Abstract
Objective:
This study examined relationships between anticholinergic (AC) medication burden and brain integrity in people living with HIV (PLWH) and people without HIV (HIV−).
Methods:
Neuropsychological performance (NP) Z-scores (learning, retention, executive function, motor/psychomotor speed, language domains and global cognition), and neuroimaging measures (brain volumetrics and white matter fractional anisotropy (FA)) were analyzed in PLWH (n=209) and HIV− (n=95) grouped according to the Anticholinergic Cognitive Burden (ACB) scale (0= no burden, 1-3 = low burden, >3 = high burden). NP and neuroimaging outcomes were compared between HIV− and PLWH with high AC burden. Within a cohort of PLWH (n=90), longitudinal change in ACB score over ~2 years was correlated to the rate of change per month of study interval in NP and neuroimaging measures.
Results:
A higher number of AC medications and ACB was observed in PLWH compared to HIV− (p<.05). A higher ACB was associated with worse motor/psychomotor performance, smaller occipital lobe, putamen, subcortical gray matter and total gray matter volumes in HIV−; and poorer executive function, retention and global cognition, smaller brain volumes (frontal, parietal and temporal lobes, hippocampus, amygdala, cortex, subcortical gray matter and total gray matter), and reduced FA (posterior corpus callosum, perforant pathway) in PLWH. PLWH with high AC burden performed worse on tests of learning and executive function compared to HIV− with high AC burden. Longitudinally, PLWH who reduced their ACB over time had better NP and neuroimaging measures.
Conclusions:
AC medications were associated with worse NP and reduced structural brain integrity, and these effects were more widespread in PLWH. Use of AC medications should be carefully monitored in older adults with deprescription considered when possible.
Keywords: Anticholinergics, HIV, neuroimaging, cognition
The implementation of combination antiretroviral therapy (cART) has resulted in an increased lifespan for persons living with HIV (PLWH) that is approximately equivalent to persons without HIV (HIV−) [1]. As the population of PLWH ages, new clinical challenges develop, such as the management of age-related comorbidities (e.g. diabetes, cardiovascular disease) and associated use of pharmacological interventions. Consequently, there is growing concern about the health consequences associated with the iatrogenic effects of HIV and non-HIV specific medications in PLWH [2,3].
Anticholinergic (AC) medications block the neurotransmitter acetylcholine and are often prescribed for multiple medical conditions including depression and other psychiatric disorders, allergies, asthma, diarrhea, bladder issues, and chronic obstructive pulmonary disorder [4]. AC medications can increase the risk of cognitive decline and dementia in older HIV− [5-7], reduce brain volumes and alter white matter integrity [8-10]. Relatedly, clinical guidelines (e.g., American Geriatric Society Beers criteria) suggest limited use of AC medication for older (≥65 years) adults [11].
Recent studies indicate that AC use is common among PLWH ≥50 years [12, 13]. Few studies have examined the effects of AC medications in PLWH. One study of female PLWH reported relationships between number of AC medications and poorer neuropsychological performance (NP) [14]. A gap remains regarding the effects of AC medication on brain integrity (both NP and neuroimaging measures) as a function of HIV status, in a cohort of both male and female individuals. Deprescribing an AC medication, or replacing a medication that has significant AC effects with one that has little to no AC effect, is an easily modifiable factor that could improve brain integrity in PLWH. The current study examined the effects of AC medication burden on brain integrity (NP, brain volumetrics, and diffusion tensor imaging (DTI)) in both HIV− individuals and PLWH. Within a subset of PLWH we examined the effects of longitudinal changes in AC burden on NP and neuroimaging measures.
Methods
Participants
PLWH were chronically infected (>1 year), on stable cART for at least 6 months, and who were virologically controlled (≤ 50 copies/mL) were recruited from the Washington University School of Medicine (WUSM) Infectious Disease clinic and AIDS Clinical Trial Unit (Barnes-Jewish Hospital; Saint Louis, MO) (n=209). HIV− (age ≥40 years old) were recruited from the Research Participant Registry at Washington University School of Medicine and leaflets distributed throughout the St. Louis community (n=95).
Exclusion criteria included <8 years of education, inability to read or write in English, a history of confounding neurological disorders (including cerebrovascular events), current of past opportunistic central nervous system infections, history of traumatic brain injury with loss of consciousness >30 minutes, self-reported major psychiatric disorders (e.g. severe depression, schizophrenia, Bipolar disorder), severe depressive symptoms (Beck Depression Inventory II score (BDI-II) ≥29), or current use of illicit drugs other than marijuana at time of study (assessed by urine drug screen). Serostatus of HIV− was confirmed using a buccal oral swab test at the study visit. This study was approved by the WUSM Institutional Review Board and all participants provided written informed consent and were financially compensated for participating.
Demographic information (age, sex, race, education) was recorded at the study visit. Current substance use (as assessed by a urine drug screen; tests for presence of cocaine, amphetamines, methamphetamines, marijuana, methadone, opiates, PCP and barbiturates), current self-reported smoker status, and systolic and diastolic blood pressure were also assessed at the time of study. Clinical characteristics of PLWH participants (current CD4 t-cell count, nadir CD4 t-cell count, current viral load, duration of infection and current cART regimen) were collected through self-report or review of medical records when available.
The subset of PLWH participants included in longitudinal analyses (n=90) met the above inclusion criteria at both time points. The average duration between study visits was 23.9 months.
Anticholinergic Burden Quantification
AC burden was quantified using the Anticholinergic Cognitive Burden (ACB) scale [15]. This scale assigns a value of 1 (possible anticholinergic effects) to 3 (definite anticholinergic effects) for each AC medication based on serum anticholinergic activity or affinity to muscarinic receptors, with values summed to create a total AC burden. Participants were further classified according to ACB guidelines as high (score >3), low (1-3) or no (score 0) AC burden. The ACB scale has been validated against adverse clinical outcomes, such as hospitalizations, emergency department visits, and incident dementia [16].
NP Evaluation
Participants were administered a comprehensive neuropsychological battery that included tests representing five cognitive domains, including learning, retention, executive function, motor/psychomotor speed, and language. Raw test scores were transformed into demographically-corrected (age, sex, race, education) Z-scores using standard norms [17]. Individual Z-scores for all tests within a single cognitive domain were averaged to create domain Z-scores, and all domain Z-scores were averaged to create a global Z-score. This test battery has previously been used to assess cognitive impairment in PLWH [18].
Acquisition and Processing of Brain Volumes
Neuroimaging was performed on a 3T Siemens Tim Trio MR scanner (Siemens AG, Erlangen Germany) with a 12-channel head coil. A high-resolution, 3-dimensional, sagittal, magnetization-prepared rapid gradient echo scan (MPRAGE) T1 scan was acquired (repetition time [TR] = 2400ms, echo time (TE) = 3.16 ms, flip angle = 8°, inversion time = 1000 ms, voxel size = 1×1×1 mm3 voxels, 256×256×256 acquisition matrix, 162 slices). FreeSurfer v.5.3.0 (Martinos Center, Harvard University, Boston, MA, USA) was used to reconstruct the cortical surface for volumetric segmentation.
Structural volumes from brain regions were generated using the Desikan-Killiany atlas [19]. Visual inspection of the automated segmentation results was performed for quality assurance purposes, and manual corrections were made when necessary. Cortical regional volumes were aggregated into lobes (frontal, parietal, temporal, occipital). Additionally, subcortical volumes (thalamus, caudate, putamen, hippocampus, and amygdala) were analyzed, as were global measures of total gray matter volume, total white matter volume, and total subcortical gray matter volume. These regions have been previously been shown to be affected by HIV [20,21].
DTI Processing
DTI assesses white matter (WM) microstructural integrity by measuring the diffusion of water in the brain. A subset of individuals (PLWH n= 149; HIV− n= 53) had DTI available. DTI preprocessing included correction for motion and eddy current distortions, followed by skull stripping using FSL 5.0.9 (www.fmrib.ox.ac.uk/fsl). Scans were inspected to validate that head movement was < 3 mm during acquisition. Tensor calculation for fractional anisotropy (FA) was generated from DTIFIT. Diffusion maps were warped to the FA-FMRIB_58 space, and WM voxels were condensed to a 1-mm-thick skeleton derived from tract-based spatial statistics (TBSS) [22]. The TBSS approach performs a searching algorithm that identifies the highest FA in the eminent vicinity and alleviates potential partial volume effects and registration errors. Skeletonized voxels were overlaid onto 12 predefined WM tracts and averaged to yield tract-wise values [23]. Tracts analyzed included the anterior and posterior corpus callosum, cortico-spinal tract, forceps major, forceps minor, frontal aslant tract, frontal-occipital fasciculus, inferior longitudinal fasciculus, cingulum, perforant pathway, superior longitudinal fasciculus, and uncinate fasciculus.
Statistical Analyses
Demographic variables (age, sex, race, education), depressive symptoms (BDI-II), blood pressure, and substance use were compared first between HIV− and PLWH, and then between no AC burden, low AC burden, and high AC burden groups within HIV− and PLWH cohorts using analysis of variance (ANOVAs), t-tests or chi-square analyses where appropriate to assess for the need for covariates in subsequent analyses. HIV− and PLWH were compared with regards to total ACB score and total number of AC medications using Mann-Whitney U test for nonparametric variables. A chi-square analysis compared the proportion of individuals with a low (ACB score 1-3) or high (ACB score >3) AC burden.
A series of univariate general linear models assessed the effect of ACB group (none, low, or high AC burden) on NP and neuroimaging measures in HIV− and PLWH separately. The first series of general linear models included each NP domain Z-score or global Z-score as the dependent variables. The next series included each brain volume as the dependent variable, and included intracranial volume as a covariate. The final series included FA for each WM tract as the dependent variable. Demographic variables were also included as covariates as necessary. Post-hoc Tukey’s tests were applied to evaluate specific group differences. False discovery rate (FDR) was used to correct for multiple comparisons in all analyses. Univariate analyses with appropriate covariates examined differences in NP and neuroimaging outcomes (volumes and FA) specifically between HIV− individuals with high AC burden and PLWH with high AC burden.
Secondary analyses evaluated longitudinal changes in ACB score, NP (n=90), and neuroimaging measures (brain volumes (n=90) and white matter tract FA (n=40)) in PLWH. Change in ACB score was calculated by subtracting ACB score at time point 1 from time point 2. Rate of change (per month) in cognition and neuroimaging variables was calculated by subtracting values at time point 1 from values at time point 2, and dividing by the number of months between study visits. Relationships between rate of change in NP or imaging variables and change in ACB scores were assessed using regression analyses, with covariates entered as the first step when appropriate. FDR was used to correct for multiple comparisons.
Results
Demographics
Table 1 summarizes group demographics and clinical information for all participants. A group difference between PLWH and HIV− was found for sex (p<.001), with a higher proportion of male PLWH (75%) compared to HIV− (52%). PLWH also exhibited higher depressive symptoms (mean=9.2, SD=7.6) compared to HIV− (mean=5.9, SD=6.1), a higher rate of current smokers (47% versus 31%), and a higher rate of marijuana use (49% versus 26%). Sex, BDI-II scores, current smoker status, and marijuana use were included as covariates in appropriate analyses. When comparing demographic and clinical variables between AC burden groups within each serostatus group, sex and depressive symptoms significantly differed by AC burden within the PLWH cohort. The high AC burden group exhibited a higher proportion of female PLWH compared to the no AC and low AC burden groups, while the no AC burden group exhibited fewer depressive symptoms compared to both the low and high AC burden groups (see Supplemental Table 1). No significant differences were identified within the HIV− cohort. Sex and BDI-II score were included as covariates in subsequent analyses when appropriate.
Table 1.
Demographic and clinical characteristics by HIV serostatus
| HIV− (n=95) | PLWH (n=209) | P-value | |
|---|---|---|---|
| Age in years; M (SD) | 54.5 (8.5) | 54.8 (9.8) | 0.92 |
| Sex (% males) | 52% | 75% | <.001 |
| Race (% AAa) | 65% | 64% | 0.83 |
| Education in years; M (SD) | 13.9 (2.2) | 13.3 (2.8) | 0.06 |
| Beck Depression Inventory-II; M(SD) | 5.9 (6.1) | 9.2 (7.6) | <.001 |
| Current smoker (% Yes) | 31% | 47% | .005 |
| Systolic blood pressure, mm Hg; M(SD) | 120.1 (17.2) | 122.1 (12.8) | 0.29 |
| Diastolic blood pressure, mm Hg; M(SD) | 79.7 (10.9) | 79.0 (9.7) | 0.58 |
| Urine drug screen results | |||
| Cocaine (% positive) | 7% | 12% | 0.35 |
| Amphetamines (% positive) | 0% | 6% | 0.06 |
| Methamphetamines (% positive) | 1% | 5% | 0.17 |
| Marijuana (% positive) | 26% | 49% | .001 |
| Methadone (% positive) | 1% | 2% | 0.72 |
| Opiates (% positive) | 8% | 6% | 0.55 |
| PCP (% positive) | 3% | 1% | 0.12 |
| Barbiturates (% positive) | 0% | 0% | N/A |
| Recent CD4 T-cell count; median (IQR) | N/A | 556 (391,831) | N/A |
| Nadir CD4 T-cell count; median (IQR) | N/A | 175 (28, 300) | N/A |
| Duration of infection (in months); median (IQR) | N/A | 188 (103, 278) | N/A |
| AC group membershipb | <.001 | ||
| No AC burden (% ACB =0) | 71.5% | 46.0% | |
| Low AC burden (% ACB=1-3) | 18.0% | 38.0% | |
| High AC burden (% ACB >3) | 10.5% | 16.0% |
AA=African American
AC (anti-cholinergic) group membership = classification according to the Anticholinergic Cognitive Burden Scale (ACB); N/A = not applicable; SD= standard deviation, IQR= interquartile range, bolded p-values indication those significant at p<.05
Demographic and clinical characteristics for PLWH included in the longitudinal analyses did not significantly differ from those who did not have follow-up data available (all p-values >.05).
HIV serostatus differences in ACB scores and number of AC medications
AC medication use was more common among PLWH (mean number of AC medications= 1.0, SD= 1.3, range= 0-7) compared to HIV− (mean=0.5, SD= 1.1, range=0-6) (p=.005). PLWH also had a higher total ACB score (mean=1.53, SD = 2.1, range=0-14) compared to HIV− (mean=0.82, SD=1.7, range=0-8) (p=.01).
After classifying individuals into none (ACB score = 0), low (ACB score 1-3), or high (ACB score >3) AC burden, a significant difference in AC group proportions by serostatus (p<.001) was observed. Sixty-eight (71.5%) of the HIV− group had no AC burden, 17 (18%) had low AC burden, and 10 (10.5%) had high AC burden. Conversely, 96 (46%) of the PLWH had no AC burden, 80 individuals (38%) had low AC burden, and 33 participants (16%) had high AC burden.
The most common AC medications taken by HIV− include cetirizine (antihistamine; 30% of HIV− who report at least one AC medication), loratadine (antihistamine; 26% of HIV− who report at least one AC medication), and trazodone (antidepressant; 19% of HIV− who report at least one AC medication). Within PLWH who report taking at least one AC medication, the most common medications included trazodone (antidepressant; 20%), metoprolol (antihypertensive beta blocker; 19%), bupropion (antidepressant; 13%), ranitidine (antihistamine and antacid; 13%) and cetirizine (antihistamine; 13%).
Relationships between HIV, AC burden, and NP
Within HIV− controls, a significant effect of AC burden on NP was observed for the motor/psychomotor domain (p=.04). Post-hoc pairwise comparisons revealed a trend that HIV− individuals with a high AC burden performed significantly worse on tests of motor/psychomotor compared to HIV− individuals no AC burden (p=.08). Within PLWH, significant effects of AC burden were identified within the retention (p=.002) and executive function (p=.002) domains, as well as global cognition (p<.001) (Figure 1). Post-hoc analyses revealed significantly worse performance globally and on tests of retention and executive function in PLWH with high AC burden compared to PLWH with low or no AC burden (p-values <.01).
Figure 1.
Significant relationships between high AC burden and worse performance on (A) motor/psychomotor speed (in HIV−), (B) retention (in PLWH), (C) executive function (in PLWH), and (D) global cognition (in PLWH). (*p<.05; **p<.01; ***p<.001)
When HIV− and PLWH with high AC burden were compared on NP, significant differences by serostatus were observed for the learning (p=.01) and executive function (p=.01) domains, with PLWH with a high AC burden performing worse compared to HIV− individuals with high AC burden.
Relationships between HIV, AC burden, and brain volumes
Within HIV− individuals, a significant effect of AC burden on brain volumes was observed for the occipital lobe (p=.03), putamen (p=.03), total subcortical gray matter (p=.04) and total gray matter (p=.02). Post-hoc pairwise comparisons revealed significantly smaller volumes in the high AC burden group compared to the no AC burden group for each previously stated region of interest (p-values <.05). Additionally, significantly smaller volumes were observed for the high AC burden group compared to the low AC burden group in the total subcortical gray matter (p=.02) volumes. There were no significant group differences between the no AC burden and low AC burden groups.
Within PLWH, a significant effect of AC burden was identified for the frontal lobe (p=.01), parietal lobe (p=.001), temporal lobe (p=.003), hippocampus (p=.02), amygdala (p=.01), cortex (p=.005), subcortical gray matter (p=.008), and total gray matter volumes (p=.004) (representative plots shown in Figure 2). For each region, the high AC burden PLWH exhibited smaller volumes compared to the no AC burden PLWH (p-values <.05) with no other significant group differences.
Figure 2.
Representative plots of the significant relationships between AC burden and brain volumes. PLWH with a high AC burden had smaller (A) frontal lobe volume, (B) temporal lobe volume, (C) hippocampal volume, and (D) total gray matter volume compared to PLWH with no AC burden. (*p<.05; **p<.01; ***p<.001)
No significant differences in brain volumes were observed between HIV− individuals with high AC burden and PLWH with high AC burden.
Relationships between HIV, AC burden, and white matter microstructural integrity
A subset of PLWH (no AC burden n=70, low AC burden n=57, high AC burden n=22) and HIV− (no AC burden n=35, low AC burden n=10, high AC burden n=8) had DTI data. Demographic and clinical characteristics of participants with DTI did not differ from those without DTI (p-values >.05).
Within HIV− individuals, no significant effects of AC burden were observed for white matter microstructural integrity. Within PLWH, a significant effect of AC burden was identified in the perforant pathway (p<.001) and posterior corpus callosum (p=.006) (Figure 3). PLWH with high ACB had significantly lower FA in the perforant pathway and posterior callosum compared to PLWH in both the low AC and no AC groups (p-values <.05). There were no significant differences between the no AC and low ACB groups. Additionally, no significant differences were observed between HIV− individuals with high AC burden and PLWH with high AC burden (p-values<.05).
Figure 3.
High AC burden was associated with lower FA of the posterior corpus callosum (A) and perforant pathway (B) compared to individuals with no and low AC burden in PLWH. (*p<.05; **p<.01; ***p<.001)
Longitudinal changes in ACB score, NP, and volumetrics in PLWH
Change in total ACB score from time point 1 to time point 2 ranged from −4 to 2 (mean = −0.48, SD = 1.3) (n=90). Forty-two percent (42%) of PLWH had no change in ACB score, 38% reported a decrease in ACB score, and 20% reported an increase in ACB score. Inverse relationships were observed between ACB score executive function Z-score (r=−.40, adjusted R2=.14, p<.001) (Figure 4), retention Z-score (r=−.26, adjusted R2=.05, p=.017), and global Z-score (r=−.26, adjusted R2=.05, p=.02).
Figure 4.
Change in ACB score from the first study visit to the second study visit significantly correlated with the rate of change (per month of study visit interval) of the executive function Z-score.
Regression analyses also examined relationships between change in ACB score and change in neuroimaging measures. Inverse relationships were identified between an increase in ACB score and a decline in brain volumes within the frontal lobe (r=−.33, adjusted R2=.08, p=.003), parietal lobe (r=−.29, adjusted R2=.06, p=.008), caudate (r=−.30, adjusted R2=.08, p=.003), and total gray matter (r=−.31, adjusted R2=.06, p=.005). No significant relationships were observed between change in ACB score and change in FA.
Discussion
The present study examined the effects of AC medication burden on brain integrity in HIV− individuals and PLWH. Use of AC medications was more commonly reported by PLWH compared to demographically similar HIV− individuals. A high AC burden was associated with worse NP, smaller brain volumes, and poorer white matter structural integrity, with more widespread relationships observed in the PLWH cohort compared to the HIV− cohort.
A high AC burden was associated with worse motor/psychomotor performance in HIV−, and poorer retention, executive function and global cognition in PLWH. Individuals with a high AC burden performed significantly worse compared to participants in the low or no AC burden groups. Longitudinally, a decrease in the ACB score was correlated with better performance in the executive function domain, retention domain, and global cognition. Our results complement a robust literature supporting worse cognitive performance, particularly in memory and executive functioning, that is associated with AC medications in the general population of older adults [7, 24-25]. Similar deleterious effects have been identified in women living with HIV with an increasing number of AC medications associated with worse performance in the learning, fluency, speed, and motor domains [14]. Although we did not observe widespread relationships between NP and AC burden in our HIV− individuals, it is likely due to the relatively young average age and low AC burden of these participants. Relatedly, AC medications and disruptions to the cholinergic system are a major area of research within Alzheimer disease (AD) with acetylcholinesterase inhibitors representing one of the primary FDA-approved treatments prescribed to slow the progression of memory impairments.
Significant effects of AC burden were also observed in brain volumes. A high AC burden was significantly associated with smaller volumes in occipital lobe, putamen, subcortical gray matter, and total gray matter volumes in HIV− individuals, and widespread cortical (frontal, parietal and temporal lobes), subcortical (hippocampus, amygdala) and global (cortex, subcortical gray matter, and total gray matter) volumes in PLWH. These results suggest that AC medications may leads to diffuse reductions in brain volume, although the pattern of these reductions may differ in PLWH compared to HIV−. Additionally, a longitudinal decrease in ACB score was associated with a smaller decline in frontal, parietal, caudate, and total gray matter volumes. While these relationships had not previously been examined in PLWH, our results are similar to those seen in aging and AD populations. For example, the use of AC medications has been associated with neurodegeneration and significantly lower total cortical volume in the general population of older adults [24]. Longitudinally, a faster rate of atrophy was reported in frontal volumes of cognitively normal older adults using ACs [8].
Finally, analyses examined the associations between WM integrity, as measured by DTI, and AC burden in PLWH and HIV−. A significant effect of AC burden was identified in PLWH only, with the high AC burden group demonstrating reduced FA in the perforant pathway and the posterior corpus callosum compared to the low and no AC burden groups. Importantly, the perforant pathway is a WM tract that connects the entorhinal cortex to the hippocampus, a structure with important cholinergic connections that are involved in memory consolidation [26]. Links have also previously been observed between reduced FA of the posterior corpus callosum and atrophy of the brain’s cholinergic system in older adults [10]. Our study did not identify any significant changes in these tracts over a 24-month period, potentially due to the small number of PLWH with longitudinal DTI data available.
Our study has several limitations that should be considered when interpreting the current results. Importantly, we were not able to consider the dosage, frequency, reason for prescription, and duration of prescription of AC medications in this analyses, or when changes in AC medications occurred during longitudinal analyses. This data was not available in the present study and may be important for future studies to include in order to fully understand the effects of AC medications on brain integrity in PLWH and HIV−. Additionally, the total number of HIV− who had a high AC burden was low, potentially limiting our power to identify small, but significant interactions between AC burden and HIV status. However, the total proportion of HIV− taking medications with AC effects (28%) is not considerably different than previous studies of a similar population older HIV− (23%-56%) [27]. Our HIV− and PLWH groups also were not well-matched according to sex, current depressive symptoms, and current tobacco and marijuana use. Although we included these variables as covariates in analyses where appropriate in order to reduce potential confounding effects, we recognize there may still be effects of these variables and other unmeasured confounding factors on results. Additionally, these cohort differences limit our ability to fully draw conclusions when comparing results from the HIV− and PLWH groups. Finally, we did not include the other prescribed medications in the current analyses. Future studies will need to consider the potential interactions between AC medications and other commonly prescribed medications. These could include cART regimens in PLWH that may alter the concentrations of AC medications [28].
Overall, AC medications represent a significant risk factor in PLWH for poorer cognition, smaller brain volumes, and reduced WM microstructural integrity both cross-sectionally and longitudinally. Notably, cortical regions, especially tracts radiating from the hippocampus, appear to be particularly vulnerable to AC medications, and these regions may in turn affect NP. Our results suggest that great care should be taken when prescribing AC medications in older adults, particularly when other risk factors for reduced brain integrity, such as HIV, are also present. Because of increased polypharmacy in older PLWH, clinicians treating these individuals should continually review all medications and consider deprescribing or limiting AC medications when possible.
Supplementary Material
Acknowledgements
This work was supported by grants from the National Institute for Nursing Research (R01NR014449 and R01NR015738), and the National Institute of Mental Health (R01MH118031). Research was conducted and supported by the Washington University Institute of Clinical and Translational Sciences (UL-TR000448 from the National Center for Advancing Translational Sciences).
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