Abstract
Background
Anxiety disorders (ANX) affect 30–60% of individuals with bipolar disorder (BD), yet limited research has systematically examined clinical characteristics and treatment patterns in this comorbid population. This study investigated demographic, clinical, and pharmacotherapeutic differences between individuals with BD with and without comorbid ANX.
Methods
Cross-sectional data from 2,225 adults with BD enrolled in the Mayo Clinic Bipolar Disorder Biobank were analyzed. Participants were assessed for comorbid ANX, demographics, clinical characteristics, medication use, and treatment response using the Alda-A scale.
Results
Overall, 61% (n = 1,366) had comorbid ANX. Individuals with BD + ANX were younger (40.4 vs. 43.6 years, p < 0.001), more likely female (66.6% vs. 54.8%, p < 0.001), and exhibited higher rates of rapid cycling (64.2% vs. 45.2%, p < 0.001), suicide attempts (40.4% vs. 24.8%, p < 0.001), substance use disorders (63.5% vs. 54.8%, p < 0.001), and somatic comorbidities (MCIRS: 6.68 vs. 5.42, p < 0.001). Pharmacotherapeutically, individuals with BD + ANX were less likely to be currently prescribed lithium, a trend‑level difference (37.1% vs. 47.8%, p = 0.005) and showed a trend towards lower valproic acid use (21.7% vs. 29.6%, p = 0.047), but more likely to receive antidepressants (53.8% vs. 39.5%, p < 0.001), benzodiazepines (39.9% vs. 26.6%, p < 0.001), and gabapentinoids (8.5% vs. 4.5%, p < 0.001). Notably, 17.3% of individuals with BD + ANX received antidepressants without mood stabilizer coverage. Treatment response (Alda-A) scores were significantly lower in BD + ANX group for lithium (4.91 vs. 6.05, p < 0.001) and second-generation antipsychotics (4.67 vs. 5.73, p < 0.001), with a trend‑level reduction observed for mood-stabilizing anticonvulsants (5.16 vs. 6.01, p = 0.005). Similar patterns were observed in both BD-I and BD-II subtypes.
Conclusions
Individuals with BD + ANX represent a more severely affected subgroup with distinct prescribing patterns favoring antidepressants over mood stabilizers and attenuated response to mood stabilizers. These findings highlight the need for anxiety-informed treatment algorithms recognizing anxiety comorbidity as a negative prognostic factor.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40345-026-00415-z.
Keywords: Bipolar disorder, Anxiety disorders, Comorbidity, Pharmacotherapy, Treatment response, Mood stabilizers, Antidepressants
Introduction
A significant challenge in effectively managing bipolar disorder (BD) is the high prevalence of comorbidities, particularly anxiety disorders (ANX) (Pavlova et al. 2015; Singh et al. 2024), with clinical estimates up to 60% (Kinrys et al. 2019; McIntyre et al. 2006; Pavlova et al. 2015; Singh et al. 2025b). Those with ANX are at increased risk for developing substance use disorders (SUDs) and frequently self-medicate with alcohol (Smith and Book 2008). Previous research has indicated that BD with comorbid ANX (BD + ANX) correlates with substantial functional impairment, risk for suicidality, and poor quality of life (Burdick et al. 2022; Simon et al. 2004). A recent systematic review reported the current prevalence of ANX in BD at approximately 38%, with generalized anxiety disorder (GAD) being the most common subtype (Yapici Eser et al. 2018). Although some studies suggest the lifetime risk of ANX may be as high as 60–85% (Merikangas et al. 2007; Mitchell et al. 2013), there remains a lack of consistent data on prevalence rates across bipolar I and bipolar II subtypes, as well as on associated clinical outcomes (Cullen et al. 2021; Galimberti et al. 2020; Kauer-Sant’Anna et al. 2009; Keck et al. 2006; Kinrys et al. 2019; Schaffer et al. 2012; Vazquez et al. 2014). There is a paucity of recent data examining pharmacotherapeutic approaches and treatment response differences among bipolar individuals with (BD + ANX) and without lifetime anxiety (BD+NoANX).
A critical discussion within the field of BD concerns the prescription rates of monoaminergic antidepressants (Elmosalamy et al. 2025; Park et al. 2022), which may vary between 30 and 70%, depending on the region and study populations (Singh et al. 2024; Yocum and Singh 2025). Evidence suggests that individuals with ANX are frequently prescribed monoaminergic antidepressants (Yatham et al. 2018), which may increase the risk of affective switching in BD. This raises the question of whether pharmacotherapy patterns differ between individuals with BD + ANX and BD+NoANX, with the former typically receiving higher rates of antidepressant prescriptions (Keck et al. 2006). Understanding these prescribing trends can inform clinical practices and contribute to strategies aimed at enhancing treatment outcomes in future research.
Recent data from the Global Bipolar Cohort Collaboration showed prevalence rates of BD + ANX as high as 60% in the North American cohorts compared to 30% in the European cohorts (Singh et al. 2024). We also observed regional variations in prescription patterns; however, data regarding the BD + ANX phenotype were not available. Leveraging data from the Mayo Clinic Bipolar Disorder Biobank, this study examined the differences in clinical and demographic characteristics as well as pharmacotherapeutic prescription patterns among individuals with BD + ANX and BD+NoANX. Furthermore, we analyzed prescription patterns among individuals with bipolar I (BD-I) and bipolar II disorder (BD-II) with and without ANX, as those with BD-II tend to be prescribed more monoaminergic antidepressants (Singh et al. 2024).
Methods
Detailed information about the Mayo Clinic Bipolar Disorder Biobank has been previously published (Frye et al. 2015). In summary, cross-sectional data were collected from study participants at enrollment (Gardea-Resendez et al. 2022; Pahwa et al., 2021b). Participants were recruited from five sites: Mayo Clinic, Rochester, Minnesota; Lindner Center of HOPE/University of Cincinnati College of Medicine, Cincinnati, Ohio; University of Minnesota, Minneapolis, Minnesota; Universidad Autónoma de Nuevo León, Mexico; and Universidad de los Andes, Chile. The inclusion criteria consisted of adults aged 18–80 with BD who spoke English at the U.S. sites and Spanish at Mexico and Chile sites, provided informed consent, and met DSM-IV-TR criteria for BD-I/BD-II or schizoaffective BD. Participants exhibiting active psychosis or suicidal ideation were excluded.
At the time of study enrollment, data were collected on demographics, family history, psychiatric conditions (including adult and childhood attention deficit hyperactivity disorder [ADHD], anorexia nervosa, bulimia nervosa, binge eating disorder (BED), GAD, obsessive-compulsive disorder (OCD), panic disorder, post-traumatic stress disorder (PTSD), and social anxiety disorder (SAD), medications, and somatic comorbidities. ANX and other psychiatric diagnoses were determined using the Bipolar Biobank Clinical Questionnaire and medical record documentation. Patients were classified as BD + ANX if they met criteria for one or more ANX. In the DSM-IV-TR, OCD and PTSD were classified under ANX; however, in the DSM-5, they were moved to independent diagnostic categories. For this study, only GAD, panic disorder, SAD, and phobias were categorized under the ANX category. Since most ANX persist as psychiatric diagnoses throughout an individual’s life, our study concentrated on lifetime ANX diagnoses. The overall burden of medical illness was measured using the Modified Cumulative Illness Rating Scale (MCIRS) (Salvi et al. 2008), with psychiatric comorbidity data excluded. Data for additional somatic comorbidities were extracted using structured surveys.
We collected data on current and lifetime prescriptions for second-generation antipsychotics (SGAs), and mood stabilizers, including lithium and mood-stabilizing anticonvulsants (MSACs) such as valproate, carbamazepine, and lamotrigine. Additional data were obtained for first-generation antipsychotics (FGAs), monoaminergic antidepressants—including selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and tricyclic antidepressants (TCAs)—as well as benzodiazepines, gabapentinoids (gabapentin or pregabalin), stimulants, wakefulness-promoting agents, and dopamine agonists. We examined patterns of polypharmacotherapy, defined as the concurrent use of two or more SGAs or mood stabilizers, and assessed the frequency of monoaminergic antidepressant prescriptions without a concurrent mood stabilizer.
Treatment response to lithium, SGAs, and MSACs was assessed using the Alda-A score from the Alda Scale (Grof et al. 2002). The Alda-A score measures clinical improvement in illness severity during the treatment, rated from 0 (no improvement or worsening) to 10 (full recovery or absence of symptoms). The B score reflects the extent of confounding factors that may influence treatment response; a lower B score indicates a smaller contribution from confounding treatments. For patients with Alda-A scores for more than one mood stabilizer, we used the score from the treatment episode with the lowest B score. If they had a missing B score, we use the medication with non-missing B score. Although initially developed to retrospectively measure response to lithium, it has been modified to assess response to other mood stabilizers in previous studies (Cuellar-Barboza et al. 2020; Ho et al. 2020; Joseph et al. 2023; Pahwa et al. 2021a; Singh et al. 2025a).
Statistical analysis
Clinical and demographic characteristics were compared between participants with and without a lifetime anxiety diagnosis (BD + ANX and BD+NoANX, respectively) using the arsenal package in R. The modelsum function was applied to perform multiple univariate tests, using linear models for continuous variables and logistic models for categorical variables. All models were adjusted for age, sex, and recruitment site (Mayo Clinic, Lindner Center of HOPE, University of Minnesota, Chile, or Mexico). Given the large number of comparisons, a threshold of p < 0.001 was used to define statistical significance. Analyses were conducted using R version 4.2.2.
Results
The study cohort consisted of 2,225 adults with BD (1,451 BD-I, 723 BD-II, 51 schizoaffective disorder BD). The mean age was 41.6 years; 62.1% were female, 84.0% White, and 12.4% Hispanic (Table 1). Rapid cycling was prevalent in 57.1% of individuals, with a history of psychosis in 39.6%, and 38.1% had early onset of BD.
Table 1.
Comparisons of demographic and clinical characteristics between individuals with BD, with and without ANX, adjusted for age, sex, and recruitment site
| Total (N = 2225) |
BD+NoANX (N = 859) |
BD + ANX (N = 1366) |
p-value | |
|---|---|---|---|---|
| Site of recruitment, n | 2225 | 859 | 1366 | |
| Mayo, n (%) | 1154 (51.9%) | 495 (57.6%) | 659 (48.2%) | < 0.001* |
| Lindner, n (%) | 770 (34.6%) | 219 (25.5%) | 551 (40.3%) | |
| University of Minnesota, n (%) | 75 (3.4%) | 29 (3.4%) | 46 (3.4%) | |
| Chile, n (%) | 75 (3.4%) | 51 (5.9%) | 24 (1.8%) | |
| Mexico, n (%) | 151 (6.8%) | 65 (7.6%) | 86 (6.3%) | |
| Age, n | 2219 | 856 | 1363 | |
| Mean (SD) | 41.6 (15.0) | 43.6 (15.9) | 40.4 (14.3) | < 0.001* |
| Sex, n | 2225 | 859 | 1366 | |
| Male, n (%) | 844 (37.9%) | 388 (45.2%) | 456 (33.4%) | < 0.001* |
| Female, n (%) | 1381 (62.1%) | 471 (54.8%) | 910 (66.6%) | |
| Race, n | 2191 | 840 | 1351 | |
| White, n (%) | 1840 (84.0%) | 705 (83.9%) | 1135 (84.0%) | 0.005* |
| Black, n (%) | 62 (2.8%) | 18 (2.1%) | 44 (3.3%) | |
| Asian, n (%) | 22 (1.0%) | 15 (1.8%) | 7 (0.5%) | |
| Other, n (%) | 170 (7.8%) | 73 (8.7%) | 97 (7.2%) | |
| Multiracial, n (%) | 97 (4.4%) | 29 (3.5%) | 68 (5.0%) | |
| Hispanic, n | 2179 | 833 | 1346 | |
| Yes, n (%) | 271 (12.4%) | 124 (14.9%) | 147 (10.9%) | 0.223 |
| Body mass index, n | 2118 | 818 | 1300 | |
| Mean (SD) | 29.9 (7.4) | 29.4 (6.8) | 30.2 (7.7) | 0.094 |
| Currently married, n | 2201 | 848 | 1353 | |
| Yes, n (%) | 997 (45.3%) | 403 (47.5%) | 594 (43.9%) | 0.821 |
| Employment, n | 2106 | 809 | 1297 | |
| Full-time, n (%) | 544 (25.8%) | 226 (27.9%) | 318 (24.5%) | 0.012 |
| Part-time, n (%) | 401 (19.0%) | 170 (21.0%) | 231 (17.8%) | |
| Not working for pay at present, n (%) | 1161 (55.1%) | 413 (51.1%) | 748 (57.7%) | |
| Education, n | 2134 | 814 | 1320 | |
| High school or less, n (%) | 61 (2.9%) | 26 (3.2%) | 35 (2.7%) | 0.271 |
| High school graduated, n (%) | 284 (13.3%) | 97 (11.9%) | 187 (14.2%) | |
| Beyond high school graduation, n (%) | 1789 (83.8%) | 691 (84.9%) | 1098 (83.2%) | |
| Bipolar Disorder Characteristics | ||||
| Bipolar type, n | 2225 | 859 | 1366 | |
| Bipolar I, n (%) | 1451 (65.2%) | 573 (66.7%) | 878 (64.3%) | 0.234 |
| Bipolar II, n (%) | 723 (32.5%) | 263 (30.6%) | 460 (33.7%) | |
| Schizoaffective, n (%) | 51 (2.3%) | 23 (2.7%) | 28 (2.0%) | |
| Rapid cycling, n | 2083 | 783 | 1300 | |
| Yes, n (%) | 1189 (57.1%) | 354 (45.2%) | 835 (64.2%) | < 0.001 |
| History of psychosis, n | 2207 | 852 | 1355 | |
| Yes, n (%) | 874 (39.6%) | 352 (41.3%) | 522 (38.5%) | 0.302 |
| Manic psychosis, n | 2207 | 852 | 1355 | |
| Yes, n (%) | 693 (31.4%) | 301 (35.3%) | 392 (28.9%) | 0.012 |
| Early onset (≤ 19 years old), n | 2094 | 791 | 1303 | |
| Yes, n (%) | 797 (38.1%) | 275 (34.8%) | 522 (40.1%) | 0.016 |
| Suicide attempt, n | 2211 | 854 | 1357 | |
| Yes, n (%) | 760 (34.4%) | 212 (24.8%) | 548 (40.4%) | < 0.001 |
| Suicide attempt (≤ 19 years old), n | 571 | 156 | 415 | |
| Yes, n (%) | 256 (44.8%) | 66 (42.3%) | 190 (45.8%) | 0.457 |
| Lifetime Psychiatric Illness History | ||||
| Childhood ADHD, n | 2185 | 853 | 1332 | |
| Yes, n (%) | 347 (15.9%) | 110 (12.9%) | 237 (17.8%) | 0.015 |
| Anorexia or bulimia, n | 2200 | 857 | 1343 | |
| Yes, n (%) | 194 (8.8%) | 50 (5.8%) | 144 (10.7%) | 0.005 |
| Binge eating disorder, n | 2202 | 855 | 1347 | |
| Yes, n (%) | 270 (12.3%) | 64 (7.5%) | 206 (15.3%) | < 0.001 |
| Post-traumatic stress disorder, n | 2202 | 855 | 1347 | |
| Yes, n (%) | 574 (26.1%) | 108 (12.6%) | 466 (34.6%) | < 0.001 |
| Obsessive compulsive disorder, n | 2196 | 857 | 1339 | |
| Yes, n (%) | 358 (16.3%) | 73 (8.5%) | 285 (21.3%) | < 0.001 |
| Lifetime Substance Use Disorder | ||||
| Substance use disorder, n | 2135 | 836 | 1299 | |
| Yes, n (%) | 1283 (60.1%) | 458 (54.8%) | 825 (63.5%) | < 0.001 |
| Tobacco use disorder, n | 2200 | 854 | 1346 | |
| Yes, n (%) | 880 (40.0%) | 300 (35.1%) | 580 (43.1%) | < 0.001 |
| Alcohol use disorder, n | 2207 | 856 | 1351 | |
| Yes, n (%) | 879 (39.8%) | 308 (36.0%) | 571 (42.3%) | 0.002 |
| Cocaine use disorder, n | 2193 | 852 | 1341 | |
| Yes, n (%) | 313 (14.3%) | 90 (10.6%) | 223 (16.6%) | < 0.001 |
| Cannabis use disorder, n | 2205 | 855 | 1350 | |
| Yes, n (%) | 658 (29.8%) | 209 (24.4%) | 449 (33.3%) | < 0.001 |
| Methamphetamine use disorder, n | 2200 | 855 | 1345 | |
| Yes, n (%) | 200 (9.1%) | 60 (7.0%) | 140 (10.4%) | 0.002 |
| Opioid use disorder, n | 2189 | 854 | 1335 | |
| Yes, n (%) | 230 (10.5%) | 59 (6.9%) | 171 (12.8%) | < 0.001 |
| Family History (First-degree Relatives) | ||||
| Bipolar disorder, n | 1692 | 648 | 1044 | |
| Yes, n (%) | 799 (47.2%) | 261 (40.3%) | 538 (51.5%) | < 0.001 |
| Anxiety, n | 722 | 296 | 426 | |
| Yes, n (%) | 439 (60.8%) | 147 (49.7%) | 292 (68.5%) | < 0.001 |
| Depression, n | 1877 | 707 | 1170 | |
| Yes, n (%) | 1505 (80.2%) | 513 (72.6%) | 992 (84.8%) | < 0.001 |
| Alcohol use disorder, n | 1894 | 721 | 1173 | |
| Yes, n (%) | 928 (49.0%) | 294 (40.8%) | 634 (54.0%) | < 0.001 |
| Suicide, n | 1873 | 721 | 1152 | |
| Yes, n (%) | 167 (8.9%) | 55 (7.6%) | 112 (9.7%) | 0.124 |
| MCIRS, mean (SD) | 6.19 (6.57) | 5.42 (6.21) | 6.68 (6.75) | < 0.001 |
| Medical comorbidities, n | 758 | 312 | 446 | |
| Hypertension, n (%) | 130 (17.2%) | 52 (16.7%) | 78 (17.5%) | 0.522 |
| Musculoskeletal, integumentary, n (%) | 6 (0.8%) | 2 (0.6%) | 4 (0.9%) | 0.707 |
| Eczema, n (%) | 69 (9.1%) | 27 (8.7%) | 42 (9.4%) | 0.275 |
| Psoriasis, n (%) | 24 (3.2%) | 10 (3.2%) | 14 (3.1%) | 0.721 |
| Vitiligo, n (%) | 4 (0.5%) | 2 (0.6%) | 2 (0.4%) | 0.390 |
| Diabetes, n (%) | 58 (7.7%) | 21 (6.7%) | 37 (8.3%) | 0.193 |
| PCOS, n (%) | 40 (5.3%) | 11 (3.5%) | 29 (6.5%) | 0.172 |
| Thyroid, n (%) | 135 (17.8%) | 61 (19.6%) | 74 (16.6%) | 0.734 |
| Rheum arthritis, n (%) | 14 (1.8%) | 5 (1.6%) | 9 (2.0%) | 0.529 |
| Fibromyalgia, n (%) | 44 (5.8%) | 11 (3.5%) | 33 (7.4%) | 0.068 |
| Stroke, n (%) | 4 (0.5%) | 1 (0.3%) | 3 (0.7%) | 0.882 |
| Epilepsy, n (%) | 50 (6.6%) | 11 (3.5%) | 39 (8.7%) | 0.038 |
| Obstructive sleep apnea, n (%) | 82 (10.8%) | 32 (10.3%) | 50 (11.2%) | 0.666 |
| Migraines, n (%) | 223 (29.4%) | 59 (18.9%) | 164 (36.8%) | < 0.001 |
| Osteoporosis, n (%) | 31 (4.1%) | 7 (2.2%) | 24 (5.4%) | 0.010 |
| Osteoarthritis, n (%) | 94 (12.4%) | 28 (9.0%) | 66 (14.8%) | 0.098 |
| COPD, n (%) | 23 (3.0%) | 8 (2.6%) | 15 (3.4%) | 0.345 |
| Obesity, n | 2118 | 818 | 1300 | |
| Yes, n (%) | 888 (41.9%) | 317 (38.8%) | 571 (43.9%) | 0.140 |
| Current psychotropics, n | 2216 | 854 | 1362 | |
| Mean (SD) | 2.72 (1.58) | 2.50 (1.47) | 2.86 (1.63) | < 0.001 |
| Lifetime psychotropics, n | 2216 | 854 | 1362 | |
| Mean (SD) | 6.68 (5.46) | 5.74 (4.77) | 7.23 (5.77) | < 0.001 |
| Antidepressant-induced mania, n | 1848 | 672 | 1176 | 0.207 |
| Mean (SD) | 384 (20.8%) | 118 (17.6%) | 266 (22.6%) | |
| Tardive dyskinesia, n | 729 | 293 | 436 | |
| Yes, n (%) | 67 (9.2%) | 25 (8.5%) | 42 (9.6%) | 0.391 |
*Not adjusted for age, sex, and site of recruitment
90% of ANX data came from the US, with the rest from Mexico and Chile. Overall, 61.4% (n = 1366) had ANX, with similar rates in BD-I (66.7%) and BD-II (64.3%). Among individuals with BD-I and BD-II, the most common ANX was GAD (50%), followed by panic disorder (30.3%), SAD (21.8%), and phobias (9.6%). There was a trend toward a higher prevalence of GAD in BD-II compared to BD-I (54.0% vs. 47.7%, p = 0.02), although this did not meet the prespecified threshold of p < 0.001; rates of other ANX disorders were similar across BD subtypes (Fig. 1).
Fig. 1.
Prevalence of Anxiety Disorders in Bipolar I and Bipolar II disorders. GAD: generalized anxiety disorder; SAD: social anxiety disorder. *p < 0.05 (trend-level)
Comparison of individuals with BD + ANX (n = 1366) and BD+NoANX (n = 859)
Table 1 shows the comparisons of demographic and clinical characteristics between individuals with BD + ANX and BD+NoANX, adjusted for age, sex, and recruitment site. Individuals with BD + ANX were younger (mean age 40.4 vs. 43.6 years, p < 0.001) and more likely female (66.6% vs. 54.8%, p < 0.001) compared to those with BD+NoANX. They exhibited higher rates of rapid cycling (64.2% vs. 45.2%, p < 0.001), BED (12.3% vs. 7.5%, p < 0.001), PTSD (34.6% vs. 12.6%, p < 0.001), OCD (21.3% vs. 8.5%, p < 0.001), and SUDs (63.5% vs. 54.8%, p < 0.001). Suicidal attempts (40.4% vs. 24.8%, p < 0.001), tobacco use disorder (43.1% vs. 35.1%, p < 0.001), cocaine use disorder (16.6% vs. 10.6%, p < 0.001), cannabis use disorder (33.3% vs. 24.4%, p < 0.001), and opioid use disorder (12.8% vs. 6.9%, p < 0.001) were more prevalent in individuals with BD + ANX, while alcohol use disorder (42.3% vs. 36.0%, p = 0.002) and methamphetamine use disorder (10.4% vs. 7.0%, p = 0.002) showed trend-level differences. A family history of BD (51.5% vs. 40.3%, p < 0.001), anxiety (68.5% vs. 49.7%, p < 0.001), depression (84.8% vs. 72.6%, p < 0.001), and alcohol use disorder (54.0% vs. 40.8%, p < 0.001) was also more common in BD + ANX group. Individuals with BD + ANX had higher MCIRS scores (6.68 vs. 5.42, p < 0.001), more migraines (36.8% vs. 18.9%, p < 0.001), and more current (2.86 vs. 2.50, p < 0.001) and lifetime use of psychotropics (7.27 vs. 5.74, p < 0.001).
Pharmacotherapeutic differences among individuals with BD + ANX (n = 1366) and BD+NoANX (n = 859)
Individuals with BD + ANX (n = 1366) differed significantly from those with BD+NoANX (n = 859) across multiple pharmacotherapeutic variables (Table 2; Fig. 2). Individuals with BD + ANX were less likely to be currently prescribed lithium, a trend‑level difference (37.1% vs. 47.8%, p = 0.005), and showed a trend towards lower valproic acid use (21.7% vs. 29.6%, p = 0.047). In contrast, they were significantly more likely to receive gabapentinoids (8.5% vs. 4.5%, p < 0.001) and benzodiazepines (39.9% vs. 26.6%, p < 0.001). They were also significantly more likely to receive antidepressant treatment (53.8% vs. 39.5%, p < 0.001), including two or more concurrent antidepressants (13.0% vs. 6.9%, p < 0.001), particularly SSRIs (28.8% vs. 16.8%, p < 0.001), and to be prescribed antidepressants without a concomitant mood stabilizer (17.3% vs. 9.7%, p < 0.001). Olanzapine prescriptions were lower among individuals with BD + ANX at a trend-level (8.0% vs. 14.0%, p = 0.008), and there was a trend toward slightly higher lamotrigine use (39.7% vs. 35.8%, p = 0.027). No significant differences were observed for carbamazepine, quetiapine, FGAs, thyroid hormones, stimulants, or dopamine agonists.
Table 2.
Differences in pharmacotherapeutic prescriptions among individuals with bipolar disorder, with and without comorbid anxiety disorder (ANX)
| Total (N = 2225) |
BD+NoANX (N = 859) |
BD + ANX (N = 1366) |
p-value* | |
|---|---|---|---|---|
| Current Prescriptions | ||||
| Lithium, n | 1516 | 579 | 937 | 0.005 |
| Yes, n (%) | 625 (41.2%) | 277 (47.8%) | 348 (37.1%) | |
| Lamotrigine, n | 1700 | 628 | 1072 | 0.027 |
| Yes, n (%) | 651 (38.3%) | 225 (35.8%) | 426 (39.7%) | |
| Valproic acid, n | 1638 | 631 | 1007 | 0.047 |
| Yes, n (%) | 406 (24.8%) | 187 (29.6%) | 219 (21.7%) | |
| Carbamazepine, n | 1543 | 578 | 965 | 0.807 |
| Yes, n (%) | 43 (2.8%) | 15 (2.6%) | 28 (2.9%) | |
| Gabapentinoids, n | 2198 | 848 | 1350 | < 0.001 |
| Yes, n (%) | 153 (7.0%) | 38 (4.5%) | 115 (8.5%) | |
| Benzodiazepines, n | 1985 | 743 | 1242 | < 0.001 |
| Yes, n (%) | 693 (34.9%) | 198 (26.6%) | 495 (39.9%) | |
| Non-BZD sedatives, n | 1974 | 740 | 1234 | 0.387 |
| Yes, n (%) | 154 (7.8%) | 52 (7.0%) | 102 (8.3%) | |
| SGA, n | 2092 | 801 | 1291 | 0.635 |
| Yes, n (%) | 1110 (53.1%) | 418 (52.2%) | 692 (53.6%) | |
| Olanzapine, n | 1544 | 601 | 943 | 0.008 |
| Yes, n (%) | 159 (10.3%) | 84 (14.0%) | 75 (8.0%) | |
| Clozapine, n | 1514 | 585 | 929 | 0.740 |
| Yes, n (%) | 23 (1.5%) | 9 (1.5%) | 14 (1.5%) | |
| Quetiapine, n | 1625 | 620 | 1005 | 0.141 |
| Yes, n (%) | 461 (28.4%) | 171 (27.6%) | 290 (28.9%) | |
| Aripiprazole, n | 1566 | 599 | 967 | 0.353 |
| Yes, n (%) | 250 (16.0%) | 84 (14.0%) | 166 (17.2%) | |
| Risperidone, n | 1551 | 597 | 954 | 0.735 |
| Yes, n (%) | 128 (8.3%) | 48 (8.0%) | 80 (8.4%) | |
| FGA, n | 1583 | 602 | 981 | 0.388 |
| Yes, n (%) | 25 (1.6%) | 12 (2.0%) | 13 (1.3%) | |
| Any antidepressants, n | 2102 | 783 | 1319 | < 0.001 |
| Yes, n (%) | 1018 (48.4%) | 309 (39.5%) | 709 (53.8%) | |
| Two or more antidepressants, n | 2102 | 783 | 1319 | <0.001 |
| Yes, n (%) | 226 (10.8%) | 54 (6.9%) | 172 (13.0%) | |
| SSRI, n | 2078 | 772 | 1306 | < 0.001 |
| Yes, n (%) | 506 (24.4%) | 130 (16.8%) | 376 (28.8%) | |
| SNRI, n | 1764 | 656 | 1108 | 0.254 |
| Yes, n (%) | 228 (12.9%) | 77 (11.7%) | 151 (13.6%) | |
| TCA, n | 1894 | 684 | 1210 | 0.140 |
| Yes, n (%) | 83 (4.4%) | 26 (3.8%) | 57 (4.7%) | |
| Antidepressant without MS, n | 2160 | 828 | 1332 | < 0.001 |
| Yes, n (%) | 311 (14.4%) | 80 (9.7%) | 231 (17.3%) | |
| Thyroid hormone, n | 1185 | 431 | 754 | 0.067 |
| Yes, n (%) | 262 (22.1%) | 113 (26.2%) | 149 (19.8%) | |
| Stimulants/wakefulness agents, n | 1675 | 610 | 1065 | 0.939 |
| Yes, n (%) | 199 (11.9%) | 67 (11.0%) | 132 (12.4%) | |
| Dopamine agonist, n | 1684 | 618 | 1066 | 0.769 |
| Yes, n (%) | 24 (1.4%) | 9 (1.5%) | 15 (1.4%) | |
| Two or more SGA, n | 2092 | 801 | 1291 | 0.430 |
| Yes, n (%) | 91 (4.3%) | 39 (4.9%) | 52 (4.0%) | |
| One or more MS, n | 2165 | 833 | 1332 | 0.060 |
| Yes, n (%) | 1503 (69.4%) | 617 (74.1%) | 886 (66.5%) | |
| Two or more MSs, n | 2165 | 833 | 1332 | 0.597 |
| Yes, n (%) | 220 (10.2%) | 87 (10.4%) | 133 (10.0%) | |
| Three or more MSs, n | 2165 | 833 | 1332 | 0.957 |
| Yes, n (%) | 2 (0.1%) | 0 (0.0%) | 2 (0.2%) | |
| No MS, n | 2165 | 833 | 1332 | 0.060 |
| Yes, n (%) | 662 (30.6%) | 216 (25.9%) | 446 (33.5%) | |
| No medications, n | 2216 | 854 | 1362 | 0.762 |
| Yes, n (%) | 183 (8.3%) | 62 (7.3%) | 121 (8.9%) | |
| Lifetime Prescriptions | ||||
| Lithium ever, n | 1876 | 717 | 1159 | 0.030 |
| Yes, n (%) | 1009 (53.8%) | 415 (57.9%) | 594 (51.3%) | |
| Lamotrigine ever, n | 2063 | 781 | 1282 | 0.150 |
| Yes, n (%) | 880 (42.7%) | 310 (39.7%) | 570 (44.5%) | |
| Valproate ever, n | 2071 | 783 | 1288 | 0.013 |
| Yes, n (%) | 769 (37.1%) | 323 (41.3%) | 446 (34.6%) | |
| Gabapentinoids ever, n | 2198 | 848 | 1350 | < 0.001 |
| Yes, n (%) | 286 (13.0%) | 75 (8.8%) | 211 (15.6%) | |
| Benzodiazepines ever, n | 1985 | 743 | 1242 | < 0.001 |
| Yes, n (%) | 1060 (53.4%) | 330 (44.4%) | 730 (58.8%) | |
| FGA ever, n | 1583 | 602 | 981 | 0.897 |
| n (%) | 163 (10.3%) | 66 (11.0%) | 97 (9.9%) | |
| SGA ever, n | 2092 | 801 | 1291 | 0.172 |
| n (%) | 1457 (69.6%) | 534 (66.7%) | 923 (71.5%) | |
| Olanzapine ever, n | 2043 | 788 | 1255 | 0.034 |
| Yes, n (%) | 374 (18.3%) | 171 (21.7%) | 203 (16.2%) | |
| Quetiapine ever, n | 2050 | 788 | 1262 | 0.010 |
| Yes, n (%) | 787 (38.4%) | 274 (34.8%) | 513 (40.6%) | |
| Aripiprazole ever, n | 2042 | 785 | 1257 | 0.386 |
| n (%) | 543 (26.6%) | 178 (22.7%) | 365 (29.0%) | |
| Risperidone ever, n | 2045 | 789 | 1256 | 0.773 |
| n (%) | 419 (20.5%) | 153 (19.4%) | 266 (21.2%) | |
| Any antidepressant ever, n | 2102 | 783 | 1319 | < 0.001 |
| n (%) | 1710 (81.4%) | 590 (75.4%) | 1120 (84.9%) | |
| SSRI ever, n | 2078 | 772 | 1306 | < 0.001 |
| Yes, n (%) | 1373 (66.1%) | 452 (58.5%) | 921 (70.6%) | |
| SNRI ever, n | 1764 | 656 | 1108 | 0.002 |
| Yes, n (%) | 644 (36.5%) | 200 (30.5%) | 444 (40.1%) | |
| TCA ever, n | 1894 | 684 | 1210 | 0.012 |
| Yes, n (%) | 350 (18.5%) | 105 (15.4%) | 245 (20.2%) | |
| Antidepressant without MS ever, n | 2164 | 831 | 1333 | < 0.001 |
| n (%) | 267 (12.3%) | 72 (8.7%) | 195 (14.6%) | |
| Thyroid hormone ever, n | 1185 | 431 | 754 | 0.300 |
| n (%) | 120 (10.1%) | 51 (11.8%) | 69 (9.2%) | |
| Stimulants/wakefulness agents ever, n | 1675 | 610 | 1065 | 0.060 |
| n (%) | 432 (25.8%) | 129 (21.1%) | 303 (28.5%) | |
| Dopamine agonist ever, n | 1684 | 618 | 1066 | 0.736 |
| n (%) | 44 (2.6%) | 17 (2.8%) | 27 (2.5%) | |
| Two or more SGAs ever, n | 2092 | 801 | 1291 | 0.866 |
| n (%) | 640 (30.6%) | 229 (28.6%) | 411 (31.8%) | |
| One or more MS ever, n | 2165 | 833 | 1332 | 0.043 |
| n (%) | 1799 (83.1%) | 714 (85.7%) | 1085 (81.5%) | |
| Two or more MSs ever, n | 2165 | 833 | 1332 | 0.873 |
| n (%) | 721 (33.3%) | 279 (33.5%) | 442 (33.2%) | |
| Three or more MSs ever, n | 2165 | 833 | 1332 | 0.415 |
| n (%) | 249 (11.5%) | 101 (12.1%) | 148 (11.1%) | |
| No MS ever, n | 2165 | 833 | 1332 | 0.043 |
| n (%) | 366 (16.9%) | 119 (14.3%) | 247 (18.5%) | |
| No medications ever, n | 2216 | 854 | 1362 | 0.827 |
| n (%) | 36 (1.6%) | 15 (1.8%) | 21 (1.5%) | |
*Models adjusted for age, sex, and recruitment site
ANX: anxiety disorders; BZD: benzodiazepines; FGA: first-generation antipsychotics; MS: mood stabilizer (includes valproate, lamotrigine, carbamazepine, and lithium); SGA: second-generation antipsychotic; SNRI: serotonin-norepinephrine reuptake inhibitor; SSRI: selective serotonin reuptake inhibitor; TCA: tricyclic antidepressant
Fig. 2.
Current and lifetime pharmacotherapeutic prescribing patterns among individuals with bipolar disorder, with and without comorbid anxiety disorders (ANX), by Bipolar I and Bipolar II subtypes. Benzo: benzodiazepines; CBZ: carbamazepine; FGA: first-generation antipsychotics; SGA: second-generation antipsychotics; SNRI: serotonin-norepinephrine reuptake inhibitor; SSRI: selective serotonin reuptake inhibitor; TCA: tricyclic antidepressants. *p < 0.05 (trend-level);. **p < 0.001
Lifetime treatment patterns mirrored these trends: individuals with BD + ANX showed a trend toward lower lifetime use of lithium (51.3% vs. 57.9%, p = 0.030) and a trend toward lower valproate use (34.6% vs. 41.3%, p = 0.013) but were significantly more likely to have received gabapentinoids (15.6% vs. 8.8%, p < 0.001), benzodiazepines (58.8% vs. 44.4%, p < 0.001), SSRIs (70.6% vs. 58.5%, p < 0.001), and SNRIs (40.1% vs. 30.5%, p = 0.002; trend-level). They were also more likely to have been treated with antidepressants without a mood stabilizer (14.6% vs. 8.7%, p < 0.001).
Pharmacotherapeutic differences among individuals with BD-I and ANX (n = 878) and those without ANX (n = 573)
Among individuals with BD-I (ANX n = 878; NoANX n = 573), significant differences were observed in several pharmacotherapeutic variables (Supplementary Table 1, Fig. 2). Individuals with BD-I + ANX were less likely to be currently prescribed lithium, a trend‑level difference (37.1% vs. 50.7%, p = 0.009), while valproic acid (22.4% vs. 31.2%, p = 0.137) and lamotrigine use (34.6% vs. 31.8%, p = 0.092) showed no statistically significant differences. There was a trend toward higher use of gabapentinoids (7.8% vs. 4.8%, p = 0.041) and significantly higher use of benzodiazepines (39.4% vs. 27.7%, p < 0.001). Olanzapine prescriptions trended lower among individuals with BD-I + ANX (9.4% vs. 16.9%, p = 0.023), while no significant differences were observed for carbamazepine, quetiapine, aripiprazole, risperidone, FGAs, or SGA overall. Regarding antidepressants, individuals with BD-I + ANX were significantly more likely to be prescribed any antidepressant (48.8% vs. 37.0%, p < 0.001), two or more concurrent antidepressants (10.5% vs. 4.0%, p < 0.001), and SSRIs (26.5% vs. 15.5%, p < 0.001), and demonstrated a trend‑level higher likelihood of receiving antidepressants without a concomitant mood stabilizer (15.9% vs. 7.7%, p = 0.001). Thyroid hormone use was lower at a trend level among ANX individuals (17.2% vs. 27.2%, p = 0.014).
Lifetime treatment patterns reflected similar trends: individuals with BD-I + ANX showed a trend toward lower lithium use (54.6% vs. 61.9%, p = 0.035) and a trend toward higher use of gabapentinoids (15.6% vs. 9.0%, p = 0.015), and quetiapine (41.3% vs. 35.7%, p = 0.052). They were also more likely to have received benzodiazepines (59.2% vs. 46.7%, p < 0.001) and SSRIs (70.9% vs. 56.6%, p < 0.001), with only these reaching statistical significance.
Pharmacotherapeutic differences among individuals with BD-II and ANX (n = 460) and those without ANX (n = 263)
Among individuals with BD-II (ANX n = 460; No ANX n = 263), similar differences emerged in pharmacotherapy patterns (Supplementary Table 2, Fig. 2). Individuals with BD-II + ANX were more likely to receive gabapentinoids, a trend‑level difference (9.5% vs. 3.5%, p = 0.005) and were significantly more likely to receive benzodiazepines (40.3% vs. 24.1%, p < 0.001) compared to those without ANX. They were also significantly more likely to be prescribed SSRIs (31.8% vs. 18.9%, p < 0.001) and any antidepressant (62.6% vs. 43.9%, p < 0.001). There was a trend-level increase in antidepressant use without a mood stabilizer (19.2% vs. 13.0%, p = 0.046). Current use of lamotrigine (51.4% vs. 46.6%, p = 0.313) and valproic acid (21.0% vs. 24.9%, p = 0.452) did not differ significantly, nor did prescriptions for carbamazepine, quetiapine, aripiprazole, risperidone, FGAs, or thyroid hormones.
Lifetime treatment patterns reflected similar trends: individuals with BD-II + ANX showed a trend toward higher gabapentinoid use (15.0% vs. 8.5%, p = 0.015), trend-level higher SNRI use (48.2% vs. 35.2%, p = 0.002), and were significantly more likely to have received benzodiazepines (57.2% vs. 37.7%, p < 0.001). There was also a trend toward higher quetiapine use (39.0% vs. 30.2%, p = 0.030) and trend-level higher overall SGA use (64.2% vs. 51.7%, p = 0.003). In contrast, lifetime lithium and valproate use showed no significant differences.
Effect of comorbid ANX on Pharmacological treatment response in BD
Individuals with BD + ANX demonstrated significantly lower treatment responses to lithium (4.91 vs. 6.05, p < 0.001) and significantly lower responses to SGAs (4.67 vs. 5.73, p < 0.001), as measured by Alda A scores (Table 3). Responses to MSACs showed a trend‑level reduction (5.16 vs. 6.01, p = 0.005). Sensitivity analyses excluding individuals with Alda B scores > 4 showed similar patterns in pharmacological treatment response (Alda A scores): individuals with BD+ANX exhibited trend-level lower responses to lithium (5.00 vs. 6.12, p = 0.008), SGAs (4.66 vs. 5.67, p = 0.002), and MSACs (5.24 vs. 6.01, p = 0.036).This pattern of reduced treatment response was evident in both BD-I and BD-II subtypes. However, the differences between groups were less pronounced in BD-II than in BD-I, and the bipolar II comparisons were further limited by smaller sample sizes.
Table 3.
Treatment responses of lithium, mood-stabilizing anticonvulsants (MSACs) and second-generation antipsychotics (SGAs), measured by Alda A scores, in individuals with bipolar disorder with and without comorbid anxiety disorders (ANX)
| All* | BD-I | BD-II | |||||||
|---|---|---|---|---|---|---|---|---|---|
| NoANX | ANX | p-value | NoANX | ANX | p-value | NoANX | ANX | p-value | |
| Lithium response (Alda-A), n | 289 | 404 | < 0.001 | 218 | 300 | 0.004 | 66 | 92 | 0.183 |
| Mean (SD) | 6.05 (2.65) | 4.91 (2.93) | 6.28 (2.59) | 5.07 (2.88) | 5.21 (2.78) | 4.39 (3.03) | |||
| MSACs (Alda-A), n | 232 | 387 | 0.005 | 153 | 248 | 0.011 | 78 | 138 | 0.165 |
| Mean (SD) | 6.01 (2.56) | 5.16 (2.59) | 6.12 (2.46) | 5.08 (2.53) | 5.77 (2.76) | 5.26 (2.67) | |||
| SGAs (Alda-A), n | 230 | 309 | < 0.001 | 131 | 257 | 0.002 | 46 | 100 | 0.080 |
| Mean (SD) | 5.73 (2.56) | 4.67 (2.69) | 5.87 (2.58) | 4.69 (2.70) | 5.48 (2.51) | 4.65 (2.71) | |||
*Including BD-I, BD-II and schizoaffective (bipolar type)
Discussion
This study represents one of the largest systematic examinations of the clinical, demographic, and pharmacotherapeutic differences between individuals with BD + ANX and BD+NoANX. Our findings reveal that anxiety comorbidity in BD is associated with a more complex clinical presentation, greater psychiatric and somatic burden, distinct prescribing patterns, and poorer treatment response to mood stabilizers. These observations have important implications for clinical management and highlight the need for tailored therapeutic approaches in this phenotypically distinct subgroup.
Clinical and demographic characteristics
Individuals with BD + ANX demonstrated a significantly more severe clinical profile compared to those of BD+NoANX. The higher prevalence of rapid cycling, suicide attempts, and multiple SUDs in the BD + ANX group suggests a more treatment-refractory illness course. Consistent with previous research showing that ANX emerge earlier and are more common in women (Simon et al. 2004; Vazquez et al. 2014), our BD + ANX group demonstrated younger age and female predominance. This pattern supports the hypothesis that anxiety may be an early predictor of BD onset (McElroy et al. 2001). Our findings align with a prior systematic review showing greater SUD prevalence and poorer treatment response in individuals with BD + ANX (Vazquez et al. 2014). The association with a history of suicide attempts is consistent with data from the Systematic Treatment Enhancement Program for Bipolar Disorder (Simon et al. 2004), reinforcing that the ANX phenotype reflects a more severe clinical profile. The substantially elevated rates of PTSD, BED, and OCD in the BD + ANX cohort further underscore the complex psychiatric burden characterizing this population. We combined heterogeneous ANX into a single cluster due to similar rates across subtypes and limited statistical power for subtype analyses. However, this approach may obscure differential effects, as certain ANX (e.g., GAD, panic disorder) may influence treatment outcomes more substantially than others (e.g., specific phobias). Future longitudinal studies with larger samples examining individual anxiety subtypes and functional outcomes would clarify their differential impacts on treatment response in BD.
The familial aggregation patterns observed in our study are particularly noteworthy. Individuals with BD + ANX had significantly higher rates of family history across multiple psychiatric conditions, including BD, ANX, depression, and alcohol use disorder. This may suggest potential shared genetic vulnerabilities (Williams et al. 2024) or environmental factors that predispose to both mood and anxiety pathology, consistent with emerging evidence of common neurobiological substrates underlying these disorders (Lopes et al. 2020; Maki et al. 2025). One hypothesis suggests that both disorders may share a core fronto-limbic network, characterized by overlapping amygdala hyperactivation and impaired prefrontal cortex regulation (Bi et al. 2022).
The increased medical comorbidity burden, as reflected by higher MCIRS scores and elevated migraine prevalence in the BD + ANX group, aligns with the growing recognition that psychiatric comorbidity often parallels increased somatic illness (Romo-Nava et al. 2021). The substantially higher medication burden—both current and lifetime—in the ANX comorbid group likely reflects both the greater clinical complexity and the challenges in achieving symptom control in this population (Feske et al. 2000).
Pharmacotherapeutic patterns and implications
A key finding of this study is the distinct pharmacotherapeutic patterns observed between individuals with and without comorbid ANX. Individuals with BD + ANX were significantly less likely to receive lithium and valproic acid, medications with robust evidence for mood stabilization and suicide prevention in BD. Conversely, they were more likely to be prescribed monoaminergic antidepressants, including SSRIs and SNRIs, as well as adjunctive agents such as benzodiazepines and gabapentinoids. Our findings are consistent with prior reports showing higher rates of antidepressant prescriptions among individuals with BD + ANX (Galimberti et al. 2020; Keck et al. 2006; Vazquez et al. 2014). Notably, a substantial proportion of BD + ANX individuals received antidepressants without concurrent mood stabilizers—a pattern that may increase risk of affective instability, rapid cycling, and mood switching, particularly in BD-I. It remains unclear whether unimodal antidepressant use, lower rates of lithium/MSAC use, anxiety comorbidity itself, or associated substance use comorbidity drives illness severity measures such as cycle acceleration and mood switching (Altshuler et al. 1995).
The lower utilization of lithium and valproate in the BD + ANX group may reflect multiple factors. Clinicians may avoid these agents due to concerns about tolerability, narrow therapeutic windows, or monitoring requirements in patients already managing complex medication regimens. The reduced valproate prescribing may also reflect growing safety concerns, particularly in women of childbearing potential due to teratogenic risks (Freeman 2022) and, more recently, emerging recommendations in European guidelines to exercise caution even in men (Singh et al. 2025b). Lamotrigine emerged as a notable exception, with slightly higher utilization in the BD + ANX group, particularly among those with BD-II. This pattern may reflect lamotrigine’s favorable tolerability profile and its efficacy in preventing depressive episodes (Keck et al. 2006). The preference for antidepressants may stem from attempts to target depressive and anxious symptoms concurrently, despite limited evidence supporting antidepressant monotherapy in BD (Elmosalamy et al. 2025). The increased prescription of benzodiazepines and gabapentinoids likely represents symptomatic management of anxiety, though benzodiazepines carry risks of dependence and may not address underlying mood dysregulation. The relative safety of gabapentinoids, and their evidence base for use in GAD and SAD, without increasing the risk of affective switching, highlight their potential role in this specific phenotype (Frye and Singh 2024; Rickels et al. 2005). The observation that individuals with BD + ANX received significantly more lifetime medications underscores the complexity of treating this difficult-to-treat population.
Treatment response and clinical outcomes
Perhaps most clinically significant is the substantially poorer treatment response to mood stabilizers among individuals with BD + ANX, as measured by the Alda-A score. Mean response scores for lithium, MSACs, and SGAs were all significantly lower in the anxiety comorbid group across both BD subtypes, with the most pronounced differences in BD-I. These findings suggest ANX comorbidity may represent a negative prognostic indicator associated with attenuated response to standard mood stabilizers. This finding aligns with previous research indicating that anxiety is a predictor of poor outcomes in BD (Feske et al. 2000; Gaudiano and Miller 2005; Simon et al. 2004). This treatment resistance may reflect distinct neurobiological underpinnings—such as hyperactive fear circuitry, dysregulated stress response systems, or altered serotonergic, GABAergic, and dopaminergic neurotransmission—not adequately addressed by conventional mood stabilizers (Freeman et al. 2002). Additionally, chronic anxiety symptoms may perpetuate mood instability (Coryell et al. 2012) through behavioral mechanisms including sleep disruption and substance use as self-medication.
The BD + ANX group showed diminished lithium response, consistent with earlier reports of reduced efficacy in comorbid anxiety (Feske et al. 2000; Young et al. 1993), despite lithium’s antisuicidal effects. A recent open-label trial suggests lithium may improve comorbid anxiety in bipolar disorder, with similar effects at low (< 0.5) and high (> 0.5) doses, and improvements correlated with depressive symptoms (Jones et al. 2022). We lack lithium dose data and adherence information in our cohort, which should be addressed in future studies. However, randomized controlled trial evidence for this population remains limited (Kauer-Sant’Anna et al. 2009; Yatham et al. 2018). It is unclear whether anxiety comorbidity alters the neurobiology of BD, reducing lithium responsiveness, or whether factors such as suboptimal dosing, adherence challenges, or early discontinuation contribute. The cross-sectional design of this study limits causal conclusions, highlighting the need for future research using longitudinal treatment data and objective adherence measures.
Subtype-Specific considerations
Subgroup analyses revealed shared and distinct patterns in BD-I and BD-II with comorbid anxiety. In both subtypes, anxiety was associated with increased antidepressant use, higher rates of benzodiazepine and gabapentinoid prescriptions, and reduced lithium utilization. However, several differences emerged. Among individuals with BD-I, anxiety comorbidity was associated with significantly lower olanzapine use and reduced thyroid hormone supplementation. In contrast, individuals with BD-II + ANX showed higher lifetime use of SGAs, particularly quetiapine, possibly reflecting its efficacy for both BD depression and GAD.
GAD was more prevalent in BD-II than BD-I, consistent with the depressive predominance of BD-II and the frequent co-occurrence of anxiety with depressive states. The particularly high rate of antidepressant monotherapy in BD-II individuals with anxiety (19.2% without mood stabilizer coverage) raises concerns about potential mood destabilization, even though the risk of treatment-emergent mania may be lower in BD-II than BD-I.
Clinical and research implications
The findings of this study have several important clinical implications. First, the presence of comorbid ANX should be recognized as a marker of illness severity and treatment complexity in BD. Clinicians should maintain heightened vigilance for suicide risk, substance use, and somatic comorbidities in this population, implementing comprehensive assessment and monitoring strategies. Second, the prevalent use of antidepressant monotherapy in the BD + ANX group—particularly without mood stabilizer coverage—warrants critical reconsideration. Antidepressant-induced activation, agitation, and insomnia may mimic or exacerbate anxiety symptoms, potentially perpetuating inappropriate antidepressant use through symptom misattribution. While anxiety symptoms are distressing and merit treatment, antidepressant monotherapy in BD carries risks of mood destabilization. Current clinical guidelines recommend mood stabilizer therapy as the foundation of BD treatment, with adjunctive agents added as needed for residual symptoms. Our data suggest this principle may be frequently overlooked in individuals with prominent anxiety, potentially contributing to the poorer outcomes observed in this group. Third, the reduced treatment response to standard mood stabilizers in BD + ANX highlights the need for alternative or augmentation strategies. Potential approaches might include psychotherapy (particularly cognitive-behavioral therapy with anxiety-specific modules), optimization of mood stabilizer dosing before adding additional agents, or investigation of novel pharmacological targets. Short-term (8 weeks) data support quetiapine as a helpful option for BD + ANX (Hirschfeld et al. 2006; Sheehan et al. 2013).
Limitations
Several limitations of this study merit consideration. First, the cross-sectional design precludes causal inferences about the relationships between ANX comorbidity, prescribing patterns, and treatment outcomes. Because data on current anxiety symptoms were not available, we were unable to assess the influence of symptom severity on pharmacotherapeutic patterns. Our study did not capture the anxious distress specifier (Bartoli et al. 2024), limiting our ability to distinguish persistent comorbid ANX from episodic anxious distress during mood episodes. Additionally, we did not assess predominant polarity, which may be associated with anxiety comorbidity and could confound the observed relationships between anxiety and treatment outcomes (Bartoli et al. 2024). Future studies incorporating both measures would provide more comprehensive characterization of anxiety presentations in bipolar disorder and their treatment implications. Longitudinal studies tracking symptom trajectories and medication changes over time would provide more definitive insights. Second, ANX diagnoses were determined at enrollment and may not reflect the full longitudinal course of anxiety symptoms, which can fluctuate with mood state. Third, our sample was predominantly recruited from U.S. sites and largely composed of white participants, with limited representation from Mexico and Chile, which may limit generalizability. Fourth, we did not assess affective temperaments, which may influence clinical presentation and treatment response in BD (Fico et al. 2023; Karam et al. 2023). Anxious temperament could potentially mediate the observed associations between anxiety comorbidity and treatment outcomes, limiting our ability to distinguish effects of ANX from underlying temperamental vulnerabilities. Fifth, our dataset did not include information on mixed features, which frequently co-occur with ANX in BD (Bartoli et al. 2020). Mixed states may confound the observed associations between anxiety comorbidity and treatment outcomes. Finally, we did not have detailed data on antidepressant treatment duration, dosing, or specific indications, which would help clarify whether these agents were prescribed primarily for anxiety, depression, or both.
Conclusions
This large-scale study reports that 60% of individuals with BD have comorbid ANX, forming a clinically distinct subgroup with greater psychiatric and somatic complexity, unique pharmacotherapy patterns, and reduced response to standard mood stabilizers. Widespread antidepressant use—often without adequate mood stabilizer coverage—and high benzodiazepine use raise concerns about current prescribing practices. These findings require replication and emphasize the need for anxiety-informed treatment strategies, further research into neurobiological mechanisms of poor treatment response, and more intensive monitoring and tailored interventions for this high-risk population.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We deeply thank the Mayo Clinic Bipolar Disorder Biobank participants for their time and generous contributions.
Author contributions
Balwinder Singh: Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Data curation, Conceptualization.Ada Man-Choi Ho: Writing – original draft, Writing – review & editing, Investigation, Visualization, Validation, Conceptualization.Brandon J. Coombes: Writing – review & editing, Methodology, Formal analysis, Data curation.Richard Pendegraft: Methodology, Formal analysis, Data curation.Manuel Gardea-Resendez: Writing – review & editing, Methodology, Investigation.David J. Bond: Writing – review & editing, Methodology, Investigation.Miguel L. Prieto: Writing – review & editing, Methodology, Investigation.Marin Veldic: Writing – review & editing, Methodology, Investigation.Susan L. McElroy: Writing – review & editing, Validation, Methodology.Joanna M. Biernacka: review & editing, Investigation, Funding acquisition.Alfredo Cuellar-Barboza: Writing – review & editing, Methodology, Investigation.Francisco Romo-Nava: Writing – review & editing, Methodology, Investigation.Mark A. Frye: Writing – review & editing, Investigation, Funding acquisition.All authors contributed to reviewing and editing the manuscript.
Funding
The Mayo Clinic Bipolar Disorder Biobank was supported by the Marriott Foundation, and by the Thomas and Elizabeth Grainger Fund in Bipolar Functional Genomics and Drug Development. Project Generation was supported in part by Mayo Clinic Center for Individualized Medicine. This publication was supported by CTSA Grant Number KL2 TR002379 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Ethics approval for The Mayo Clinic Bipolar Disorder Biobank has been obtained from the Mayo Clinic Institutional Review Board (reference ID: 08-008794). Written informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
BS reports research grant support from Mayo Clinic, the National Network of Depression Centers, and Breakthrough Discoveries for Thriving with Bipolar Disorder (BD2). He is a KL2 Mentored Career Development Program scholar, supported by CTSA Grant Number KL2 TR002379 from the National Center for Advancing Translational Science (NCATS). He has received honoraria (to Mayo Clinic) from Elsevier for editing a Clinical Overview on Treatment-Resistant Depression. DJB reports research grant support from Breakthrough Discoveries for Thriving with Bipolar Disorder (BD2). MAF received grant support from Assurex Health and Mayo Foundation, received CME travel and honoraria from Carnot Laboratories and American Physician Institute, and has Financial Interest/Stock ownership/Royalties from Chymia LLC. SLM is a consultant to, or member of the scientific advisory boards of, in the past year: Axsome, Idorsia, Levo, Kallyope, Novo Nordisk, Otsuka, and Soleno. SLM is presently or has been in the past year a principal or co-investigator on research studies sponsored by: Axsome, Idorsia, Marriott Foundation, National Institute of Mental Health, Novo Nordisk, and Otsuka. Patents: SLM is also inventor on United States Patent No. 6,323,236 B2, Use of Sulfamate Derivatives for Treating Impulse Control Disorders, and, along with the patent’s assignee, University of Cincinnati, Cincinnati, OH, has received payments from Johnson & Johnson Pharmaceutical Research & Development, L.L.C., which has exclusive rights under the patent. FRN is supported in part by NIMH grants K23MH120503 and 1R61MH133770-01A1; is the inventor on a U.S. Patent and Trademark Office patent # 10,857,356. MGR receives research support from Conahcyt CONAHCYT (Mexico). The rest of the co-authors have no conflict of interest to declare.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- Altshuler LL, Post RM, Leverich GS, Mikalauskas K, Rosoff A, Ackerman L. Antidepressant-induced mania and cycle acceleration: a controversy revisited. Am J Psychiatry. 1995;152:1130–8. 10.1176/ajp.152.8.1130. [DOI] [PubMed] [Google Scholar]
- Bartoli F, Crocamo C, Carra G. Clinical correlates of DSM-5 mixed features in bipolar disorder: A meta-analysis. J Affect Disord. 2020;276:234–40. 10.1016/j.jad.2020.07.035. [DOI] [PubMed] [Google Scholar]
- Bartoli F, Bachi B, Callovini T, Palpella D, Piacenti S, Morreale M, Di Lella ME, Crocamo C, Carra G, Investigators N. Anxious distress in people with major depressive episodes: a cross-sectional analysis of clinical correlates. CNS Spectr. 2024;29:49–53. 10.1017/S1092852923002377. [DOI] [PubMed] [Google Scholar]
- Bi B, Che D, Bai Y. Neural network of bipolar disorder: toward integration of neuroimaging and neurocircuit-based treatment strategies. Transl Psychiatry. 2022;12:143. 10.1038/s41398-022-01917-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Burdick KE, Millett CE, Yocum AK, Altimus CM, Andreassen OA, Aubin V, Belzeaux R, Berk M, Biernacka JM, Blumberg HP, Cleare AJ, Diaz-Byrd C, Dubertret C, Etain B, Eyler LT, Forester BP, Fullerton JM, Frye MA, Gard S, Godin O, Haffen E, Klaus F, Lagerberg TV, Leboyer M, Martinez-Aran A, McElroy S, Mitchell PB, Olie E, Olorunfemi P, Passerieux C, Peters AT, Pham DL, Polosan M, Potter JR, Sajatovic M, Samalin L, Schwan R, Shanahan M, Sole B, Strawbridge R, Stuart AL, Torres I, Ueland T, Vieta E, Williams LJ, Wrobel AL, Yatham LN, Young AH, Nierenberg AA, McInnis MG. Predictors of functional impairment in bipolar disorder: results from 13 cohorts from seven countries by the global bipolar cohort collaborative. Bipolar Disord. 2022. 10.1111/bdi.13208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Coryell W, Fiedorowicz JG, Solomon D, Leon AC, Rice JP, Keller MB. Effects of anxiety on the long-term course of depressive disorders. Br J Psychiatry. 2012;200:210–5. 10.1192/bjp.bp.110.081992. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cuellar-Barboza AB, McElroy SL, Veldic M, Singh B, Kung S, Romo-Nava F, Nunez NA, Cabello-Arreola A, Coombes BJ, Prieto M, Betcher HK, Moore KM, Winham SJ, Biernacka JM, Frye MA. Potential Pharmacogenomic targets in bipolar disorder: considerations for current testing and the development of decision support tools to individualize treatment selection. Int J Bipolar Disord. 2020;8:23. 10.1186/s40345-020-00184-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cullen C, Kappelmann N, Umer M, Abdolizadeh A, Husain MO, Bonato S, Sharma G, Xue S, Ortiz A, Kloiber SM, Mulsant BH, Husain MI. Efficacy and acceptability of pharmacotherapy for comorbid anxiety symptoms in bipolar disorder: A systematic review and meta-analysis. Bipolar Disord. 2021;23:754–66. 10.1111/bdi.13125. [DOI] [PubMed] [Google Scholar]
- Elmosalamy A, Keeth N, Park JH, Gerberi DJ, McElroy SL, Frye MA, Singh B. Systematic review of Second-Generation antidepressant monotherapy for acute Bipolar-II depression. Psychopharmacol Bull. 2025;55:79–103. 10.64719/pb.4545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Feske U, Frank E, Mallinger AG, Houck PR, Fagiolini A, Shear MK, Grochocinski VJ, Kupfer DJ. Anxiety as a correlate of response to the acute treatment of bipolar I disorder. Am J Psychiatry. 2000;157:956–62. 10.1176/appi.ajp.157.6.956. [DOI] [PubMed] [Google Scholar]
- Fico G, Janiri D, Pinna M, Sague-Vilavella M, Gimenez Palomo A, Oliva V, De Prisco M, Cortez PG, Anmella G, Gonda X, Sani G, Tondo L, Vieta E, Murru A. Affective temperaments mediate aggressive dimensions in bipolar disorders: A cluster analysis from a large, cross-sectional, international study. J Affect Disord. 2023;323:327–35. 10.1016/j.jad.2022.11.084. [DOI] [PubMed] [Google Scholar]
- Freeman MP. Prescribing guideline for valproic acid and women of reproductive potential: forget it exists. J Clin Psychiatry. 2022;83. 10.4088/JCP.22ed14609. [DOI] [PubMed]
- Freeman MP, Freeman SA, McElroy SL. The comorbidity of bipolar and anxiety disorders: prevalence, psychobiology, and treatment issues. J Affect Disord. 2002;68:1–23. 10.1016/s0165-0327(00)00299-8. [DOI] [PubMed] [Google Scholar]
- Frye MA, Singh B. Other anticonvulsants: Pregabalin, gabapentin, and topiramate. In: Nemeroff CB, Craighead WE, editors. Anxiety and depression association of America patient guide to mood and anxiety disorders. Amer Psychiatric Pub Inc; 2024. pp. 275–80.
- Frye MA, McElroy SL, Fuentes M, Sutor B, Schak KM, Galardy CW, Palmer BA, Prieto ML, Kung S, Sola CL, Ryu E, Veldic M, Geske J, Cuellar-Barboza A, Seymour LR, Mori N, Crowe S, Rummans TA, Biernacka JM. Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses. Int J Bipolar Disord. 2015;3:30. 10.1186/s40345-015-0030-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Galimberti C, Caricasole V, Bosi MF, Vigano CA, Ketter TA, Dell’Osso B. Clinical features and patterns of psychopharmacological prescription in bipolar patients with vs without anxiety disorders at onset. Early Interv Psychiatry. 2020;14:714–22. 10.1111/eip.12900. [DOI] [PubMed] [Google Scholar]
- Gardea-Resendez M, Winham SJ, Romo-Nava F, Cuellar-Barboza A, Clark MM, Andreazza AC, Cabello-Arreola A, Veldic M, Bond DJ, Singh B, Prieto ML, Nunez NA, Betcher H, Moore KM, Blom T, Colby C, Pendegraft RS, Kelpin SS, Ozerdem A, Miola A, De Filippis E, Biernacka JM, McElroy SL, Frye MA. Quantification of diet quality utilizing the rapid eating assessment for participants-shortened version in bipolar disorder: implications for prospective depression and cardiometabolic studies. J Affect Disord. 2022;310:150–5. 10.1016/j.jad.2022.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gaudiano BA, Miller IW. Anxiety disorder comobidity in bipolar I disorder: relationship to depression severity and treatment outcome. Depress Anxiety. 2005;21:71–7. 10.1002/da.20053. [DOI] [PubMed] [Google Scholar]
- Grof P, Duffy A, Cavazzoni P, Grof E, Garnham J, MacDougall M, O’Donovan C, Alda M. Is response to prophylactic lithium a Familial trait? J Clin Psychiatry. 2002;63:942–7. [DOI] [PubMed] [Google Scholar]
- Hirschfeld RM, Weisler RH, Raines SR, Macfadden W, Group BS. Quetiapine in the treatment of anxiety in patients with bipolar I or II depression: a secondary analysis from a randomized, double-blind, placebo-controlled study. J Clin Psychiatry. 2006;67:355–62. 10.4088/jcp.v67n0304. [DOI] [PubMed] [Google Scholar]
- Ho AM, Coombes BJ, Nguyen TTL, Liu D, McElroy SL, Singh B, Nassan M, Colby CL, Larrabee BR, Weinshilboum RM, Frye MA, Biernacka JM. Mood-Stabilizing antiepileptic treatment response in bipolar disorder: A Genome-Wide association study. Clin Pharmacol Ther. 2020;108:1233–42. 10.1002/cpt.1982. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jones G, Rong C, Vecera CM, Gurguis CI, Chudal R, Khairova R, Leung E, Ruiz AC, Shahani L, Zanetti MV, de Sousa RT, Busatto G, Soares J, Gattaz WF, Machado-Vieira R. The role of lithium treatment on comorbid anxiety symptoms in patients with bipolar depression. J Affect Disord. 2022;308:71–5. 10.1016/j.jad.2022.04.025. [DOI] [PubMed] [Google Scholar]
- Joseph B, Nunez NA, Pazdernik V, Kumar R, Pahwa M, Ercis M, Ozerdem A, Cuellar-Barboza AB, Romo-Nava F, McElroy SL, Coombes BJ, Biernacka JM, Stan MN, Frye MA, Singh B. Long-Term lithium therapy and thyroid disorders in bipolar disorder: A historical cohort study. Brain Sci. 2023;13. 10.3390/brainsci13010133. [DOI] [PMC free article] [PubMed]
- Karam EG, Saab D, Jabbour S, Karam GE, Hantouche E, Angst J. The role of affective temperaments in bipolar disorder: the solid role of the cyclothymic, the contentious role of the hyperthymic, and the neglected role of the irritable temperaments. Eur Psychiatry. 2023;66:e37. 10.1192/j.eurpsy.2023.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kauer-Sant’Anna M, Kapczinski F, Vieta E. Epidemiology and management of anxiety in patients with bipolar disorder. CNS Drugs. 2009;23:953–64. 10.2165/11310850-000000000-00000. [DOI] [PubMed] [Google Scholar]
- Keck PE Jr., Strawn JR, McElroy SL. Pharmacologic treatment considerations in co-occurring bipolar and anxiety disorders. J Clin Psychiatry. 2006;67(Suppl 1):8–15. [PubMed] [Google Scholar]
- Kinrys G, Bowden CL, Nierenberg AA, Hearing CM, Gold AK, Rabideau DJ, Sylvia LG, Gao K, Kamali M, Bobo WV, Tohen M, Deckersbach T, McElroy SL, Ketter TA, Shelton RC, Friedman ES, Calabrese JR, McInnis MG, Kocsis J, Thase ME, Singh V, Reilly-Harrington NA. Comorbid anxiety in bipolar CHOICE: insights from the bipolar inventory of symptoms scale. J Affect Disord. 2019;246:126–31. 10.1016/j.jad.2018.12.039. [DOI] [PubMed] [Google Scholar]
- Lopes FL, Zhu K, Purves KL, Song C, Ahn K, Hou L, Akula N, Kassem L, Bergen SE, Landen M, Veras AB, Nardi AE, Study BG, McMahon C, F.J. Polygenic risk for anxiety influences anxiety comorbidity and suicidal behavior in bipolar disorder. Transl Psychiatry. 2020;10:298. 10.1038/s41398-020-00981-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Maki H, Sakai N, Kataoka M, Fujii K, Kageyama Y, Hayama T, Matsuo K, Nishioka M, Kato T. Family study of bipolar disorder with comorbid anxiety disorder points to THSD7A with possible role of parent-of-origin effect. PCN Rep. 2025;4:e70071. 10.1002/pcn5.70071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McElroy SL, Altshuler LL, Suppes T, Keck PE Jr., Frye MA, Denicoff KD, Nolen WA, Kupka RW, Leverich GS, Rochussen JR, Rush AJ, Post RM. Axis I psychiatric comorbidity and its relationship to historical illness variables in 288 patients with bipolar disorder. Am J Psychiatry. 2001;158:420–6. 10.1176/appi.ajp.158.3.420. [DOI] [PubMed] [Google Scholar]
- McIntyre RS, Soczynska JK, Bottas A, Bordbar K, Konarski JZ, Kennedy SH. Anxiety disorders and bipolar disorder: a review. Bipolar Disord. 2006;8:665–76. 10.1111/j.1399-5618.2006.00355.x. [DOI] [PubMed] [Google Scholar]
- Merikangas KR, Akiskal HS, Angst J, Greenberg PE, Hirschfeld RM, Petukhova M, Kessler RC. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National comorbidity survey replication. Arch Gen Psychiatry. 2007;64:543–52. 10.1001/archpsyc.64.5.543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mitchell PB, Johnston AK, Frankland A, Slade T, Green MJ, Roberts G, Wright A, Corry J, Hadzi-Pavlovic D. Bipolar disorder in a National survey using the world mental health version of the composite international diagnostic interview: the impact of differing diagnostic algorithms. Acta Psychiatr Scand. 2013;127:381–93. 10.1111/acps.12005. [DOI] [PubMed] [Google Scholar]
- Pahwa M, Joseph B, Nunez NA, Jenkins GD, Colby CL, Kashani KB, Marin V, Moore KM, Betcher HK, Ozerdem A, Cuellar-Barboza AB, McElroy SL, Biernacka JM, Frye MA, Singh B. Long-term lithium therapy and risk of chronic kidney disease in bipolar disorder: A historical cohort study. Bipolar Disord. 2021a;23:715–23. 10.1111/bdi.13052. [DOI] [PubMed] [Google Scholar]
- Pahwa M, Joseph B, Nunez NA, Jenkins GD, Colby CL, Kashani KB, Marin V, Moore KM, Betcher HK, Ozerdem A, Cuellar-Barboza AB, McElroy SL, Biernacka JM, Frye MA, Singh B. 2021b. Long-term lithium therapy and risk of chronic kidney disease in bipolar disorder: A historical cohort study. Bipolar Disord. 10.1111/bdi.13052 [DOI] [PubMed]
- Park JH, Nunez NA, Gardea-Resendez M, Gerberi DJ, Breitinger S, Veldic M, Frye MA, Singh B. Short term Second-Generation antidepressant monotherapy in acute depressive episodes of bipolar II disorder: A systematic review and Meta-Analysis. Psychopharmacol Bull. 2022;52:45–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pavlova B, Perlis RH, Alda M, Uher R. Lifetime prevalence of anxiety disorders in people with bipolar disorder: a systematic review and meta-analysis. Lancet Psychiatry. 2015;2:710–7. 10.1016/S2215-0366(15)00112-1. [DOI] [PubMed] [Google Scholar]
- Rickels K, Pollack MH, Feltner DE, Lydiard RB, Zimbroff DL, Bielski RJ, Tobias K, Brock JD, Zornberg GL, Pande AC. Pregabalin for treatment of generalized anxiety disorder: a 4-week, multicenter, double-blind, placebo-controlled trial of Pregabalin and Alprazolam. Arch Gen Psychiatry. 2005;62:1022–30. 10.1001/archpsyc.62.9.1022. [DOI] [PubMed] [Google Scholar]
- Romo-Nava F, Blom T, Cuellar-Barboza AB, Awosika OO, Martens BE, Mori NN, Colby CL, Prieto ML, Veldic M, Singh B, Gardea-Resendez M, Nunez NA, Ozerdem A, Biernacka JM, Frye MA, McElroy SL. Revisiting the bipolar disorder with migraine phenotype: clinical features and comorbidity. J Affect Disord. 2021;295:156–62. 10.1016/j.jad.2021.08.026. [DOI] [PubMed] [Google Scholar]
- Salvi F, Miller MD, Grilli A, Giorgi R, Towers AL, Morichi V, Spazzafumo L, Mancinelli L, Espinosa E, Rappelli A, Dessi-Fulgheri P. A manual of guidelines to score the modified cumulative illness rating scale and its validation in acute hospitalized elderly patients. J Am Geriatr Soc. 2008;56:1926–31. 10.1111/j.1532-5415.2008.01935.x. [DOI] [PubMed] [Google Scholar]
- Schaffer A, McIntosh D, Goldstein BI, Rector NA, McIntyre RS, Beaulieu S, Swinson R, Yatham LN, Canadian Network for, Task MAT. F., 2012. The CANMAT task force recommendations for the management of patients with mood disorders and comorbid anxiety disorders. Ann Clin Psychiatry. 24, 6–22. [PubMed]
- Sheehan DV, Harnett-Sheehan K, Hidalgo RB, Janavs J, McElroy SL, Amado D, Suppes T. Randomized, placebo-controlled trial of quetiapine XR and divalproex ER monotherapies in the treatment of the anxious bipolar patient. J Affect Disord. 2013;145:83–94. 10.1016/j.jad.2012.07.016. [DOI] [PubMed] [Google Scholar]
- Simon NM, Otto MW, Wisniewski SR, Fossey M, Sagduyu K, Frank E, Sachs GS, Nierenberg AA, Thase ME, Pollack MH. Anxiety disorder comorbidity in bipolar disorder patients: data from the first 500 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Am J Psychiatry. 2004;161:2222–9. 10.1176/appi.ajp.161.12.2222. [DOI] [PubMed] [Google Scholar]
- Singh B, Yocum AK, Strawbridge R, Burdick KE, Millett CE, Peters AT, Sperry SH, Fico G, Vieta E, Verdolini N, Godin O, Leboyer M, Etain B, Tso IF, Coombes BJ, McInnis MG, Nierenberg AA, Young AH, Ashton MM, Berk M, Williams LJ, Keramatian K, Yatham LN, Overs BJ, Fullerton JM, Roberts G, Mitchell PB, Andreassen OA, Andreazza AC, Zandi PP, Pham D, Biernacka JM, Frye MA, Collaborators F-B. T.G.B.C.C., 2024. Patterns of pharmacotherapy for bipolar disorder: A GBC survey. Bipolar Disord. 26, 22–32. 10.1111/bdi.13366 [DOI] [PMC free article] [PubMed]
- Singh B, Ho AM, Coombes BJ, Romo-Nava F, Bond DJ, Veldic M, Pendegraft RS, Batzler A, Cuellar-Barboza AB, Gardea-Resendez M, Prieto ML, Ozerdem A, McElroy SL, Biernacka JM, Frye MA. Antipsychotic use in bipolar disorder: clinical and genomic correlates- a Mayo clinic bipolar disorder biobank study. Int J Bipolar Disord. 2025a. 10.1186/s40345-025-00405-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Singh B, Swartz HA, Cuellar-Barboza AB, Schaffer A, Kato T, Dols A, Sperry SH, Vassilev AB, Burdick KE, Frye MA. Bipolar Disorder Lancet. 2025b;406:963–78. 10.1016/S0140-6736(25)01140-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Smith JP, Book SW. Anxiety and substance use disorders: A review. Psychiatr Times. 2008;25:19–23. [PMC free article] [PubMed] [Google Scholar]
- Vazquez GH, Baldessarini RJ, Tondo L. Co-occurrence of anxiety and bipolar disorders: clinical and therapeutic overview. Depress Anxiety. 2014;31:196–206. 10.1002/da.22248. [DOI] [PubMed] [Google Scholar]
- Williams CM, Peyre H, Wolfram T, Lee YH, Seidlitz J, Ge T, Smoller JW, Mallard TT, Ramus F. Characterizing the phenotypic and genetic structure of psychopathology in UK biobank. Nat Ment Health. 2024;2:960–74. 10.1038/s44220-024-00272-8. [Google Scholar]
- Yapici Eser H, Kacar AS, Kilciksiz CM, Yalcinay-Inan M, Ongur D. Prevalence and associated features of anxiety disorder comorbidity in bipolar disorder: A Meta-Analysis and Meta-Regression study. Front Psychiatry. 2018;9:229. 10.3389/fpsyt.2018.00229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yatham LN, Kennedy SH, Parikh SV, Schaffer A, Bond DJ, Frey BN, Sharma V, Goldstein BI, Rej S, Beaulieu S, Alda M, MacQueen G, Milev RV, Ravindran A, O’Donovan C, McIntosh D, Lam RW, Vazquez G, Kapczinski F, McIntyre RS, Kozicky J, Kanba S, Lafer B, Suppes T, Calabrese JR, Vieta E, Malhi G, Post RM, Berk M. Canadian network for mood and anxiety treatments (CANMAT) and international society for bipolar disorders (ISBD) 2018 guidelines for the management of patients with bipolar disorder. Bipolar Disord. 2018;20:97–170. 10.1111/bdi.12609. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yocum AK, Singh B. Global trends in the use of pharmacotherapy for the treatment of bipolar disorder. Curr Psychiatry Rep. 2025;27:239–47. 10.1007/s11920-025-01606-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Young LT, Cooke RG, Robb JC, Levitt AJ, Joffe RT. Anxious and non-anxious bipolar disorder. J Affect Disord. 1993;29:49–52. 10.1016/0165-0327(93)90118-4. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.