Long-Term Outcomes of Adolescent THC Exposure on Translational Cognitive Measures in Adulthood in an Animal Model and Computational Assessment of Human Data
Notice bibliographique
Résumé
Importance: Although perceived as relatively harmless and nonaddictive, adolescent cannabis use significantly increases the likelihood of developing cannabis use disorder in adulthood, especially for high-potency cannabis. Risky decision-making is associated with chronic cannabis use, but given confounds of human studies, it remains unclear whether adolescent cannabis exposure and Δ9-tetrahydrocannabinol (THC) potency specifically predicts risky decision-making or influences cognitive response to the drug later in life. Objective: To leverage a human data set of cannabis users and a rat model to evaluate the long-term outcomes of adolescent THC exposure on adult decision-making and impulse control. Design, Setting, and Participants: This translational rat study tested the link between adolescent THC exposure and adulthood decision-making. A reanalysis of a previously published dataset of human chronic cannabis users was conducted to evaluate decision-making phenotypes. Computational modeling assessed the human and animal results in a single framework. Data were collected from 2017 to 2020 and analyzed from 2020 to 2022. Main Outcomes and Measures: Decision-making was measured by the Iowa Gambling Task (IGT) and Rat Gambling Task (rGT). Impulse control was assessed in the rat model. Computational modeling was used to determine reward and punishment learning rates and learning strategy used by cannabis users and THC-exposed rats. Cell-specific molecular measures were conducted in the prefrontal cortex and amygdala. Results: Of 37 participants, 24 (65%) were male, and the mean (SD) age was 33.0 (8.3) years. Chronic cannabis users (n = 22; mean [SE] IGT score, -5.182 [1.262]) showed disadvantageous decision-making compared with controls (n = 15; mean [SE] IGT score, 7.133 [2.687]; Cohen d = 1.436). Risky choice was associated with increased reward learning (mean [SE] IGT score: cannabis user, 0.170 [0.018]; control, 0.046 [0.008]; Cohen d = 1.895) and a strategy favoring exploration vs long-term gains (mean [SE] IGT score: cannabis user, 0.088 [0.012]; control, 0.020 [0.002]; Cohen d = 2.218). Rats exposed to high-dose THC but not low-dose THC during adolescence also showed increased risky decision-making (mean [SE] rGT score: vehicle, 46.17 [7.02]; low-dose THC, 69.45 [6.01]; high-dose THC, 21.97 [11.98]; Cohen d = 0.433) and elevated reward learning rates (mean [SE] rGT score: vehicle, 0.17 [0.01]; low-dose THC, 0.10 [0.01]; high-dose THC, 0.24 [0.06]; Cohen d = 1.541) during task acquisition. These animals were also uniquely susceptible to increased cognitive impairments after reexposure to THC in adulthood, which was correlated with even greater reward learning (r = -0.525; P < .001) and a shift in strategy (r = 0.502; P < .001), similar to results seen in human cannabis users. Molecular studies revealed that adolescent THC dose differentially affected cannabinoid-1 receptor messenger RNA expression in the prelimbic cortex and basolateral amygdala in a layer- and cell-specific manner. Further, astrocyte glial fibrillary acidic protein messenger RNA expression associated with cognitive deficits apparent with adult THC reexposure. Conclusions and Relevance: In this translational study, high-dose adolescent THC exposure was associated with cognitive vulnerability in adulthood, especially with THC re-exposure. These data also suggest a link between astrocytes and cognition that altogether provides important insights regarding the neurobiological genesis of risky cannabis use that may help promote prevention and treatment efforts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».