Paul RH, Cho K, Belden A, Carrico AW, Martin E, Bolzenius J, Luckett P, Cooley SA, Mannarino J, Gilman JM, et al. Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach. J Neuroimmune Pharmacol. 2022.
AbstractThe current study applied data-driven methods to identify and explain novel cognitive phenotypes of HIV. Methods: 388 people with HIV (PWH) with an average age of 46 (15.8) and median plasma CD4+ T-cell count of 555 copies/mL (79% virally suppressed) underwent cognitive testing and 3T neuroimaging. Demographics, HIV disease variables, and health comorbidities were recorded within three months of cognitive testing/neuroimaging. Hierarchical clustering was employed to identify cognitive phenotypes followed by ensemble machine learning to delineate the features that determined membership in the cognitive phenotypes. Hierarchical clustering identified five cognitive phenotypes. Cluster 1 (n=97) was comprised of individuals with normative performance on all cognitive tests. The remaining clusters were defined by impairment on action fluency (Cluster 2; n=46); verbal learning/memory (Cluster 3; n=73); action fluency and verbal learning/memory (Cluster 4; n=56); and action fluency, verbal learning/memory, and tests of executive function (Cluster 5; n=114). HIV detectability was most common in Cluster 5. Machine learning revealed that polysubstance use, race, educational attainment, and volumes of the precuneus, cingulate, nucleus accumbens, and thalamus differentiated membership in the normal vs. impaired clusters. The determinants of persistent cognitive impairment among PWH receiving suppressive treatment are multifactorial nature. Viral replication after ART plays a role in the causal pathway, but psychosocial factors (race inequities, substance use) merit increased attention as critical determinants of cognitive impairment in the context of ART. Results underscore the need for comprehensive person-centered interventions that go beyond adherence to patient care to achieve optimal cognitive health among PWH.
Gilman JM, Schmitt WA, Potter K, Kendzior B, Pachas GN, Hickey S, Makary M, Huestis MA, Evins EA.
Correction to: Identification of ∆9-tetrahydrocannabinol (THC) impairment using functional brain imaging. Neuropsychopharmacology. 2022.
Gilman JM, Schuster RM, Potter KW, Schmitt W, Wheeler G, Pachas GN, Hickey S, Cooke ME, Dechert A, Plummer R, et al. Effect of Medical Marijuana Card Ownership on Pain, Insomnia, and Affective Disorder Symptoms in Adults: A Randomized Clinical Trial. JAMA Netw Open. 2022;5 (3) :e222106.
AbstractImportance: Despite the legalization and widespread use of cannabis products for a variety of medical concerns in the US, there is not yet a strong clinical literature to support such use. The risks and benefits of obtaining a medical marijuana card for common clinical outcomes are largely unknown.
Objective: To evaluate the effect of obtaining a medical marijuana card on target clinical and cannabis use disorder (CUD) symptoms in adults with a chief concern of chronic pain, insomnia, or anxiety or depressive symptoms.
Design, Setting, and Participants: This pragmatic, single-site, single-blind randomized clinical trial was conducted in the Greater Boston area from July 1, 2017, to July 31, 2020. Participants were adults aged 18 to 65 years with a chief concern of pain, insomnia, or anxiety or depressive symptoms. Participants were randomized 2:1 to either the immediate card acquisition group (n = 105) or the delayed card acquisition group (n = 81). Randomization was stratified by chief concern, age, and sex. The statistical analysis followed an evaluable population approach.
Interventions: The immediate card acquisition group was allowed to obtain a medical marijuana card immediately after randomization. The delayed card acquisition group was asked to wait 12 weeks before obtaining a medical marijuana card. All participants could choose cannabis products from a dispensary, the dose, and the frequency of use. Participants could continue their usual medical or psychiatric care.
Main Outcomes and Measures: Primary outcomes were changes in CUD symptoms, anxiety and depressive symptoms, pain severity, and insomnia symptoms during the trial. A logistic regression model was used to estimate the odds ratio (OR) for CUD diagnosis, and linear models were used for continuous outcomes to estimate the mean difference (MD) in symptom scores.
Results: A total of 186 participants (mean [SD] age 37.2 [14.4] years; 122 women [65.6%]) were randomized and included in the analyses. Compared with the delayed card acquisition group, the immediate card acquisition group had more CUD symptoms (MD, 0.28; 95% CI, 0.15-0.40; P < .001); fewer self-rated insomnia symptoms (MD, -2.90; 95% CI, -4.31 to -1.51; P < .001); and reported no significant changes in pain severity or anxiety or depressive symptoms. Participants in the immediate card acquisition group also had a higher incidence of CUD during the intervention (17.1% [n = 18] in the immediate card acquisition group vs 8.6% [n = 7] in the delayed card acquisition group; adjusted odds ratio, 2.88; 95% CI, 1.17-7.07; P = .02), particularly those with a chief concern of anxiety or depressive symptoms.
Conclusions and Relevance: This randomized clinical trial found that immediate acquisition of a medical marijuana card led to a higher incidence and severity of CUD; resulted in no significant improvement in pain, anxiety, or depressive symptoms; and improved self-rating of insomnia symptoms. Further investigation of the benefits of medical marijuana card ownership for insomnia and the risk of CUD are needed, particularly for individuals with anxiety or depressive symptoms.
Trial Registration: ClinicalTrials.gov Identifier: NCT03224468.
Gilman JM, Schmitt WA, Potter K, Kendzior B, Pachas GN, Hickey S, Makary M, Huestis MA, Evins EA.
Identification of ∆9-tetrahydrocannabinol (THC) impairment using functional brain imaging. Neuropsychopharmacology. 2022;47 (4) :944-952.
AbstractThe primary cannabinoid in cannabis, Δ9-tetrahydrocannabinol (THC), causes intoxication and impaired function, with implications for traffic, workplace, and other situational safety risks. There are currently no evidence-based methods to detect cannabis-impaired driving, and current field sobriety tests with gold-standard, drug recognition evaluations are resource-intensive and may be prone to bias. This study evaluated the capability of a simple, portable imaging method to accurately detect individuals with THC impairment. In this double-blind, randomized, cross-over study, 169 cannabis users, aged 18-55 years, underwent functional near-infrared spectroscopy (fNIRS) before and after receiving oral THC and placebo, at study visits one week apart. Impairment was defined by convergent classification by consensus clinical ratings and an algorithm based on post-dose tachycardia and self-rated "high." Our primary outcome, prefrontal cortex (PFC) oxygenated hemoglobin concentration (HbO), was increased after THC only in participants operationalized as impaired, independent of THC dose. ML models using fNIRS time course features and connectivity matrices identified impairment with 76.4% accuracy, 69.8% positive predictive value (PPV), and 10% false-positive rate using convergent classification as ground truth, which exceeded Drug Recognition Evaluator-conducted expanded field sobriety examination (67.8% accuracy, 35.4% PPV, and 35.4% false-positive rate). These findings demonstrate that PFC response activation patterns and connectivity produce a neural signature of impairment, and that PFC signal, measured with fNIRS, can be used as a sole input to ML models to objectively determine impairment from THC intoxication at the individual level. Future work is warranted to determine the specificity of this classifier to acute THC impairment.ClinicalTrials.gov Identifier: NCT03655717.
Cooke ME, Clifford JS, Do EK, Gilman JM, Maes HH, Peterson RE, Prom-Wormley EC, Evins EA, Schuster RM.
Polygenic score for cigarette smoking is associated with ever electronic-cigarette use in a college-aged sample. Addiction. 2022;117 (4) :1071-1078.
AbstractBACKGROUND AND AIMS: Electronic cigarette use has escalated rapidly in recent years, particularly among youth. Little is known about the genetic influences on e-cigarette use. This study aimed to determine whether genetic risk for regular use of combustible cigarettes or for number of cigarettes smoked per day confers risk for ever e-cigarette use or frequency of e-cigarette use.
DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We used data from 9541 young adults from the Spit for Science longitudinal cohort study (2011-2019). Polygenic scores (PGS) of regular combustible cigarette use (PGS-RCU) and cigarettes per day (PGS-CPD) were constructed using summary statistics from the two largest available genome-wide association study (GWAS) meta-analysis of European ancestry and East Asian ancestry of combustible cigarette use and used to test whether the PGS of RCU or CPD predicted lifetime e-cigarette use and frequency of past 30-day e-cigarette use in a diverse sample of young adults of African (AFR), Admixed American (AMR), East Asian (EAS), European (EUR), and South Asian (SAS) ancestry.
FINDINGS: The PGS-RCU was associated with lifetime e-cigarette use in the EUR sample (OR = 1.27, 95% CI = 1.19-1.36, P = 7.53 × 10-12 ), but not in the other subsamples (ps > 0.12). This association remained significant after excluding regular combustible cigarette smokers (OR = 1.21, 95% CI = 1.12-1.31, P = 3.36 × 10-6 ). There was no statistically significant association between PGS-CPD and lifetime e-cigarette use and neither the PGS-RCU nor the PGS-CPD were associated with frequency of e-cigarette use in the past 30 days in any of the subsamples.
CONCLUSIONS: Genetic factors associated with regular combustible cigarette use appear to be associated with ever e-cigarette use in young adults. We did not find evidence for shared genetic factors influencing heaviness of use of combustible cigarettes and current e-cigarette use frequency.