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17 cbd studies

The goal of this study is to find an alternative to heavy pain medication, which can lead to prescription drug-abuse.

The findings from these studies are broad, ranging from treatment of serious disorders to quirky ancillary information. Ultimately, these aren’t the first studies to be done on the efficacy of CBD as a medical therapy.

This study’s true focus was the varied dosage; seeking whether administration of CBD produces an “inverted U-Shaped dose response.”

The Impact

This study investigated how CBD could affect subjects with liver injuries resulting from chronic and binge alcohol consumption. CBD was given to subjects (in this case, mice and human blood samples) that had been fed alcohol. In short, the analysis demonstrated that CBD lessened the elevated liver enzymes and the increased liver triglyceride. It also reduced fat droplet accumulation.

Arterial Ischemic Stroke occurs when blood flow in an artery to the brain is blocked due to narrowness of the artery or the formation of a blood clot. Neonatal (i.e. newborn) Arterial Ischemic Stroke grimly means there is a condition specific to infants. Woefully little is known about NAIS, but it can certainly lead to lifelong disabilities and/or brain injury. Currently, there is no effective treatment.

Author Gerhard Nahler found it most surprising that an entire group of authors were “tempted to over-interpret results.” However, he felt that misinterpretations are not entirely uncommon, stating “People overlook quite frequently that “in vitro” results may differ significantly from conditions “in vivo”, particularly in man. In vitro results are suggestions, not proofs for processes in real life.

Authors: Vanessa P. Soares and Alline C. Campos

Our primary measure of recognition memory performance was d’, but Table 3 shows other performance measures for completeness, including the hit and false alarm rates used to calculate d’. Table 3 shows a measure of response bias (c = − 1/2 * [zH − zFA]), where negative values indicate a liberal bias to respond “old” and positive values indicate a conservative bias to respond “new”. Table 3 also shows response time (RT). Each of these performance measures were separately analyzed in a mixed-design analysis of variance (ANOVA) with session (pretest, posttest) as a within-subject factor, strain group (THC and THC + CBD) as a between-subject factor, and THC + metabolite levels as a covariate.

In addition to being a small feasibility study that needs to be replicated, there are three primary limitations of the present study. First, like Morgan et al. (2010a, 2010b), assignment of subjects to strains was not completely random, so pre-existing differences between participants could have influenced the results. For example, regular users of high potency THC concentrates may be more or less susceptible to its acute effects than other subjects. Bidwell et al. (2018) and Englund et al. (2013) used random assignment, but only Bidwell et al. (2018) used naturalistic administration. Second, the 50 min that elapsed after consumption prior to the memory assessment (which occurred

Analysis of cannabinoid plasma biomarker levels revealed a main effect of session, F(1,29) = 11.44, p < .001, \( <\eta>_p^2 \) = 0.28, and a significant main effect of cannabinoid type, F(1,29) = 16.12, p < .001, \( <\eta>_p^2 \) = 0.36. Cannabinoid type interacted with strain group, F(1,29) = 5.25, p < .05, \( <\eta>_p^2 \) = 0.15, showing that sum THC + metabolite levels were higher for the THC group compared to the THC + CBD group (pbf < .05). Cannabinoid type interacted with session, F(1,29) = 7.69, p < .01, \( <\eta>_p^2 \) = 0.21, showing that the level of sum THC + metabolites was higher at posttest (i.e., after cannabis use) compared to pretest (pbf < .001). There was a significant 3-way interaction between cannabinoid type, strain group, and session, F(1,29) = 5.42, p < .05, \( <\eta>_p^2 \) = 0.16. When this interaction was decomposed with Bonferroni-corrected post hoc tests, they indicated that the strain groups did not differ on any pretest levels, but posttest sum THC + metabolites levels were higher for the THC group than the THC + CBD group (pbf < .001). When testing each measure separately (Table 2), we only observed a significant difference for THC levels at pretest. Posttest CBD levels were higher for the THC + CBD group than the THC group, whereas posttest THC levels and sum THC + metabolites were higher for the THC group than the THC + CBD group.


Participants were instructed not to use cannabis on the day of their baseline appointment, which took place at the research team’s on-campus laboratory. After completing the informed consent process, a Breathalyzer (Intoximeter, Inc., St. Louis, MO) and urinalysis test was administered to ensure that participants had no alcohol, sedatives, cocaine, opiates, or amphetamines in their system. If either test was positive, the baseline appointment was rescheduled, and participants with repeated positives were terminated from the study. Female participants were required to take a urine pregnancy test, to ensure that they were not currently pregnant. Participants completed questionnaires on demographics, lifestyle, substance use, and medical history. After baseline questionnaires were completed, participants provided a blood draw.

35 min after blood draw to assess peak cannabinoid levels) may have limited the observed effects of THC and CBD. On the other hand, we have found the effects of THC on verbal recall memory to be relatively persistent when international shopping list test (ISLT) performance was compared between 15 and 30 min after use versus 60–75 min after use (Bidwell et al. 2020). Third, given the nature of this observational pilot study we were not powered to include all relevant covariates or ethically able to match the groups on important characteristics such as cannabis use history, preferred form of cannabis (e.g. flower vs. concentrate), or preferred route of inhaled administration (e.g. bong, pipe, etc.). Furthermore, compared to the THC group, the THC + CBD group tended to be older (with age also ranging more widely), started regular cannabis use later, used less cannabis in the past month, and consumed significantly less THC in their assigned strain. Although the first three demographic trends were not significant, that may be attributable to the small sample size, so these factors could have contributed to group differences on memory. Despite these concerns, our strongest memory effects were shown in the THC group, which had the heaviest levels of use prior to the study sessions mitigating a concern that our findings are driven by tolerance effects in heavy users. Typically, heavier users are less likely to show acute decrements in memory performance (Ranganathan and D’Souza 2006; Schoeler and Bhattacharyya 2013).

Cannabinoid plasma biomarker levels taken immediately post-use were our primary assessment of the strength of the effects of each cannabinoid, but cannabinoid content weight is also reported to facilitate comparison with other studies. The total weight of the product that each participant used was measured as the difference between pre- and post-use weight (mg Total, Table 2). The amount of each cannabinoid consumed by each participant was estimated by multiplying the total weight used by the percentage of THC and CBD in that subject’s strain (mg THC and mg CBD, Table 2). To examine differences in cannabinoid content across groups, analyses were performed in a mixed-design ANOVA with cannabinoid type (CBD, THC) as a within-subject factor and strain group (THC, THC + CBD) as a between-subject factor.

As is typical in recognition memory research (Macmillan and Creelman 2005; Malmberg 2008; Neath and Surprenant 2003; Wixted 2007) and consistent with previous studies on the effects of THC and CBD on recognition memory (Morgan et al. 2012; Morgan et al. 2010b), d’ (accuracy in discriminating old vs. new words) was used as the primary measure of memory performance. The hit rate (H, proportion of correct “old” responses to studied words) and false alarm rate (FA, proportion of incorrect “old” responses to non-studied words) are used to calculate d’ (d′ = zH − zFA, where z is the standard normal distribution). Given the distribution of the metabolites, we performed a log transformation of the metabolite data.