Introduction
Quality of life (QoL) is a broad construct defined as satisfaction in the physical, psychological, social, and environmental aspects of life, which is significantly lower for individuals with BD in remission than those without lifetime BD [Reference Pascual-Sánchez, Jenaro and Montes-Rodríguez1]. BD is associated with a reduction in the self-reported QoL to a similar [Reference Yen, Cheng, Huang, Yen, Ko and Chen2] or even greater extent than schizophrenia [Reference Atkinson, Zibin and Chuang3]. Among the several dimensions of QoL, health-related QoL (HRQoL) refers to the individual’s perception of physical and mental health over time and its impact on the ability to live a fulfilling life. HRQoL is particularly low in BD [Reference Rhee, Gillissie, Nierenberg and McIntyre4].
Several studies have reported positive associations between QoL and neuropsychological performance in executive functioning and verbal abstraction [Reference Cotrena, Branco, Shansis and Fonseca5–Reference Toyoshima, Kako, Toyomaki, Shimizu, Tanaka and Nakagawa7]. This association may be particularly present following remission of the first manic episode [Reference Mackala, Torres, Kozicky, Michalak and Yatham8]. Another study involving 224 participants reported that cognitive reserve was positively associated with the physical component of HRQoL but negatively associated with its mental component [Reference Anaya, Torrent, Caballero, Vieta, Bonnin and Ayuso-Mateos9]. The association between good neuropsychological performance and a satisfying QoL thus needs to be confirmed in studies with large sample sizes.
Moreover, the studies investigating associations between cognition and QoL did not consider the potential mediating role of functioning. Condition-specific functioning measures [Reference Pogue, Lavelle, Hodgkin, Sylvia, Ritter and Nierenberg10] and psychological functioning [Reference Di Vincenzo, Sampogna, Della Rocca, Brandi, Mancuso and Landolfi11] are indeed positively associated with QoL in BD. When measured with BD-specific tools, QoL is also positively associated with functioning [Reference Provencher, Morton, Guillemette, Rheault and Mérette12]. Cognition is a critical determinant of functioning in BD: a study using elastic net regression reported that executive functioning was associated with functioning (β = 0.30) [Reference Tsapekos, Strawbridge, Cella, Wykes and Young13]. A previous SEM study reported a significant association (β = 0.18) between cognition and functioning when controlling for premorbid IQ, number of episodes, and lithium response [Reference Saito, Fujii, Ozeki, Ohmori, Honda and Mori14]. This association was confirmed with longitudinal studies: processing speed predicts long-term functioning [Reference Burdick, Goldberg and Harrow15] and verbal memory middle-term functioning [Reference Bonnin, Martinez-Aran, Torrent, Pacchiarotti, Rosa and Franco16]. It is thus crucial to investigate whether the positive association sometimes found between cognition and QoL is mediated by functioning.
Previous studies investigating the association between cognition and QoL have also ignored essential confounders. Several determinants of cognition, functioning, and QoL have been identified in BD. First, residual depressive symptoms are associated with QoL [Reference Gao, Su, Sweet and Calabrese17], cognition [Reference Bonnín, Del, González-Pinto, Solé, Reinares, González-Ortega and Alberich18], and functioning [Reference Léda-Rêgo, Bezerra-Filho and Miranda-Scippa19] in individuals with BD in euthymia. Subsyndromal anxiety has also been identified as an important predictor of poor QoL in BD [Reference Yoldi-Negrete, Morera, Palacios-Cruz, Camarena, Ortega and Castañeda-Franco20] and comorbid anxiety is associated with poor neuropsychological performance in BD-II [Reference Wu, Chang, Lai, Wu, Chen and Chu21]. Moreover, a history of psychosis in BD is associated with poorer cognition [Reference Bora22]. Finally, medication is associated with QoL in BD through adverse effects [Reference Yen, Cheng, Huang, Yen, Ko and Chen2]. The prescription of antipsychotics has also been shown to be associated with cognitive impairment in a meta-analysis of cross-sectional studies, including euthymic participants with BD [Reference Bourne, Aydemir, Balanza-Martinez, Bora, Brissos and Cavanagh23]. It is, thus, likely of particular importance to control for antipsychotic prescription when investigating the relationship between cognition, functioning, and QoL in BD.
This study aimed to explore the relationship between cognition, functioning, and HRQoL in a large sample of individuals with euthymic BD using SEM and controlling for several covariates, such as subthreshold depression, anxiety, psychotic features, and antipsychotic medication. We hypothesized that the positive association between cognition and HRQoL would be mediated by functioning and that these covariates would not confound this mediation.
Methods
The present report followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Study design and characteristics of the recruiting network
This multicenter transversal study included patients recruited into the FACE-BD (FondaMental Advanced Centers of Expertise for Bipolar Disorders) cohort within a French national network of 10 centers (Bordeaux, Colombes, Créteil, Grenoble, Marseille, Monaco, Montpellier, Nancy, Paris, and Versailles). All procedures contributing to this work were approved by the local ethics committee (Comité de Protection des Personnes Ile de France IX) on January 18, 2010.
Participants
The diagnosis of BD was based on the Structured Clinical Interview for DSM-IV-TR (SCID) criteria [Reference First, Spitzer, Gibbon and Williams24]. Outpatients with type 1, type 2, or not-otherwise-specified (NOS) BD between 18 and 65 years of age were eligible for this analysis. All patients included in the analyses were euthymic at the time of testing according to the DSM-IV-TR criteria, with scores on the Montgomery-Asberg Depression Rating Scale (MADRS) ≤ 11 [Reference Montgomery and Asberg25] and the Young Mania Rating Scale (YMRS) < 12 [Reference Young, Biggs, Ziegler and Meyer26]. Patients who met the following criteria at any time of testing were excluded to control for a cognitive impairment resulting from a cause other than BD itself: history of significant neurological disorder, dyslexia, dysorthographia, dyscalculia, dysphasia, dyspraxia, substance-related disorders in the previous month (except tobacco use), electroconvulsive therapy in the past year. Patients with anomalous trichromatism and any disabling visual impairment were also excluded to avoid an artefactual cognitive impairment.
Assessment tools
Clinical evaluation
The subtype of BD and history of psychotic features were established according to DSM-IV-TR criteria. The MADRS total score measured residual depressive symptoms. The presence of antipsychotics during the time of testing was reported as a binary variable. The state of anxiety was measured using the state subscale of the State–Trait Anxiety Inventory, form Y-A (STAI-Y-A) [Reference Spielberger, Sydeman and Maruish27].
Cognition
The neuropsychological battery explored five cognitive domains through 10 tests, five of which were subtests from the WAIS version III [Reference WAIS-28] or version IV [Reference Wechsler, Coalson and Raiford29], as the French version of the WAIS-IV started to be used as it became available.
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- Verbal memory: California Verbal Learning Test [Reference Delis30] short- and long-delay free recall
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- Working memory: WAIS digit span (total score) and spatial span (forward and backward scores) from the Wechsler Memory Scale version III [Reference Wechsler31]
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- Executive functions: Trail Making Test (TMT) part B [Reference Reitan32], color/word condition of the Stroop test [Reference Golden33], semantic and phonemic verbal fluency [Reference Lezak34]
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- Processing speed: Digit symbol coding (WAIS-III) or coding (WAIS-IV), WAIS symbol search, and TMT part A
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- Verbal and perceptual reasoning: WAIS vocabulary and matrices
Raw scores were transformed into demographically corrected standardized z-scores based on normative data [Reference Golden33, Reference Conners and Staff35–Reference Poitrenaud, Deweer, Kalafat and Van der Linden37]. Higher scores reflect better performance. We computed a mean score for each cognitive domain.
Functioning
Global functioning was measured using the Global Assessment of Functioning (GAF) [Reference Jones, Thornicroft, Coffey and Dunn38]. Domain-based psychosocial functioning in everyday life was assessed using the Functioning Assessment Short Test (FAST) [Reference Rosa, Sanchez-Moreno, Martinez-Aran, Salamero, Torrent and Reinares39]. For the GAF, higher scores are associated with higher functioning, whereas the inverse is true for the FAST.
HRQoL
HRQoL was measured with the European Quality of Life 5 Dimensions and 3 Lines (EQ-5D-3L) value index. It is a generic preference-based measure developed to describe and value health across various disease areas [40]. The scale evaluates five aspects of health: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has three levels: no, some, and extreme. EQ-5D health states were converted into a single summary number, the index value, using a time trade-off valuation technique [Reference Chevalier and de Pouvourville41]. An index value of 1 corresponds to the best possible health state according to the preferences of the general population of a country/region, while an index value of <0 represents the worst possible health state. It has shown good convergent validity [Reference Arbuckle, Frye, Brecher, Paulsson, Rajagopalan and Palmer42, Reference Hayhurst, Palmer, Abbott, Johnson and Scott43] and known-groups validity in patients with and without BD [Reference Hayhurst, Palmer, Abbott, Johnson and Scott43–Reference Vojta, Kinosian, Glick, Altshuler and Bauer45].
Statistical analyses
Rationale to map the latent variables with their indicators
The five average scores for the different cognitive domains were included in the SEM model as indicators of the latent variable cognition. They all corresponded to theoretically separated, albeit partially overlapping, cognitive functions. Factor analyses previously identified working memory [Reference Czobor, Jaeger, Berns, Gonzalez and Loftus46], conceptual reasoning [Reference Langenecker, Saunders, Kade, Ransom and McInnis47], psychomotor speed, verbal memory, and executive functioning [Reference Schretlen, Pena, Aretouli, Orue, Cascella and Pearlson48] as autonomous cognitive dimensions in BD.
Two measures were included in the SEM model as indicators for the latent variable functioning: GAF & FAST total score. They were chosen because they measured complementary aspects of functioning. GAF assesses how much a person’s symptoms affect day-to-day life. In contrast, the FAST-total score combines specific domains of functioning (autonomy, occupational functioning, cognition, financial issues, interpersonal relationships, and leisure), irrespective of the symptoms’ level. Several studies have reported that these two types of functioning assessments are closely associated in BD [Reference Bonnin, Martinez-Aran, Reinares, Valenti, Sole and Jimenez49, Reference Roux, Brunet-Gouet, Ehrminger, Aouizerate, Aubin and Azorin50].
Rationale for covariates selection
Four covariates were included in the SEM model because they have all been associated with cognitive deficits in BD: prescription of antipsychotics [Reference Bourne, Aydemir, Balanza-Martinez, Bora, Brissos and Cavanagh23], psychotic features [Reference Bora22], residual depressive symptoms [Reference Bonnín, Del, González-Pinto, Solé, Reinares, González-Ortega and Alberich18], and anxiety [Reference Wu, Chang, Lai, Wu, Chen and Chu21].
Model specifications, estimation, and testing
Zero-order correlations between each measure (cognition, functioning, HRQoL, and covariates) were calculated using Spearman’s correlation coefficients.
Linear regression analyses evaluated relationships between cognition, functioning, and HRQoL. They were indexed using standardized coefficients: factor loading for the relationship between indicators and latent variables and standardized regression coefficients (β) for the relationship between covariates, cognition, functioning, and HRQoL. Two cognitive domain scores were computed from two cognitive variables acquired during the same cognitive test (the TMT, in which part A was included in speed of processing, whereas part B was included in executive functions), leading to a shared method variance. To account for the artefactual influence of the current assessment method, the residual variances between the implied indicators were allowed to be correlated, as recommended [Reference Cole, Ciesla and Steiger51].
A robust estimator was used: the maximum likelihood estimation with robust (Huber-White) standard errors and a scaled test statistic (asymptotically) equal to the Yuan-Bentler test statistic. Missing data were managed using the full information maximum likelihood (FIML). We examined consensual fit indices with recommended cutoff criteria for good fit [Reference Hu and Bentler52]: Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) > 0.95, root mean square error of approximation (RMSEA) ≤ 0.05 (p of close-fit >0.05, and 90% confidence intervals), standardized root mean square residual (SRMR) < 0.08. For the mediation analysis, the direct effect was the standardized path coefficient between cognition and functioning, and the indirect effect was the product of the direct effect and the standardized path coefficient between functioning and HRQoL. The total effect of cognition on HRQoL was the sum of direct and indirect effects.
Results
Characteristics of the participants
The flowchart of the study participants’ selection process is reported in Figure 1. The inclusion period began in June 2013, when HRQoL started to be routinely collected by all centers, and ended in January 2021. We confirmed the eligibility for 1190 participants whose data were then analyzed. The participants’ sociodemographic, clinical, and functional characteristics are reported in Table 1. The participants mainly consisted of women with type 1 BD.
Figure 1. Flowchart of the study participants’ selection process.
Table 1. Participant socio-demographic, clinical, and functional characteristics
Abbreviations: N missing: Number of participants with missing information; MADRS: Montgomery-Asberg Depression Rating Scale; YMRS: Young Mania Rating Scale; STAI-YA: State–Trait Anxiety Inventory, form Y-A; FAST: Functioning Assessment Short Test; GAF: Global Assessment of Functioning; EQ-5D-3L: European Quality of Life 5 dimensions and 3 lines.
The neuropsychological results are presented in Table 2. WAIS-III was proposed to 60 participants and WAIS-IV to 927 participants. The worst performances were found for verbal memory (−0.07 ± 1.11) and executive functioning (−0.05 ± 0.73), and the best for reasoning (0.21 ± 0.82).
Table 2. Cognitive performance expressed in demographically corrected standardized z-scores
Abbreviations: CVLT: California Verbal Learning Test; TMT: Trail Making Test; CPT: Continuous Performance Test.
The mean FAST total score was 14.2 (± 11.1), and the mean GAF score was 73.1 (± 12.6), corresponding to mild functional impairment [Reference Bonnin, Martinez-Aran, Reinares, Valenti, Sole and Jimenez49].
The mean EQ-5D-3L index was 0.831 (± 0.178), which was below the scores reported for different age classes below 65 in the general French population (ranging from 0.948 in the 18–24 year class to 0.853 in the 55–64 year class) [Reference Janssen, Szende, Cabases, Ramos-Goñi, Vilagut and König53].
SEM model
The zero-order correlations matrix is reported in Supplementary Table 1. There were 14.8% missing data with 99 different patterns of missingness. The covariance coverage matrix of missing data is reported in Supplementary Table 2. The observed variances for EGF, FAST, & STA-Y (state subscale) were at least 1000 times greater than the observed variance of some other variables. They were thus converted to z-scores. The model provided a good fit for the data, as suggested by TLI and CFI above 0.95 (TLI = 0.97, CFI = 0.982), RMSEA not statistically greater than 0.05 (RMSEA = 0.031, one-sided p-value of the test of the null hypothesis that the RMSEA equals .05: 1) and an SRMR below 0.08 (0.026). All indicator variables were reliable and valid measures of their respective latent variables, as supported by significant moderate to high factor loadings (absolute values of standardized factor loadings ≥0.5, p < 0.001, see Supplementary Figure 1 and Table 3). The model explained 36.3% of the variance in functioning and 37.4% of the variance in HRQoL.
Table 3. Statistics for the estimated factor loadings, standardized path coefficients, and correlation coefficients of the SEM mediation model
Abbreviation: GAF: Global Assessment of Functioning; FAST: Functioning Assessment Short Test; MADRS: Montgomery-Asberg Depression Rating Scale; STAI-YA: State–Trait Anxiety Inventory, form Y-A; EQ-5D-3L: European. Quality of Life 5 dimensions and 3 lines.
The association between cognition and HRQoL was not significant (see Figure 2), whereas cognition was significantly and positively associated with functioning (β = 0.20, z = 5, p < 0.001), and functioning was significantly and positively associated with HRQoL (β = 0.25, z = 5.4, p < 0.001). The mediation analysis showed a nonsignificant direct effect of cognition on HRQoL (β = − 0.03, z = −0.78, p = 0.433), a significant indirect effect of cognition on HRQoL (β = 0.05, z = 3.6, p < 0.001) and a non-significant total effect of cognition on HRQoL (β = 0.02, z = 0.56, p = 0.567). The significant indirect effect in the absence of a significant direct effect was interpreted as a full mediation (or indirect-only mediation) of the effect of cognition on HRQoL by functioning. The significant indirect effect in the absence of a significant total effect of cognition on HRQoL was interpreted as an inconsistent mediation: the positive indirect effect of cognition on HRQoL was neutralized by its negative direct effect.
Figure 2. Simplified diagram of the model. Indicators of latent variables were omitted for readability (see Supplementary Figure 1 to see the model with the indicators). Rectangles indicate the observed variables, ovals the latent variables, single-headed arrows the regressions (freely estimated regression weight), and double-headed arrows the covariances. Path coefficients were standardized. (Significance levels are as follows: ***p < 0.001, **p < 0.01, *p < 0.05).
Antipsychotic prescription negatively affected cognition (β = −0.23, z = −5.7, p < 0.001, see Figure 2 and Table 3). Two covariates were significantly and negatively associated with functioning: depression (β = −0.35, z = −9.5, p < 0.001) and state anxiety (β = −0.27, z = −7.4, p < 0.001). Beyond functioning, HRQoL was mainly negatively associated with state anxiety (β = −0.39, z = −11.8, p < 0.001).
Discussion
The present study aimed to investigate the relationships between cognition, functioning, and HRQoL in a large sample of individuals with euthymic BD, controlling for a set of covariates.
The main result of this study is that cognition was only indirectly associated with HRQoL in euthymic participants with BD: better cognition was associated with better psychosocial functioning, which was, in turn, associated with better HRQoL. There was no direct link between cognition and HRQoL, thus confirming previous findings in more symptomatic participants with BD, controlling for cognitive reserve [Reference Cotrena, Branco, Shansis and Fonseca54]. One explanation for this lack of a direct association could have been the use of a global measure of QoL, which may combine subdimensions with opposite associations with cognition. For example, a previous study reported a negative association between cognitive reserve and the psychological dimension of HRQoL, whereas its physical dimension was positively associated with cognitive reserve [Reference Anaya, Torrent, Caballero, Vieta, Bonnin and Ayuso-Mateos9]. Therefore, further studies are encouraged using more specific measures of QoL when investigating its association with cognition in BD. This result has an important impact on recommendations about cognitive remediation in BD. Clinicians should expect an improvement in HRQoL after cognitive remediation for individuals with BD and cognitive impairment through functioning enhancement. A recent RCT investigated the transfer of cognitive remediation into functioning and goal attainment, with mixed findings [Reference Tsapekos, Strawbridge, Cella, Young and Wykes55]. First, improvement in cognition accounts for more than one-third of the cognitive remediation effect on functioning. Second, cognitive improvements did not account for changes in goal attainment, a key component of QoL. However, the cognitive level achieved following remediation accounts for differences in goal attainment since only those performing above a certain level showed significant improvement in goal attainment. It is important to note that the comparison between the two studies is limited by the fact that the goal attainment measured in this study was quite different from the HRQoL measured in ours. Another RCT reported a positive effect of cognitive remediation on executive function in BD but a lack of improvement of functioning and QoL, thus suggesting again that cognitive improvement without functional improvement is insufficient to enhance QoL [Reference Ott, Vinberg, Kessing, Bowie, Forman and Miskowiak56]. An inconsistent mediation of the effect of cognition on QoL by functioning has been previously identified in schizophrenia [Reference Ehrminger, Roux, Urbach, André, Aouizerate and Berna57]. The neutralization of the positive indirect effect of cognition on HRQoL by its negative direct effect may be explained by the fact that individuals with better cognition, resulting from a higher cognitive reserve, may have an increased awareness of their disorder and thus overestimate its consequences compared to their premorbid situation, leading to a lower perceived HRQoL. Further studies should control from clinical insight when investigating the associations between cognition, functioning, and QoL in BD.
Several modifiable factors of HRQoL were identified in our study, suggesting certain amendments to the classification of QoL determinants in BD between marked, moderate, mild, possible, and unknown proposed by Grunze and Born [Reference Grunze and Born58]. Our results confirm the marked impact of anxiety on decreased QoL reported in this review. They contrast those in a previous study showing the lack of an association between anxiety and QoL in a sample of individuals with BD when residual depressive symptoms were accounted for [Reference Gao, Su, Sweet and Calabrese17]. Several approaches have been proposed to relieve anxiety in BD, such as mindfulness-based cognitive therapy [Reference Perich, Manicavasagar, Mitchell, Ball and Hadzi-Pavlovic59] and medication optimization [Reference Sheehan, Harnett-Sheehan, Hidalgo, Janavs, McElroy and Amado60, Reference Vieta, Martinez-Arán, Nieto, Colom, Reinares and Benabarre61]. Our results suggest these approaches may also improve functioning and HRQoL in BD at euthymia. Functioning should also be added to the category of marked moderators of HRQoL in BD. Functional remediation, which aims to develop cognitive strategies, psychoeducation about cognition, and problem-solving in the context of everyday life improves the functional outcome in euthymic individuals with BD [Reference Bonnin, Torrent, Arango, Amann, Sole and Gonzalez-Pinto62, Reference Torrent, Bonnin, Martínez-Arán, Valle, Amann and González-Pinto63]. Our results thus suggest that this intervention may also improve HRQoL in BD. Potential concerns have been raised toward the use of the EQ-5D in BD, as this measure has been considered to more strongly reflect depression rather than other consequences of BD and misses large areas of mental HRQoL [Reference Brazier, Connell, Papaioannou, Mukuria, Mulhern and Peasgood64]. Our results obtained in a sample of euthymic participants suggest the opposite: the EQ-5D-3L index score was independently influenced by anxiety and functioning and, to a much lower extent, by residual depression. The classification of Grunze and Born [Reference Grunze and Born58] considered the possible impact of cognition on QoL in BD. Still, our results support neither a direct nor a total association between these two variables. In this classification, subsyndromal depression was considered a moderate correlate of QoL in BD, which was not supported by our study, perhaps due to the stringency of the criterion used to include euthymic participants. Finally, this classification reported a history of psychosis as having an unknown impact on QoL, whereas our results suggest a small albeit significant positive association between these two variables.
Several modifiable factors of psychosocial functioning were also identified in our study. Functioning was mainly influenced by subsyndromal depression, even though this variable showed a low range due to the stringent criterion used for inclusion. This finding is in accordance with previous cross-sectional [Reference Bowie, Depp, McGrath, Wolyniec, Mausbach and Thornquist65] and longitudinal studies [Reference Bonnin, Martinez-Aran, Torrent, Pacchiarotti, Rosa and Franco16]. Our results suggest that targeting residual depressive symptoms, for instance, with adjunctive medication [Reference Garriga, Solé, González-Pinto, Selva-Vera, Arranz and Amann66] and therapy programs [Reference Deckersbach, Nierenberg, Kessler, Lund, Ametrano and Sachs67–Reference Solé, Bonnin, Mayoral, Amann, Torres and González-Pinto69], would improve functioning and QoL in BD at euthymia. The two other important correlates of functioning were cognition and anxiety. Although numerous studies showed a consistent association between cognition and functioning in BD (see, for example [Reference Bonnín, Jiménez, Solé, Torrent, Radua and Reinares70]), they are much more scarce concerning the association between anxiety and functioning (see [Reference Léda-Rêgo, Bezerra-Filho and Miranda-Scippa19] for a review). These results suggest tackling cognitive deficits with cognitive remediation programs [Reference Tsapekos, Strawbridge, Cella, Young and Wykes55, Reference Ott, Vinberg, Kessing, Bowie, Forman and Miskowiak56] would improve functioning in BD at euthymia. The history of psychosis was unrelated to functioning, thus confirming a previous finding that this specifier explains very little of the variance in functioning [Reference Jiménez-López, Sánchez-Morla, Aparicio, López-Villarreal, Martínez-Vizcaíno and Rodriguez-Jimenez71].
Finally, the model presented in this study revealed few modifiable factors for cognition, except the prescription of antipsychotics, which was mildly associated with poorer cognitive performance, and anxiety, which was weakly associated with it. A previous individual patient data meta-analysis reported that the use of antipsychotics was associated with specific measures of verbal memory [Reference Bourne, Aydemir, Balanza-Martinez, Bora, Brissos and Cavanagh23]. However, it is impossible to draw any conclusions concerning a causal link between antipsychotics and cognitive impairment. Longitudinal studies are needed to clarify the effect of antipsychotics on cognition in BD. They should consider each molecule’s specific psychopharmacological properties, serum level, duration of exposure, and therapeutic response. Our results support a small negative impact of the history of psychosis on cognition in BD. Previous reports showed that cognitive impairments were associated with recent [Reference Levy, Medina and Weiss72] and active psychotic features in BD but not with a history of remitted psychotic symptoms [Reference Bowie, Best, Depp, Mausbach, Patterson and Pulver73].
The present results must be interpreted in light of several limitations. First, the neuropsychological battery did not include measures of attention. Previous studies have identified attention as an important cognitive factor of the cognitive structure in BD through various factor analyses [Reference Czobor, Jaeger, Berns, Gonzalez and Loftus46, Reference Schretlen, Pena, Aretouli, Orue, Cascella and Pearlson48]. In addition, our study lacked records of several important variables for QoL: medical and personality comorbidities, internalized stigma [Reference Post, Pardeller, Frajo-Apor, Kemmler, Sondermann and Hausmann74], and circadian rhythms, which have also been shown to be associated with QoL in BD [Reference Slyepchenko, Allega, Leng, Minuzzi, Eltayebani and Skelly75]. There is little consensus within the literature on measuring QoL most accurately. Still, it may be argued that a measure of QoL specifically designed for BD [Reference Michalak and Murray76] would have been preferable. Finally, the design of this study had two important limitations: the lack of comparison with a control group, which is required to infer whether the relationships presented in this model are specific to BD, and the cross-sectional design of this study, which makes it impossible to infer the direction of causality. The choice of the arrow direction from functioning to QoL was questionable, as some authors have postulated that psychological functioning measured with the Personal and Social Performance Scale may be influenced by perceived QoL in BD [Reference Di Vincenzo, Sampogna, Della Rocca, Brandi, Mancuso and Landolfi11]. Longitudinal studies are needed to test the direction of the association between functioning and QoL, using, for instance, cross-lagged panel modeling, dual change score model, or latent difference score.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1192/j.eurpsy.2024.1789.
Data availability statement
Due to ethical and legal restrictions, data involving clinical participants cannot be made publicly available. All relevant data are available upon request to the Fondation FondaMental for researchers who meet the criteria for access to confidential data.
Acknowledgments
We thank the Centre Hospitalier de Versailles and William Hempel (Alex Edelman & Associates) for editorial assistance.
Financial support
This study was supported by a grant from the Centre Hospitalier de Versailles (Bourse Registre 2019), the Fondation FondaMental, Créteil, France, and the Investissements d’Avenir Programs managed by the ANR under references ANR-11-IDEX-0004-02 and ANR-10-COHO-10-01.
Competing interest
The authors declare no competing interests exist.
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