Introduction
Cognitive function in bipolar I disorder (BDI) shows evidence of heterogeneity. We and others have shown that patients with BDI can be stratified based on their cognitive functioning into groups displaying global cognitive impairment, selective cognitive impairment, and preserved or even above-average cognitive functioning (Bora et al., Reference Bora, Hidiroglu, Ozerdem, Kacar, Sarisoy, Civil Arslan and Tumkaya2016; Burdick et al., Reference Burdick, Russo, Frangou, Mahon, Braga, Shanahan and Malhotra2014; Chakrabarty et al., Reference Chakrabarty, Torres, Su, Sawatzky, Keramatian and Yatham2020). Variability in cognitive function is also present during the premorbid period; population-based studies that have used scholastic performance as a proxy for cognitive function have found that the risk for BDI is higher at both the lowest and the highest levels of academic performance (Kendler, Ohlsson, Mezuk, Sundquist, & Sundquist, Reference Kendler, Ohlsson, Mezuk, Sundquist and Sundquist2016), and that children with either low or high intelligence are at higher risk of developing BD (Papachristou et al., Reference Papachristou, Oldehinkel, Ormel, Raven, Hartman, Frangou and Reichenberg2017; Parellada, Gomez-Vallejo, Burdeus, & Arango, Reference Parellada, Gomez-Vallejo, Burdeus and Arango2017).
At present, there is no definitive model that can account for this heterogeneity in cognitive functioning. Initially, cognitive dysfunction in BDI was attributed to progressive changes occurring after illness onset, possibly as the cumulative result of medication exposure and neurotoxicity from increased systemic inflammation, oxidative stress and recurrent mood episodes (López-Jaramillo et al., Reference López-Jaramillo, Lopera-Vásquez, Gallo, Ospina-Duque, Bell, Torrent and Vieta2010). Although there is evidence of cognitive decline around the time of disease onset (Lee et al., Reference Lee, Hermens, Scott, Redoblado-Hodge, Naismith, Lagopoulos and Hickie2014; Zanelli et al., Reference Zanelli, Reichenberg, Morgan, Fearon, Kravariti, Dazzan and Murray2010), most longitudinal studies now suggest that progressive cognitive deterioration is not the rule in BDI, though it may occur in a subset of patients (Jiménez-López et al., Reference Jiménez-López, Sánchez-Morla, López-Villarreal, Aparicio, Martínez-Vizcaíno, Vieta and Santos2019; Samamé, Cattaneo, Richaud, Strejilevich, & Aprahamian, Reference Samamé, Cattaneo, Richaud, Strejilevich and Aprahamian2021; Santos et al., Reference Santos, Aparicio, Bagney, Sánchez-Morla, Rodríguez-Jiménez, Mateo and Jiménez-Arriero2014; Sánchez-Morla et al., Reference Sánchez-Morla, López-Villarreal, Jiménez-López, Aparicio, Martínez-Vizcaíno, Roberto and Santos2019; Sparding et al., Reference Sparding, Joas, Clements, Sellgren, Pålsson and Landén2021; Torres et al., Reference Torres, Qian, Basivireddy, Chakrabarty, Wong, Lam and Yatham2020). Deviation in maturational processes represents another plausible, but less studied, mechanism for cognitive dysfunction in BDI. Population-level data indicate that first episode of BD almost always occurs in adolescence/young adulthood, a critical period of neurodevelopment (Lewinsohn, Seeley, & Klein, Reference Lewinsohn, Seeley and Klein2003; Valli, Fabbri, & Young, Reference Valli, Fabbri and Young2019). Some studies have also shown that youth at clinical or familial high risk for BD show mild to moderate cognitive dysfunction, indicative of neurodevelopmental abnormalities (Bora, Reference Bora2015). In our own sample of patients with first-episode mania (the same as used in the current study), those presenting with global cognitive dysfunction also had higher total gray matter volumes compared to healthy individuals (Chakrabarty et al., Reference Chakrabarty, Torres, Su, Sawatzky, Keramatian and Yatham2020), suggesting delay from typical maturational gray matter changes in adolescence/young adulthood (Dima et al., Reference Dima, Modabbernia, Papachristou, Doucet, Agartz, Aghajani and Frangou2021; Frangou et al., Reference Frangou, Modabbernia, Williams, Papachristou, Doucet, Agartz and Dima2021; Tamnes et al., Reference Tamnes, Herting, Goddings, Meuwese, Blakemore, Dahl and Mills2017). It is therefore possible that the cognitive heterogeneity in BDI reflects different pathophysiological ‘pathways’, with neurodevelopmental and neuroprogressive mechanisms influencing the nature and/or degree of cognitive problems experienced by individual patients.
Structural brain imaging studies show significant age-related changes in brain morphometry throughout the lifespan that varies across individuals (Dima et al., Reference Dima, Modabbernia, Papachristou, Doucet, Agartz, Aghajani and Frangou2021; Frangou et al., Reference Frangou, Modabbernia, Williams, Papachristou, Doucet, Agartz and Dima2021; Wierenga et al., Reference Wierenga, Doucet, Dima, Agartz, Aghajani, Akudjedu and Tamnes2022). Application of machine algorithms to structural neuroimaging data enables an individual-level estimation of the biological age of the brain (brain-age) (Cole & Franke, Reference Cole and Franke2017; Franke & Gaser, Reference Franke and Gaser2019). The difference between an individual's brain-age and their chronological age (referred to here as the brain-predicted age difference, or brainPAD) provides an indication as to whether age-related biological processes appear accelerated or delayed (Cole & Franke, Reference Cole and Franke2017; Franke & Gaser, Reference Franke and Gaser2019). In the largest study to date on brainPAD in neuropsychiatric disorders, Kaufmann and colleagues reported that patients with chronic BDI had, on average, a moderate increase in brainPAD compared to healthy individuals (effect size d = 0.29), suggesting apparent acceleration in age-related brain structural changes in patients with established BD (Kaufmann et al., Reference Kaufmann, van der Meer, Doan, Schwarz, Lund, Agartz and Westlye2019). Conversely, a study in early-stage BD found that brainPAD was lower in patients compared to healthy individuals (d = −0.23) (Hajek et al., Reference Hajek, Franke, Kolenic, Capkova, Matejka, Propper and Alda2019). Collectively, there is some support for the notion that while a moderate degree of age-acceleration may be present in chronic cases, delayed brain maturation may be present in early-stage BD. During typical development, general intellectual ability and multiple aspects of executive function have been primarily associated with age-related reduction in cortical thickness, possibly reflective of synaptic pruning and improved synaptic efficiency during brain maturation, which continues well into the 2nd and 3rd decade of life (de Chastelaine, Donley, Kennedy, & Rugg, Reference de Chastelaine, Donley, Kennedy and Rugg2019; Porter, Collins, Muetzel, Lim, & Luciana, Reference Porter, Collins, Muetzel, Lim and Luciana2011; Schnack et al., Reference Schnack, van Haren, Brouwer, Evans, Durston, Boomsma and Hulshoff Pol2015; Squeglia, Jacobus, Sorg, Jernigan, & Tapert, Reference Squeglia, Jacobus, Sorg, Jernigan and Tapert2013). This and our previous findings suggest that a delay in normal neurodevelopmental trajectories may contribute to cognitive deficits early in the course of BD (Chakrabarty et al., Reference Chakrabarty, Torres, Su, Sawatzky, Keramatian and Yatham2020). In this context, the present study is the first to examine associations between brainPAD and cognitive functioning in a sample of patients with first-episode mania. Based on our previous data, we hypothesized that cognitive dysfunction across domains would be most pronounced in patients with a negative brainPAD, indicative of delayed maturation.
Materials and methods
Participants
Clinically stable patients meeting Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV TR) criteria for BDI were enrolled in the Systematic Treatment Optimization Program for Early Mania [STOP-EM] from UBC Hospital (UBCH) and affiliated sites, and community referrals (Association, 2000). The complete protocol has been previously described (Yatham, Kauer-Sant'Anna, Bond, Lam, & Torres, Reference Yatham, Kauer-Sant'Anna, Bond, Lam and Torres2009). Patients in the STOP-EM program were recruited based on the following eligibility criteria: (1) age 14–35 years; (2) experienced the first manic/mixed episode in the preceding 3 months; (3) clinically stable and receiving mood-stabilizing treatment at enrollment; (4) no lifetime history of neurological disorder or traumatic brain injury; (5) sufficient English proficiency to understand and follow study procedures.
Clinical procedures
Board-certified psychiatrists confirmed the diagnostic status (including substance and other comorbidities) of all participants via the Mini International Neuropsychiatric Interview (M.I.N.I.) (Sheehan et al., Reference Sheehan, Lecrubier, Sheehan, Amorim, Janavs, Weiller and Dunbar1998). Mood symptoms were assessed with the Montgomery-Asberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS) (Montgomery & Asberg, Reference Montgomery and Asberg1979; Young, Biggs, Ziegler, & Meyer, Reference Young, Biggs, Ziegler and Meyer1978).
Cognitive assessment
The cognitive assessment battery included tests which have previously been shown to be directly relevant to BD (Chakrabarty et al., Reference Chakrabarty, Torres, Su, Sawatzky, Keramatian and Yatham2020; Torres et al., Reference Torres, Kozicky, Popuri, Bond, Honer, Lam and Yatham2014). All participants were administered the North American Adult Reading Test (NAART) to assess premorbid IQ (Bain, Reference Bain2010; Uttl, Reference Uttl2002), while current cognitive function was evaluated using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and additional tests from the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Cognitive Consensus Battery (MCCB) (Nuechterlein et al., Reference Nuechterlein, Green, Kern, Baade, Barch, Cohen and Marder2008). Details of individual tests and their corresponding domains are shown in online Supplementary Table S1. Test scores were transformed into demographically corrected z-scores based on normative data derived from healthy populations external to this study and provided in individual test manual (Delis, Kramer, Kaplan, & Ober, Reference Delis, Kramer, Kaplan and Ober2000; Golden, Reference Golden2002; Lezak & Lezak, Reference Lezak and Lezak2004; Reitan & Wolfson, Reference Reitan and Wolfson1993; Robbins et al., Reference Robbins, James, Owen, Sahakian, McInnes and Rabbitt1994; Wechsler, Reference Wechsler1997). Demographically corrected test z-scores were averaged to obtain domain scores (Torres et al., Reference Torres, Kozicky, Popuri, Bond, Honer, Lam and Yatham2014). Processing speed z-scores were coded such that negative scores indicated slower performance. Similar to the MATRICS composite score, a composite global cognition score was calculated as the average of the z-scores of the individual cognitive domains. Standardized NAART full-scale IQ scores were also calculated for each subject (Blair & Spreen, Reference Blair and Spreen1989).
Imaging
Whole-brain T1-weighted magnetic resonance images were acquired on a Philips Achieva 3.0 Tesla scanner (Koninklijke Philips N.V, The Netherlands) using a three-dimensional axial inversion recovery-weighted spoiled gradient recalled sequence and the following parameters: FOV = 20 cm (RL) × 25.6 cm (AP), ACQ matrix = 256 × 256, isotropic image voxels (1 × 1 × 1 mm3), autoshim, TR/TE = autoset shortest, T/R head coil, flip angle = 8 degrees, and 1 mm thick contiguous 180 slices of the whole brain.
Cortical reconstruction and subcortical segmentation of the T1-weighted datasets were implemented in FreeSurfer image analysis suite (version 7.1.0; http://surfer.nmr.mgh.harvard.edu/). Processing included removal of non-brain tissue using a hybrid watershed/surface deformation procedure, segmentation of the subcortical white matter and deep gray matter volumetric structures, intensity normalization, tessellation of the boundary of gray matter and white matter, automated topology correction and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class. The Desikan-Killiany atlas was used to parcellate the brain into 68 cortical regions (Desikan et al., Reference Desikan, Ségonne, Fischl, Quinn, Dickerson, Blacker and Killiany2006), while subcortical segmentation was performed using the probabilistic atlas in FreeSurfer (Fischl et al., Reference Fischl, van der Kouwe, Destrieux, Halgren, Segonne, Salat and Dale2004). This procedure yielded measures of total intracranial volume (ICV), regional cortical thickness, surface area and subcortical volumes.
Participants with high-quality T1-images and high-quality FreeSurfeer parcellation and segmentation were included. Quality assessment of images involved two steps:
(a) Using visual inspection, each T1-weighted scan was assessed using 4 criteria: (1) image affected by movement, (2) temporal poles missing (even partly) in the reconstruction, (3) other parts of the cortex missing in the reconstruction, (4) non-brain tissue (e.g. dural/skull) still visible in the reconstructed pial surface. These criteria were applied separately for the left and right hemisphere. Each criterion was scored as 0 if there were ‘no errors visible’ or as 1 if there were ‘errors visible in at least 3 consecutive slices’. Scans with total scores of 1 or 2 were considered of good quality; those with higher scores were excluded.
(b) The quality of the FreeSurfer parcellation and segmentation of the T1-scans included after visual inspection was assessed using the Qoala-T tool (Klapwijk, van de Kamp, van der Meulen, Peters, & Wierenga, Reference Klapwijk, van de Kamp, van der Meulen, Peters and Wierenga2019) which uses a pre-trained supervised-learning model to classify each individual's FreeSurfer output as good or poor.
Estimation of BrainPAD
We used a model developed by the Enhancing NeuroImaging Genetics through Meta-analysis (ENIGMA) Consortium to obtain brain-based age estimates (Han et al., Reference Han, Dinga, Hahn, Ching, Eyler, Aftanas and Schmaal2020). The model was trained and validated using the same Freesurfer outputs as in the current study, which were derived from a sample of 4314 healthy individuals (43.3% males; age range: 18–75 years). Because the literature suggests sex differences in age-related brain structural changes (Jahanshad & Thompson, Reference Jahanshad and Thompson2017), the ENIGMA model was specified separately for males and females. Accordingly, the parameters from the ENIGMA model were applied to the data from male and female participants of the current study to obtain brain-age estimates. Subsequently, the brainPAD for each participant was computed by subtracting chronological age from brain-age, and expressed in units of years. Positive brainPAD values indicate apparent acceleration in age-related brain structural changes. Conversely, negative values indicate apparent delay in age-related brain structural changes. In subsequent analyses, chronological age was included as a covariate to remove any residual effect on brainPAD (Le et al., Reference Le, Kuplicki, McKinney, Yeh, Thompson and Paulus2018).
Statistical analysis
As we were primarily interested in examining the degree of cognitive dysfunction in individuals at the extremes of the brainPAD distribution (i.e. showing the greatest degree of delay or acceleration compared to chronological age), the patient cohort was divided into tertiles based on brainPAD values, resulting in 3 groups: (1) a ‘delayed’ group (with brainPAD values in the lowest tertile); (2) an ‘age-congruent’ group (intermediate tertile, suggesting the least discrepancy between predicted brain-age and chronological age); and (3) an ‘accelerated’ group (with brainPAD values in the highest tertile). Mean values of demographically normed global cognition and individual domain z-scores were calculated for the delayed, age-congruent and accelerated brainPAD tertiles. The primary analysis was a comparison of global cognitive z-scores amongst tertiles, using ANCOVA correcting for chronological age, with a significance threshold of p ⩽ 0.05. To determine the specific nature of cognitive changes amongst tertiles, differences in individual domain z-scores were also assessed, with a Bonferroni correction for multiple comparisons (p = 0.05/6 individual domains = 0.008). Omnibus tests reaching the a priori level of significance were followed with Bonferroni corrected pairwise comparisons. To quantify the magnitude of differences amongst tertiles, Cohen's d effect sizes using the delayed tertile as a reference group were calculated.
Exploratory analyses compared differences in demographic or clinical variables (e.g. total illness duration, age of index episode, number of previous depressive episodes, psychotic features in mania, years of education, comorbid substance use, lithium/valproate/antipsychotic use) amongst tertiles, using ANCOVA controlling for chronological age or χ2 test, as appropriate. As these analyses were exploratory in nature, no correction for multiple comparisons was applied.
Results
Participant characteristics
Of the 91 participants who completed baseline cognitive assessment, 82 completed MRI scan at baseline. A further 2 of these were excluded due to poor scan quality, leaving a final sample of 80 participants with first-episode BDI. The demographic, clinical and cognitive characteristics of the entire patient group are shown in Tables 1 and 2.
Table 1. Demographic and clinical characteristics of entire first episode BDI patients and patients by brainPAD tertile
YMRS, Young Mania Rating Scale; MADRS, Montgomery Asberg Depression Rating Scale.
a Age at first mood episode, either depressive, hypomanic or manic.
b Years between index episode (either depressive, hypomanic or manic) and assessment.
*p ⩽ 0.05.
Table 2. Comparison of demographically normed cognitive z-scores amongst brainPAD tertiles
a Negative processing speed scores indicate slower performance.
*Significant at level of p ⩽ 0.05 for global cognition or p ⩽ 0.008 for individual cognitive domains.
Participant brainPAD and clinical/demographic variables
Mean brainPAD of patients was −1.00 (s.d. 8.22), indicating a small delay in normative age-related brain structural changes in the whole sample. Patients in the lowest (delayed) tertile (n = 27) had a mean brainPAD of −10.79 (s.d. 2.99), with brainPAD values ranging −17.95 to −6.49. Those in the age-congruent tertile (n = 26) had mean brainPAD of −1.45 (s.d. 2.49; range −5.30 to 2.38) and those in the accelerated tertile (n = 27) had a mean brainPAD of 7.79 (s.d. 3.73; range 2.81 to 16.10). The distribution of brainPAD values is shown in online Supplementary Fig. S1.
The demographic and clinical characteristics of patients in each brainPAD tertile are shown in Table 1. The accelerated tertile had a lower mean chronological age (p = 0.004) and a lower proportion of individuals with psychotic features in mania (p = 0.018) compared to the age-congruent tertile. The delayed tertile had higher YMRS scores compared to age-congruent (p = 0.047) and accelerated (p = 0.029) tertiles. There were no differences amongst brainPAD tertiles in any other clinical or demographic variable, including premorbid IQ (Table 1).
Cognitive performance in patients split by brainPAD tertiles
Normative cognitive scores for each tertile are shown in Table 2 and Fig. 1. In ANCOVA analyses controlling for age, there were significant differences in global cognitive scores amongst tertiles (F (2,76) = 3.90, p = 0.025), with post-hoc testing showing the delayed tertile scoring significantly lower than the age-congruent tertile (p = 0.031), with a moderate effect size (d = 0.74). Verbal memory also significantly differed amongst tertiles (F (2,79) = 7.76, p = 0.001), with the delayed tertile scoring significantly lower than the age-congruent (p = 0.005, d = 0.94) and accelerated (p = 0.003, d = 0.84) tertiles, with large effect sizes. There were no differences between accelerated and age-congruent tertiles in post-hoc analyses. A plot of global cognition v. brainPAD values as a continuous measure is shown in online Supplementary Fig. S2.
Fig. 1. Cognitive performance in delayed, age-congruent and accelerated brainPAD tertiles. Mean demographically normed cognitive domain z-scores with 95% CI shown. *Significant difference following correction for multiple comparisons.
ANCOVAs which additionally included YMRS scores or psychotic features in mania showed a similar pattern of significant results for global cognition and verbal memory (online Supplementary Table S2). After controlling for psychosis, tertiles showed trend-level differences in attention scores, with the accelerated tertile scoring lower than age-congruent. This however did not survive correction for multiple comparisons (uncorrected p = 0.048).
Discussion
Using a brain-age estimation model, we examined how deviations from typical age-related structural changes were associated with cognitive functioning in a sample of patients with BDI presenting with first-episode mania. Individuals in the delayed tertile of brainPAD values showed the greatest magnitude of global cognitive impairments, with moderate decrements compared to age-congruent patients. Those in the delayed tertile also showed significant, large decrements in verbal memory compared to other patients.
These findings suggest that global cognitive impairment, and specifically verbal memory deficits, in the early stages of BD are related to an apparent delay in typical age-related brain changes. This is aligned with prior observations that in a subset of patients, BD is associated with abnormalities in neurodevelopment characterized by early childhood subthreshold manic-like symptoms and suboptimal premorbid cognitive function (Papachristou et al., Reference Papachristou, Oldehinkel, Ormel, Raven, Hartman, Frangou and Reichenberg2017; Seidman et al., Reference Seidman, Cherkerzian, Goldstein, Agnew-Blais, Tsuang and Buka2013). In the Tracking Adolescents' Individual Lives Survey (TRAILS), a prospective Dutch community cohort (n = 2230), children with high manic-like symptoms and low IQ had the highest risk of developing BDI at age 19, although most TRAILS participants presenting with BDI had no cognitive difficulties as children (Papachristou et al., Reference Papachristou, Oldehinkel, Ormel, Raven, Hartman, Frangou and Reichenberg2017). Other studies have similarly found that some patients with BD display compromised childhood/adolescent intellectual and adaptive functioning, indicative of neurodevelopmental difficulties in a subset of patients (MacCabe et al., Reference MacCabe, Lambe, Cnattingius, Sham, David, Reichenberg and Hultman2010; Parellada et al., Reference Parellada, Gomez-Vallejo, Burdeus and Arango2017). Our results are also in line with previous analyses of this first episode cohort showing that patients with global cognitive and verbal memory deficits displayed gray matter volumetric changes suggestive of a delay in normal adolescent brain maturational processes (Chakrabarty et al., Reference Chakrabarty, Torres, Su, Sawatzky, Keramatian and Yatham2020; Chakrabarty, Kozicky, Torres, Lam, & Yatham, Reference Chakrabarty, Kozicky, Torres, Lam and Yatham2015). This study, which demonstrates that first-episode patients with the highest degree of delay from normal neurodevelopmental trajectories show the most pronounced cognitive deficits, lends support to the hypothesis that a subset of patients with BDI experience an aberrant neurodevelopmental course while also clarifying the nature of these neurodevelopmental changes.
Notably, patients in the delayed brainPAD tertile showed the most pronounced deficits in verbal memory, scoring close to 1 standard deviation below the normative mean and significantly lower than other patients in this cohort. Verbal memory is a core deficit in BDI, with multiple studies finding dysfunction in first-episode patients and in euthymia (Chakrabarty et al., Reference Chakrabarty, Kozicky, Torres, Lam and Yatham2015), and has also been consistently associated with social and occupational functioning in BD (Bonnín Cdel et al., Reference Bonnín Cdel, González-Pinto, Solé, Reinares, González-Ortega, Alberich and Torrent2014; Lomastro, Valerio, Blasco, Tagni, & Martino, Reference Lomastro, Valerio, Blasco, Tagni and Martino2020; Martinez-Aran et al., Reference Martinez-Aran, Vieta, Torrent, Sanchez-Moreno, Goikolea, Salamero and Ayuso-Mateos2007; Szmulewicz et al., Reference Szmulewicz, Valerio, Lomastro, Smith, Chiappe, Martino and Igoa2018; Torres et al., Reference Torres, DeFreitas, DeFreitas, Bond, Kunz, Honer and Yatham2011). Our results provide a potential neurobiological substrate for this deficit. Conversely, all brainPAD tertiles showed some degree of processing speed deficits, with normative scores ranging from −0.57 to −0.39. Some, (Daban et al., Reference Daban, Mathieu, Raust, Cochet, Scott, Etain and Bellivier2012; Glahn et al., Reference Glahn, Almasy, Barguil, Hare, Peralta, Kent and Escamilla2010) but not all (Luperdi et al., Reference Luperdi, Tabarés-Seisdedos, Livianos, Vieta, Cuesta and Balanzá-Martínez2019), studies have suggested that delays in processing speed may be a marker of genetic vulnerability to BD; our results similarly indicate that processing speed deficits may be a trait feature in early BD. Interestingly, brainPAD was not associated with premorbid IQ. This may be due to the measure used in this study to assess premorbid IQ. While the NAART provides a reliable estimate of premorbid crystallized intelligence, it has limited utility in predicting premorbid abilities in cognitive domains such as memory, processing speed and executive functioning (Frick, Wahlin, Pachana, & Byrne, Reference Frick, Wahlin, Pachana and Byrne2011; Schretlen, Buffington, Meyer, & Pearlson, Reference Schretlen, Buffington, Meyer and Pearlson2005). The NAART therefore may not capture dysfunction in these domains during the neurodevelopmental period. The NAART is also heavily influenced by education; the limited variability in years of education amongst tertiles in this cohort may have restricted ability to detect differences in the NAART (Hayat et al., Reference Hayat, Luben, Dalzell, Moore, Anuj, Matthews and Khaw2016; Uttl, Reference Uttl2002).
Individuals in the accelerated tertile did not show statistically significant differences compared to the age-congruent tertile in any cognitive measure. One possibility is that the sample was underpowered to detect significant differences in this tertile. The accelerated tertile displayed the lowest attention scores which, after controlling for the presence of psychotic features in mania, were lower than the age-congruent tertile at an uncorrected threshold of significance. In a larger sample, this difference may have reached the corrected level of significance. Another possible explanation for this negative finding is the first episode cohort used in this analysis. Some, though not all (Serafini et al., Reference Serafini, Pardini, Monacelli, Orso, Girtler and Brugnolo2021), individuals with BD longitudinally display cognitive decline and progressive structural brain changes, both of which have been linked to number of mood episodes (Abé et al., Reference Abé, Ekman, Sellgren, Petrovic, Ingvar and Landén2015; Chen et al., Reference Chen, Huang, Lee, Chang, Lin, Chiu and Kuo2021; Sánchez-Morla et al., Reference Sánchez-Morla, López-Villarreal, Jiménez-López, Aparicio, Martínez-Vizcaíno, Roberto and Santos2019). Consistent with this idea of a subgroup of patients displaying evidence of neuroprogression with time and mood recurrence, those with accelerated brain aging may show more pronounced cognitive deficits in a cohort with longer duration of illness.
This study has a number of strengths, foremost of which is the inclusion of a well characterized, exclusively first episode mania cohort. It also represents the first examination of brain-age and cognition in BD; to our knowledge, there has only been one previous study of brain-age and cognition in mood disorders, which examined middle age and geriatric MDD (Christman et al., Reference Christman, Bermudez, Hao, Landman, Boyd, Albert and Taylor2020). However, some limitations and unanswered questions remain. The cross-sectional nature of the dataset precludes any causative associations and does not inform either about the timing of the potential developmental delay nor about the evolution of age-related brain changes following illness onset. To answer this, a well powered longitudinal study assessing cognition and brain structure over multiple timepoints is needed. The longitudinal impact of mood stabilizer and comorbid conditions requires further examination. Additionally, higher brain-age relative to chronological age (i.e. higher brainPAD) has been associated with adverse physical outcomes and cognitive decline in mid-life and older adults (Anatürk et al., Reference Anatürk, Kaufmann, Cole, Suri, Griffanti, Zsoldos and de Lange2021; Cole et al., Reference Cole, Ritchie, Bastin, Valdes Hernandez, Munoz Maniega, Royle and Deary2018; Elliott et al., Reference Elliott, Belsky, Knodt, Ireland, Melzer, Poulton and Hariri2019; Ronan et al., Reference Ronan, Alexander-Bloch, Wagstyl, Farooqi, Brayne, Tyler and Fletcher2016). It would therefore be of interest to examine longitudinal associations between brainPAD and physical health markers, and associations between brainPAD, physical health and cognitive functioning in older adults with BD. Patient participants in this cohort were generally high functioning and had preserved premorbid intelligence, potentially limiting generalizability of these results to other patient cohorts. Information regarding family history of neurodevelopmental disorders and birth trauma was not systematically collected in this cohort, but may be additional variables of interest associated with cognitive functioning and predicted brain-age. However, this study provides a useful starting point for understanding the potential neurodevelopmental basis of cognitive deficits in BD.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291722002136
Financial support
The data for this manuscript was generated from the Systematic Treatment Optimization Program for Early Mania, which was supported by unrestricted grant funding from AstraZeneca to LNY. AstraZeneca had no input in the design of the study, data collection, data interpretation or writing of this manuscript.
Conflict of interest
TC has received grant funding from the Michael Smith Foundation and National Research Council-Canada. SF and RG report no conflicts of interest. IT has received grant funding from Canadian Institutes of Health Research and Michael Smith Foundation. He has also served as a consultant for Community Living British Columbia. LNY is a consultant and/or has received speaker fees and/or sits on the advisory board and/or receives research funding from Abbvie, Alkermes, Allergan, Canadian Network for Mood and Anxiety Treatments (CANMAT), Canadian Institutes of Health Research (CIHR), Dainippon Sumitomo Pharma, Gedeon Richter, GSK, Intracellular Therapies, Lundbeck, Merck, Otsuka, Sanofi and Sunovion over the past 3 years.
Ethical standards
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.
Open access
