Symptom Profiles,But Not Prefrontal Neurochemistry,Differentiate ADHD Youth With and Without a Family History of Bipolar I Disorder

Abstract

Objective: To identify clinical and central features that differentiate ADHD youth with and without familial risk for bipolar I disorder (BD). Methods: Psychostimulant-free ADHD youth (10–18 years) with and without a first-degree relative with BD and healthy controls were enrolled. Bilateral ventrolateral prefrontal cortex (VLPFC) proton magnetic resonance spectroscopy (¹H MRS) scans and a range of symptom ratings were performed. Results: A total of n = 145 youth were enrolled. ADHD youth with a family history of BD exhibited greater manic and depressive symptom severity, ADHD hyperactivity/impulsive symptom severity, and higher parent-reported ratings of dysregulation compared with ADHD youth without a BD family history. Although VLPFC metabolite levels did not differ across groups, choline levels in the left VLPFC correlated with different symptom ratings. Conclusion: Symptom profiles including more severe mood and externalizing symptoms, but not VLPFC neurochemistry, differentiate psychostimulant-free ADHD youth with and without a family history of BD.

Keywords

bipolar I disorder familiar risk mania high-risk adolescent choline

Introduction

Prodromal clinical and biological features predictive of the onset of bipolar I disorder (BD) are needed to guide clinical staging and early intervention strategies (McGorry et al., 2014; McNamara et al., 2010). The initial onset of BD most frequently occurs during late childhood and adolescence (Perlis et al., 2009), and is often preceded by attention-deficit/hyperactivity disorder (ADHD; Axelson et al., 2015; Singh et al., 2006). Prevalence rates of ADHD in youth with BD are substantially higher than the general population, particularly in pre-pubescent children, and prodromal ADHD is associated with a lower age at onset of BD (Donfrancesco et al., 2011; Faraone et al., 1997; Kowatch et al., 2005; Masi et al., 2006; Perlis et al., 2004; Wozniak et al., 1995). A recent meta-analysis of prospective studies found that prodromal ADHD is a significant risk factor for developing BD compared with heathy youth (risk ratio: 8.97; Brancati et al., 2021). Additionally, having a first-degree relative with BD robustly increases the risk of developing BD (DelBello & Geller, 2001; Mortensen et al., 2003; Smoller et al., 2003), and youth with a first-degree BD relative exhibit higher rates of ADHD (Lau et al., 2018; Propper et al., 2021) and more severe ADHD symptoms (Kim et al., 2015) compared with ADHD youth with healthy parents. Moreover, a higher rate of ADHD youth with a first-degree BD relative developed BD compared with healthy youth and the general population (Tillman & Geller, 2006). While these findings suggest that ADHD in conjunction with familial risk for BD may represent a different phenotype that confers greater risk for developing BD compared with ADHD alone, associated neuropathophysiological features remain poorly understood.

Evidence from proton magnetic resonance spectroscopy (¹H MRS) studies suggest that the pathophysiology of ADHD is associated with neurochemical abnormalities in frontal lobe regions, including the medial prefrontal cortex (MPFC) and anterior cingulate cortex (ACC). Specifically, abnormalities in prefrontal glutamate + glutamine (Glx), N-acetylaspartate (NAA), and/or choline (Cho) have most consistently been observed in youth with ADHD (Aoki et al., 2013; Perlov et al., 2009; Spencer et al., 2014). Abnormalities in Glx, Cho, and/or NAA levels have also been observed in the dorsolateral PFC (DLPFC) and ACC of youth with BD (Caetano et al., 2011; Castillo et al., 2000; Chang et al., 2003; Davanzo et al., 2003; Olvera et al., 2007; Sassi et al., 2005; Singh et al., 2010). One study found that youth with ADHD alone had a higher Glx to myo-inositol ratio in the ACC compared with youth with both BD and ADHD as well as healthy controls (Moore et al., 2006). While extant evidence suggests that asymptomatic BD offspring do not exhibit abnormalities in prefrontal metabolite levels (Chang et al., 2003; Olvera et al., 2007; Nery et al., 2019; Singh et al., 2010), no studies have specifically investigated prefrontal neurochemical profiles in ADHD youth with and without a first-degree BD relative. Furthermore, there have been no studies investigating neurochemical profiles in the ventrolateral prefrontal cortex (VLPFC), which is more closely associated with emotional regulation (Berboth et al., 2021; Yamasaki et al., 2002). Lastly, few studies have assessed ADHD youth free of psychostimulant medications which have been shown to alter prefrontal metabolite levels (Benamor, 2014; Carrey et al., 2003; Husarova et al., 2014; Kronenberg et al., 2008).

With these considerations in mind, the aim of this cross-sectional study was to compare neurochemical profiles in the bilateral VLPFC of psychostimulant-free ADHD youth with (“high-risk,” HR) and without (“low-risk,” LR) a first-degree relative with BD, as well as a typically developing healthy control group (HC). A second objective was to compare symptom profiles and investigate associations among VLPFC metabolite levels and symptom ratings previously found to precede and predict the initial onset of BD, including attenuated manic and depressive symptoms (Axelson et al., 2015; Bechdolf et al., 2014; Faedda et al., 2019), impaired global functioning (Tillman & Geller, 2006), and higher parent-reported ratings of emotional dysregulation (Biederman et al., 2009). It was hypothesized that increasing risk (HR > LR > HC) for BD would be associated with graded alterations in VLPFC metabolite levels, and that these alterations would be associated with more severe manic and depressive symptoms, lower ratings of global functioning, and greater dysregulation.

Method

Participants

Three groups of psychostimulant-free youth (10–18 years) were recruited: (1) youth with ADHD and at least one biological parent or sibling with BD (“high-risk”), (2) youth with ADHD and no first- or second-degree relative with a mood or psychotic disorder (“low-risk”), and (3) typically developing healthy controls (HC) with no personal or family history of a DSM-5 Axis I psychiatric disorder. The Structured Clinical Interview for DSM-5 (SCID-5-CV) confirmed a parental diagnosis of BD (First et al., 1996), and the Family Interview for Genetics Studies (FIGS; Maxwell, 1999) was used to confirm DSM-5 BD diagnoses in first- or second-degree relatives including siblings. Pubertal status was determined with the Duke Tanner Stage Self-assessment (Duke et al., 1980), handedness with the Crovitz Handedness Questionnaire (Crovitz & Zener, 1962), and socioeconomic status with the Hollingshead Four-Factor Index of Social Status (Hollingshead, 1975).

All subjects had no contraindication to an MRI scan (e.g., braces or claustrophobia), had an IQ ≥ 80 as determined by the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999), had no major medical or neurological illness that could influence MR results or any significant episode (>10 minutes) of loss of consciousness, and had no lifetime DSM-5 substance use disorder. All ADHD youth met DSM-5 criteria for ADHD (any type) using the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-PL; Kaufman et al., 1997), had no current DSM-5 mood, anxiety (other than specific phobias), conduct, eating or psychotic disorders, Tourette’s disorder, chronic tic disorder, or autism spectrum disorder, had no exposure to psychostimulants (prescription or recreational) or other medications used for the treatment of ADHD (e.g., atomoxetine) for at least 3 months prior to screening, had no lifetime exposure to mood stabilizers or antipsychotic medications, had no psychotropic medication exposure during the 30 days prior to screening, and had no clinically significant ECG or blood pressure abnormalities. This study was approved by the Institutional Review Board of University of Cincinnati and was registered at clinicaltrials.gov with identifier NCT02478788. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Rating Scales

ADHD symptom ratings were obtained using the clinician-administered Attention-Deficit Hyperactivity Disorder Rating Scale (ADHD-RS; Farles et al., 2001), and inattention and hyperactivity/impulsivity subscale scores were analyzed separately. Depression symptom severity was determined using the Children’s Depression Rating Scale-Revised (CDRS-R; Poznanski et al., 1979, 1983), and manic symptom severity was determined using the Young Mania Rating Scale (YMRS; Young et al., 1978). Global functioning was assessed using the Children’s Global Assessment Scale (CGAS; Shaffer et al., 1983). ADHD youth were also rated using the Clinical Global Impression-Severity Scale (CGI-S) to assess overall illness severity (Guy, 1970). All clinician ratings were administered by a child and adolescent psychiatrist with established inter-rater reliabilities (kappa > .9). Parents completed the Child Behavior Checklist (CBCL ages 6–18, Achenbach & Rescorla, 2001), and CBCL total score, internalization and externalization subscale scores, and Dysregulation Profile (CBCL-DP) scores (i.e., the sum of the attention, aggression, and anxious/depressed scores) were assessed.

1H MRS

All participants underwent a ¹H MRS scan on a Philips Ingenia 3.0 Tesla MR scanner, equipped with a 32-channel radio frequency head coil. A 3D, high-resolution, isotropic, T1-weighted fast Fourier echo (FFE) anatomical sequence was conducted using 8.1 ms repetition time (T_R), 3.7 ms echo time (T_E), 940 ms inversion time, 8° flip angle, sensitivity encoding factor (SENSE) of 2, and contiguous slices of 1 mm thickness with a voxel size 1 mm × 1 mm that was used for voxel placement. We positioned two MRS voxels (20 mm × 20 mm × 20 mm) in the right and left VLPFC which included Brodmann areas 44, 45, and 46 (Supplemental Figure S1). MRS data were acquired using a short echo time, single-voxel Point RESolved Spectroscopy (PRESS) pulse sequence with T_R = 2,000 ms, T_E = 30 ms, and 96 averages. Water signal was suppressed by the VAriable Pulse powers and Optimizing Relaxation delays (VAPOR) method (Tkac et al., 1999). The raw spectroscopy data was processed using Linear Combination of Model spectra (LCModel; Provencher, 1993) to determine metabolite (glutamate [Glu], glutamate + glutamine [Glx], N-acetyl aspartate [NAA], phosphocreatine plus creatine [PCr + Cr], choline-containing compounds [Cho], and myo-inositol [mI]) levels in institutional units (IU). SPM12 (Statistical Parametrical Mapping) was then used to segment each T1 FFE image and calculate the percentage of each tissue type (gray matter, white matter, and cerebrospinal fluid [CSF]) within each MRS voxel (Wellcome Department of Cognitive Neurology, London, UK). The raw metabolite levels were adjusted for tissue contributions (Woolrich et al., 2009), adjusted to the T1 and T2 relaxation decay rate of the corrected water concentration, and corrected for literature reported T1 and T2 relaxation decay rates of the primary metabolites (Tal et al., 2012; Träber et al., 2004; Wansapura et al., 1999). Glutamate concentrations were unadjusted for metabolite T1 and T2 relaxation decay (Gussew et al., 2012). Metabolites with a Cramer-Rao Lower Bound of >15% were excluded from further analysis.

Data analysis

Statistical analyses were performed using the Statistical Analysis System (SAS Institute, Cary, NC, USA). Group differences in demographic variables were identified using one-way ANOVAs for continuous variables and Chi-Square tests for categorical variables. MRS data were evaluated with ANCOVA models using age, sex, and BMI as covariates, and individual group differences evaluated with t-tests adjusted for multiple comparisons (α = .001). Exploratory linear correlation analyses examined relationships among neurometabolite levels, demographic variables, and symptom ratings using R software (4.1.1 version). For these analyses a significance threshold of p ≤ .01 was used to mitigate the risk of type I error, and group interactions were also calculated to test for group moderation.

Results

Subject demographics

A total of n = 145 (high-risk, n = 45; low-risk, n = 51; HC, n = 49) male and female (66% male) youth (mean age: 14.0 ± 2.5 years) were enrolled, and group demographic and diagnostic characteristics are presented in Table 1. Groups were similar in age, sex, race, pubertal status, hand dominance, and BMI. Compared with the low-risk ADHD group, the high-risk ADHD group had a higher rate of ADHD combined versus inattentive type (p = .0007) and a lower SES (p = .00001).

Table 1.

Demographic and Diagnostic Characteristics.

Variables^a	HC (n = 49)	Low-risk (n = 51)	High-risk (n = 45)	p-Value^b
Age (years)	14.6 ± 2.4	14.1 ± 2.5	13.4 ± 2.4	.1
Gender, n (%) female	19 (39)	17 (33)	21 (47)	.7
Race, n (%) Caucasian	31 (63)	34 (67)	26 (58)	.9
Prepubescent, n (%)	9 (18)	13 (25)	14 (31)	.5
Right handed, n (%)	37 (75)	41 (80)	38 (84)	.9
Height (cm)	166.3 ± 11.6	162.9 ± 12.2	160.1 ± 13.8	.1
Weight (kg)	63.0 ± 16.9	63.9 ± 21.9	61.4 ± 23.3	.9
BMI (kg/m²)	22.5 ± 4.6	23.7 ± 6.7	23.4 ± 7.2	.6
BMI z score	0.6 ± 1.1	0.8 ± 1.2	0.7 ± 1.3	.7
BMI percentile	65.5 ± 29.0	68.2 ± 29.1	68.3 ± 33.4	.9
Hollingshead SES ADHD Type, n (%)	3.9 ± 1.0	3.8 ± 0.9	2.9 ± 1.0	.001
Combined	—	22 (43)	34 (76)	.001
Inattentive	—	29 (57)	10 (22)
Prior psychostimulant exposure, n (%)	—	17 (33)	23 (51)	.3

Values are group mean SD or number of subjects (n) and percent (%).

One-way ANOVA or χ².

Symptom ratings

Clinician ratings

Significant main effects of group were observed for ADHD-RS total score (p < .0001), both inattentive (p < .0001) and hyperactivity/impulsivity (p < .0001) subscale scores, YMRS total score (p < .0001), CDRS-R total score (p < .0001), and CGAS total score (p < .0001; Figure 1). Both high- and low-risk ADHD groups differed significantly from HCs on all ratings (all ps < .0001). High-risk ADHD subjects had significantly higher hyperactivity/impulsivity subscale scores (p = .002), YMRS total scores (p = .01), CDRS-R total scores (p = .03), and CGI-S scores (p = .04) compared with the low-risk ADHD group. Significant group by ADHD “type” (inattentive/combined) interactions were observed for YMRS (p = .02) and CDRS-R (p = .04) but not other ratings. YMRS (p = .006) and CDRS-R (p = .01) scores were significantly greater in the high-risk ADHD combined-type group compared with the low-risk ADHD combined-type group despite having similar hyperactivity/impulsivity subscale scores (p = .44; Supplemental Figure S2). A larger percentage of high-risk ADHD subjects (13%) had a YMRS total score of ≥12 (≤12 remission or minimal or no symptoms) compared with the low-risk ADHD group (2%, p = .047), and a similar percentage of subjects in the high-risk (31%) and low-risk (20%) groups had a CDRS-R total score of ≥28 (≤28 remission or minimal or no symptoms; p = .31). There was a trend for a larger percentage of subjects in the high-risk ADHD group (40%) with a CGI-S score of ≥5 (markedly or severely ill) compared with the low-risk group (18%, p = .07).

Figure 1.

ADHD-RS total scores (a), ADHD-RS inattention (b), and hyperactivity/impulsivity (H/I) (c), subscale scores, CDRS-R (d), YMRS (e), and CGAS (f) total scores in healthy controls (HC, n = 49), ADHD youth without BD family history (low-risk, LR, n = 51), and ADHD youth with a first-degree relative with BD (high-risk, HR, n = 45). Values are group mean ± S.E.M.

Among all subjects (n = 145), sex, race, and body mass index were not significantly correlated with any rating measure. Age was inversely correlated with ADHD-RS total scores (r = -0.32, p < .0001) and hyperactivity/impulsivity subscale scores (r = −.39, p < .0001) but not other ratings (ps > .01). Age was also inversely correlated with ADHD-RS total scores in both low-risk (r = −.47, p = .0005) and high-risk groups (r = −.37, p = .013), as were hyperactivity/impulsivity subscale scores in both low-risk (r = −.42, p = .002) and high-risk groups (r = −.47, p = .001). ADHD-RS total scores were positively correlated with YMRS (r = .44, p < .0001) and CDRS-R (r = .52, p < .0001) total scores, and inversely correlated with CGAS total score (r = −.86, p < .0001). Among ADHD youth only (n = 96), ADHD-RS total scores were positively correlated with CGI-S total scores (r = .39, p < .0001) and inversely correlated with CGAS total scores (r = −.36, p = .0004). ADHD-RS total scores were more robustly correlated with CDRS-R, YMRS, CGAS, and CGI-S total scores in the high-risk ADHD group (n = 45) compared with the low-risk ADHD group (n = 51), and the group interaction was significant for YMRS (p = .04; Supplemental Figure S3).

Parent-reported ratings

For CBCL scores, significant group effects were observed for CBCL total score (p < .0001), internalization (p < .0001), and externalization (p < .0001) subscale scores, and CBCL-DP scores (p < .0001; Figure 2). The high-risk ADHD group had a significantly higher CBCL total score (p < .0001), internalization (p < .0001), and externalization (p < .0001) subscale scores, and CBCL-DP scores (p < .0001) compared with the low-risk ADHD group.

Figure 2.

CBCL total scores (a), CBCL-DP (b), externalization (c), and internalization (d) subscale scores in healthy controls (HC, n = 49), ADHD youth without BD family history (low-risk, LR, n = 51), and ADHD youth with a first-degree relative with BD (high-risk, HR, n = 45). Values are group mean ± SEM

¹H MRS

A total of n = 141 subjects had usable MRS data (CRLB < 15%) and were included in the final analysis (high-risk, n = 45; low-risk, n = 49; HC, n = 47). There were no group differences in demographic variables including age, sex, race, and BMI (all ps > .05). For the right and left VLPFC, there were no group differences in the signal-to-noise ratio (right: p = .98, left: p = .77), or between gray matter (right: p = .73, left: p = .87), white matter (right: p = .85, left: p = .92), and CSF (right: p = .97, left: p = .62) segmentation. For the left VLPFC, the main effect of group was not significant for Glu (p = .84), Glx (p = .82), mI (p = .15), Cho (p = .53), NAA (p = .53), or PCr + Cr (p = .69; Figure 3a). For the right VLPFC, the main effect of group was not significant for Glu (p = .27), Glx (p = .24), mI (p = .19), Cho (p = .84), NAA (p = .63), or PCr + Cr (p = .55; Figure 3b). Adjusting for age and BMI, which were significantly correlated different metabolites, did not change the results for either left or right VLPFC. There were no significant group by ADHD “type” (inattentive/combined) interactions for any metabolite. There were no significant group differences for any metabolite when comparing all ADHD youth (n = 94) with HCs (n = 47), when metabolite levels were expressed as a ratio to Cr, or when mI, PCr + Cr, and Cho were combined (Zhang et al., 2021).

Figure 3.

Metabolite levels in the left (a) and right (b) VLPFC of healthy controls (HC, n = 47), ADHD youth without BD family history (low-risk, LR, n = 49), and ADHD youth with a first-degree relative with BD (high-risk, HR, n = 45). Values are group mean ± SEM.

VLPFC Metabolites and Symptom Ratings

Among all subjects with usable MRS data (n = 141), ADHD-RS total score, inattention and hyperactivity/impulsivity subscale scores, CDRS-R, YMRS, and CGI-S total scores were not significantly correlated with any metabolite in the right or left VLPFC before or after adjusting for age and BMI (all ps > .01). CGAS scores were inversely correlated with left VLPFC Cho levels after adjusting for age and BMI (r = −.28, p = .009). Among ADHD youth (n = 94), CGAS scores correlated with Cho levels in the left (r = .34, p = .0007) and right (r = .28, p = .006) VLPFC, and CGI-S scores correlated with Cho in the left VLPFC (r = −.36, p = .0003) but not right VLPFC (r = −.21, p = .049). CDRS-R and YMRS total scores and ADHD-RS total score and subscale scores were not correlated with VLPFC Cho levels in either adjusted or unadjusted models (all ps > .01). Left, but not right, VLPFC Cho levels correlated with ADHD-RS total score in the low-risk ADHD group (n = 49) but not in the high-risk ADHD group (n = 45), and the group interaction was significant (p = .0017; Figure 4a and b). Similarly, hyperactivity/impulsivity subscale scores correlated with left, but not right, VLPFC Cho levels within the low-risk ADHD group (r = .42, p = .003) but not high-risk ADHD group (r = −.12, p = .45), and the group interaction was significant (p = .005). The group interactions were not significant for CGAS scores (Figure 4c and d) or CGI-S scores. There was a trend for CBCL-DP scores to be inversely correlated with left, but not right, VLPFC Cho levels in the high-risk group only (r = −.31, p = .047), and the group interaction was significant (p = .023; Figure 4e and f).

Figure 4.

Linear correlations between Cho levels and ADHD-RS (a and b), CGAS (c and d), and CBCL-DP (e and f) total scores in the left (L-VLPFC) and right VLPFC (R-VLPFC) of ADHD youth with (high-risk, HR, n = 45) and without (low-risk, LR, n = 49) a BD family history. Within group correlation coefficients and group interaction terms are presented.

Discussion and Conclusions

This study provides cross-sectional evidence that psychostimulant-free ADHD youth, regardless of family history of BD, exhibit elevated ratings of manic and depressive symptom severity and lower ratings of global functioning, as well as higher parent-reported ratings of externalizing and internalizing symptoms and dysregulation, compared with healthy typically developing youth. Youth with ADHD and a family history of BD had a higher rate of combined- versus inattentive-type ADHD, as well as greater ADHD, manic, and depressive symptom severity, compared with ADHD youth without a BD family history. Moreover, ADHD symptoms were more robustly correlated with manic and depressive symptoms, as well as ratings of global symptom severity and global functioning, in ADHD youth with a BD family history. Higher parent-reported ratings of externalizing and internalizing symptoms and dysregulation were found in ADHD youth with versus without BD family history. Contrary to our hypothesis, ADHD youth with or without BD family history did not exhibit alterations in any metabolite level in the left or right VLPFC compared with health youth. However, exploratory analyses found that left VLPFC Cho levels were consistently associated with different ratings, and were differentially associated with ADHD symptom severity and CBCL-dysregulation in ADHD youth with versus without a BD family history. Together, these findings demonstrate that symptom profiles differentiates psychostimulant-free ADHD youth with a BD family history from ADHD youth without family history. Although group differences in VLPFC metabolite profiles were not observed, common and differential associations between left VLPFC Cho levels and different symptom ratings warrant additional investigation.

Regarding ADHD symptom profiles, ADHD youth with a family history of BD had a higher rate of combined-type ADHD compared with ADHD youth without a BD family history. Higher rates of combined-type ADHD are also observed in youth with both BD and ADHD compared with ADHD alone (Donfrancesco et al., 2017; Faraone et al., 1998). Although youth with and without a BD family history with combined-type ADHD exhibited similar hyperactive/impulsive subscale scores, greater hyperactive/impulsive subscale scores were observed in youth with a BD family history and inattentive-type ADHD compared with ADHD youth without BD family history. It is relevant, therefore, that more severe hyperactive/impulsive symptoms have also observed in youth with both BD and ADHD compared with ADHD alone (Donfrancesco et al., 2017; Serrano et al., 2013; Wilens et al., 2009). Consistent with prior longitudinal evidence (Hart et al., 1995; Kim et al., 2015), ADHD-RS hyperactivity/impulsivity subscale scores similarly decreased with age in both ADHD groups. ADHD symptom severity, including hyperactivity/impulsivity symptoms, were highly correlated with ratings of global functioning in ADHD youth with, but not without, a BD family history. Interestingly, a 6-year prospective study found that lower baseline global functioning ratings using the CGAS were a significant predictor of future BD conversion in youth with combined- and hyperactive-type ADHD (Tillman & Geller, 2006). The present cross-sectional evidence therefore extends prior research by demonstrating that the ADHD symptom profile observed in psychostimulant-free ADHD youth with a BD family history differs from ADHD youth without a BD family history.

Regarding mood symptoms, both ADHD groups exhibited greater manic and depressive symptom severity compared with healthy controls, and ADHD youth with a BD family history exhibited greater manic and depressive symptom severity scores compared with ADHD youth without a BD family history. Moreover, ADHD and manic symptom severity were positively correlated in ADHD youth with but not without a BD family history. Depression and manic symptom severity were greater in youth with combined-type ADHD and a BD family history compared with ADHD youth without a BD family history, despite having similar hyperactive/impulsive subscale scores. Although the mean YMRS and CDRS-R total scores exhibited by both ADHD groups do not exceed thresholds for “remission” (i.e., minimal or no symptoms), a larger percentage of ADHD youth with a BD family history (13%) had a YMRS total score of ≥12 compared with ADHD youth without a BD family history (2%). In view of extant evidence that attenuated manic and depressive symptoms commonly precede the initial onset of BD (Axelson et al., 2015; Bechdolf et al., 2014; Faedda et al., 2019), the present findings suggest that ADHD youth with a BD family history, particularly those with combined-type ADHD, would be at greater risk for developing BD than ADHD youth without BD family history. Future prospective research is therefore warranted to determine whether elevated prodromal mood symptom severity ratings are also associated with BD risk progression in youth with ADHD.

Parent-reported ratings on the CBCL provide corroborating evidence that ADHD youth with a BD family history exhibit a more severe symptom profile, including greater externalizing, internalizing, and dysregulation symptoms, compared with ADHD youth without a BD family history. Previous studies have found that youth with both BD and ADHD also exhibit more severe externalizing symptoms compared with youth with ADHD alone (Doerfler et al., 2011; Serrano et al., 2013). We also found that ADHD youth with a BD family history had higher parent-reported ratings on the CBCL-DP, also referred to as the pediatric or juvenile bipolar disorder profile, compared with ADHD youth without a BD family history. A meta-analysis reported that children with BD had significantly higher CBCL-DP scores compared with children with ADHD (Mick et al., 2003). Moreover, a longitudinal prospective study found that higher CBCL-DP scores predicted the subsequent diagnoses of BD and higher rates of psychiatric hospitalization in youth with ADHD (Biederman et al., 2009). Together, these findings further suggest that ADHD youth with a BD family history resemble youth with both BD and ADHD as well as ADHD youth that eventually developed BD.

The ¹H MRS results demonstrate that ADHD youth, with or without a BD family history, do not exhibit differences in neurometabolite levels in the bilateral VLPFC compared with healthy youth. This result contrasts with previous meta-analytic evidence that youth with ADHD exhibit alterations in NAA, Glx, and/or Cho levels in the ACC and MPFC compared with healthy youth (Aoki et al., 2013; Perlov et al., 2009; Spencer et al., 2014). However, our results are consistent with the observation that youth with a first-degree BD relative do not exhibit differences in VLPFC metabolite levels compared with healthy youth (Nery et al., 2019). These findings suggest that the mood and cognitive symptoms exhibited by ADHD youth with and without a BD family history cannot be attributed to abnormalities in VLPFC neurochemistry. We did however find that Cho levels in the left VLPFC were most consistently correlated with different symptom ratings in both ADHD groups, and were differentially associated with ADHD total and hyperactivity/impulsivity subscale scores, as well as parent-reported rated CBCL-dysregulation scores, in ADHD youth with versus without a BD family history. In view of evidence that prefrontal Cho levels decrease following psychostimulant treatment (Kronenberg et al., 2008; Wiguna et al., 2012), such differential associations in ADHD youth with and without BD family history would predict dissimilar symptom responses to psychostimulant treatment and warrant additional prospective research.

Taken collectively, these cross-sectional findings demonstrate that psychostimulant-free ADHD youth with a BD family history exhibit a different symptom profile compared with ADHD youth without a BD family history. This profile is characterized by greater manic and depressive symptom severity, greater ADHD hyperactivity/impulsive symptom severity, and higher parent-reported ratings of externalization, internalization, and dysregulation. This profile resembles youth with both BD and ADHD as well as ADHD youth that eventually developed BD, and therefore warrants further evaluation in future prospective research. Although group differences in VLPFC metabolite profiles were not observed, common and differential associations between left VLPFC Cho levels and different symptom ratings also warrant additional investigation.

Limitations

This study has notable limitations. First, the cross-sectional design prevents attributing causal relationships and it is not known whether subjects in the different risk groups will develop BD in the future. Therefore, prospective longitudinal studies are warranted to evaluate the predicative validity of the primary findings. Second, this study only investigated neurometabolite levels in the VLPFC, and other prefrontal regions including the ACC, MPFC, and DLPFC may have yielded different results. Moreover, other neuroimaging modalities, including functional MRI, may be more sensitive to detect group differences in VLPFC functional integrity. Third, analyses of clinical ratings were not corrected for multiple comparisons and should be considered preliminary due to the increased risk of Type I error. Study strengths include a well-characterized cohort of psychostimulant-free youth with ADHD and BD family history, similar group demographics, both clinician and parent-reported ratings, and ¹H MRS assessment of prefrontal neurochemistry.

Supplemental Material

sj-docx-1-jad-10.1177_10870547221101645 – Supplemental material for Symptom Profiles, But Not Prefrontal Neurochemistry, Differentiate ADHD Youth With and Without a Family History of Bipolar I Disorder

Supplemental material, sj-docx-1-jad-10.1177_10870547221101645 for Symptom Profiles, But Not Prefrontal Neurochemistry, Differentiate ADHD Youth With and Without a Family History of Bipolar I Disorder by Constance Chen, Maxwell J. Tallman, Kim M. Cecil, Luis Rodrigo Patino, Thomas J. Blom, Melissa P. DelBello and Robert K. McNamara in Journal of Attention Disorders

Footnotes

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: R.K.M. has received research support from Martek Biosciences Inc, Royal DSM Nutritional Products, LLC, Inflammation Research Foundation, Ortho-McNeil Janssen, AstraZeneca, Eli Lilly, NARSAD, and national institutes of health (NIH), and previously served on the scientific advisory board of the Inflammation Research Foundation. M.P.D. receives research support from NIH, PCORI, Acadia, Allergan, Alkermes, Janssen, Johnson and Johnson, Lundbeck, Otsuka, Pfizer, Sage, and Sunovion. She is also a consultant, on the advisory board for Alkermes, Allergan, Janssen, Johnson and Johnson, Lundbeck, Medscape, Myriad, Pfizer and Sage. The remaining authors do not have disclosures.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported in part by R01 NIMH grant 097818 to R.K.M and M.P.D (Co-PIs); NIH had no further role in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication.

ORCID iD

Robert K. McNamara

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Constance Chen is a medical student at the University of Cincinnati College of Medicine and was a participant in Medical Student Scholars Program in the Department of Psychiatry and Behavioral Neuroscience. Her research interests include risk factors and biomarkers for psychiatric disorders and understanding the role of neurodevelopmental factors in the etiology of mood dysregulation.

Maxwell J. Tallman is a senior Imaging Analyst in the Department of Psychiatry and Behavioral Neuroscience at the University of Cincinnati College of Medicine. His research interests include multimodal neuroimaging techniques used to evaluate risk, resilience and treatment responses in children and adolescents with or at high risk for psychiatric disorders.

Kim M. Cecil is Professor of Radiology, Pediatrics, Neuroscience, and Environmental and Public Health Sciences at Cincinnati Children's Hospital Medical Center and University of Cincinnati. She is the Director of Basic and Translational Radiology Research and leads the Imaging Research Center at Cincinnati Children’s Hospital Medical Center. Her research interest include using magnetic resonance imaging and spectroscopy in infants, children, adolescents, and adults.

Luis Rodrigo Patino is Assistant Professor of Clinical Medicine in the Department of Psychiatry and Behavioral Neuroscience at the University of Cincinnati College of Medicine. Dr. Patino is a child and adolescent psychiatrist, and his primary research interests include deciphering risk, resilience and treatment response biomarkers for mood disorders and understanding neurodevelopmental factors that contribute to mood dysregulation.

Thomas J. Blom is a Senior Research Associate in the Department of Psychiatry and Behavioral Neuroscience at the University of Cincinnati College of Medicine. His interests include biostatistics in the context of psychiatry research, and the application of multivariate mixed models to complex datasets.

Melissa P. DelBello is Professor of Psychiatry, Pediatrics, and Psychology and Chair of the Department of Psychiatry and Behavioral Neuroscience at the University of Cincinnati College of Medicine. She is a child and adolescent psychiatrist with a research focus in the neurodevelopment and treatment of children and adolescents with and at risk for mood disorders.

Robert K. McNamara is Professor of Psychiatry and Behavioral Neuroscience at the University of Cincinnati College of Medicine. He also serves as the Director of the Lipidomics Research Program which is dedicated to understanding the role of bioactive lipids in human health and disease. His research is primarily focused on the understanding the role of omega-3 polyunsaturated fatty acids in the pathophysiology and neuroprogression of different psychiatric disorders.

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