnia from Q1 2004 to Q4 2024.

Table 1.

Clinical characteristics of reported insomnia.

Characteristics	Reports, n (%)
Overall	179697
Age, y
Median, IQR	55.00 (40.00,67.00)
<18	5706 (3.18)
18–64	73297 (40.79)
≥65	33935 (18.88)
Unknown	66759 (37.15)
Gender
Female	104192 (57.98)
Male	59805 (33.28)
Unknown	15700 (8.74)
Reported country
United States	97397 (54.20)
United Kingdom	9408 (5.24)
Canada	8501 (4.73)
France	4942 (2.75)
Germany	2052 (1.14)
Indications
Depression	7732 (4.30)
Hepatitis C	5000 (2.78)
Smoking cessation therapy	4455 (2.48)
Osteoporosis	3177 (1.77)
Bipolar disorder	2658 (1.48)
Parkinson disease	2520 (1.40)
Product used for unknown indication	32410 (18.04)
Time to onset, d
Median, IQR	2.00 (0.00,43.00)

IQR = interquartile range.

3.2. Disproportionality analysis

Figure 2 summarizes the top 30 drugs ranked by the frequency of AERs in the current study. These drugs were classified according to the World Health Organization (WHO) ATC system. As for the frequencies of AERs, varenicline (5421 reports) is the most frequently reported drug, followed by thyroxine (4546 reports), duloxetine (3879 reports), quetiapine (3383 reports), and pregabalin (2723 reports). The majority are primarily nervous system medications (17, 57%) and anti-infectives for systemic use (4, 13.3%). Of these 30 drugs, only 4 drugs didn’t indicate insomnia risk on the label, while the remaining 26 drugs did.

Figure 2.

Top 30 drugs with the highest number of reported insomnia.

According to the signal strength, the top 30 drugs ranked by ROR are listed in Table 2, all of which showed positive disproportionality signals. Positive signal drugs are mainly concentrated on the nervous system. The results of ROR, PRR, IC, and EBGM are consistent. To provide an intuitive ranking of signal strength, the drugs that simultaneously met the thresholds of all 4 algorithms were ranked according to ROR values in the present study. The corresponding PRR, IC, and EBGM values for each drug are fully presented in Table 2. The top 5 drugs ranked by ROR were: mefloquine (ROR 22.22, 95% CI 19.11–25.85), viloxazine (ROR 12.29, 95% CI 9.46–15.96), flibanserin (ROR 12.02, 95% CI 10.28–14.05), finasteride (ROR 10.93, 95% CI 10.31–11.59), and dasabuvir/ombitasvir/paritaprevir/ritonavir (ROR 10.68, 95% CI 9.97–11.44). According to drug classification, the most common type of drugs is nervous system medications (18, 60%), followed by respiratory system medications (3, 10%), and genitourinary system and sex hormones (3, 10%). Of the top 30 drugs, 29 drugs indicate insomnia risk on the label. Several drugs not labeled for insomnia, including fexofenadine, telaprevir, and pimavanserin, generated considerable case reports despite not appearing in the top 30 signal rankings. Their full signal metrics are detailed in Table S3, Supplemental Digital Content, https://links.lww.com/MD/R670.

Table 2.

Signal strength for drugs associated with insomnia.

WHO ATC category	Drug name	Case reports	ROR (95% CI)	PRR (95% CI)	IC (IC025)	EBGM (EBGM05)	Label
Nervous system	Viloxazine	63	12.29 (9.46, 15.96)	11.08 (8.76, 14.02)	3.47 (3.1)	11.08 (8.9)	Y
	Selegiline	128	9.94 (8.29, 11.92)	9.15 (7.82, 10.7)	3.19 (2.93)	9.15 (7.86)	Y
	Vilazodone	434	9.2 (8.34, 10.15)	8.53 (7.73, 9.41)	3.09 (2.95)	8.51 (7.84)	Y
	Duloxetine	3879	8.5 (8.23, 8.79)	7.94 (7.63, 8.26)	2.96 (2.91)	7.79 (7.58)	Y
	Ramelteon	174	8.31 (7.12, 9.7)	7.76 (6.77, 8.9)	2.96 (2.73)	7.76 (6.82)	Y
	Varenicline	5421	7.71 (7.5, 7.93)	7.25 (7.11, 7.39)	2.82 (2.78)	7.06 (6.9)	Y
	Ropinirole	511	7.22 (6.6, 7.89)	6.81 (6.3, 7.37)	2.76 (2.63)	6.79 (6.3)	Y
	Pitolisant	33	6.3 (4.43, 8.96)	6 (4.3, 8.37)	2.58 (2.08)	6 (4.47)	Y
	Phenelzine	61	6.3 (4.87, 8.16)	5.99 (4.73, 7.58)	2.58 (2.21)	5.99 (4.83)	Y
	Tranylcypromine	23	6.21 (4.08, 9.46)	5.91 (3.99, 8.75)	2.56 (1.97)	5.91 (4.16)	Y
	Atomoxetine	999	6.07 (5.69, 6.47)	5.79 (5.46, 6.14)	2.53 (2.43)	5.76 (5.46)	Y
	Lurasidone	790	5.86 (5.45, 6.29)	5.59 (5.27, 5.93)	2.48 (2.38)	5.57 (5.25)	Y
	Armodafinil	257	5.8 (5.12, 6.58)	5.55 (4.93, 6.24)	2.47 (2.29)	5.54 (4.99)	Y
	Bupropion	2513	5.68 (5.46, 5.92)	5.44 (5.23, 5.66)	2.43 (2.37)	5.38 (5.2)	Y
	Vortioxetine	430	5.58 (5.06, 6.14)	5.34 (4.84, 5.89)	2.41 (2.27)	5.33 (4.91)	Y
	Amantadine	152	5.56 (4.72, 6.54)	5.32 (4.55, 6.22)	2.41 (2.18)	5.32 (4.64)	Y
	Paroxetine	1856	5.21 (4.97, 5.46)	5 (4.81, 5.2)	2.31 (2.24)	4.96 (4.77)	Y
	Amphetamine	1725	5.11 (4.87, 5.37)	4.92 (4.73, 5.12)	2.29 (2.22)	4.88 (4.69)	Y
Respiratory system	Roflumilast	249	8.64 (7.59, 9.83)	8.05 (7.16, 9.05)	3.01 (2.82)	8.04 (7.21)	Y
	Pseudoephedrine	1763	5.61 (5.35, 5.89)	5.38 (5.17, 5.6)	2.42 (2.35)	5.33 (5.12)	Y
	Dextromethorphan	37	5.58 (4.01, 7.77)	5.34 (3.9, 7.31)	2.42 (1.95)	5.34 (4.05)	Y
Genitourinary system and sex hormones	Flibanserin	176	12.02 (10.28, 14.05)	10.86 (9.47, 12.46)	3.44 (3.22)	10.86 (9.53)	Y
	Finasteride	1250	10.93 (10.31, 11.59)	9.98 (9.41, 10.58)	3.31 (3.23)	9.91 (9.44)	N
	Fezolinetant	65	8.41 (6.53, 10.83)	7.85 (6.2, 9.93)	2.97 (2.61)	7.85 (6.35)	Y
Antiparasitic products, insecticides and repellents	Mefloquine	205	22.22 (19.11, 25.85)	18.45 (16.4, 20.75)	4.2 (3.99)	18.43 (16.24)	Y
Anti-infectives for systemic use	Dasabuvir/Ombitasvir/Paritaprevir/Ritonavir	901	10.68 (9.97, 11.44)	9.77 (9.21, 10.36)	3.28 (3.18)	9.72 (9.18)	Y
Systemic hormonal preparations	Levothyroxine	4546	9.96 (9.66, 10.27)	9.19 (9.01, 9.37)	3.17 (3.12)	8.98 (8.75)	Y
Antineoplastic and immunomodulating agents	Interferon	1816	6.34 (6.04, 6.65)	6.03 (5.8, 6.27)	2.58 (2.51)	5.98 (5.74)	Y
Alimentary tract and metabolism	Phentermine	351	5.95 (5.34, 6.62)	5.68 (5.15, 6.26)	2.5 (2.35)	5.67 (5.18)	Y
	Sibutramine	61	5.28 (4.08, 6.82)	5.07 (4.01, 6.41)	2.34 (1.97)	5.07 (4.08)	Y

CI = confidence interval, EBGM = empirical Bayes geometric mean, IC = information component, N = no, PRR = proportional reporting ratio, ROR = reporting odds ratio, Y = yes.

3.3. Subgroup analysis

Figure 3 presents the top 10 insomnia-related signals across different age subgroups. Disproportionality analysis revealed distinct medication profiles: in children, signals were dominated by nervous system agents such as viloxazine, selegiline, zaleplon, and temazepam, along with anti-infectives including mefloquine and the dasabuvir combination, and respiratory system drugs such as dextromethorphan. The adult profile broadened to incorporate systemic hormonal preparations, notably finasteride, flibanserin, and thyroxine, in addition to nervous system drugs and anti-infectives. Among the elderly, leading signals combined nervous system medications and hormonal therapies represented by fezolinetant.

Figure 3.

Signals detection based on age. (A) Age group under 18 years old. (B) Age group 18–64 years old. (C) Age group over 65 years old.

Figure 4 shows the disproportionality results based on gender. We listed the top 10 drugs related to insomnia in males and females based on ROR values. The male-specific top 10 list included: mefloquine, viloxazine, dasabuvir combination, vilazodone, finasteride, roflumilast, phentermine, selegiline, varenicline, and levothyroxine. The female-specific top 10 list comprised: mefloquine, viloxazine, flibanserin, selegiline, levothyroxine, dasabuvir combination, roflumilast, vilazodone, ramelteon, and fezolinetant. Notably, mefloquine and viloxazine emerged as the leading signals in both genders. Several drugs appeared in both lists, including selegiline, thyroxine, the dasabuvir combination, roflumilast, and vilazodone. However, distinct gender specific patterns were also observed: finasteride and phentermine ranked prominently only in males, whereas flibanserin and fezolinetant were unique to the female top 10 list.

Figure 4.

Signals detection among different gender groups. (A) Male group. (B) Female group.

4. Discussion

To our knowledge, this is the first pharmacovigilance study to comprehensively investigate drug-associated insomnia risk signals using real-world data from the FAERS database. By integrating reporting frequency and 4 disproportionality analysis methods, this study found that central nervous system agents dominate the signal profile. The strongest cluster of insomnia risk signals includes antidepressants, central nervous system stimulants,anti-Parkinson agents, antipsychotics, and wakefulness-promoting drugs. Endocrine medications and specific anti-infectives also exhibited strong signals. Furthermore, respiratory system drugs ranked prominently among high signal agents. These findings indicate that insomnia is not only a common adverse reaction to psychotropic medications but also a risk across multiple nonpsychiatric therapeutic domains.

Insomnia is a prevalent adverse effect of medications. Studies have identified that antidepressants, prescription opioids, and caffeine can lead to insomnia.^[16,18,19] However, existing research on drug-induced insomnia is relatively scarce, primarily comprising a limited number of case reports and systematic reviews. Considering that the mechanisms of sleep-wake regulation involve intricate neural and endocrine pathways, numerous medications have the potential to cause insomnia, which can impact patients’ occupational performance, daily activities, and overall health. Therefore, pharmacovigilance studies in real-world settings are essential for enhancing drug safety information and aiding clinical decision-making.

This study found that the highest frequencies and signal strengths of reports were predominantly associated with central nervous system medications. These included antidepressants, antipsychotics, central nervous system stimulants, addiction medications, and anti-Parkinson drugs, etc. Through analysis of the FAERS database, we found that certain antidepressants are associated with insomnia as a notable side effect. This adverse effect was predominantly observed among monoamine oxidase inhibitors, selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, and norepinephrine dopamine reuptake inhibitors. Antidepressants primarily exert their antidepressant effects by influencing the 5-hydroxytryptamine (5-HT) and norepinephrine (NE) systems in the brain.^[20] However, the mechanisms by which they affect sleep have not yet been fully elucidated. It is currently believed that antidepressants may impact sleep quality and architecture by modulating the levels of neurotransmitters associated with the sleep-wake cycle. Key cell groups in the body, including cholinergic, noradrenergic, serotonergic, dopaminergic, and histaminergic neurons, stimulate ascending arousal pathways through their projections to promote wakefulness.^[21,22] By selectively inhibiting the reuptake of serotonin, dopamine, and NE, antidepressants increase the levels of NE and serotonin in the brain, thereby suppressing REM sleep and reducing overall sleep duration.^[23] Although the mechanisms causing insomnia are unclear, monoamine oxidase inhibitors have still been observed to suppress rapid eye movement (REM) sleep.^[24]

This study identified insomnia risk signals in attention-deficit/hyperactivity disorder (ADHD) medications, with the highest report frequencies linked to stimulants (methylphenidate, amphetamine) and the strong statistical signals associated with non-stimulants (atomoxetine, viloxazine). Stimulants likely disrupt sleep through direct central nervous system excitation via rapid dopamine or NE release, while non-stimulants interfere with sleep-wake balance via sustained NE reuptake inhibition. Real-world data from this study demonstrate that insomnia risk warrants vigilance in ADHD treatment. The high signal drugs identified in this study, the monoamine oxidase B inhibitor selegiline and the non-ergot dopamine receptor agonist ropinirole, both exert their therapeutic effects in Parkinson disease by enhancing central dopaminergic signaling. The dopamine system not only regulates motor function but also profoundly influences arousal.^[25] Increased dopaminergic activity is a well-established mechanism underlying neuropsychiatric adverse reactions such as insomnia.

This study identified insomnia signals with 3 atypical antipsychotics, each exhibiting distinct mechanisms. Quetiapine, despite its common use as a sedative via H1 antagonism, showed a high frequency of insomnia reports. This likely reflects its dose-dependent effects: higher therapeutic doses preferentially block α1-adrenergic receptors, often inducing akathisia that disrupts sleep. Its extensive prescription volume and off-label use for sleep also contribute to the high report count, along with possible rebound insomnia upon abrupt discontinuation. A meta-analysis has indicated that insomnia and akathisia are common adverse reactions to aripiprazole.^[26] The mechanism underlying aripiprazole related insomnia is rooted in its unique pharmacology as a dopamine D2 receptor partial agonist: in the mesolimbic pathway, which is involved in emotion and motivation, its agonist activity may enhance psychological arousal and delay sleep onset,^[27,28] whereas in the nigrostriatal pathway, which regulates movement, its functional antagonistic effect is the primary cause of akathisia. Lurasidone carries a distinct insomnia risk due to its unique receptor profile. It has very low affinity for the histamine H₁ receptor, which means it lacks the common sedative side effect. However, it exhibits strong antagonistic activity at the 5-HT₇ receptor.^[29,30] This receptor is widely distributed in sleep-regulating centers such as the hypothalamus and is involved in the homeostasis of circadian rhythms and sleep architecture. Therefore, the insomnia risk associated with lurasidone can be attributed to its direct interference with sleep-wake neural circuits via 5-HT₇ receptor antagonism, combined with the absence of H₁ receptor-mediated sedation to buffer this effect. The side effect of insomnia associated with the addiction medication varenicline has been reported in clinical settings.^[31] The mechanism may involve its action on the brain neurotransmitter system, particularly its partial agonistic effect on nicotinic acetylcholine receptors, which can disrupt the normal sleep-wake cycle.

The antimalarial drug mefloquine demonstrated the strongest insomnia risk signal. This aligns with its known adverse effects on the central nervous system.^[32] Mefloquine is highly lipophilic and readily accumulates in the central nervous system. Its insomnia-inducing mechanism is primarily attributed to direct neurotoxic effects, including binding to neuroreceptors and cholinesterases, inhibition of sarcoendoplasmic reticulum ATPase, and interference with cellular Ca²⁺ homeostasis. These actions lead to neuronal hyperexcitability and abnormal discharge. This neurotoxicity underlies the spectrum of severe neuropsychiatric adverse reactions associated with mefloquine, such as insomnia, anxiety, nightmares, and dizziness.^[33,34] Interferon, as an immunomodulator, does not directly kill viruses but acts by activating the immune system. It can stimulate the production of pro-inflammatory cytokines, which can cross the blood-brain barrier and act on glial cells in the brain. Dysregulation of inflammatory cytokines is a potential mechanism underlying insomnia, affecting sleep-regulating centers and leading to reduced sleep quality.^[35] Studies have also found that interferon may disrupt the balance of neurotransmitters in the brain, such as reducing dopamine release.^[36] These neurotransmitters are crucial for regulating sleep cycles, and changes in their levels can contribute to insomnia symptoms, including difficulty falling asleep, shallow sleep, or early morning awakenings. This study also detected insomnia signals linked to direct-acting antiviral therapy for hepatitis C, consistent with prior clinical reports.^[37–39] The mechanism remains unclear, but immune reconstitution and inflammatory responses likely contribute. Chronic HCV infection exhausts the immune system.^[40] Following rapid viral clearance by direct-acting antivirals, the suppressed immune system undergoes abrupt reconstitution, accompanied by cytokine fluctuations. These pro-inflammatory cytokines can cross the blood-brain barrier and disrupt sleep-regulatory centers (e.g., hypothalamus, brainstem), potentially driving insomnia and related neuropsychiatric effects. The mechanism by which quinolone antibiotics cause insomnia is not fully understood, but one possible reason is their inhibition of γ-aminobutyric acid (GABA) binding to its receptors. GABA is an amino acid that has a calming and inhibitory effect on the brain, and this may be a reason for sleep disturbances.^[41]

This study identified 3 drugs within the genitourinary system and sex hormones category that exhibit significant insomnia risk signals. Their potential mechanisms may all involve interference with the homeostatic network of the hypothalamus-limbic system. Fezolinetant is a highly selective neurokinin-3 receptor antagonist that primarily acts on KNDy neurons in the hypothalamic thermoregulatory center. Given the close anatomical and functional connections between the thermoregulatory, sleep, and arousal networks within the hypothalamus, this targeted intervention may indirectly affect the circadian output of the suprachiasmatic nucleus or the arousal-maintaining function of the tuberomammillary nucleus through functional crosstalk in neural circuits, potentially leading to insomnia. The insomnia risk associated with finasteride may be related to the clinical phenomenon known as “post-finasteride syndrome.” The proposed mechanism involves the irreversible inhibition of 5α-reductase, leading to long-term impairment in the synthesis of endogenous neuroactive steroids, thereby weakening the inhibitory function of the GABAergic system. Such long-term neuroendocrine alterations may manifest as persistent symptoms including anxiety, mood disorders, and insomnia.^[42] Notably, despite ranking highly in both reporting frequency and signal strength in analysis, insomnia is not listed as an adverse reaction in finasteride’s current prescribing information. This discrepancy suggests that the risk may be more substantial or prevalent than currently recognized, highlighting a gap in existing safety data and underscoring the need for heightened clinical vigilance. Flibanserin, as a 5-HT1A receptor agonist and 5-HT2A receptor antagonist, exerts its therapeutic effects by modulating serotonergic pathways in the prefrontal-limbic system. This modulation may indirectly influence the balance of interacting dopaminergic and noradrenergic systems, which could subsequently lead to sleep disturbances.

Subgroup analysis revealed that while the ranking of insomnia-related drug signals varied across age groups, the core high-risk agents, including mefloquine, ramelteon, duloxetine, and the dasabuvir/ombitasvir/paritaprevir/ritonavir combination, remained consistently prominent across all 3 age strata, underscoring the age-independent robustness of these signals. However, each age group exhibited distinct signal profiles that closely aligned with its characteristic disease spectrum and medication patterns. The pediatric spectrum was dominated by nervous system agents, anti-infectives, and respiratory drugs, reflecting the central role of pharmacotherapy for ADHD, infections, and respiratory symptoms in children. The adult profile expanded to include systemic hormonal preparations and genitourinary and sex-hormone drugs, mirroring the growing therapeutic needs for thyroid dysfunction, sex hormone-related conditions, and menopausal management in adulthood. The elderly profile further incorporated agents such as roflumilast for chronic obstructive pulmonary disease and fezolinetant for menopausal vasomotor symptoms, illustrating the complex polypharmacy landscape in older adults where neuropsychiatric, chronic respiratory, and hormonal metabolic conditions intersect to shape sleep-related drug risks. Similarly, subgroup analysis stratified by sex further clarified distinct patterns of drug-associated insomnia risk between genders. Male-specific high-risk signals included finasteride and varenicline, corresponding to health conditions that are more prevalent in adult males, such as prostatic disorders, androgenetic alopecia, and higher rates of smoking. Female-specific high-risk signals comprised flibanserin and fezolinetant, both of which directly target conditions unique to the female life course, such as hypoactive sexual desire disorder and menopausal vasomotor symptoms, highlighting how insomnia risk is closely tied to therapies addressing distinct female physiological stages. These age and sex specific signals demonstrate that medication-related insomnia risk is not only linked to pharmacological mechanisms but is also deeply rooted in population differences in disease profiles, health behaviors, and treatment choices.

In addition to finasteride, this study identified several other clinically significant drugs that do not list insomnia in their prescribing information. Medications including pimavanserin, telaprevir, and fexofenadine all generated signals and were associated with a high number of case reports. Existing literature has also suggested a potential link between these drugs and insomnia.^[38,43] This discrepancy between frequent clinical reports and the absence of formal label warnings highlights a potential gap in current safety information. The signals emerging from this large-scale dataset suggest that heightened clinical vigilance is warranted when prescribing these drugs. Furthermore, targeted follow-up studies are necessary to confirm these associations and elucidate the underlying mechanisms.

FAERS provides a large repository of AERs, encompassing a wide range of drugs and patient populations, ensuring a comprehensive data collection that would be difficult to detect in traditional epidemiological studies. Despite this, this study also has certain limitations. First, due to limitations in the proactive, accurate, and timely reporting of adverse events by physicians and other healthcare providers, there may be possibilities of underreporting and misreporting. In particular, reporting delays and recall bias may affect the accuracy of data regarding the onset time of insomnia. Additionally, as a spontaneous reporting system, FAERS has a high rate of missing information. In this study, a substantial proportion of reports lacked complete age or gender records, preventing their inclusion in subgroup analyses, which may affect the completeness and representativeness of sex and age-specific risk signals. It is important to note that these subgroup analyses cannot assess the robustness of the identified signals to this missing demographic information. Second, this study focuses on the safety signal analysis of single drugs. However, in actual clinical practice, combination therapy exists, and risk factors such as concomitant medications, drug interactions, administration methods, and dosages need further refinement in future research. Furthermore, although this study proactively excluded indications likely to cause insomnia, the indication field itself in spontaneous reports is often incomplete. Notably, 18.04% of reports were filed under “Product used for unknown indication.” More importantly, observational report-based studies cannot fully capture and adjust for all potential confounding factors, such as unrecorded comorbidities, patient lifestyle habits, socioeconomic factors, and disease severity. These unmeasured confounders may introduce residual bias into the estimated association between drugs and insomnia. It should also be emphasized that this study is primarily a statistical surveillance of adverse drug reaction signals based on the FAERS database. The results reflect the strength of reporting associations between drugs and insomnia events, not proven causality. The signals identified here should be regarded as hypothesis-generating, intended to provide risk alerts for clinical practice and to guide the direction of subsequent causal inference research. Nevertheless, the FAERS database remains an important tool for pharmacovigilance analysis and guides clinical practitioners to be alert to the risks associated with related drugs.

5. Conclusions

This large-scale pharmacovigilance analysis based on the FAERS indicates that drugs associated with insomnia risk span multiple therapeutic classes, including nervous system agents, endocrine medications, and anti-infectives. Although spontaneous reporting systems and disproportionality analyses have inherent limitations and cannot establish causality directly, the high-signal drugs identified in this study provide important risk alerts for clinical medication monitoring. Future prospective studies are still needed to further verify and clarify the causal relationships between these drugs and insomnia.

Author contributions

Conceptualization: Xinyao Huang, Yingying Chen.

Data curation: Xinyao Huang, Yingying Chen.

Formal analysis: Xinyao Huang, Yingying Chen, Song Li.

Methodology: Xinyao Huang, Hao Liu, Gang Cao.

Software: Xinyao Huang, Yingying Chen, Yan Wen.

Writing – original draft: Xinyao Huang, Yingying Chen.

Writing – review & editing: Song Li.