Abstract
Background
Physical pain is a common issue in people with bipolar disorder (BD). It worsens mental health and quality of life, negatively impacts treatment response, and increases the risk of suicide. Lithium, which is prescribed in BD as a mood stabilizer, has shown promising effects on pain.
Methods
This naturalistic study included 760 subjects with BD ( FACE-BD cohort) divided in two groups: with and without self-reported pain (evaluated with the EQ-5D-5L questionnaire). In this sample, 176 subjects were treated with lithium salts. The objectives of the study were to determine whether patients receiving lithium reported less pain, and whether this effect was associated with the recommended mood-stabilizing blood concentration of lithium.
Results
Subjects with lithium intake were less likely to report pain (odds ratio [OR] = 0.59, 95% confidence interval [CI], 0.35–0.95; p = 0.036) after controlling for sociodemographic variables, BD type, lifetime history of psychiatric disorders, suicide attempt, personality traits, current depression and anxiety levels, sleep quality, and psychomotor activity. Subjects taking lithium were even less likely to report pain when lithium concentration in blood was ≥0.5 mmol/l (OR = 0.45, 95% CI, 0.24–0.79; p = 0.008).
Conclusions
This is the first naturalistic study to show lithium’s promising effect on pain in subjects suffering from BD after controlling for many confounding variables. This analgesic effect seems independent of BD severity and comorbid conditions. Randomized controlled trials are needed to confirm the analgesic effect of lithium salts and to determine whether lithium decreases pain in other vulnerable populations.
Keywords: bipolar disorder, lithium, lithium plasma level, mood disorders, pain
Introduction
Physical pain is highly prevalent in subjects suffering from bipolar disorder (BD) (~30%) [1]. Pain experienced by subjects suffering from BD seems to be mainly idiopathic (e.g. headache or chronic back pain) [1]. Pain worsens mental health and quality of life [2]. Indeed, subjects reporting pain have a longer time to remission [3], poorer treatment response [4], and higher suicidal rates [5]. Despite the large number of available pain-killer drugs, many subjects still report pain and experience high disability levels [6]. Opioids are among the most efficient analgesic drugs, but they have important side effects: substance use disorder, mood-altering effects, and higher suicide mortality [7–11].
Lithium is recommended and widely used in BD as a mood stabilizer. Some case reports show a promising effect of lithium on pain [12–14]. A randomized controlled trial (RCT) demonstrated that lithium reduces pain in subjects with spinal cord injury compared to placebo [15]. Moreover, lithium reduces pain in subjects with chronic cluster headaches [16, 17] and is recommended as a prophylactic treatment in this pathology [18]. Animal studies also support the lithium analgesic effect. In neuropathic rat models, lithium decreases thermal hyperalgesia, and mechanical and cold allodynia [19–22]. These effects are not observed when lithium is combined with naloxone, suggesting that it acts through the opioid system [20].
We hypothesized that (1) subjects suffering from BD and taking lithium are less likely to report physical pain (H1); and (2) subjects suffering from BD and with blood lithium concentration ≥ 0.5 mmol/l (that is the threshold recommended to observe the mood stabilizing effect) [23] are even less likely to report pain (H2).
Materials and methods
Study population
Participants were recruited from the FACE-BD cohort. This is a French prospective, naturalistic cohort of outpatients with BD enrolled at the advanced Centers of Expertise in Bipolar Disorder (CEBD) and coordinated by the FondaMental Foundation [24, 25]. Subjects are referred by a general practitioner or a psychiatrist to the expert center where they are evaluated and followed. Participants had a diagnosis of BD type I, II, or not otherwise specified, according to the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), were older than 18 years, and without ongoing severe mood episode at evaluation.
From all the subjects included in the database (n = 2,835), subjects with exhaustive baseline data (that is first evaluation at the CEBD) on pain levels, sex, age, depression level, sleep quality, and affectivity, lability, and intensity, impulsivity and hostility levels were selected (n = 977). Then, subjects treated with lithium but without available blood lithium measurements were excluded (n = 217). Therefore, the final sample included 760 subjects among whom 176 were treated with lithium salts. As treatment at baseline was the one prescribed by the referring physician (that is, current treating physician), the small number of participants taking lithium salts at inclusion could be explained by the current decrease in lithium prescriptions [26].
Assessments
From the database, the following data were extracted: sociodemographic variables (age, sex, marital status, education), current psychotropic medications, age at BD onset, number of thymic episodes, number of lifetime suicide attempts and psychiatric comorbidities (recorded by trained psychiatrists or psychologists using the SCID-I), quality of sleep (self-evaluated by the subjects with the Pittsburgh Sleep Quality Index), and potentially painful somatic comorbidities (e.g. multiple sclerosis, cancer, ulcer, rheumatoid arthritis).
Pain
Pain was self-evaluated with the EQ-5D-5L questionnaire [27, 28]. The EQ-5D-5L is a standardized quality-of-life scale developed by the European EuroQol group. This questionnaire has been validated in several countries, including France [29, 30]. It includes five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension is rated on a 5-point Likert scale: no problem, slight problems, moderate problems, severe problems, and extreme problems. The standard reference period for the response is the respondent’s “own health state today.”
Using the reported EQ-5D-5L scores, subjects were classified in two groups in function of the presence or absence of moderate or severe problems for the pain/discomfort dimension [31]. The EQ-5D-5L pain dimension has good psychometric properties and has shown good responsiveness and discriminative validity in various diseases where pain is a major symptom [32–37]. It is correlated with the scores of specific pain measuring tools, such as pain visual analog scales and the Brief Pain Inventory [34, 38].
Lithium
Lithium plasma levels (mmol/l) were extracted from the database. For all participants, blood samples were collected 12 hours after the last lithium intake and after fasting.
Affective state
The scores of the following tests were extracted from the database: Young Mania Rating Scale (YMRS; manic state assessment), Quick Inventory of Depressive Symptoms (QIDS) scale (depression level), and Spielberger Anxiety Inventory (STAI Y-A; self-evaluation of anxiety state). Subscales of the Multidimensional Assessment of Thymic States (MAThyS) for subjects suffering from BD were used to evaluate emotional reactivity, cognition, motivation, psychomotor activity, and sensory perception.
Personality traits
Affective traits were self-assessed with the Affective Lability Scale (ALS), the Affect Intensity Measure (AIM), the Barrat Impulsiveness Scale (BIS-10), and the Buss-Durkee Hostility Inventory (BDHI). Two dimensions, overt and covert aggressiveness, were derived from the BDHI because its construction produces these two distinct loaded factors [39, 40].
Ethical concerns
A web-based application, e-bipolar©, was developed and is used to collect data for clinical monitoring and research purposes [25]. Access to this web-based system is carefully regulated and the application was approved by the French body overseeing the safety of computerized databases (that is, Commission Nationale de l’Informatique et des Libertés, CNIL) [25]. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects were approved by an ethics committee (CPP-Ile de France IX). All individuals provided written informed consent before entering the study.
Statistical analysis
Variables in the two groups (with and without pain) were compared by univariate analysis. Another univariate analysis was done to compare subjects with and without lithium intake. For quantitative variables, mean and standard deviation (SD) were used. For qualitative variables, number of occurrences and frequencies per class were used. Quantitative and quantitative variables were compared between groups with the t-test or Mann–Whitney test, and the chi-square or Fisher test, respectively.
To test whether pain was associated with lithium intake, two multivariate regression logistic models were used. Model 1 tested whether subjects with lithium intake were less likely to report pain compared with those without lithium intake. Model 2 tested whether subjects with blood lithium concentration ≥ 0.5 mmol/l were even less likely to report pain. To this aim, the sample was classified in three groups: (1) subjects without lithium intake, (2) subjects with blood lithium concentration < 0.5 mmol/l, (3) subjects with blood lithium concentration ≥ 0.5 mmol/l. All analyses were done with R, version 4.2.1 [41].
For each model, confounders were selected from the literature and from our univariate analysis (p < 0.15). The following variables were selected as potential confounders: age, sex, BD type, lifetime psychiatric disorders (anxiety, eating and substance use disorders), past history of suicide attempt, personality traits (affective lability and intensity, impulsivity, aggressiveness according to the ALS, AIM, BIS-10 and BDHI scores, respectively), depression and anxiety levels (QIDS and STAI Y-A scores), sleep quality (PSQI score), and MAThyS subscale scores. Their normal distribution was evaluated. The Box Cox transformation was used for the QIDS score to reduce the influence of positive skewness and of outliers. MAThyS sub-scores were categorized in three classes (that is terciles) because the relation between MAThyS sub-scores and pain was nonlinear [2].
To avoid overfitting, an automatic stepwise forward and backward selection was performedwith the MASS package [42]. Only variables with the best fit were retained, according to the Akaike information criterion (AIC). The odds ratios (OR) and their 95% confidence intervals (CI) were estimated. To discuss the risk accurately, odds ratios were transformed to averaged risk ratios according to Grant [43].
In sensitivity analyses, the following variables were added sequentially to the best model to test whether lithium intake was still associated with pain: psychotropic drugs (anticonvulsants, antipsychotics, anxiolytics, hypnotics, antidepressants) and variables associated in univariate analyses but with missing data (high-school diploma, single, age of first episode, number of depressive episodes, anxiety score, cancer, and ulcer). The number of observations without missing data are reported for each sensitivity analysis.
Results
Sample description
The sample included 463 (60.9%) women, and 339 (44.6%) subjects who had BD type 1. The mean age was 40.2 years (SD = 12.62), the mean QIDS score was 9.56 (SD = 5.75), suggesting a low level of depressive symptomatology, and the mean YMRS score was 2.34 (SD = 3.67), indicating the absence of hypomanic symptoms. According to the EQ-5D-5L score, 171 subjects (22.5%) reported pain. Depressive episodes, lifetime psychiatric comorbidities (anxiety, eating and substance abuse/dependence disorders), and history of suicide attempts were more frequent in subjects who reported pain than in those who did not. Moreover, the levels of depression and anxiety and also of affective lability and intensity, hostility, and impulsivity were higher, and sleep quality was lower in subjects who reported pain. The percentages of subjects taking anxiolytics and lithium salts were higher and lower, respectively, in the group who reported pain (Table 1). Few subjects had somatic comorbidities that were not associated with self-reported pain (Table 1).
Table 1.
Sociodemographic and clinical characteristics of the groups with and without pain
| Variable | Without pain Mean (sd)/Number (%) | With pain Mean (sd)/ Number (%) | Statistical analysis | p-value* | |
|---|---|---|---|---|---|
| Sociodemographic | |||||
| n | 589 | 171 | |||
| Age (years) | 39.54 (12.83) | 42.48 (11.63) | F = 7.24 | 0.007 | |
| Sex | Men | 242 (41.1) | 55 (32.2) | χ2 = 4.1 | 0.04 |
| Women | 347 (58.9) | 116 (67.8) | |||
| Single | No | 270 (50.8) | 82 (52.9) | χ2 = 0.15 | 0.7 |
| Yes | 362 (49.2) | 73 (47.1) | |||
| Education | No | 193 (36.3) | 62 (40.8) | χ2 = 0.82 | 0.37 |
| (High-school diploma) | Yes | 338 (63.7) | 90 (59.2) | ||
| Clinical | |||||
| BD subtype | I | 271 (46) | 68 (39.8) | χ2 = 2.19 | 0.34 |
| II | 252 (42.8) | 83 (48.5) | |||
| NOS | 66 (11.2) | 20 (11.7) | |||
| Age at BD onset (years) | 23.3 (9.14) | 24.1 (9.7) | F = 0.91 | 0.34 | |
| Number of depressive episodes | 5.01 (4.6) | 6.4 (5.6) | F = 8.3 | 0.004 | |
| Number of manic episodes | 1.0 (1.91) | 1.02 (2.6) | F = 0.01 | 0.94 | |
| Number of hypomanic episodes | 3.29 (4.93) | 3.75 (4.98) | F = 0.81 | 0.37 | |
| Number of hospitalizations | 0 | 141 (26.1) | 46 (29.3) | χ2 = 2.88 | 0.41 |
| 1 | 113 (20.9) | 32 (20.4) | |||
| 2–3 | 165 (30.6) | 38 (24.2) | |||
| > 3 | 121 (22.4) | 41 (26.1) | |||
| History of suicide attempt | No | 402 (68.3) | 100 (58.5) | χ2 = 5.22 | 0.02 |
| Yes | 187 (31.7) | 71 (41.5) | |||
| Lifetime substance use disorder | No | 396 (67.2) | 101 (59.1) | χ2 = 3.55 | 0.06 |
| (abuse/dependence) | Yes | 193 (32.8) | 70 (40.9) | ||
| Lifetime anxiety disorder | No | 362 (61.5) | 80 (46.8) | χ2 = 11.14 | 0.0008 |
| Yes | 227 (38.5) | 91 (53.2) | |||
| Lifetime eating disorder | No | 483 (82) | 126 (73.7) | χ2 = 5.25 | 0.02 |
| Yes | 106 (18) | 45 (26.3) | |||
| Multiple sclerosis | No | 570 (99.5) | 164 (99.4) | 1 | |
| Yes | 3 (0.5) | 1 (0.5) | |||
| Cancer | No | 539 (97.6) | 150 (95.5) | 0.17 | |
| Yes | 13 (2.4) | 7 (4.5) | |||
| Inflammatory bowel disease | No | 564 (99.1) | 161 (99.4) | 1 | |
| Yes | 5 (0.9) | 1 (0.6) | |||
| Rheumatoid arthritis | No | 577 (99.8) | 167 (100) | 1 | |
| Yes | 1 (0.2) | 0 (0) | |||
| Peptic ulcer disease | No | 584 (96.6) | 153 (94.4) | 0.29 | |
| Yes | 19 (3.4) | 9 (5.6) | |||
| QIDS-SR | 8.7 (5.4) | 12.5 (5.8) | F = 60.94 | <0.0001 | |
| YMRS | 0 | 323 (54.9) | 80 (47.3) | χ2 = 3.4 | 0.18 |
| (1–7) | 213 (36.2) | 69 (40.8) | |||
| >7 | 52 (8.8) | 20 (11.8) | |||
| PSQI (0–21) | 6.46 (3.55) | 9.42 (4.15) | F = 84.94 | <0.0001 | |
| STAI Y-A (state) (0–60) | 40.6 (13.94) | 48.0 (13.8) | F = 37.14 | <0.0001 | |
| MAThyS emotional (0–40) | 21.22 (6.33) | 23.15 (7.02) | F = 11.77 | 0.0006 | |
| MAThyS motivation (0–40) | 17.56 (6.46) | 16.64 (8.01) | F = 2.38 | 0.12 | |
| MAThyS cognition (0–40) | 20.32 (5.8) | 21.24 (6.67) | F = 3.13 | 0.08 | |
| MAThyS sensory perception (0–50) | 25.91 (4.38) | 26.34 (6.81) | F = 0.94 | 0.33 | |
| MAThyS psychomotor (0–30) |
12.9 (5.21) | 12.2 (6.27) | F = 2.05 | 0.15 | |
| AIM | 3.66 (0.68) | 3.9 (0.63) | F = 17.19 | <0.0001 | |
| ALS | 1.19 (0.66) | 1.54 (0.67) | F = 38.62 | <0.0001 | |
| BDHI Expressive component |
20.2 (7.89) | 23.23 (7.84) | F = 19.7 | 0.0001 | |
| BDHI Attitudinal component |
7.09 (4.34) | 8.75 (4.27) | F = 19.57 | <0.0001 | |
| BIS-10 | 66.67 (11.01) | 70.38 (12.87) | F = 14.24 | <0.0001 | |
| Drugs | |||||
| Lithium carbonate | No | 438 (74.5) | 146 (85.4) | χ2 = 8.43 | 0.004 |
| Yes | 151 (25.6) | 25 (14.6) | |||
| Anticonvulsants | No | 294 (49.9) | 80 (46.8) | χ2 = 0.4 | 0.53 |
| Yes | 295 (50.1) | 91 (53.2) | |||
| Antipsychotics | No | 334 (56.7) | 103 (60.2) | χ2 = 0.54 | 0.46 |
| Yes | 255 (43.3) | 68 (39.8) | |||
| Anxiolytics | No | 466 (79.1) | 119 (69.6) | χ2 = 6.26 | 0.01 |
| Yes | 123 (20.9) | 52 (30.4) | |||
| Hypnotics | No | 510 (86.6) | 146 (85.4) | χ2 = 0.08 | 0.78 |
| Yes | 79 (13.4) | 25(14.6) | |||
| Antidepressants | No | 362 (61.5) | 93 (54.4) | χ2 = 2.47 | 0.12 |
| Yes | 227 (38.5) | 78 (45.6) |
Note: *p-values are not corrected for multiple comparison, nor adjusted for other variables.
Abbreviations: AIM, affect intensity measure; ALS, affective lability scale; BD, bipolar disorder; BDHI, Buss–Durkee Hostility Inventory; BIS-10, Barratt Impulsiveness Scale; MAThyS, multidimensional assessment of thymic states; NOS, not otherwise specified; PSQI, Pittsburgh Sleep Quality Index; QIDS-SR, quick inventory of depressive self-report; STAI Y-A, State–Trait Anxiety Inventory; YMRS, Young Mania Rating Scale.
Compared with subjects not treated with lithium salts, subjects taking lithium salts were more often men, with BD type I, had less frequent lifetime anxious and substance abuse/dependence disorders, took less often anticonvulsants, but reported a higher number of past psychiatric hospitalizations. They had lower levels of affective lability and intensity, hostility and impulsivity, and better sleep quality (Supplementary Table S1).
Multivariate analyses
In multivariate analysis (model 1), lithium intake was significantly and negatively associated with reporting pain, that is, subjects on lithium were significantly less likely to report pain (OR = 0.59, 95% CI, 0.35–0.95; p = 0.036) compared with the other subjects (Table 2). The risk of reporting pain was reduced by 36% in subjects on lithium (RR = 0.64, 95% CI, 0.41, 0.96). Model 1 with all confounding variables gave similar results (OR = 0.58, 95% CI, 0.34–0.95; p = 0.035) (Supplementary Table S2), as well as the sensitivity analyses (Supplementary Table S3).
Table 2.
Odds ratios for the best model 1 selected based on the AIC
| OR [CI] | Statistic (z-value) | p-value | |
|---|---|---|---|
| Lithium salts a [yes] | 0.59 [0.35–0.95] | −2.1 | 0.036 |
| Age | 1.02 [1.01–1.04] | 2.58 | 0.010 |
| QIDS-SR (Box–Cox) | 1.24 [1.11–1.38] | 3.86 | 0.0001 |
| PSQI | 1.13 [1.07–1.19] | 4.42 | <0.0001 |
| MAThyS Motivation b | |||
| [16 – 20] | 1.61 [0.99–2.66] | 1.89 | 0.059 |
| MAThyS Motivation b | |||
| [21 – 40] | 1.90 [1.11–3.28] | 2.33 | 0.020 |
| BDHI Expressive Component |
1.02 [0.99–1.05] | 1.68 | 0.09 |
The reference for comparison is no lithium intake.
The reference for comparison is the score [0–15] of the MAThyS motivation subscale.
Abbreviations: BDH, Buss–Durkee Hostility Inventory; MAThyS, multidimensional assessment of thymic states; PSQI, Pittsburgh sleep quality index; QIDS-SR, quick inventory of depressive self-report.
Among the 176 subjects with lithium blood level data, 139 had a lithium concentration ≥ 0.5 mmol/l (that is therapeutic concentration). The multivariate analysis (model 2) showed a significant and negative association between lithium concentration and reporting pain (OR = 0.45, 95% CI, 0.24–0.79; p = 0.008), but not in subjects with lithium <0.5 mmol/l (OR = 1.15, 95% CI, 0.49–2.51: p = 0.73) (Table 3). The risk of reporting pain was halved in subjects with lithium ≥0.5 mmol/l (RR = 0.5. 95% CI, 0.28, 0.83). Model 2 with all confounding variables gave the same results (OR = 0.45, 95% CI, 0.23–0.81; p = 0.01), and also the sensitivity analyses (Supplementary Table S4).
Table 3.
Odds ratios for the best model 2 selected based on the AIC
| OR [CI] | Statistic (z-value) | p-value | |
|---|---|---|---|
| Lithium salts
a
[< 0.5 mmol/l] |
1.15 [0.49–2.51] | 0.34 | 0.735 |
| Lithium salts
a
[≥ 0.5 mmol/l] |
0.45 [0.24–0.79] | −2.64 | 0.008 |
| Age | 1.02 [1.00–1.04] | 2.49 | 0.013 |
| QIDS-SR | 1.23 [1.11–1.38] | 3.84 | 0.0001 |
| PSQI | 1.13 [1.07–1.19] | 4.39 | <0.0001 |
| MAThyS Motivation b | |||
| [16 –20] | 1.63 [0.99–2.70] | 1.93 | 0.053 |
| MAThyS Motivation b | |||
| [21 – 40] | 1.85 [1.08–3.18] | 2.33 | 0.026 |
| BDHI Expressive Component |
1.02 [0.99–1.05] | 1.72 | 0.086 |
The reference for comparison is no lithium intake.
The reference for comparison is the score [0–15] of the MAThyS motivation subscale.
Abbreviations: BDHI, Buss–Durkee Hostility Inventory; MAThyS, multidimensional assessment of thymic states; PSQI, Pittsburgh Sleep Quality Index; QIDS-SR, quick inventory of depressive self-report.
Discussion
In our study, individuals treated with lithium were less likely to report physical pain. Moreover, they were two times less likely to report pain when they had the recommended lithium blood level. This indicates that the recommended threshold of lithium efficiency for mood stabilization [23] is also effective for pain relief. These results remained significant after controlling for many variables, leading us to conclude that our results were not influenced by BD severity or emotional state. Subjects who reported pain also took more drugs, particularly antidepressants and anticonvulsants known to have analgesic effects. However, the intake of psychotropic medications (except lithium) was not associated with a reduced risk of reporting pain. Therefore, it is unlikely that the other psychotropic medications might explain lithium’s positive effect on pain. This negative result may be explained by the naturalistic design of our study. Generally, the analgesic effects of antidepressants and anticonvulsants are reduced in psychiatric patients [44]. Future studies should investigate the analgesic effect of antidepressants and anticonvulsants in subjects suffering from BD and reporting pain.
Lithium could be a promising strategy for subjects suffering from BD reporting pain and could also be tested in subjects with chronic pain, independently of mood disorders. Case reports and animal studies suggest that lithium is efficient for different painful pathologies, such as fibromyalgia and neuropathic pain [12, 13, 20]. The current analgesic medications have limited efficiency [45–48] and opioids have serious side effects [7, 8, 11]. Moreover, developing new drugs to alleviate pain with acceptable adverse effects is a complex and long process [49–51]. Lithium is a well-known drug, cheap, with monitorable adverse effects [52]. Lithium effect occurs through multiple mechanisms at different levels, but its blood concentration needs to be >0.5 mmol/l to observe its full mood-stabilizing effect [53]. Lithium prevents the reduction of gray matter volume in brain, restores the balance between excitatory and inhibitory neurotransmission in neurons, and promotes the synthesis of neuroprotective proteins [54]. All these mechanisms are impaired also in pain (e.g. neuropathic pain) [55–57], and could be improved with lithium. For example, painful pathologies are associated with hyperalgesia due to N-methyl-D-aspartate (NMDA) receptor activity [58]. Chronic lithium intake reduces NMDA receptor activity, promotes glutamate reuptake, and restores the normal excitatory activity [54].
Some limitations must be highlighted. First, the EQ-5D-5L questionnaire is a validated measure of pain intensity, but does not provide any useful information on pain location, duration, and frequency. Thus, we could not determine whether lithium was effective on chronic pain. Second, due to the database format, we could not determine whether the pain reported by patients was related to specific medical conditions or was idiopathic. We also could not assess the effect of lithium on specific painful pathologies. Fibromyalgia and headache are frequently reported by subjects suffering from BD, and lithium could be efficient particularly for these pathologies [59–61]. Third, some variables could have confounded the effect of lithium on pain, such as opioid intake or other somatic comorbidities not recorded in the database. Fourth, the cross-sectional design of our study does not allow highlighting of causal relationships. Lithium could have been prescribed to people with lower depression levels and lower emotional instability, thus favoring the selection of individuals who were less likely to report pain. RCTs are necessary to rule out these potential confounding variables and to confirm our results.
The study has some strengths. We included many variables that could have confounded the effect of lithium on pain, particularly potential painful commodities, and we had the blood lithium concentration. Moreover, we assessed lithium’s effect on pain in naturalistic conditions and this gives an ecological validity to our results.
In conclusion, this is the first naturalistic study showing that lithium has a promising effect on pain in subjects suffering from BD. However, RCTs are necessary to confirm this result. It would be important to determine whether lithium could also decrease pain in subjects reporting chronic pain and in subjects vulnerable to pain.
Supporting information
Risch et al. supplementary material
Acknowledgments
We thank the FondaMental Foundation (www-fondation-fondamental.org), a private foundation supporting research in mental health that develops a new model of translational research in psychiatry in France and supports the infrastructure of the Bipolar Expert Centers. We express all our thanks to the nurses, and to the individuals who were included in the present study. We thank Hakim Laouamri, and his team (Seif Ben Salem, Karmène Souyris, Victor Barteau, and Mohamed Laaidi) for the development of the FACE-BD computer interface, data management, quality control, and regulatory aspects. We would like to Elisabetta Andermarcher for the careful proofreading.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1192/j.eurpsy.2023.2476.
Data availability statement
The datasets generated and/or analyzed during the current study are not publicly available due to the sensitive and identifiable nature of health data but are available from the corresponding author upon reasonable request.
Author contribution
Olié E., Courtet Ph., Dubois J., and Risch N. formulated the hypotheses, designed the study, interpreted the results, and wrote the article. Dubois J and Risch N performed statistical analyses. Etain B, Aouizerate B, Bellivier F, Belzeaux R, Dubertret C, Haffen E, Januel D, Lefrere Antoine, Walter M, Rey R, Schwan R, Samalin L, Roux P, Polosan M, Leboyer M, Olié E and Courtet P and FACE BD collaborators participated in partcipants’ inclusion and assessment. All authors contributed to revise the final manuscript. All authors approved the submitted version of the article.
Financial support
This work was funded by Fondation FondaMental (RTRS Santé Mentale), by the Investissements d’Avenir program managed by ANR (references ANR-11-IDEX-0004-02 and ANR-10-COHO-10-01), and by INSERM (Institut National de la Santé et de la Recherche Médicale).
Competing interest
The authors and the FACE BD Collaborators declare none.
Ethical standard
The study was performed according to the Declaration of Helsinki. The protocol was approved by an ethics committee (CPP-Ile de France IX).
Transparency declaration
All of the authors declare that the manuscript is an honest, accurate, and transparent account of the study being reported; and that no important aspects of the study have been omitted.
References
- [1].Stubbs B, Eggermont L, Mitchell AJ, De Hert M, Correll CU, Soundy A, et al. The prevalence of pain in bipolar disorder: a systematic review and large-scale meta-analysis. Acta Psychiatr Scand. 2015;131:75–88. doi: 10.1111/acps.12325. [DOI] [PubMed] [Google Scholar]
- [2].Risch N, Dubois J, M’bailara K, Cussac I, Etain B, Belzeaux R, et al. Self-reported pain and emotional reactivity in bipolar disorder: a prospective FACE-BD study. J Clin Med. 2022;11:893. doi: 10.3390/jcm11030893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Karp JF, Scott J, Houck P, Reynolds CF, Kupfer DJ, Frank E. Pain predicts longer time to remission during treatment of recurrent depression. J Clin Psychiatry. 2005;66:591–7. doi: 10.4088/JCP.v66n0508. [DOI] [PubMed] [Google Scholar]
- [4].Kroenke K, Shen J, Oxman TE, Williams JW, Dietrich AJ. Impact of pain on the outcomes of depression treatment: results from the RESPECT trial: Pain. 2008;134:209–15. doi: 10.1016/j.pain.2007.09.021. [DOI] [PubMed] [Google Scholar]
- [5].Calati R, Laglaoui Bakhiyi C, Artero S, Ilgen M, Courtet P. The impact of physical pain on suicidal thoughts and behaviors: meta-analyses. J Psychiatr Res. 2015;71:16–32. doi: 10.1016/j.jpsychires.2015.09.004. [DOI] [PubMed] [Google Scholar]
- [6].Rice ASC, Smith BH, Blyth FM. Pain and the global burden of disease. Pain. 2016;157:791–6. doi: 10.1097/j.pain.0000000000000454. [DOI] [PubMed] [Google Scholar]
- [7].Bohnert ASB. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305:1315. doi: 10.1001/jama.2011.370. [DOI] [PubMed] [Google Scholar]
- [8].Gomes T, Mamdani MM, Dhalla IA, Paterson JM, Juurlink DN. Opioid dose and drug-related mortality in patients with nonmalignant pain. Arch Intern Med. 2011;171:686–691. doi: 10.1001/archinternmed.2011.117. [DOI] [PubMed] [Google Scholar]
- [9].Schaffer CB, Nordahl TE, Schaffer LC, Howe J. Mood-elevating effects of opioid analgesics in patients with bipolar disorder. J Neuropsychiatry Clin Neurosci. 2007;19:449–52. doi: 10.1176/jnp.2007.19.4.449. [DOI] [PubMed] [Google Scholar]
- [10].Scherrer JF, Svrakic DM, Freedland KE, Chrusciel T, Balasubramanian S, Bucholz KK, et al. Prescription opioid analgesics increase the risk of depression. J Gen Intern Med. 2014;29:491–9. doi: 10.1007/s11606-013-2648-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Vowles KE, McEntee ML, Julnes PS, Frohe T, Ney JP, van der Goes DN. Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and data synthesis. Pain. 2015;156:569–76. doi: 10.1097/01.j.pain.0000460357.01998.f1. [DOI] [PubMed] [Google Scholar]
- [12].Tyber MA. Lithium carbonate augmentation therapy in fibromyalgia. CMAJ Can Med Assoc J. 1990;143:902. [PMC free article] [PubMed] [Google Scholar]
- [13].Fontrier T. Lithium for fibromyalgia. Anesth Analg. 2004;98:1505. doi: 10.1213/01.ANE.0000114584.16029.0D. [DOI] [PubMed] [Google Scholar]
- [14].Sugawara H, Sakamoto K, Ishigooka J. Lithium for bipolar depression with chronic pain. Clin Neuropsychopharmacol Ther. 2010;1:32–4. doi: 10.5234/cnpt.1.32. [DOI] [Google Scholar]
- [15].Yang ML, Li JJ, So KF, Chen JYH, Cheng WS, Wu J, et al. Efficacy and safety of lithium carbonate treatment of chronic spinal cord injuries: a double-blind, randomized, placebo-controlled clinical trial. Spinal Cord. 2012;50:141–6. doi: 10.1038/sc.2011.126. [DOI] [PubMed] [Google Scholar]
- [16].Boiardi A, Bussone G, Merati B, Tansini E, Boeri R. Course of chronic cluster headache. Ital J Neurol Sci. 1983;4:75–7. doi: 10.1007/BF02043441. [DOI] [PubMed] [Google Scholar]
- [17].Bussone G, Leone M, Peccarisi C, Micieli G, Granella F, Magri M, et al. Double blind comparison of lithium and verapamil in cluster headache prophylaxis. Headache J Head Face Pain 1990;30:411–7. doi: 10.1111/j.1526-4610.1990.hed3007411.x. [DOI] [PubMed] [Google Scholar]
- [18].Leroux E, Ducros A. Cluster headache. Orphanet J Rare Dis. 2008;3:20. doi: 10.1186/1750-1172-3-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Shimizu T, Shibata M, Wakisaka S, Inoue T, Mashimo T, Yoshiya I. Intrathecal lithium reduces neuropathic pain responses in a rat model of peripheral neuropathy. Pain. 2000;85:59–64. doi: 10.1016/S0304-3959(99)00249-3. [DOI] [PubMed] [Google Scholar]
- [20].Banafshe HR, Mesdaghinia A, Arani MN, Ramezani MH, Heydari A, Hamidi GA. Lithium attenuates pain-related behavior in a rat model of neuropathic pain: possible involvement of opioid system. Pharmacol Biochem Behav. 2012;100:425–30. doi: 10.1016/j.pbb.2011.10.004. [DOI] [PubMed] [Google Scholar]
- [21].Banafshe HR, Hamidi GA, Brahimipoor M. The effect of lithium on painful diabetic neuropathy in streptozotocin-induced diabetic rats. J Neurol Sci. 2013;333:e527. doi: 10.1016/j.jns.2013.07.1858. [DOI] [Google Scholar]
- [22].Pourmohammadi N, Alimoradi H, Mehr SE, Hassanzadeh G, Hadian MR, Sharifzadeh M, et al. Lithium attenuates peripheral neuropathy induced by paclitaxel in rats. Basic Clin Pharmacol Toxicol. 2012;110:231–7. doi: 10.1111/j.1742-7843.2011.00795.x. [DOI] [PubMed] [Google Scholar]
- [23].Sproule B. Lithium in bipolar disorder: can drug concentrations predict therapeutic effect? Clin Pharmacokinet. 2002;41:639–60. doi: 10.2165/00003088-200241090-00002. [DOI] [PubMed] [Google Scholar]
- [24].Henry C, Etain B, Mathieu F, Raust A, Vibert J-F, Scott J, et al. A French network of bipolar expert centres: a model to close the gap between evidence-based medicine and routine practice. J Affect Disord. 2011;131:358–63. doi: 10.1016/j.jad.2010.11.013. [DOI] [PubMed] [Google Scholar]
- [25].Henry C, Godin O, Courtet P, Azorin J-M, Gard S, Bellivier F, et al. Outcomes for bipolar patients assessed in the French expert center network: a 2-year follow-up observational study (FondaMental advanced centers of expertise for bipolar disorder [FACE-BD]). Bipolar Disord. 2017;19:651–60. doi: 10.1111/bdi.12539. [DOI] [PubMed] [Google Scholar]
- [26].Kessing LV, Vradi E, Andersen PK. Nationwide and population-based prescription patterns in bipolar disorder. Bipolar Disord. 2016;18:174–82. doi: 10.1111/bdi.12371. [DOI] [PubMed] [Google Scholar]
- [27].Janssen MF, Pickard AS, Golicki D, Gudex C, Niewada M, Scalone L, et al. Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. Qual Life Res. 2013;22:1717–27. doi: 10.1007/s11136-012-0322-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Feng Y-S, Kohlmann T, Janssen MF, Buchholz I. Psychometric properties of the EQ-5D-5L: a systematic review of the literature. Qual Life Res. 2021;30:647–73. doi: 10.1007/s11136-020-02688-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Luo N, Li M, Chevalier J, Lloyd A, Herdman M. A comparison of the scaling properties of the English, Spanish, French, and Chinese EQ-5D descriptive systems. Qual Life Res. 2013;22:2237–43. doi: 10.1007/s11136-012-0342-0. [DOI] [PubMed] [Google Scholar]
- [30].Andrade LF, Ludwig K, Goni JMR, Oppe M, de Pouvourville G. A French value set for the EQ-5D-5L. PharmacoEconomics. 2020;38:413–25. doi: 10.1007/s40273-019-00876-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Fond G, Boyer L, Andrianarisoa M, Godin O, Bulzacka E, Berna F, et al. Self-reported pain in patients with schizophrenia. Results from the national first-step FACE-SZ cohort. Prog Neuro-Psychopharmacol Biol Psychiatry. 2018;85:62–8. doi: 10.1016/j.pnpbp.2018.04.007. [DOI] [PubMed] [Google Scholar]
- [32].Soer R, Reneman MF, Speijer BLGN, Coppes MH, Vroomen PCAJ. Clinimetric properties of the EuroQol-5D in patients with chronic low back pain. Spine J. 2012;12:1035–9. doi: 10.1016/j.spinee.2012.10.030. [DOI] [PubMed] [Google Scholar]
- [33].Obradovic M, Lal A, Liedgens H. Validity and responsiveness of EuroQol-5 dimension (EQ-5D) versus short Form-6 dimension (SF-6D) questionnaire in chronic pain. Health Qual Life Outcomes. 2013;11:110. doi: 10.1186/1477-7525-11-110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Whynes DK, McCahon RA, Ravenscroft A, Hodgkinson V, Evley R, Hardman JG. Responsiveness of the EQ-5D health-related quality-of-life instrument in assessing low back pain. Value Health. 2013;16:124–32. doi: 10.1016/j.jval.2012.09.003. [DOI] [PubMed] [Google Scholar]
- [35].Durham J, Steele JG, Breckons M, Story W, Vale L. DEEP study: does EQ-5D-5L measure the impacts of persistent oro-facial pain? J Oral Rehabil. 2015;42:643–50. doi: 10.1111/joor.12296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Tawiah AK, Al Sayah F, Ohinmaa A, Johnson JA. Discriminative validity of the EQ-5D-5 L and SF-12 in older adults with arthritis. Health Qual Life Outcomes. 2019;17:68. doi: 10.1186/s12955-019-1129-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Spronk I, Bonsel GJ, Polinder S, van Baar ME, Janssen MF, Haagsma JA. Exploring the relation between the EQ-5D-5L pain/discomfort and pain and itching in a sample of burn patients. Health Qual Life Outcomes. 2020;18:144. doi: 10.1186/s12955-020-01394-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Garratt AM, Furunes H, Hellum C, Solberg T, Brox JI, Storheim K, et al. Evaluation of the EQ-5D-3L and 5L versions in low back pain patients. Health Qual Life Outcomes. 2021;19:155. doi: 10.1186/s12955-021-01792-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Bushman BJ, Cooper HM, Lemke KM. Meta-analysis of factor analyses: an illustration using the buss-Durkee hostility inventory. Personal Soc Psychol Bull. 1991;17:344–9. doi: 10.1177/0146167291173015. [DOI] [Google Scholar]
- [40].Fernandez E, Day A, Boyle GJ. Measures of anger and hostility in adults. In: Measures of personality and social psychological constructs. The Netherlands: Elsevier; 2015, p. 74–100. doi: 10.1016/B978-0-12-386915-9.00004-8. [DOI] [Google Scholar]
- [41].R Core Team. R: a language and environment for statistical computing. Vienna: R foundation; 2022. [Google Scholar]
- [42].Venables WN, Ripley BD, Venables WN. Modern applied statistics with S. 4th ed. New York: Springer; 2002. [Google Scholar]
- [43].Grant RL. Converting an odds ratio to a range of plausible relative risks for better communication of research findings. BMJ. 2014;348:f7450. doi: 10.1136/bmj.f7450. [DOI] [PubMed] [Google Scholar]
- [44].Marchettini P, Wilhelm S, Petto H, Tesfaye S, Tölle T, Bouhassira D, et al. Are there different predictors of analgesic response between antidepressants and anticonvulsants in painful diabetic neuropathy? Eur J Pain. 2016;20:472–82. doi: 10.1002/ejp.763. [DOI] [PubMed] [Google Scholar]
- [45].Finnerup NB, Attal N, Haroutounian S, McNicol E, Baron R, Dworkin RH, et al. Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurol. 2015;14:162–73. doi: 10.1016/S1474-4422(14)70251-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46].van de Laar M. Pain treatment in arthritis-related pain: beyond NSAIDs. Open Rheumatol J. 2012;6:320–30. doi: 10.2174/1874312901206010320. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47].Chou R, Deyo R, Friedly J, Skelly A, Weimer M, Fu R, et al. Systemic pharmacologic therapies for low back pain: a systematic review for an American College of Physicians Clinical Practice Guideline. Ann Intern Med. 2017;166:480. doi: 10.7326/M16-2458. [DOI] [PubMed] [Google Scholar]
- [48].Jackson JL, Cogbill E, Santana-Davila R, Eldredge C, Collier W, Gradall A, et al. A comparative effectiveness meta-analysis of drugs for the prophylaxis of migraine headache. PLoS One. 2015;10:e0130733. doi: 10.1371/journal.pone.0130733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [49].Percie du Sert N, Rice ASC. Improving the translation of analgesic drugs to the clinic: animal models of neuropathic pain: Improving models of neuropathic pain. Br J Pharmacol. 2014;171:2951–63. doi: 10.1111/bph.12645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [50].Yaksh TL, Woller SA, Ramachandran R, Sorkin LS. The search for novel analgesics: targets and mechanisms. F1000Prime Rep. 2015;7:56. doi: 10.12703/P7-56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [51].Knezevic NN, Yekkirala A, Yaksh TL. Basic/translational development of forthcoming opioid- and nonopioid-targeted pain therapeutics. Anesth Analg. 2017;125:1714–32. doi: 10.1213/ANE.0000000000002442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [52].Volkmann C, Bschor T, Köhler S. Lithium treatment over the lifespan in bipolar disorders. Front Psychiatry. 2020;11:377. doi: 10.3389/fpsyt.2020.00377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [53].Alda M. Lithium in the treatment of bipolar disorder: pharmacology and pharmacogenetics. Mol Psychiatry. 2015;20:661–70. doi: 10.1038/mp.2015.4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [54].Malhi GS, Tanious M, Das P, Coulston CM, Berk M. Potential mechanisms of action of lithium in bipolar disorder: current understanding. CNS Drugs. 2013;27:135–53. doi: 10.1007/s40263-013-0039-0. [DOI] [PubMed] [Google Scholar]
- [55].Pan PL, Zhong JG, Shang HF, Zhu YL, Xiao PR, Dai ZY, et al. Quantitative meta-analysis of grey matter anomalies in neuropathic pain. Eur J Pain. 2015;19:1224–31. doi: 10.1002/ejp.670. [DOI] [PubMed] [Google Scholar]
- [56].Petrou M, Pop-Busui R, Foerster BR, Edden RA, Callaghan BC, Harte SE, et al. Altered excitation-inhibition balance in the brain of patients with diabetic neuropathy. Acad Radiol. 2012;19:607–12. doi: 10.1016/j.acra.2012.02.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [57].Eaton MJ, Blits B, Ruitenberg MJ, Verhaagen J, Oudega M. Amelioration of chronic neuropathic pain after partial nerve injury by adeno-associated viral (AAV) vector-mediated over-expression of BDNF in the rat spinal cord. Gene Ther. 2002;9:1387–95. doi: 10.1038/sj.gt.3301814. [DOI] [PubMed] [Google Scholar]
- [58].Basbaum AI, Bautista DM, Scherrer G, Julius D. Cellular and molecular mechanisms of pain. Cell. 2009;139:267–84. doi: 10.1016/j.cell.2009.09.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [59].Fornaro M, Stubbs B. A meta-analysis investigating the prevalence and moderators of migraines among people with bipolar disorder. J Affect Disord. 2015;178:88–97. doi: 10.1016/j.jad.2015.02.032. [DOI] [PubMed] [Google Scholar]
- [60].Kudlow PA, Rosenblat JD, Weissman CR, Cha DS, Kakar R, McIntyre RS, et al. Prevalence of fibromyalgia and co-morbid bipolar disorder: a systematic review and meta-analysis. J Affect Disord. 2015;188:134–42. doi: 10.1016/j.jad.2015.08.030. [DOI] [PubMed] [Google Scholar]
- [61].Stubbs B. A random effects meta-analysis investigating the prevalence of bipolar disorder in people with fibromyalgia: an updated analysis. J Affect Disord. 2016;191:308–9. doi: 10.1016/j.jad.2015.12.006. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Risch et al. supplementary material
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to the sensitive and identifiable nature of health data but are available from the corresponding author upon reasonable request.