Anticonvulsant Use in Older Age Bipolar Disorder in a Global Sample from the Global Aging and Geriatric Experiments in Bipolar Disorder Project: Utilisation d’anticonvulsivants pour le traitement des troubles bipolaires du sujet âgé auprès d’un échantillon mondial provenant du projet GAGE-BD

Abstract

French

Background:

Anticonvulsants are an essential treatment for bipolar disorder; however, there is relatively little known about their use in older age bipolar disorder (OABD). In this paper, which leverages a large international dataset, we aim to 1) describe the use of anticonvulsants in OABD compared to younger age bipolar disorder (YABD; ages <50 years old) and 2) explore any demographic/clinical correlates.

Methods:

A secondary analysis was conducted on the international data from the Global Aging and Geriatric Experiments in Bipolar Disorder project. The main objective was to report the prevalence of anticonvulsant use in OABD over 50 years old (mean age = 62.27) and the most prescribed anticonvulsant. Additional analysis explored any demographic and clinical correlates associated with anticonvulsant use. Generalized linear mixed models were used for this analysis.

Results:

Of the 2,691 participants with bipolar disorder who had anticonvulsant prescribing data, 34.4% (n = 926) used anticonvulsants at the time of study. Rates of anticonvulsant prescribing did not significantly differ between OABD and YABD groups (36.7% (n = 666) vs. 29.7% (n = 260)). Anticonvulsant prescribing patterns for OABD and YABD did not significantly differ, with valproate as the most prescribed anticonvulsant. OABD anticonvulsant users had less lithium use, more antidepressant use, more rapid cycling, more mood episodes and more cardiovascular comorbidities compared to nonusers.

Conclusion:

Anticonvulsant use was similar in OABD and YABD. A number of important clinical correlates of anticonvulsant use were identified.

Keywords

older age bipolar disorder anticonvulsants geriatric

Introduction

Bipolar disorder (BD) affects 0.5–1.0% of older adults,¹ and with the rapidly aging population, this number of individuals with older age bipolar disorder (OABD) will only continue to grow. Of all mood disorders, BD has the largest negative impact on individual well-being, as well as the highest cost to healthcare systems.² A wide domain of research has aimed to address which medications are best suited to manage BD. However, recommendations for OABD are limited, as most of the studies currently available have focused on younger adults.³

This leaves clinicians with minimal direction to prescribe medications for OABD.^4,5 Lithium is often hailed as the gold standard treatment for BD, as determined in the Delphi survey of maintenance lithium treatment.^5–7 However, concerns about potential renal complications have led clinicians to avoid recommending the use of lithium for the management of younger and older adults with BD.^5,6 Consequently, there has been a significant increase in the use of anticonvulsants over the past three decades, making them the second most frequently prescribed class of medication after antidepressants.^4,7,8 Numerous randomized controlled trials have demonstrated the efficacy of these medications in managing acute mania and bipolar depression.⁸ However, anticonvulsants have unique side effects and safety profiles, necessitating specific precautions for patient safety during both short-term and long-term use.^8,9 While existing literature addresses anticonvulsant prescription guidelines for younger populations with BD, there is a noticeable gap in research on the use of anticonvulsants in OABD. This gap extends to the clinical correlates of anticonvulsant use in this population, highlighting the need for further investigation in this area.

Using the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project dataset,¹⁰ we examined the prevalence of anticonvulsant use in OABD compared to younger adults with bipolar disorder (YABD, age <50). We also determined the most commonly prescribed anticonvulsants used, and sociodemographic and clinical correlates of anticonvulsant use that could provide helpful information to guide clinicians on anticonvulsant use in OABD.

Methods

Participants and Dataset

Participants were recruited for a large multisite cross-sectional international study for the GAGE-BD project.¹⁰ There were 32 contributing study cohorts and 17 sites for this dataset. Tables S1 and S2 describe the inclusion/exclusion criteria for each study. Of the 3,429 participants in the Wave 1 and 2 harmonized GAGE-BD dataset, we identified a total of 2,691 participants with BD and with anticonvulsant prescribing data. This sample was split into two categories: OABD (n = 1,815) and YABD (n = 876). The age cut off for OABD was determined to be ≥50 years old, as suggested by the ISBD Task Force due to the shortened life-span of BD.¹¹ The methods were previously described by Sajatovic et al., 2015.^10,11 All methods were performed in accordance with ethics guidelines at sites of contributing data and approved by relevant research ethics boards.

Variables of Interest

The primary outcome of this secondary analysis was anticonvulsant use (yes/no), with secondary outcomes to report on prescription patterns between OABD and YABD, including the most prescribed anticonvulsant. Exploratory variables included any sociodemographic or clinical correlates such as age, sex, smoking status, number of hospitalizations, harmonized depression score, cognitive function (Mini-Mental State Examination),¹² medical comorbidities, mania scores (Young Mania Rating Scale),¹³ among others. (Refer to Tables 1 and 2). Depression scores were measured by the Hamilton Depression Rating Scale (HAM-D),¹⁴ Montgomery-Asberg Depression Rating Scale (MADRS)¹⁵ or the Centre for Epidemiologic Studies Depression Scale (CES-D).¹⁶ This data was harmonized into one categorical depression variable with the following cut-offs: No depression (HAM-D ≤ 7; MADRS ≤6; CES-D ≤ 15), mild-moderate depression (HAM-D 8–23; MADRS 7–34; CES-D 16–27) and severe depression (HAM-D ≥ 24; MADRS ≥35; CES-D ≥ 28).¹⁷

Table 1.

Demographic and Clinical Correlates of OABD in Anticonvulsant Users and Nonusers (n = 1815).

Correlates (n = 1815)	Anticonvulsant users (n = 666)	Anticonvulsant nonusers (n = 1149)	Statistics
Age	61.71 (SD = 8.33)	62.60 (SD = 8.77)	t(1) = 1.257, p = .209
Sex (Female)	59.0% (n = 393)	57.2% (n = 657)	t(1) = .049, p = .961
Age of onset	31.75 (SD = 14.72)	32.71 (SD = 15.39)	t(1) = 1.435, p = .152
Education	12.41 (SD = 4.55)	12.83 (SD = 3.93)	t(1) = .489, p = .625
Lithium users	27.2% (n = 180)	44.8% (n = 510)	t(1) = 11.275, p < .001
Antidepressant users	46.9% (n = 292)	34.9% (n = 375)	t(1) = 3.885, p < .001
Antipsychotic users	51.5% (n = 342)	43.4% (n = 496)	t(1) = .737, p = .461
Antipsychotic generation			F(2) = 1.814, p = .164
−1^st generation	3.9% (n = 12)	5.9% (n = 27)
−2^nd generation	90.5% (n = 275)	90.8% (n = 416)
-Both	5.6% (n = 17)	3.3% (n = 15)
Previously hospitalized (yes)	69.8% (n = 308)	69.2% (n = 467)	t(1) = .517, p = .605
Number of hospitalizations	3.29 (SD = 5.274)	2.67 (SD = 4.005)	t(1) = 1.835, p = .067
Rapid cycling			F(2) = 6.242, p = .002
-No	77.3% (n = 228)	91.6% (n = 488)
-Yes	20.3% (n = 60)	6.8% (n = 36)
-Other	2.4% (n = 7)	1.7% (n = 9)
Number of affective episodes	38.52 (SD = 201.70)	14.34 (SD = 40.17)	t(1) = 2.278, p = .023
Cardiovascular comorbidity	52.2% (n = 302)	43.2% (n = 441)	t(1) = 3.275, p = .001
Respiratory comorbidity	35.3% (n = 169)	34.3% (n = 311)	t(1) = .005, p = .996
Gastrointestinal comorbidity	26.6% (n = 114)	22.4% (n = 182)	t(1) = 1.522, p = .128
Hepatic/Pancreatic comorbidity	8.7% (n = 34)	6.8% (n = 51)	t(1) = .918, p = .359
Renal comorbidity	7.1% (n = 29)	6.3% (n = 46)	t(1) = .137, p = .891
Genito-urinary comorbidity	21.5% (n = 73)	20.5% (n = 127)	t(1) = .980, p = .327
Musculoskeletal comorbidity	39.8% (n = 179)	41.1% (n = 337)	t(1) = .237, p = .813
Endocrine comorbidity	39.9% (n = 225)	33.8% (n = 336)	t(1) = 1.426, p = .154
Smoking status			F(3) = 1.326, p = .264
-Never	22.3% (n = 85)	18.4% (n = 140)
-Current	31.7% (n = 121)	35.2% (n = 267)
-Past	17.8% (n = 68)	12.3% (n = 93)
-Unclear	28.3% (n = 108)	34.1% (n = 259)
Cognition: MMSE score	27.05 (SD = 3.253)	27.59 (SD = 2.465)	t(1) = 1.492, p = .136
Harmonized Depression			F(2) = .114, p = .893
-No	62.0% (n = 330)	54.0% (n = 522)
-Mild to moderate	33.6% (n = 179)	41.9% (n = 405)
-Severe	4.3% (n = 23)	4.1% (n = 40)
Mania (YMRS total)	4.78 (SD = 7.77)	8.0 (SD = 10.50)	t(1) = .219, p = .826

The bold values indicate the significant of p valuses < .05. OABD = older age bipolar disorder.

Table 2.

Demographic and Clinical Correlates of YABD in Anticonvulsant Users and Nonusers (n = 876).

Correlates (n = 876)	Anticonvulsant users (n = 260)	Anticonvulsant nonusers (n = 616)	Statistics
Age	39.87 (SD = 7.17)	36.07 (SD = 8.81)	t(1) = 2.128, p = .034
Sex (Female)	62.7% (n = 163)	68.5% (n = 421)	t(1) = 1.501, p = .134
Age of onset	23.33 (SD = 9.60)	24.97 (SD = 9.51)	t(1) = .484, p = .628
Education	13.40 (SD = 3.91)	13.54 (SD = 3.22)	t(1) = .921, p = .357
Lithium users	30.8% (n = 80)	30.2% (n = 186)	t(1) = 3.949, p < .001
Antidepressant users	45.6% (n = 118)	37.5% (n = 230)	t(1) = .133, p = .894
Antipsychotic users	57.5% (n = 149)	44.2% (n = 272)	t(1) = .599, p = .549
Antipsychotic generation			F(2) = .825, p = .439
−1^st generation	4.4% (n = 6)	4.8% (n = 12)
−2^nd generation	94.8% (n = 128)	94.0% (n = 237)
-Both	0.7% (n = 1)	1.2% (n = 3)
Previously hospitalized (yes)	74.4% (n = 163)	60.7% (n = 334)	t(1) = 1.084, p = .279
Number of hospitalizations	3.82 (6.07)	2.17 (4.12)	t(1) = 2.047, p = .041
Rapid cycling			F(2) = .889, p = .412
-No	39.3% (n = 22)	64.4% (n = 177)
-Yes	58.9% (n = 33)	33.5% (n = 92)
-Other	1.8% (n = 1)	2.2% (n = 6)
Number of affective episodes	27.85 (SD = 82.44)	20.77 (SD = 57.30)	t(1) = .306, p = .760
Cardiovascular comorbidity	21.8% (n = 38)	7.7% (n = 35)	t(1) = 1.486, p = .138
Respiratory comorbidity	35.2% (n = 44)	14.6% (n = 61)	t(1) = 1.235, p = .217
Gastrointestinal comorbidity	13.4% (n = 18)	5.9% (n = 25)	t(1) = .342, p = .732
Hepatic/Pancreatic comorbidity	2.4% (n = 3)	0.7% (n = 3)	t(1) = .769, p = .442
Renal comorbidity	2.2% (n = 3)	0.7% (n = 3)	t(1) = .932, p = .352
Genito-urinary comorbidity	2.5% (n = 2)	1.2% (n = 4)	t(1) = .449, p = .654
Musculoskeletal comorbidity	30.7% (n = 39)	13.9% (n = 58)	t(1) = .505, p = .614
Endocrine comorbidity	14.6% (n = 24)	9.6% (n = 42)	t(1) = .370, p = .712
Smoking status			F(3) = 1.762, p = .153
-Never	12.8% (n = 24)	25.1% (n = 124)
-Current	50.3% (n = 94)	46.7% (n = 231)
-Past	3.2% (n = 6)	7.5% (n = 37)
-Unclear	33.7% (n = 63)	20.8% (n = 103)
Cognition: MMSE score	24.33 (4.62)^a	24.75 (3.20)^b	t(1) = .731, p = .498
Depression			F(2) = .154, p = .857
-No	44.8% (n = 73)	33.2% (n = 151)
-Mild to moderate	49.7% (n = 81)	62.4% (n = 284)
-Severe	5.5% (n = 9)	4.4% (n = 20)
Mania (YMRS total)	7.29 (SD = 8.78)	6.08 (SD = 6.56)	t(1) = .199, p = .842

^an = 3; ^bn = 4.

The bold values indicate the significant p values < .05. YABD=younger age bipolar disorder; MMSE=Mini-Mental State Examination.

Statistical Analysis

Statistical analyses were conducted using IBM SPSS Statistics (Version 29). Demographic and clinical characteristics were described using frequencies, percentages, means and standard deviations. Independent t-tests and chi-square tests, and generalized linear mixed models (GLMM) were used to explore relationships between OABD vs YABD for primary, secondary and exploratory outcomes. GLMM using a binomial probability distribution with a logit link function and random effect of study cohort were used to control for study cohorts.

Results

Demographics and Clinical Characteristics

Table 3 summarizes the demographic and clinical characteristics of the sample. The mean age for OABD and YABD was 62.27 (SD = 8.62) and 37.20 (SD = 8.53), respectively. There was a higher proportion of females with YABD (66.7%) compared to OABD (57.9%), χ2(1) = 19.57, p < .001. The mean age of onset was higher in participants with OABD (32.37, SD = 15.16) compared to YABD (24.47, SD = 9.56), p < .001, 95% CI [-8.95, −6.86].

Table 3.

Demographic Characteristics of Participants with Bipolar Disorder from the GAGE-BD Dataset (n = 2691).

	OABD (≥50 years old) (n = 1815)	YABD (<50 years old) (n = 876)
Age (years)	62.3 (SD = 8.62)	37.2 (SD = 8.53)
Mean (SD)
Sex (Female)	57.9% (n = 1050)	66.7% (n = 584)
Diagnosis (BD Sub-Type)	BD 1: 76.7% (n = 1381)	BD 1: 73.0% (n = 634)
	BD 2: 20.4% (n = 368)	BD 2: 23.6% (n = 205)
	Unknown: 2.9% (n = 52)	Unknown: 3.3% (n = 29)
Age of onset	32.4 (SD = 15.16)	24.5 (SD = 9.56)
Mean (SD)
Education in years	12.7 (SD = 4.15)	13.5 (SD = 3.43)
Mean (SD)
Occupation (working)	23.1% (n = 257)	37.0% (n = 179)
Relationship status
-In relationship	40.2% (n = 594)	35.2% (n = 173)
-Single	57.9% (n = 856)	64.4% (n = 316)
-Unknown	1.9% (n = 28)	0.4% (n = 2)
Hospitalized for psychiatric condition (yes)	69.4% (n = 775)	64.6% (n = 497)

OABD=older age bipolar disorder; YABD=younger age bipolar disorder; GAGE-BD=Global Aging and Geriatric Experiments in Bipolar Disorder.

Anticonvulsant use in OABD vs. YABD

Anticonvulsant use was 36.7% in OABD and 29.7% in YABD, t(1) = 1.40, p = .161 (Table 4). The number of anticonvulsants used was similar for OABD and YABD, with most participants using only one anticonvulsant.

Table 4.

Anticonvulsant users in OABD vs YABD (n = 2,691).

	OABD (≥50 years old) (n = 1815)	YABD (<50 years old) (n = 876)	Statistics
Anticonvulsant use; % (n)	36.7% (n = 666)	29.7% (n = 260)	t(1) = 1.40, p = .161
Number of anticonvulsants; % (n)	0: 63.3% (n = 1149)	0: 70.3% (n = 616)	F(4) = .143, p = .966
	1: 33.2% (n = 603)	1: 26.8% (n = 235)
	2: 3.3% (n = 60)	2: 2.6% (n = 23)
	3: 0.2% (n = 3)	3: 0.1% (n = 1)
	4: 0% (n = 0)	4: 0.1% (n = 1)

OABD=older age bipolar disorder; YABD=younger age bipolar disorder.

Prescription Patterns of Anticonvulsant use in OABD vs YABD

Table 5 describes the different anticonvulsants prescribed for OABD and YABD. Prescription patterns did not differ significantly between OABD and YABD. Valproate (VPA) was the most commonly used anticonvulsant (OABD: VPA comprised 46.4% of all anticonvulsant use in OABD; YABD: 41.2%) followed by lamotrigine (OABD: 34.5%; YABD: 35.4%). Although gabapentin had a p < 0.05, our significance was set to p = 0.005 due to Bonferroni correction and therefore gabapentin use was not significantly different between OABD and YABD groups.

Table 5.

Prescribing Patterns Amongst OABD and YABD Anticonvulsant Users (n = 926).

Name of anticonvulsant	OABD (≥50 years) (n = 666)	YABD (<50 years) (n = 260)	Statistics
Carbamazepine	13.1% (n = 87)	13.5% (n = 35)	χ2(1) = .026, p = .872
Oxcarbazepine	4.1% (n = 27)	5.0% (n = 13)	χ2(1) = .405, p = .525
Gabapentin	5.3% (n = 35)	8.8% (n = 23)	χ2(1) = 4.107, p = .043
Lamotrigine	34.5% (n = 230)	35.4% (n = 92)	χ2(1) = .060, p = .807
Pregabalin	1.4% (n = 9)	0.8% (n = 2)	Fisher's Exact Test, p = .737
Primidone	0.2% (n = 1)	0% (n = 0)	Fisher's Exact Test, p = 1.0
Topiramate	4.2% (n = 28)	5.8% (n = 15)	χ2(1) = 1.034, p = .309
Valproate	46.4% (n = 309)	41.2% (n = 107)	χ2(1) = 2.077, p = .150
Levetiracetam	0% (n = 0)	0.4% (n = 1)	Fisher's Exact Test, p = .281
Other	1.8% (n = 12)	0% (n = 0)	Fisher's Exact Test, p = .024

*Statistical sig is when p < 0.05/10 < 0.005. YABD=younger age bipolar disorder.

Correlates of Anticonvulsant use

Tables 1 and 2 summarize the demographic and clinical correlates of anticonvulsant use in OABD (Table 1) and YABD (Table 2). Of the 1,815 older adults who have BD, n = 666 were anticonvulsant users and n = 1,149 were nonusers.

In OABD anticonvulsant users (compared to nonusers) (Table 1), lithium use was less common (27.2% vs. 44.8%, t(1) = 11.275, p < .001), while antidepressant use was more common (46.9% vs. 34.9%, t(1) = −3.885, p < .001). Cardiovascular comorbidities were more common in anticonvulsant users compared to nonusers (52.2% vs. 43.2%, t(1) = 3.275, p = .001). Rapid cycling was also more common in anticonvulsant users (20.3% vs. 6.8%, F(2) = 6.242, p = .002), and the number of lifetime mood episodes was higher (mean = 38.52 vs. 14.34). The age, proportion of females, age of onset, education level, antipsychotic use, number of hospitalizations, other comorbidities, cognition, as well as depression and mania scores were not significantly different in anticonvulsant users and nonusers.

In YABD (Table 2), the mean age for anticonvulsant users and nonusers was 39.87 (SD = 7.17) and 36.07 (SD = 8.81), respectively, t(1) = −2.128, p = .034. Lithium use was more common in anticonvulsant users compared to nonusers (30.8% vs. 30.2%, t(1) = 3.949, p < .001), and number of hospitalizations was also higher in anticonvulsant users compared to nonusers (3.82 vs 2.17, t(1) = −2.047, p = .041). The proportion of females, age of onset, education level, antidepressant use, antipsychotic use, comorbidities, cognition, depression and mania scores were not significantly different in anticonvulsant users and nonusers.

Discussion

This paper leveraged the large international GAGE-BD dataset to 1) describe the use of anticonvulsants in OABD compared to YABD, and 2) explore any demographic/clinical correlates. We had anticonvulsant prescribing data on 2,691 individuals with BD, including OABD (n = 1,815) and YABD (n = 876). We also identified important correlates of anticonvulsant use in OABD.

In this analysis, anticonvulsant use in OABD (36.7%) was not statistically different compared to YABD (29.7%). Clinicians may be following similar prescribing patterns for both older and younger adults, possibly due to the limited availability of clinical trials and specific guidelines tailored to older populations.¹⁸ Although the similar use of anticonvulsants in OABD and YABD may be due to anticonvulsants being perceived as safer than alternative medications in OABD, e.g., VPA is safer than lithium regarding kidney risk.^18,19 The previous literature cites similar potential reasons for the (relatively) more common anticonvulsant use in OABD.^18,19 Possible additional explanations for this could be gleaned from examining the concurrent prescribing patterns of other psychotropic medications in OABD anticonvulsant users (e.g., antipsychotic use – a medication that requires less frequent blood monitoring).

The type of anticonvulsant prescribed was similar amongst anticonvulsant users regardless of age (OABD vs YABD), with VPA (46.4%) being the most prescribed anticonvulsant, followed by lamotrigine (34.5%) and carbamazepine (13.1%). In our current era this level of use of carbamazepine may seem high in older adults given its hematological concerns and propensity towards drug–drug interactions. However, perhaps this speaks to carbamazepine's efficacy²⁰ and clinicians’ wish to use it despite its adverse effect profile. This suggests that the prescribers may not perceive differences between OABD and YABD in terms of anticonvulsants’ efficacy and safety. Since there are relatively few randomized clinical trials on OABD,²¹ most research suggests similar prescribing patterns to YABD and this can be seen in the current data. Future studies could assess a few interesting hypotheses: (1) whether clinicians find certain anticonvulsants to be more efficacious than the more commonly prescribed medications (antipsychotics) and (2) whether that explains higher anticonvulsant use despite many clinicians’ perceptions that antipsychotics are safer (require less monitoring, less drug–drug interactions).

There are some interesting clinical correlates of anticonvulsant use in OABD. Lithium use was significantly lower in anticonvulsant users compared to nonusers. It is possible that physicians perceive lithium use as riskier in many OABD patients¹⁹ and opt to prescribe anticonvulsants in those patients. Alternatively, the sample collected could reflect a lithium treatment resistant group. In contrast, for YABD, anticonvulsant use was associated with more lithium use, which likely illustrates the hesitancy to prescribe lithium to older adults. The results also demonstrate that anticonvulsant users (compared to nonusers) more commonly use antidepressants, suggesting that anticonvulsants are used in more atypical forms of BD (not the typical familial classical lithium-responsive BD²²), or that multiple medication classes are needed. Anticonvulsant users also had more rapid cycling and a higher amount of mood episodes, which suggests that anticonvulsants are again used in the more atypical forms of BD. Future hypotheses that could be tested include: (1) whether clinicians tend to switch patients from lithium to anticonvulsants around age 60, and (2) prospectively compare the efficacy of anticonvulsants vs lithium vs antipsychotics in OABD, including subgroup analyses of atypical forms of BD.

Cardiovascular comorbidities were more prevalent in anticonvulsant users (compared to nonusers), which is consistent with the literature that suggests individuals with BD have an elevated risk for cardiovascular mortality.^23,24 In addition, anticonvulsant use has also been associated with metabolic changes (in cytochrome P450 enzyme) that may increase cardiovascular risk.^25,26 This suggests a few possibilities, 1) physicians perceive anticonvulsant use to be safer for OABD patients with cardiovascular comorbidities, or 2) anticonvulsant use increases cardiovascular risk. However, due to the cross-sectional nature of this study, causality cannot be inferred, and future research is needed.

Strengths and Limitations

There are many strengths of this study, such as its large sample size (n = 2,691), and the use of various international sites, which increases the external validity of the findings. An important limitation includes the cross-sectional nature of this study, which has difficulty establishing temporality or causality.²⁷ The data collected was from a sample with a mean age of 62, which is not necessarily generalizable to people in their mid-70s or older. In addition, the data was collected over a long period of time (∼20 years) and the exact dates of data collection for each study were not available, so we could not examine the impact of pharmaceutical marketing on prescription patterns. Due to the heterogeneity of OABD, including those with both early-onset and late-onset BD can be seen as a limitation since we did not separate these subgroups for our analysis.

Although we have used the GLMM for our primary analysis, we used a chi-square to compare younger and older adults’ type of anticonvulsant use (e.g., VPA, lamotrigine, etc.). Future studies can further examine types of anticonvulsant use using GLMM and 95% confidence intervals. In addition, we did not have information about efficacy, adverse effects, or medication adherence, which could be assessed in future longitudinal studies.

Conclusions

Our data suggest anticonvulsants are widely used in OABD, with VPA as the most common anticonvulsant for clinicians treating OABD. Anticonvulsant use was similar for OABD and YABD, and prescription patterns of individual anticonvulsants remained similar regardless of age. The clinical correlates of anticonvulsant use in OABD include less lithium use, more antidepressant use, more rapid cycling, higher number of mood episodes and more cardiovascular comorbidities. Future longitudinal studies could help us understand the relationship between anticonvulsant use and the correlates identified.

Data Access

The data are available from the corresponding author on reasonable request.

Supplemental Material

sj-docx-1-cpa-10.1177_07067437251372190 - Supplemental material for Anticonvulsant Use in Older Age Bipolar Disorder in a Global Sample from the Global Aging and Geriatric Experiments in Bipolar Disorder Project: Utilisation d’anticonvulsivants pour le traitement des troubles bipolaires du sujet âgé auprès d’un échantillon mondial provenant du projet GAGE-BD

Supplemental material, sj-docx-1-cpa-10.1177_07067437251372190 for Anticonvulsant Use in Older Age Bipolar Disorder in a Global Sample from the Global Aging and Geriatric Experiments in Bipolar Disorder Project: Utilisation d’anticonvulsivants pour le traitement des troubles bipolaires du sujet âgé auprès d’un échantillon mondial provenant du projet GAGE-BD by Katie C. Bodenstein, Myriam Lesage, Paola Lavin, Sigfried Schouws, Melis Orhan, Alexandra Beunders, Osvaldo P. Almeida, Kursat Altinbas, Vicent Balanzá-Martínez, Izabela G. Barbosa, Hilary P. Blumberg, Farren B.S. Briggs, Cynthia V. Calkin, Orestes V. Forlenza, Brent Forester, Ariel G. Gildengers, Benno C.M. Haarman, Tomas Hajek, Beny Lafer, Paula V. Nunes, Benoit Mulsant, Andrew T. Olagunju, Regan E. Patrick, Joquim Radua, Kaylee Sarna, Christian Simhandl, Jair C. Soares, Ashley N. Sutherland, Nicole Fiorelli, Antonio L. Teixeira, Shangying Tsai, Eduard Vieta, Joy Yala, Lisa Eyler, Annemiek Dols, Martha Sajatovic and Soham Rej in The Canadian Journal of Psychiatry

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by the International Society for Bipolar Disorders (ISBD) Bowden Massey Strategic Research Initiative. This publication's contents are solely the responsibility of the authors and do not necessarily represent the official views of ISBD. The ISBD is a 401c3 non-profit organization whose mission is to foster international collaboration in education and research. For more information, visit www.isbd.org and .

ORCID iDs

Katie C. Bodenstein

Benoit Mulsant

Andrew T. Olagunju

Supplemental Material

Supplemental material for this article is available online.

References

Kessler

Berglund

Demler

, et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. 2005;62(6):593–602.

Whiteford

Degenhardt

Rehm

, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382(9904):1575–1586.

Dols

Kessing

Strejilevich

, et al. Do current national and international guidelines have specific recommendations for older adults with bipolar disorder? A brief report. Int J Geriatr Psychiatry. 2016;31(12):1295–1300.

Shulman

Rochon

Sykora

, et al. Changing prescription patterns for lithium and valproic acid in old age: shifting practice without evidence. Br Med J. 2003;326(7396):960–961.

Shulman

Almeida

Herrmann

, et al. Delphi survey of maintenance lithium treatment in older adults with bipolar disorder: an ISBD task force report. Bipolar Disord. 2019;21(2):117–123.

Soh

Bodenstein

OHY

, et al. Atorvastatin lowers serum calcium levels in lithium-users: results from a randomized controlled trial. BMC Endocr Disord. 2022;22(1):238.

Rhee

Olfson

Nierenberg

Wilkinson

. 20-year trends in the pharmacologic treatment of bipolar disorder by psychiatrists in outpatient care settings. Am J Psychiatry. 2020;177(8):706–715.

Pichler

Hattwich

Grunze

Muehlbacher

. Safety and tolerability of anticonvulsant medication in bipolar disorder. Expert Opin Drug Saf. 2015;14(11):1703–1724.

Mezuk

Morden

Ganoczy

Post

Kilbourne

. Anticonvulsant use, bipolar disorder, and risk of fracture among older adults in the Veterans Health Administration. Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry. 2010;18(3):245–255.

10.

Sajatovic

Eyler

Rej

, et al. The Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project: understanding older-age bipolar disorder by combining multiple datasets. Bipolar Disord. 2019;21(7):642–649.

11.

Sajatovic

Strejilevich

Gildengers

, et al. A report on older-age bipolar disorder from the international society for bipolar disorders task force. Bipolar Disord. 2015;17(7):689–704.

12.

Mitchell

Shukla

Ajumal

Stubbs

Tahir

. The Mini-Mental State Examination as a diagnostic and screening test for delirium: systematic review and meta-analysis. Gen Hosp Psychiatry. 2014;36(6):627–633.

13.

Young

Biggs

Ziegler

Meyer

. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry J Ment Sci. 1978;133:429–435.

14.

Rohan

Rough

Evans

, et al. A protocol for the Hamilton rating scale for depression: item scoring rules, rater training, and outcome accuracy with data on its application in a clinical trial. J Affect Disord. 2016;200:111–118.

15.

Quilty

Robinson

Rolland

Fruyt

Rouillon

Bagby

. The structure of the montgomery-Åsberg depression rating scale over the course of treatment for depression. Int J Methods Psychiatr Res. 2013;22(3):175–184.

16.

The CES-D Scale: A Self-Report Depression Scale for Research in the General Population - Lenore Sawyer Radloff, 1977 [Internet]. [cited 2024 Oct 16]. Available from: https://journals-sagepub-com.proxy3.library.mcgill.ca/doi/10.1177/014662167700100306

17.

Orhan

Millett

Klaus

, et al. Comparing continuous and harmonized measures of depression severity in older adults with bipolar disorder: relationship to functioning. J Affect Disord. 2022;314:44–49.

18.

Rej

Herrmann

Shulman

Fischer

Fung

Gruneir

. Current psychotropic medication prescribing patterns in late-life bipolar disorder. Int J Geriatr Psychiatry. 2017;32(12):1459–1465.

19.

Rej

Schuurmans

Elie

Stek

Shulman

Dols

. Attitudes towards pharmacotherapy in late-life bipolar disorder. Int Psychogeriatr. 2016;28(6):945–950.

20.

Yatham

Kennedy

Parikh

, et al. 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(2):97–170.

21.

Young

Mulsant

Sajatovic

, et al. GERI-BD: a randomized double-blind controlled trial of lithium and divalproex in the treatment of mania in older patients with bipolar disorder. Am J Psychiatry. 2017;174(11):1086–1093.

22.

Grof

Duffy

Cavazzoni

, et al.

Is response to prophylactic lithium a familial trait?

J Clin Psychiatry. 2002;63(10):942–947.

23.

Weiner

Warren

Fiedorowicz

. Cardiovascular morbidity and mortality in bipolar disorder. Ann Clin Psychiatry Off J Am Acad Clin Psychiatr. 2011; 23(1):40.

24.

Goldstein

. Bipolar disorder and the vascular system: mechanisms and new prevention opportunities. Can J Cardiol. 2017;33(12):1565–1576.

25.

Patorno

Glynn

Hernandez-Diaz

, et al. Risk of ischemic cerebrovascular and coronary events in adult users of anticonvulsant medications in routine care settings. J Am Heart Assoc. 2013;2(4):e000208.

26.

Svalheim

Sveberg

Mochol

Taubøll

. Interactions between antiepileptic drugs and hormones. Seizure. 2015;28:12–17.

27.

Vieta

De Prisco

. Cross-sectional studies: is pressing the pause button worth it in research. Eur Neuropsychopharmacol. 2024;85:32–33.

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