Safety and tolerability of natural and synthetic cannabinoids in adults aged over 50 years: A systematic review and meta-analysis.

BACKGROUND: Cannabinoid-based medicines (CBMs) are being used widely in the elderly. However, their safety and tolerability in older adults remains unclear. We aimed to conduct a systematic review and meta-analysis of safety and tolerability of CBMs in adults of age ≥50 years. METHODS AND
FINDINGS: A systematic search was performed using MEDLINE, PubMed, EMBASE, CINAHL PsychInfo, Cochrane Library, and ClinicalTrials.gov (1 January 1990 to 3 October 2020). Randomised clinical trials (RCTs) of CBMs in those with mean age of ≥50 years for all indications, evaluating the safety/tolerability of CBMs where adverse events have been quantified, were included. Study quality was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. Two reviewers conducted all review stages independently. Where possible, data were pooled using random-effects meta-analysis. Effect sizes were calculated as incident rate ratio (IRR) for outcome data such as adverse events (AEs), serious AEs (SAEs), and death and risk ratio (RR) for withdrawal from study and reported separately for studies using tetrahydrocannabinol (THC), THC:cannabidiol (CBD) combination, and CBD. A total of 46 RCTs were identified as suitable for inclusion of which 31 (67%) were conducted in the United Kingdom and Europe. There were 6,216 patients (mean age 58.6 ± 7.5 years; 51% male) included in the analysis, with 3,469 receiving CBMs. Compared with controls, delta-9-tetrahydrocannabinol (THC)-containing CBMs significantly increased the incidence of all-cause and treatment-related AEs: THC alone (IRR: 1.42 [95% CI, 1.12 to 1.78]) and (IRR: 1.60 [95% CI, 1.26 to 2.04]); THC:CBD combination (IRR: 1.58 [95% CI,1.26 to 1.98]) and (IRR: 1.70 [95% CI,1.24 to 2.33]), respectively. IRRs of SAEs and deaths were not significantly greater under CBMs containing THC with or without CBD. THC:CBD combination (RR: 1.40 [95% CI, 1.08 to 1.80]) but not THC alone (RR: 1.18 [95% CI, 0.89 to 1.57]) significantly increased risk of AE-related withdrawals. CBD alone did not increase the incidence of all-cause AEs (IRR: 1.02 [95% CI, 0.90 to 1.16]) or other outcomes as per qualitative synthesis. AE-related withdrawals were significantly associated with THC dose in THC only [QM (df = 1) = 4.696, p = 0.03] and THC:CBD combination treatment ([QM (df = 1) = 4.554, p = 0.033]. THC-containing CBMs significantly increased incidence of dry mouth, dizziness/light-headedness, and somnolence/drowsiness. Study limitations include inability to fully exclude data from those <50 years of age in our primary analyses as well as limitations related to weaknesses in the included trials particularly incomplete reporting of outcomes and heterogeneity in included studies.
CONCLUSIONS: This pooled analysis, using data from RCTs with mean participant age ≥50 years, suggests that although THC-containing CBMs are associated with side effects, CBMs in general are safe and acceptable in older adults. However, THC:CBD combinations may be less acceptable in the dose ranges used and their tolerability may be different in adults over 65 or 75 years of age.

Introduction

The cannabis plant (Cannabis sativa L.) has been used worldwide both for recreational and medicinal purposes for thousands of years. With a fast-growing aging population, its medicinal use has also caught up and is growing in the elderly [1-3].

Among the cannabinoids found in the cannabis plant, delta-9-tetrahydorcannabinol (THC) and cannabidiol (CBD) are the most well characterised and often considered for medicinal purposes. THC can cause intoxication [4,5] and has antiemetic, analgesic, and potentially neuroprotective and anti-inflammatory effects. On the other hand, CBD is nonintoxicating [5,6] with antiepileptic and potentially also anti-inflammatory, neuroprotective, antioxidant, and antipsychotic effects [7-9]. While several trials have used these cannabinoids for a wide range of diseases and indications, a majority of these have investigated younger people [10,11]. However, age-related pharmacodynamic and pharmacokinetic changes as well as higher prevalence of comorbidities and polypharmacy in the elderly mean that they may have a different profile of safety and tolerability to cannabinoids [12,13] compared to younger people, as is well known with other groups of medications especially those used for disorders of the central nervous system [14]. Both THC and CBD, the common constituents of most cannabinoid-based medicines in current use have prominent effects on brain function and cognition [15]. Therefore, evidence of safety and tolerability of cannabinoid-based medicines (CBM) established in studies in younger adults cannot be directly extrapolated to the older adults. Although a number of recent reviews and meta-analyses [12,16,17] have summarized the safety and tolerability profile of CBMs, they have all pooled data from studies investigating across the age spectrum, making it difficult to draw age-specific inferences. With increasing interest in their use in disorders typically affecting older people [18-20] and growing unlicensed use [21], there is a particular need to investigate the safety and tolerability of CBMs in older people. This is also relevant, as there is a widely held view that many of the naturally derived cannabinoids are generally safe as they have been around and used for a long time.

Here, we have addressed this by investigating the safety and tolerability of CBMs in people over 50 years of age through systematically reviewing all double-blind, randomised controlled trials (RCTs) using CBMs that focused on people with mean age of 50 years and over to conduct a meta-analysis. As there is a larger evidence base of studies with mean age of participants ≥50 years than the more limited set of studies that have exclusively focused on people over 50 years and even less on people over 65 or 75 years, we have focused on studies with mean age of participants ≥50 years and complemented these results with additional analyses restricted to studies that have exclusively focused on people over 50 years and even less on people over 65. Existing meta-analytic investigations [16,17] have generally considered all CBMs together, irrespective of whether they included THC, CBD, or THC:CBD in combination. However, THC can cause intoxication and may induce anxiety and transient psychotomimetic effects [5], especially at higher doses and in vulnerable individuals, while CBD does not cause intoxication when directly compared in the same individuals [5] and may potentially ameliorate anxiety and psychosis [9,22-24]. Further, there is growing evidence that THC and CBD may have opposing acute effects on autonomic arousal and brain [15] and cardiovascular function [25,26], and CBD may mitigate some of the harmful effects of THC on cognition and behaviour [15,27,28], consistent with their opposing effects on some of their molecular targets [4]. This suggests that THC and CBD may have distinct tolerability profiles, with the possibility that certain side effects may be noticeable in those taking formulations containing only THC but not in those taking formulations containing only CBD while adverse effects may even be mitigated in those taking THC and CBD in combination. This underscores the importance of examining their safety and tolerability separately. Therefore, we have addressed this issue by separately investigating the effects of THC, CBD, or THC:CBD in combination.

We hypothesized that compared to control treatments, all 3 categories of CBMs will be associated with: (i) a greater incidence of adverse events (AEs); (ii) no greater incidence of serious adverse events (SAEs) or death; and (iii) no greater risk of withdrawal from study. Further, we hypothesized a direct relationship between the dose of THC used in THC-containing CBMs and the incidence of adverse consequences in older adults.

Methods

Data sources and searches

The review was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines [29] (see S1 PRISMA checklist). The study protocol was preregistered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42019148869). Ethics approval was not required for this systematic review and meta-analysis.

A detailed description of the bibliographic search strategy is presented in Methods in S1 Text. We identified studies published from 1 January 1990 up to 31 October 2020, from several electronic databases. Studies were independently assessed by 2 researchers and disagreements resolved through consensus or discussions with a third researcher.

Study selection

Studies were included if (1) published from 1990 onwards; (2) included older adults (defined as mean age ≥50 years) or reported a distinct subgroup of older adults and provided separate results for this subgroup; and (3) provided data on the safety and tolerability of medical cannabinoids administered by any route, at any dose, for any duration and for any indication. Studies were excluded if they (1) included exclusively younger participants (mean age <50 years); (2) studied effects of cannabinoids for recreational purposes or failed to provide the dosage of cannabinoids; and (3) were not reported in English language. Here, we focus on results from RCTs.

Data extraction and quality assessment

All relevant available data for examination of the safety and tolerability of different CBMs (THC:CBD combination or THC or CBD alone) were collected from eligible studies, complemented with information from ClinicalTrials.gov and author responses.

Data were extracted for study design, participant characteristics, indication, dosage and duration of intervention, all-cause and treatment-related AEs and SAEs, and AE-related withdrawals and deaths.

AEs and SAEs were coded according to the Medical Dictionary for Regulatory Activities (MedDRA) “system organ classes” (SOC). Data were also extracted for the top 5 (as reported by each study) AEs for each SOC, where available. Data extraction and coding was verified by a medically qualified researcher and discrepancies resolved following discussions with senior researcher. The disease conditions investigated were classified into broader subgroups for analysis purpose.

Overall, quality of evidence was assessed using recommended criteria [30] and summarised to reflect confidence in estimates [31].

Data synthesis and analysis

Total exposure to active intervention in person-years was estimated by first calculating this for each individual study by multiplying the number of participants in the active intervention arm with the duration of treatment for that arm for each study and then adding up these study-specific values for all studies under each broad category (THC, THC:CBD, or CBD) of intervention investigated here. Mean exposure in person-years for each category (THC, THC:CBD, or CBD) of intervention was estimated by calculating the arithmetic mean from study-specific estimates obtained as above for each intervention category. Pooled mean ages of participants for each group of studies and treatment arms were estimated by calculating the arithmetic mean of study specific mean age as reported by individual studies for each intervention arm. Other pooled estimates (median and interquartile range) for summary study characteristics, such as duration of study in weeks, participants analysed or included, or duration of treatment for each treatment arm, were calculated by estimating them from the total number (e.g., for variables such as number of participants included or analysed; duration of study or treatment) or the mean estimate (e.g., for variables such as mean age) as reported in individual studies for each set subgroups of RCTs.

Pooled effect-sizes were estimated if there were 2 or more RCTs within each group or subgroup under the random-effects model using the restricted maximum-likelihood estimator because of anticipated heterogeneity. For each broad category of intervention, analyses combined both parallel-arm and crossover RCTs, with the latter treated as parallel-arm design [32] for pooled analyses. But we also report results by RCT design for each intervention. We estimated incident rate ratio (IRR) for outcome data such as AEs, SAEs, and death and risk ratio (RR) for withdrawal from study. Studies with more than 1 active treatment arm were treated as independent studies. In studies with more than 1 active treatment arm compared against a single control group, we also report meta-analysis of dependent effect-sizes with robust variance estimation [33-35]. For the purpose of reporting throughout the manuscript, results are reported for analyses treating all studies as independent, while corresponding dependent meta-analyses are reported in Results in S1 Text and signposted in the main text as appropriate. We investigated heterogeneity using forest plots and the I statistic and publication bias using Egger regression test [36] and the “Trim and fill” method [37]. For the analysis of AEs, data for all conditions were combined. We also examined whether estimates varied according to treatment, design, clinical condition, and dose of study drug using meta-regression.

Our primary analysis includes the results of all studies where the mean age of study participants was ≥50 years. As many participants in these included studies were <50 years of age, we also carried out separate analyses restricted to studies where all participants were ≥50 years of age and also where all participants were ≥65 years of age. These analyses were carried out where there were at least 2 studies with analysable data. Statistical analyses were performed using the metafor package in R (version 3.6.3) [38]. For meta-analysis of dependent effect-sizes with robust variance estimation, we also used the clubSandwich package (https://github.com/jepusto/clubSandwich) along with metafor in R.

Results

Data selection

Fig 1 (PRISMA flow chart) summarizes the study selection procedure. Main characteristics and outcome measures of each study are included in Tables 1–3; additional details regarding studies are presented in Results in S1 Text and Tables A and B in S1 Text. A total of 60 comparisons of CBM and control intervention using RCT design (hereafter called RCTs) (n = 6,216 participants; 1933.47 person-years of cannabinoid exposure) from 46 published articles were included (Fig 1). Of these, 4 RCTs recruited participants over age ≥65 years (n = 68; mean age, 72.4 (SD ± 4.5)), of which one was ≥75 years (Tables 1 and 2) [39-42].

Fig 1

Study disposition.

Table 1

Characteristics of included randomised controlled trials of THC in older adults (N = 30).

Study ID (country)	Study Design (RCT)	THC: Sample included/analysed NMean age (SD), Male %	Comparator: Sample included/analysed NMean age (SD), Male %	Age cut-off for enrolment	Indication	THC classification	Comparator	THC treatment duration, weeks	Calculated daily average THC dose	Overall GRADE rating for study
***Ahmed et al. 2014(the Netherlands)	Crossover	12/1172.00 (5), 50	12/1172.00 (5), 50	≥65 years	Healthy older participants	Namisol	Placebo	.4	6.5 mg	Moderate
Ahmed et al. 2015(the Netherlands)	Crossover	10/1077.30 (5.6), 70	10/1077.30 (5.6), 70	≥18 years	Dementia	Namisol	Placebo	2.6	3 mg	Moderate
Brisbois et al. 2011(Canada)	Parallel-arm	24/1167.00 (10.9), 64	22/1065.50 (8), 50	Adult	Cancer patients with chemosensory alterations	Dronabinol	Placebo	2.6	7.5 mg	Low
Carley et al. 2018(USA)‡	Parallel-arm	21/2152.70 (7.7), 76	25/2558.80 (6.1), 72	21–65 years	Obstructive sleep apnoea	Dronabinol	Placebo	6.0	2.5 mg	Low
Carley et al. 2018(USA)‡	Parallel-arm	27/2754.70 (7), 67	25/2558.80 (6.1), 72	21–65 years	Obstructive sleep apnoea	Dronabinol	Placebo	6.0	10 mg	Low
Curtis et al. 2009(UK)	Crossover	44/3752.00 (9.5), 50	44/3752.00 (9.5), 50	≥18 years	Huntington disease	Nabilone	Placebo	5.0	2 mg	Low
De Vries et al. 2016(the Netherlands)	Crossover	25/2452.00 (NR), 62	25/2452.00 (NR), 62	>18 years	Chronic pancreatitis	Namisol	Diazepam	.1	8 mg	Moderate
**Herrmann et al. 2019(Canada)	Crossover	39/3887.00 (10), 77	39/3887.00 (10), 77	≥55 years	Alzheimer disease	Nabilone	Placebo	6.0	1.6 mg	Moderate
Jatoi et al. 2002(USA)	Parallel-arm	152/15267.00 (10), 66	159/15965.00 (11), 65	≥18 years	Cancer-related anorexia	Dronabinol	Megestrol acetate	8.1	5 mg	Low
Johnson et al. 2010(UK)§	Parallel-arm	58/5861.30 (12.5), 52	59/5960.10 (12.3), 54	NR	Patients with cancer-related pain	THC extract spray	Placebo	2.0	23 mg	Moderate
Lane et al. 1991(USA)	Parallel-arm	21/2147.0 (20–68)*,†48	21/2149.0 (22–64)*,†48	18–69 years	Chemotherapy-induced nausea and vomiting	Dronabinol	Prochlorperazine	.9	40 mg	Low
Meiri et al. 2007(USA)	Parallel-arm	17/1761.60 (14.2), 53	14/1457.20 (8.6), 38	≥18 years	Chemotherapy-induced nausea and vomiting	Dronabinol	Placebo	.7	20 mg	Low
Peball et al. 2020(Austria)	Parallel-arm	19/1965.4 (7.94), 53	19/1964.0 (8.04), 74	≥30 years	Parkinson disease	Nabilone	Placebo	4.0	0.75 mg	Moderate
Sieradzan et al. 2001(UK)	Crossover	9/959.00 (NR), 44	9/959.00 (NR), 44	NR	Parkinson disease	Nabilone	Placebo	.1	2 mg	Very low
Strasser et al. 2006(Germany)§	Parallel-arm	100/10060.00 (12), 54	48/4862.00 (10), 52	Adult	Cancer-related anorexia	THC	Placebo	6.0	5 mg	Low
Svendsen et al. 2004(Denmark)	Crossover	24/2450.0 (NR), 42	24/2450.0 (NR), 42	18–55 years	Multiple sclerosis	Dronabinol	Placebo	3.0	10 mg	Moderate
Tomida et al. 2006(UK)§	Crossover	6/655.30 (5), 100	6/655.30 (5), 100	NR	Intraocular pressure	THC extract spray	Placebo	.1	5 mg	Low
Toth et al. 2012(Canada)	Parallel-arm	13/1360.80 (15.3), 38	13/1361.60 (14.6), 69	18–80	Diabetic peripheral neuropathic pain	Nabilone	Placebo	5.0	4 mg	Low
Van Amerongen et al. 2017, 2(the Netherlands)||	Crossover	24/2454.30 (8.9), 33	24/2454.30 (8.9), 33	≥18 years	Multiple sclerosis	THC	Placebo	.1	16 mg	Moderate
Van Amerongen et al. 2017, 1(the Netherlands)||	Parallel-arm	12/1257.30 (9), 33	12/1251.40 (8), 33	≥18 years	Multiple sclerosis	THC	Placebo	4.0	28.5 mg	Moderate
Van den Elsen et al. 2015, 1(the Netherlands)	Parallel-arm	24/2479.00 (8), 46	26/2678.00 (7), 54	≥40 years	Dementia	Namisol	Placebo	3.0	4.5 mg	Moderate
Van den Elsen et al. 2015, 2(the Netherlands)	Crossover	22/2276.40 (5.3), 68	22/2276.40 (5.3), 68	≥18 years	Dementia	Namisol	Placebo	2.6	3 mg	Moderate
***Volicer et al. 1997(USA)	Crossover	15/1272.70 (4.9), 92	15/1272.70 (4.9), 92	NR	Alzheimer disease	Dronabinol	Placebo	6.0	5 mg	Very low
***Walther et al. 2011(Switzerland)	Crossover	2/278.00 (NR), 100	2/278.00 (NR), 100	NR	Alzheimer disease	Dronabinol	Placebo	2.0	2.5 mg	Very low
Ware et al. 2010(Canada)	Crossover	32/3250.00 (11.2), 16	32/3250.00 (11.2), 16	≥18 years	Fibromyalgia	Nabilone	Amitriptyline	2.0	1 mg	Moderate
Weber et al. 2010(Switzerland)	Crossover	27/2257.00 (12), 74	27/2257.00 (12), 74	Adult	Amyotrophic lateral sclerosis patients with cramps	Dronabinol	Placebo	2.0	10 mg	Moderate
Zadikoff et al. 2011(Canada)	Crossover	9/960.00 (7), 0	9/960.00 (7), 0	18–75	Cervical dystonia	Dronabinol	Placebo	3.0	15 mg	Low
Zajicek et al. 2003(UK)§	Parallel-arm	216/20650.00 (8.2), 31	222/21351.00 (7.6), 37	18–64	Multiple sclerosis	Dronabinol	Placebo	14.0	25mg	Moderate
Zajicek et al. 2005(UK)§	Parallel-arm	125/12550.00 (8.2), 31	120/12051.00 (7.6), 37	18–64	Multiple sclerosis	Dronabinol	Placebo	52.0	25 mg	Moderate
Zajicek et al. 2013(UK)	Parallel-arm	332/32952.30 (7.6), 40	166/16452.00 (8.2), 41	18–65	Multiple sclerosis	Dronabinol	Placebo	160.0	28 mg	Moderate

***Studies recruited participants ≥65 years.

‡Article included more than 1 dose level.

§Article included more than 1 cannabinoid intervention.

*Median age (range).

†Included as median age for whole study population was ≥50.

||Article included the results of multiple trials.

GRADE, Grading of Recommendations Assessment, Development, and Evaluation; NR, not recorded; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Table 3

Characteristics of randomised controlled trials of CBD in older adults (N = 4).

Study ID (country)	Study Design	CBD:Sample included/analysed NMean age (SD)Male %	Comparator:Sample included/analysed NMean age (SD)Male %	Age cut-off for enrolment	Indication	Active treatment	Comparator	CBD treatment duration, weeks	Calculated daily average CBD dose	GRADE rating
Consroe et al. 1991USA	Crossover	18/1547.82 (15.3), 53	18/1547.82 (15.3), 53	NR	Huntington disease	CBD	Placebo	6	700 mg	Low
Jadoon et al. 2016§UK	Parallel-arm	13/1356.8 (9.9), 77	14/1459.0 (9.4)68	≥18 years	Type 2 diabetes	CBD	Placebo	13	200 mg	Moderate
Tomida et al. 2006‡,§UK	Crossover	6/655.3 (5.0), 100	6/655.3 (5.0), 100	NR	Intraocular pressure	CBD	Placebo	0.1	20 mg	Low
Tomida et al. 2006‡,§UK	Crossover	6/655.3 (5.0), 100	6/655.3 (5.0), 100	NR	Intraocular pressure	CBD	Placebo	0.1	40 mg	Low

§Article included more than 1 cannabinoid intervention.

‡Article included more than 1 dose level.

CBD, cannabidiol; GRADE, Grading of Recommendations Assessment, Development, and Evaluation; NR, not recorded.

Table 2

Characteristics of included randomised controlled trials of THC:CBD combination in older adults (N = 26).

Study ID (country)	Study Design	CBD/THC: Sample included/analysed NMean age (SD), Male %	Comparator: Sample included/analysed NMean age (SD), Male %	Age cut-off for enrolment	Indication	CBD/THC classification	Comparator	CBD/THC treatment duration, weeks	Calculated daily average CBD/THC dose	GRADE rating
Blake et al. 2006(UK)	Parallel-arm	31/3160.9 (10.6), 26	27/2764.9 (8.5), 15	NR	Rheumatoid arthritis	THC:CBD spray	Placebo	5.0	14.6 mg THC: 13.5 mg CBD	Low
**Carroll et al. 2004(UK)	Crossover	19/1767.0 (NR)63	19/1767.0 (NR)63	18–78 years	Levodopa-induced dyskinesia in Parkinson disease	Cannabis extract	Placebo	4.0	10.2 mg THC: 5.1 mg CBD	Moderate
Duran et al. 2010(Spain)	Parallel-arm	7/750 (41–70)*0	9/950 (34–76)*11	>18 years	Chemotherapy-induced nausea and vomiting	THC:CBD spray	Placebo	.6	13 mg THC: 12 mg CBD	Moderate
Fallon et al. 2017, 1(Multicentre)||	Parallel-arm (withdrawal study)	103/10361.4 (10.9), 61	10310361.6 (11.8), 53	≥18 years	Advanced cancer patients with pain	THC:CBD spray	Placebo	5.0	17.6 mg THC: 16.3 mg CBD	Moderate
Fallon et al. 2017, 2(Multicentre)||	Parallel-arm	200/19960.0 (11), 53	199/19859.6 (11), 49	≥18 years	Advanced cancer patients with pain	THC:CBD spray	Placebo	5.0	17 mg THC: 15.8 mg CBD	Moderate
Jadoon et al. 2016, 1(UK)‡,§	Parallel-arm	11/1159.0 (8.8), 55	14/1459.0 (7.7), 50	≥18 years	Type 2 diabetes	CBD/THCV	Placebo	13.0	10 mg THC: 10 mg CBD	Moderate
Jadoon et al. 2016, 2(UK)‡,§	Parallel-arm	12/1258.0 (8.1), 75	14/1459.0 (7.7), 50	≥18 years	Type 2 diabetes	CBD/THCV	Placebo	13.0	10 mg THC: 200 mg CBD	Moderate
Johnson et al. 2010, (UK)§	Parallel-arm	60/6059.4 (12.1), 55	59/5960.1 (12.3), 54	NR	Patients with cancer-related pain	THC:CBD spray	Placebo	2.0	25 mg THC: 23 mg CBD	Moderate
Litchman et al. 2018, (Multicentre)	Parallel-arm	199/19959.2 (12), 56	198/19860.7 (11.1), 52	≥18 years	Advanced cancer patients with pain	THC:CBD spray	Placebo	5.0	17.3 mg THC: 16 mg CBD	Moderate
Lynch et al. 2014(USA)	Crossover	18/1656.0 (10.8), 17	18/1656.0 (10.8), 17	NR	Chemotherapy-induced neuropathic pain	THC:CBD spray	Placebo	6.0	21.6 mg THC: 20 mg CBD	Low
Markova et al. 2019(Czech Republic)	Parallel-arm	53/5351.3 (10.2)30	53/5351.3 (10.2)30	≥18 years	Multiple sclerosis	THC:CBD spray	Placebo	12.0	19.7 mg THC: 18.3 mg CBD	Low
Notcutt et al. 2012(UK)	Parallel-arm (withdrawal study)	18/1859.7 (9)50	18/1854.4 (10.4)33	NR	Multiple sclerosis	THC:CBD spray	Placebo	4.0	20.8 mg THC: 19.3 mg CBD	Very low
Nurmikko et al. 2007(UK)	Parallel-arm	63/6352.4 (15.8), 44	62/6254.3 (15.2), 37	≥18 years	Neuropathic pain	THC:CBD spray	Placebo	5.0	THC 29.7 mg: CBD 27.5 mg	High
***Pickering et al. 2011, 1(UK)¶	Crossover	5/467.0 (NR), 50	5/467.0 (NR), 50	40–74 years	COPD	THC:CBD spray	Placebo	.1	4.7 mg THC: 4.4 mg CBD	Low
**Pickering et al. 2011, 2(UK)¶	Crossover	6/558.0 (NR), 80	6/558.0 (NR), 80	40–75 years	Healthy controls	THC:CBD spray	Placebo	.1	10.3 mg THC: 9.5 mg CBD	Low
Portenoy et al. 2012, 1(Multicentre)‡	Parallel-arm	91/9159.0 (12.3), 49	91/9156.0 (12.2), 48	NR	Cancer patients with chronic pain	THC:CBD spray	Placebo	5.0	10.8 mg THC: 10 mg CBD	Moderate
Portenoy et al. 2012, 2(Multicentre)‡	Parallel-arm	88/8759.0 (13.1), 56	91/9156.0 (12.2), 48	NR	Cancer patients with chronic pain	THC:CBD spray	Placebo	5.0	27 mg THC: 25 mg CBD	Moderate
Portenoy et al. 2012, 3(Multicentre)‡	Parallel-arm	90/9058.0 (11.2), 53	91/9156.0 (12.2), 48	NR	Cancer patients with chronic pain	THC:CBD spray	Placebo	5.0	43.2 mg THC: 40 mg CBD	Moderate
Riva et al. 2019(Italy)	Parallel-arm	30/2958.4 (10.6)62	30/3057.2 (13.8)53	18–80 years	Motor neurone disease	THC:CBD spray	Placebo	6.0	21.6 mg THC: 20.0 mg CBD	High
Serpell et al. 2014(UK)	Parallel-arm	128/12857.6 (14.4), 34	118/11857.0 (14.1), 45	≥18 years	Neuropathic pain	THC:CBD spray	Placebo	14.0	24 mg THC: 22 mg CBD	Moderate
Strasser et al. 2006(Germany)§	Parallel-arm	95/9561.0 (12), 56	48/4862.0 (10), 52	Adult	Cancer-related anorexia	Cannabis extract	Placebo	6.0	5 mg THC: 2 mg CBD	Moderate
Vaney et al. 2004(Switzerland)	Crossover	57/5055.0 (10), 49	57/5055.0 (10), 49	Adult	Multiple sclerosis	Cannabis extract	Placebo	2.0	27.5 mg THC: 9.9 mg CBD	Low
Wade et al. 2004(UK)	Parallel-arm	80/8051.0 (9.4), 41	80/8050.0 (9.3), 35	NR	Multiple sclerosis	THC:CBD spray	Placebo	6.0	40.5 mg THC: 37.5 mg CBD	Moderate
Zajicek et al. 2003(UK)§	Parallel-arm	219/21151.0 (7.6), 36	222/21351.0 (7.6), 37	18–64 years	Multiple sclerosis	Cannabis extract	Placebo	14.0	25 mg THC: 12.5 mg CBD	Moderate
Zajicek et al. 2005(UK)§	Parallel-arm	138/13851.0 (7.6), 36	120/12051.0 (7.6), 37	18–64 years	Multiple sclerosis	Cannabis extract	Placebo	52.0	25 mg THC: 12.5 mg CBD	Moderate
Zajicek et al. 2012(UK)	Parallel-arm	144/14351.9 (7.7), 39	135/13452.0 (7.9), 35	18–64 years	Multiple sclerosis	Cannabis extract	Placebo	12.0	25 mg THC: 12.5 mg CBD	Moderate

**Studies recruited participants ≥50years.

*Median age (range).

||Article included the results of multiple trials.

‡Article included more than 1 dose level.

§Article included more than 1 cannabinoid intervention.

***Studies recruited participants ≥65 years.

¶Article included multiple study groups/indications.

CBD, cannabidiol; COPD, chronic obstructive pulmonary disease; GRADE, Grading of Recommendations Assessment, Development, and Evaluation; NR, not recorded; THC, delta-9-tetrahydrocannabinol; THCV, tetrahydrocannabivarin.

The formulations used in THC studies were (numbers within brackets indicating the number of RCTs where each formulation was used): nabilone (6), dronabinol (marinol) (14), THC (3), THC extract spray (2), and Namisol (5). The combination THC:CBD trials used THC:CBD spray (18), cannabis extract (6), and CBD/THCV (2). The CBD studies used CBD preparations only.

The disease conditions investigated were classified into broader subgroups for analysis purpose as neurodegenerative (Alzheimer disease, Parkinson disease, Huntington disease, amyotrophic lateral sclerosis), multiple sclerosis, motor neurone disease, pain (neuropathic pain), cancer (cancer or chemotherapy-related anorexia, pain, or nausea/vomiting), and others (type 2 diabetes mellitus, chronic obstructive pulmonary disease, fibromyalgia, raised intraocular pressure, cervical dystonia, healthy, pancreatitis, obstructive sleep apnoea, and Levodopa-induced dyskinesia in Parkinson disease).

Figs 2–14 show the forest plots and summary results of the meta-analyses stratified according to study design, for all-cause and treatment-related AEs and SAEs, withdrawals, deaths, for studies using THC, THC:CBD combination, and CBD, respectively.

Fig 2

Forest plot of all-cause adverse events: THC studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Fig 14

Forest plot of all-cause adverse events: CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Overall study quality GRADE [31] is reported in Tables 1–3 and Results in S1 Text. Risk of bias estimates are reported in Figs 15–19. CBD, cannabidiol; GRADE, Grading of Recommendations Assessment, Development, and Evaluation; IRR, incident rate ratio; RCT, randomised clinical trial.

Forest plot of treatment-related adverse events: THC studies.

Forest plot of all-cause serious adverse events: THC studies.

Forest plot of treatment-related serious adverse events: THC studies.

Forest plot of adverse event-related withdrawals: THC studies.

Numbers under the “Mean Age (yrs)” and “Withdrawals (n)” columns refer to the values in active and control intervention arms, respectively. The conditions listed are the disease conditions subgrouped into broader categories for meta-regression analyses purposes. They are: ND (dementia, Alzheimer disease, PD, Huntington disease, amyotrophic lateral sclerosis); MS; Cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting, chemosensory alterations); and Other (type 2 diabetes mellitus, fibromyalgia, raised intraocular pressure, cervical dystonia, healthy, pancreatitis, obstructive sleep apnoea). MS, multiple sclerosis; NA, Not Available; ND, neurodegenerative disease; PD, Parkinson disease; RCT, randomised clinical trial; RR, risk ratio; THC, tetrahydrocannabinol.

Forest plot of all deaths: THC studies.

Forest plot of all-cause adverse events: THC:CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CBD, cannabidiol; CE, cannabis extract; IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Forest plot of treatment-related adverse events: THC:CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CBD, cannabidiol; CE, cannabis extract; IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Forest plot of all-cause serious adverse events: THC:CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CBD, cannabidiol; CE, cannabis extract; IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Forest plot of treatment-related serious adverse events: THC:CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CBD, cannabidiol; CE, cannabis extract; IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Forest plot of all withdrawals: THC:CBD studies.

Numbers under the “Mean Age (yrs)” and “Withdrawals (n)” columns refer to the values in active and control intervention arms, respectively. The conditions listed are the disease conditions subgrouped for meta-regression analyses purposes are: MS; MND; pain (neuropathic pain, rheumatoid arthritis), cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting), diabetes mellitus, COPD, HC, levodopa-induced dyskinesia in PD. CBD, cannabidiol; COPD, chronic obstructive pulmonary disease; HC, healthy controls; MS, multiple sclerosis; MND, motor neurone disease; PD, Parkinson disease; RCT, randomised clinical trial; RR, risk ratio; THC, delta-9-tetrahydrocannabinol.

Forest plot of all deaths: THC:CBD studies.

Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CBD, cannabidiol; CE, cannabis extract; IRR, incident rate ratio; RCT, randomised clinical trial; THC, delta-9-tetrahydrocannabinol.

Fig 15

Risk of bias (THC studies).

Authors’ judgements about each risk of bias domain for each individual study.

Fig 19

Risk of bias (CBD studies).

Authors’ judgements about each risk of bias domain for each individual study.

THC studies

A total of 30 RCTs (15 crossover and 15 parallel-arm) from 28 articles [39-41,43-67] (see Results in S1 Text and Table A in S1 Text, for additional details), reported on 1,461 patients on active [analysed 1,417; Total person-years of THC exposure: 1252.83 person-years; Mean person-years of THC exposure (mean ± SD): 41.76 ± 184.28 person-years] and 1,251 (analysed 1,210) patients on control intervention, ranging from 50 to 87 years in mean age (males: 0% to 100%). All except 4 studies used placebo control [43,45,54,62].

Pooled IRRs for all-cause (k = 21) and treatment-related AEs (k = 9) from all RCTs were 1.42 (95% CI, 1.12 to 1.79) and 1.60 (95% CI, 1.26 to 2.04), respectively. Pooled IRRs for all-cause (k = 27) and treatment-related (k = 23) SAEs from all RCTs were 1.08 (95% CI, 0.80 to 1.46) and 1.23 (95% CI, 0.56 to 2.69), respectively. Pooled RR for AE-related withdrawals (k = 27) and IRR for all deaths (k = 30) from all RCTs were 1.18 (95% CI, 0.89 to 1.57) and 1.09 (95% CI, 0.75 to 1.59), respectively. Neither Egger test nor “Trim and fill” method indicated publication or other selection bias for any of the outcomes except for SAEs (Results in S1 Text and Fig A in S1 Text). For all-cause SAEs, while Egger test was nonsignificant, Trim-fill method identified 10 missing studies. The estimated effect of treatment on IRR for all-cause SAEs, which was not significant previously, became significant after inclusion of potentially missing studies identified by the Trim-fill method (1.46, 95% CI, 1.09 to 1.95, p = 0.01, k = 35). For treatment-related SAEs, Egger test indicated significant publication bias and Trim-fill method identified 5 missing studies, though they did not change the direction or significance of effect-size on inclusion. Where there was nonindependence of outcome data used in analyses, results of dependent meta-analyses were consistent with the results of independent meta-analyses (Results in S1 Text).

Effect of moderators

Meta-regression analyses indicate that there was a trend-level effect [QM (df = 4) = 9.986, p = 0.084] of clinical condition on estimated effect of THC treatment on all-cause AEs, which seemed to be mainly related to a significantly lower estimated effect in RCTs investigating neurodegenerative disorder (regression coefficient = 0.905; p = 0.006) patients compared to other conditions. Except that, moderators such as study design or type of intervention did not significantly influence estimated effects of THC treatment on any of the outcomes assessed.

Effect of dose

Meta-regression analyses also indicated that there was a significant effect of daily THC dose on all-cause AEs [QM (df = 1) = 5.024, p = 0.025] as well as on AE-related withdrawals [QM (df = 1) = 4.696, p = 0.03] for all RCTs indicating that higher the dose of THC the higher was the risk of all-cause AEs and risk of withdrawal (regression coefficient = −0.905; p = 0.006) from study in THC-treated patients compared to control treatment (Fig B in S1 Text). There was no significant association of daily THC dose with any of the other estimates (SAEs and deaths).

Common side effects

Pooled IRRs of the most commonly reported AEs (Table C in S1 Text) suggested significantly higher incidence rate of dry mouth, dizziness/light-headedness, mobility/balance/coordination difficulties, somnolence/drowsiness, euphoria, and male impotence in active compared to control arms.

Analysis of studies where all participants were ≥50 years

Restricting the meta-analysis to the 4 studies [39-41,65] that recruited participants with ≥50 years of age (total n = 136; analysed n = 126), results were broadly comparable to the primary analysis in the pattern of findings, though effect on all-cause AEs was no longer significant. The pooled IRRs for all-cause AEs (k = 3) were 2.13 (95% CI, 0.46 to 9.97) and treatment-related AEs (k = 3) was 2.80 (95% CI, 1.09 to 7.21), respectively. Pooled IRRs for all-cause (k = 4) and treatment-related SAEs (k = 2) were 1.20 (95% CI, 0.39 to 3.65) and 0.50 (95% CI, 0.03 to 7.99), respectively. Pooled RR for AE-related withdrawals (k = 4) and IRR for all deaths (k = 4) were 1.11 (95% CI, 0.49 to 2.55) and 0.58 (95% CI, 0.11 to 3.06), respectively (Fig C–H in S1 Text).

Analysis of studies where all participants were ≥65 years

Restricting the meta-analysis to only the 3 studies [39-41] that recruited participants with ≥65 years of age (total n = 58; analysed n = 50), effects were also broadly comparable to the primary analysis in the pattern of findings, with the exception of effect on all-cause AEs, which was no longer significant. The pooled IRRs for all-cause AEs (k = 3) were 2.13 (95% CI, 0.46 to 9.97) and treatment-related AEs (k = 2) was 2.80 (95% CI, 1.09 to 7.21), respectively. Pooled IRRs for all-cause (k = 3) and treatment-related SAEs (k = 2) were 1.80 (95% CI, 0.13 to 8.76) and 0.50 (95% CI, 0.03 to 7.99), respectively. Pooled RR for AE-related withdrawals (k = 3) and IRR for all deaths (k = 3) were 1.00 (95% CI, 0.23 to 4.41) and 0.42 (95% CI, 0.05 to 3.42), respectively.

THC:CBD combination studies

A total of 26 studies (5 crossover and 21 were parallel-arm; see Results in S1 Text and Table B in S1 Text for additional details) from 21 articles [42,46,48,49,68-84] reported on 1,965 patients [analysed 1,940; Total person-years of THC:CBD exposure: 394.29 person-years; Mean person-years of THC exposure (mean ± SD): 15.17 ± 28.20 person-years] on active and 1,887 (analysed 1,863) on placebo, ranging from 50 to 67 years in age (males: 0% to 80%). All studies used placebo as control. Two of the 26 included studies [80] investigated a combination of CBD and THCV, and we also examined the key effects after excluding these studies, which remained unchanged (please see Fig I–N in S1 Text). Results of meta-analysis for individual AEs after excluding these studies were identical to the results including all studies (as shown in Table D in S1 Text) and hence not shown.

Pooled IRRs for all-cause (k = 16) and treatment-related (k = 9) AEs from all RCTs were 1.58 (95% CI, 1.26 to 1.98) and 1.70 (95% CI, 1.24 to 2.33), respectively. Pooled IRRs for all-cause (k = 26) and treatment-related (k = 21) SAEs from all RCTs were 1.17 (95% CI, 0.99 to 1.39) and 1.19 (95% CI, 0.88 to 1.62), respectively. Pooled RR for AE-related withdrawals (k = 26) and IRR for all deaths (k = 26) from all RCTs were 1.40 (95% CI, 1.08 to 1.80) and 1.14 (95% CI, 0.89 to 1.46), respectively. Neither Egger test nor “Trim and fill” method indicated any significant effect of publication or other selection bias for any of the outcomes except for deaths (Fig O in S1 Text). For deaths as outcome, while Egger test was not significant, Trim and fill method indicated 9 missing studies, with the estimated effect becoming significant after their inclusion (1.33, 95% CI: 1.03 to 1.71; p = 0.027, k = 35). Where there was nonindependence of outcome data used in analyses, results of dependent meta-analyses were consistent with the results of independent meta-analyses.

Meta-regression analysis indicated that there was a significant effect of clinical condition on effect-size for treatment-related AEs [QM (df = 3) = 15.948, p = 0.01] and AE-related withdrawals [QM (df = 3) = 8.987, p = 0.029]. For treatment-related AEs, this was mainly related to a significantly higher effect-size in RCTs investigating pain conditions (regression coefficient = 0.393; p = 0.022) and those investigating other conditions (regression coefficient = 1.263; p = 002) compared to cancer conditions. For withdrawals, this was related to significantly higher effect-size in RCTs investigating pain conditions (regression coefficient = 0.816; p = 0.020) and multiple sclerosis (regression coefficient = 0.675; p = 0.035) compared to cancer conditions. Except these, moderators such as study design or type of intervention did not significantly influence estimated effects of THC:CBD combination treatment on any of the outcomes assessed.

There was a significant effect of daily THC dose [QM (df = 1) = 4.554, p = 0.033] on AE-related withdrawals (Fig P in S1 Text) and a trend-level effect on all-cause [QM (df = 1) = 2.899, p = 0.089] and treatment-related AEs QM (df = 1) = 3.016, p = 0.082]. Exploratory analyses suggested that there was also a significant effect of CBD dose (QM (df = 1) = 4.539, p = 0.033) on all-cause AEs (Fig Q in S1 Text) and a trend-level effect [QM (df = 1) = 3.145, p = 0.076] on treatment-related AEs but no significant effect on withdrawals. Effects of dose of both THC and CBD were such that the higher their dose, the higher was the effect of THC:CBD combination treatment on withdrawals and AEs (all-cause and treatment-related). Except these, daily THC or CBD dose did not have any significant influence on the effects of THC:CBD combination treatment on SAEs or death.

Pooled IRRs of the most commonly reported AEs (Table D in S1 Text) suggested significantly higher incidence rate of nausea, vomiting, dry mouth, dizziness/light-headedness, somnolence/drowsiness, disorientation, fatigue, and visual symptoms in active compared to control arms.

Restricting the meta-analysis to the 3 studies [42,68] that recruited participants with ≥50 years of age (total n = 60; analysed n = 52) was similar to the primary analysis in the pattern of findings, except for treatment-related AEs and AE-related withdrawals, which were no longer significant. The pooled IRRs for all-cause AEs (k = 3) and treatment-related AEs (k = 2) were 2.65 (95% CI, 1.49 to 4.71) and 4.73 (95% CI, 0.54 to 41.65), respectively. Pooled IRRs for all-cause (k = 3) and treatment-related SAEs (k = 3) were 1.00 (95% CI, 0.10 to 9.61) and 1.00 (95% CI, 0.10 to 9.61), respectively. Pooled RR for AE-related withdrawals (k = 3) and IRR for all deaths (k = 3) were 0.77 (95% CI, 0.18 to 3.22) and 1.00 (95% CI, 0.10 to 9.61), respectively (Fig R-W in S1 Text). None of these studies reported any SAEs (either treatment-related or all-cause) or death.

As analysable data were available from only 1 study, this analysis was not carried out.

CBD studies

Four studies (3 crossover and 1 parallel-arm RCT) from 3 articles [50,80,85] reported on 43 patients (analysed 40) on active and 44 (analysed 41) on placebo, with age ranging from 53 to 59 years [53% to 100% males; person-years of total CBD exposure: 6.60 person-years; Mean person-years of CBD exposure (mean ± SD: 1.10 ± 1.23 person-years)]. Pooled IRR for all-cause AEs for all RCTs was 1.02 (95% CI, 0.90 to 1.16), based on data available only from the 2 crossover studies reporting on 3 different dosage conditions (Fig 14; Fig X in S1 Text). There was limited data to allow quantitative synthesis of other outcomes; however, there were no treatment-related SAEs, withdrawals, or death reported (see Results in S1 Text for qualitative synthesis). No analysable data was available from studies where all study participants were ≥50 years of age.

Risk of bias

A summary of the risk of bias of included studies are presented in Figs 15–19. Briefly, most RCTs reported adequate randomisation sequence generation and concealment, outcome objectiveness, and masking of outcome assessors; however, some studies had high risk of bias because of potential for unmasking of participants and study personnel and selective reporting of the safety outcome. Most studies reported objective outcome assessments; however, only 60% of studies reported that outcome assessors had been appropriately blinded. A total of 45% of studies did selective reporting, i.e., they did not report data for all the safety outcomes (AEs and SAEs) in the trial and reported them when they occurred more than once or were more common or when they had occurrence above a certain threshold (1% to 10%). The authors judged 33 (55%) trials at low risk of bias, 20 (33%) trials at unclear risk of bias, and 7 (12%) trials to have high risk of bias for safety outcome reporting (Fig 15–19). Overall, 36 trials were judged to be of moderate quality, of which 15 (42%) trials reported all AEs and SAEs. Ten (56%) of moderate-quality trials of THC only intervention reported all AEs and SAEs.

Summary of risk of bias (THC studies).

Authors’ judgements about each risk of bias domain reported as percentages across included studies.

Risk of bias (THC:CBD studies).

Summary of risk of bias (THC:CBD studies).

Authors’ judgements about each risk of bias domain reported as percentages across included studies.

Discussion

In this systematic review and meta-analysis, we investigated the safety and tolerability of medicinal cannabinoids in older adults by pooling data from double-blind RCTs with a reported mean participant age of 50 years and over. We hypothesized that compared to control treatments, all 3 categories of CBMs will be associated with a greater incidence of AEs, but no greater incidence of SAEs, death, or risk of withdrawal from study. We also expected a direct relationship between the dose of THC used in THC-containing CBMs and the risk of adverse outcomes. We found that generally moderate to high-quality evidence (about 60% studies) suggests that as hypothesized, treatment with THC-containing medications (THC alone and THC:CBD combination) was associated on average with significantly higher incidence of all-cause and treatment-related AEs compared to control treatments. Further, consistent with our hypotheses, the average incidence rates of serious AEs (all-cause and treatment-related) and death were not significantly greater under CBMs compared to controls in studies using THC with or without CBD. However, contrary to expectation, significantly higher risk of withdrawal related to AEs was noted on average for studies using THC:CBD combination, though this was not observed in studies that used THC without CBD. In contrast, generally low-quality evidence (about 67% of studies) suggests that CBD alone may not significantly increase the incidence rate of all cause of AEs. Qualitative synthesis of data on treatment-related AEs, SAEs, deaths, and withdrawals from study also did not suggest any increase associated with CBMs containing CBD alone. In terms of relationship between THC dose and adverse outcomes, as hypothesized, we found a direct relationship between daily THC dose and all-cause AEs and AE-related withdrawals in THC studies and between THC dose and AE-related withdrawals in THC:CBD studies. Contrary to our expectations, we did not find any association between THC dose and the other outcomes investigated, such as SAEs and deaths. In addition, exploratory analysis showed that CBD dose also had a significant direct relationship with all-case AEs and a strong trend-level association with treatment-related AEs in RCTs using THC-CBD combination.

Additional analyses restricted to only those studies in which all participants were ≥50 years of age or ≥65 years of age, where this was feasible, indicated a pattern of findings broadly comparable with the results of our main analyses including all studies. However, the effect of CBMs containing THC but no CBD on all-cause AEs and CBMs containing THC:CBD on treatment-related AEs and AE-related withdrawals were no longer significant, which likely reflects the lower power of these analyses which included a maximum of 4 studies in any analysis.

Collectively, our results from studies that included participants with a mean age ≥ 50 years may suggest that older adults are at significantly greater risk of both treatment-related and all-cause AEs from CBMs containing THC, but not using CBMs without THC. Despite the greater risk of AEs, older adults receiving CBMs do not seem to be at a significantly greater risk of more serious consequences such as SAEs or death. Greater risk of withdrawal from study in those receiving CBMs containing THC:CBD combination but not in those containing THC or CBD without the other cannabinoid is an unexpected finding. While we did not compare these 2 effects in a systematic manner, if true, this may suggest that combination of these cannabinoids may be less acceptable in this age group, who already may be receiving multiple treatments for comorbidities. What may underlie these effects is much less clear. One may speculate that this may be a result of the generally higher THC dose employed in THC:CBD studies compared to the THC studies, which may have made participants in the former group of studies more susceptible to AEs and withdrawal as a result compared to those receiving THC. This is supported by approximately double the median dose of THC used in THC:CBD studies [median and interquartile range: 10.3(10.2 to 21.6) mg/day in crossover and 20.8(14.6 to 25) mg/day in parallel-arm RCTs] as opposed to THC studies [median and interquartile range: 5(2.25 to 9 mg/day in crossover and 10(4.8 to 25) mg/day in parallel-arm RCTs]. In accordance with this hypothesis, there was a nominally higher incidence of AEs in general in those studies using both cannabinoids in combination compared to THC studies. However, one cannot completely rule out the possibility that greater risk of withdrawal from study in those receiving CBMs containing THC:CBD combination but not in those containing THC or CBD without the other cannabinoid, as observed here, is purely by chance. Further, THC dose was significantly associated with withdrawal in both study groups using THC-containing medications. In contrast, CBD dose was only associated with AEs but not withdrawal in those using THC:CBD combination. However, whether these results suggest that lower acceptability of THC:CBD combination is a tolerability issue in older patients because of combination of cannabinoids rather than being related to the dose of CBD remains to be formally tested. It is also worth noting that the evidence base of studies that used CBMs without THC in older adults and found it to be well tolerated is relatively sparse. Whether this may underlie the absence of significantly greater risk of all-cause AEs as a result of treatment with CBMs without THC compared to controls, contrary to our expectation, remains to be tested. However, more generally, lack of significant AEs associated with CBD treatment alone in the present study is consistent with other analyses suggesting a good tolerability profile of CBD alone in younger age groups [86,87]. Nevertheless, collectively, these results suggest that although the incidence of side effects is greater with THC-containing products, CBMs investigated in these studies are generally safe. Their overall tolerability profile also seems to be generally acceptable to patients, though combination of THC and CBD may be less so. It is important to note that although the general pattern of results of analyses restricted to studies where all participants were ≥50 years of age or ≥65 years of age were broadly comparable with results from our main analyses of studies with mean participant age ≥50 years, they were not identical, indicating that effects of CBMs may be different when focused exclusively on adults over 65 or 75 years of age.

Although we did observe a significant association between THC dose and all-cause AEs in THC studies, this was not present in THC:CBD studies. Whether this reflects independent evidence that CBD may mitigate some of the adverse effects of THC, such as on cognition, behaviour, autonomic, and cardiovascular function [15,25-28] and therefore may have obscured any dose–response relationship with AEs in THC:CBD studies, remains unclear. The present study was not designed to formally test this hypothesis, which needs independent examination.

Finally, we identified a number of specific AEs associated with CBMs containing THC:CBD combination or THC without CBD. Of the specific AEs, significantly increased incidence of dizziness/light-headedness, drowsiness, disorientation, and impaired mobility/balance/coordination is worth noting, in light of higher risk of falls in this age group, which may be further exacerbated by these treatments [88,89].

Pooled estimates of all-cause AEs under both THC and THC:CBD and of treatment-related AEs and AE-related withdrawals under THC:CBD as reported herein need to be considered carefully in light of high levels of heterogeneity (discussed further below) in the studies as evident from the estimated I statistic (>32% in all these estimates). Therefore, we have also reported the prediction intervals along with the 95% confidence intervals in the forest plots for the key pooled estimates reported herein. These prediction intervals indicate that while the average effects of CBM treatments on these outcomes are significant (as evident from the 95% confidence intervals), the range of predicted effects across different study settings that may be observed in a new study may span a wider range of effect sizes than indicated by the 95% confidence intervals. Some of these predicted effect-sizes in new studies are likely to be <1, indicating that the risk of these outcomes with THC or THC:CBD treatment may not be higher than under control treatment condition.

Previous reviews of adverse consequences of treatment with CBMs have either been qualitative [12,19,20], did not specifically focus on older adults [16,17], or did not consider the effects of THC, CBD, or their combination separately [12,16,17]. Even in studies that have pooled the adverse event data quantitatively, this has been done based on specific formulation rather than on the basis of their cannabinoid content [16,17]. Further, they have generally reported summary effect-size estimates (such as odds ratio) that do not take into account person-years of treatment, an important consideration in quantitative synthesis of outcomes from RCTs [16,17]. As in previous reviews, we found that not all studies have published all the AEs and SAEs and that there is lack of evidence of the safety, tolerability, and acceptability specifically in older patients. Nevertheless, our findings are consistent with previous reviews which found dizziness to be the most common nonserious AE with CBM treatment [12,16,17]. These results are also consistent with a prospective observational study of 901 people above 65 years of age (74.5 ± 7.5 years), who received medical cannabis from January 2015 to October 2017 in a specialized medical cannabis clinic, 31.7% reported at least 1 AE due to the treatment after 6 months, with dizziness (9.7%) and dry mouth (7.1%) as the commonest AEs [21]. Another similar study of 184 people (81.2 ± 7.5 years of age) from April 2017 to October 2018 showed 33.6% with AEs and dizziness (12.1%) and sleepiness and fatigue (11.2%) as the commonest AEs [90]. A previous systematic review of the efficacy and safety of medicinal cannabinoids which focused only on older people considered 5 controlled trials with THC (n = 3) and oral THC:CBD (n = 2) [12]. However, this review did not provide summary estimates (effect sizes) due to high heterogeneity among the included studies, lack of available data on means and standard deviations per treatment group, and very small sample sizes [12]. Therefore, only qualitative and descriptive summaries were provided. Studies included in this review reported dizziness, euphoria, drowsiness, confusion, and disorientation as the common adverse effects [12], consistent with our report. An earlier systematic review of AEs of medicinal cannabinoids for all ages by Wang and colleagues [17] included 23 RCTs published between 1966 and late 2007 and analysed the effect of oral THC and THC:CBD oral and spray formulations. However, they did not include data from studies examining the synthetic cannabinoid nabilone and found no evidence of higher incidence of SAEs after a median of 2 weeks use compared with a control group, regardless of the age of individuals (RR 1.04, 95% CI 0.78 to 1.30). Wang and colleagues [17] reported that respiratory, gastrointestinal, and nervous system disorders were the most frequently reported categories of SAEs, and dizziness was the most commonly reported nonserious AE (15.5%) among people exposed to cannabinoids. However, unlike the present review wherein we have used GRADE criteria to assess methodological quality of included RCTs, Wang and colleagues used a scale reported in a manuscript by Jadad and colleagues [91] to assess methodological quality of RCTs. As the scale from Jadad and colleagues does not adequately assess the quality of safety reporting in RCTs, most studies were rated as of good quality in the review by Wang and colleagues, despite their poor reporting of safety [17].

However, our results are not consistent with a more recent systematic review and meta-analysis by Whiting and colleagues [16], which included studies investigating all age groups and showed that SAEs and AE-related withdrawal are generally higher in those treated with CBMs. In contrast, we have shown that SAEs are not increased in older adults consistent with other reviews [12,17]. Whiting and colleagues [16] found pooled effect-size for any AE (OR 3.03, 95% CI 2.42 to 3.80), SAE (OR 1.41, 95% CI 1.04 to 1.92), and withdrawal due to AE (OR 2.94, 95% CI 2.18 to 3.96), and Wang and colleagues [17] found significantly higher nonserious AEs (RR 1.86, 95% CI 1.57 to 2.21) in those who received CBMs compared to control groups. In contrast to Whiting and colleagues, we have shown that increase in withdrawal may be more nuanced and more in those receiving THC:CBD combination and not for other types of CBMs.

It is difficult to compare our pooled effect-size with previous reviews due to a number of reasons. Firstly, Whiting reported odds ratios and Wang and collagues reported relative risk, wherein we have reported IRR. In the present context, the risk of AEs or SAEs in either study arm are unlikely to be constant over time. Therefore, IRR, which takes into consideration person-years of treatment and is a ratio of the incidence rate in the experimental treatment group to that in the control treatment group, is more meaningful and appropriate in contrast to the odds ratio, which is a ratio of the odds of an event in the experimental group to that in the control group and are not easily interpretable. Secondly, we carried out a pooled analysis for individual interventions unlike the other reviews, with Wang and colleagues also excluding studies using Nabilone [17]. Finally, the 2 previous reviews that reported summary estimates included all age groups and were not specific for older people unlike here.

Our results extend previous literature by showing that although at significantly higher risk of adverse events from CBMs containing THC, but not from those without THC, CBMs are generally safe in older adults, who typically experience comorbid health conditions [92] and receive polypharmacy [93]. Also, CBMs are generally acceptable as long as they are not a combination of THC and CBD.

Results presented here may also need to be considered against the side effect profile of common treatments for the clinical conditions investigated in studies included in the meta-analyses reported here, as safety and tolerability profile of a potential new treatment relative to existing alternatives is an important consideration in the context of prescribing. Studies included patients with Alzheimer disease, Parkinson disease, Huntington disease, amyotrophic lateral sclerosis, multiple sclerosis, motor neurone disease, neuropathic pain, cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting), type 2 diabetes mellitus, chronic obstructive pulmonary disease, fibromyalgia, raised intraocular pressure, cervical dystonia, pancreatitis, and obstructive sleep apnoea. As would be expected, these conditions are typically treated with a wide range of pharmacological treatments (opioid analgesics, nonsteroidal anti-inflammatory, antiepileptics, benzodiazepines, psychotropics, cholinesterase inhibitors, glutamate antagonists, dopamine agonists, immunosuppressants, muscle relaxants, etc.) with a varied side effect profile. While a systematic comparison of side effect profile of CBMs and these other treatments would be useful and can helpfully inform prescribing decisions, this was outside the scope of the present review and was not examined here. Therefore, future studies may need to investigate this in a systematic manner. As we pooled data from randomised controlled comparisons, as opposed to observational studies without a control arm, they are very unlikely to have confounded the results presented herein.

Strengths and limitations

Our review and meta-analysis are limited by a number of methodological weaknesses, some of which stem from the design and analytic approach of the present study and others which are related to weaknesses in the studies that were included in the present analysis. One of the key limitations inherent in the meta-analytic approach that also applies to the present study relates to the issue of heterogeneity in the pooled data [94]. Studies pooled in our analyses focused on patients with different clinical conditions and indications, used different doses, formulations, and routes of administration of the study drug for different treatment periods and employed different study designs (crossover versus parallel-arm RCTs). Further, although we attempted to control for patients from widely disparate age groups taking part in the trial by setting an inclusion criterion of mean age of 50 years and over as the cut-off, studies included in our main analyses still involved data from people below age 50 and studies also varied in terms of sex distribution. This is reflected in the heterogeneity estimates reported in our results. In order to address this wide-ranging heterogeneity and yet pool the data in a meaningful way, we employed a random-effects model in our analyses, which assumes that there will be variability in the observed estimates of treatment effects across studies, both as a result of real differences in the effect of treatment between studies as well as by chance because of sampling variability. As a result, pooled estimates reported are not precise (as may be evident from the wide confidence intervals) and should not be considered as such. Effects reported here represent an average effect of CBM treatments on safety and tolerability outcomes investigated rather than an effect that is common across studies. As the effects may be different within an individual study, we also report a prediction interval for our key reported outcomes, in order to give an estimate of the range of predicted effects across study settings that may be observed in a new study. Further, we also carried out subgroup (e.g., crossover and parallel-arm RCTs separately) and meta-regression analyses to examine the sources of heterogeneity for THC and THC:CBD studies. These analyses did not suggest that study design or type of intervention had any significant effect on the outcomes assessed, though they suggest that effects vary, sometimes significantly, across clinical conditions investigated in the RCTs. They were more pronounced in AE-related withdrawals and treatment-related AEs in THC:CBD studies and less so for all-cause AEs in studies investigating THC as a treatment. Dose of study drug also seemed to underlie some of the heterogeneity observed in all-cause and treatment-related AEs and AE-related withdrawals in the THC and THC:CBD studies. Another limitation relates to the fact that we were not able to systematically examine the sources of heterogeneity for CBD alone studies as there were fewer studies than recommended for such analyses. One other limitation of the present meta-analysis relates to our focus on studies reporting on participants aged 50 years and over.

Further, we included studies in which the mean age of study participants was ≥50 years (although the studies also included many participants who were <50 years of age). As the cut-off employed by us differs from the conventional threshold of 65 years for “elderly” [95], this may be considered as a limitation. However, this was chosen as the clinical conditions (diabetes, cancer, neurodegenerative disorders, etc.) for which CBMs are often considered become more common from around this age. This is also a period characterised by multimorbidities, polypharmacy, and age-related bodily changes that may affect pharmacokinetics [95] and tolerability of medications. Further, in order to address the limitation that many participants in the included studies were <50 years of age, we also carried out sensitivity analysis that included studies where all participants were ≥50 years of age. The results of these analyses suggest that the pooled effect-sizes were generally in the same direction as that reported from the larger set of studies, though the confidence intervals were wider, as may be expected. We have also reported the number of studies which have actually studied individuals with age ≥65 years or ≥75 years. As evident from this, there is a very limited set of studies that have exclusively focused on people at these ages. Therefore, the present meta-analysis highlights the need for studies that may need to focus on people over 65. However, given the age range as well as median and interquartile range of the mean ages of study participants included in the studies that constitute our meta-analysis, it is clear that people over 65 and 75 years are currently being recruited into studies of CBMs for various indications. As individual RCTs are often not powerful enough to unravel patterns of side effects and given the growing use of CBMs in the elderly and general perception of them being safe, it is particularly important to summarize currently available evidence to help inform about the safety and tolerability profile of CBMs in those aged 50 years and over rather than wait for the evidence base focusing only on ≥65 years to mature. In the fullness of time, future attempts at evidence synthesis need to focus only on studies of people ≥65 years when a sufficient number of studies have accumulated.

The other main source of limitation stems from methodological limitations in the included trials as identified during quality assessment [30,31], specifically pertaining to selective outcome reporting, and inadequate description of methods of randomization, allocation concealment, and blinding. Additionally, many included RCTs investigated only modestly sized samples [41,42,44,50,57]. Small samples render studies particularly underpowered when estimating serious and less serious adverse outcomes, which by their nature may not occur frequently. It is in this context that the present report addresses an important gap in extant evidence by systematic quantitative synthesis of RCT data following existing recommendations [30,31] to provide estimates from a larger pool of patients. Further, our analyses suggest that publication or other selection biases are unlikely to have influenced the pooled estimates reported here.

Unlike in previous meta-analyses, which reported summary effects separately based on indications, we pooled safety and tolerability data in older adults across a broad range of indications. While this may have added to the heterogeneity of the data synthesized, it allowed us to comprehensively estimate separately the effects of 3 broad categories of cannabinoid-based interventions, i.e., THC only, THC:CBD combination, and CBD only, something that has not been done before. This is a key strength of the present approach, given the reported opposite effects of different cannabinoids [4,15] that argue against data being combined. Another important strength of the present report relates to the analysis of the effects of moderators to examine the extent to which they may have influenced results, in particular relationship with cannabinoid doses used.

Studies evaluated various routes of CBM administration (oral capsules, tablets, sublingual spray, oromucosal spray). Also, not all studies compared CBMs with placebo, with 4 studies using active control treatments [43,45,54,62]. While all of these may have resulted in a very heterogeneous set of included studies, we used a random-effects model to mitigate these effects. Further, heterogeneity did not seem to significantly affect any of our estimates other than all-cause AEs, giving further confidence in the results reported. Nevertheless, we have also reported prediction intervals in addition for our key reported outcomes to give an estimate of the range of predicted effects across different study settings. Finally, another important potential limitation of the present study relates to the fact that we did not investigate the efficacy of CBMs in older adults. As outcome measures used to index efficacy vary widely between clinical conditions and there is a relative paucity of studies investigating a particular clinical condition, there are not enough data for any quantitative synthesis of efficacy of different CBMs in older adults to be meaningful just yet.

Clinical efficacy is one of the foremost considerations in addition to patient choice and safety/tolerability of interventions when prescribing in clinical practice. While the present study summarizes current evidence regarding safety/tolerability of CBMS, there is limited efficacy evidence for most clinical indications for which CBMS have been used in older people. Therefore, there is a pressing need for efficacy studies in specific indications where there is proof of concept or rationale for use of CBMs in older people. With regard to CBMs, potential for drug–drug interaction in light of effect on cytochrome p450 enzymes is a major concern in the context of treating older patients [13]. However, few studies have examined this, an important likely determinant of tolerability and dose adjustment, and therefore worthy of investigation in future studies.

Complete reporting of safety/tolerability data as well as improved trial designs incorporating robust methods for allocation concealment, masking of participant and outcome assessors are further important considerations for future trials. Using well-powered samples, such studies need to focus on safety, tolerability, as well as efficacy of different categories of CBMs, in particular CBD on its own, a relatively less investigated CBM in older people.

Conclusions

Results of the present study using data from RCTs with mean participant age ≥50 years suggest that although THC-containing CBMs are associated with side effects in those aged 50 years and over, in general, CBMs are safe and acceptable treatments in older adults, with a caveat that THC:CBD combinations may be less so at least in dose ranges used in studies thus far. However, tolerability may be different in adults over 65 or 75 years of age, and robust evidence of efficacy of different CBMs for specific indications is needed before they may be used in routine practice in older adults.

PRISMA checklist.

(DOC)

Click here for additional data file.

Supporting Methods, Results, Tables and Figures.

Table A in S1 Text. Summary of randomised controlled trials of THC in older adults for studies with participants with mean age ≥50 years (N = 30) and studies with participants with age ≥50 years (N = 4, 13%; one ≥50 years; two ≥65 years; one ≥75 years). Table B in S1 Text. Summary of randomised controlled trials of THC:CBD combination in older adults for studies with participants with mean age ≥50 years (N = 26) and studies with participants recruited with age ≥50 years (N = 3, 11.5%; two ≥50 years; one ≥65 years). Table C in S1 Text. Summary estimates (incident rate ratio, IRR) from meta-analysis for the most commonly reported adverse events (AEs): IRR of participants experiencing AE with cannabinoid (THC) compared to placebo or active control condition. Table D in S1 Text. Summary estimates (incident rate ratio, IRR) from meta-analysis for the most commonly reported adverse events (AEs): IRR of participants experiencing AE with cannabinoid (THC:CBD combination) compared to placebo or active control condition. Table E in S1 Text. Characteristics of unpublished randomised trials of cannabinoids in older adults. Fig A in (a) All-cause adverse events (AEs); (b) Treatment-related AEs; (c) All-cause serious adverse events (AAEs); (d) Treatment-related SAEs; (e) AE-related withdrawals; (f) deaths. Fig B in S1 Text. THC dose-related withdrawals in THC studies. Fig C in S1 Text. Forest plot of all-cause adverse events: THC studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Fig D in S1 Text. Forest plot of treatment-related serious adverse events: THC studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Fig E in S1 Text. Forest plot of all-cause serious adverse events: THC studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Fig F in S1 Text. Forest plot of treatment-related serious adverse events: THC studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Fig G in S1 Text. Forest plot of adverse event-related withdrawals: THC studies (participants with ≥50 years of age). Numbers under the “Mean Age (yrs)” and “Withdrawals (n)” columns refer to the values in active and control intervention arms, respectively. The conditions listed are the disease conditions subgrouped into broader categories for meta-regression analyses purposes. They are: neurodegenerative disorders (ND) (dementia, Alzheimer disease, Parkinson disease (PD), Huntington disease, amyotrophic lateral sclerosis); multiple sclerosis (MS); Cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting, chemosensory alterations); and Other (type 2 diabetes mellitus, fibromyalgia, raised intraocular pressure, cervical dystonia, healthy, pancreatitis, obstructive sleep apnoea). Fig H in Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. Fig I in Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig J in S1 Text. Forest plot of treatment-related adverse events: THC:CBD studies (excluding THCV). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig K in S1 Text. Forest plot of all-cause serious adverse events: THC:CBD studies. (excluding THCV). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig L in S1 Text. Forest plot of treatment-related serious adverse events: THC:CBD studies (excluding THCV). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig M in Numbers under the “Mean Age (yrs)” and “Withdrawals (n)” columns refer to the values in active and control intervention arms, respectively. The conditions listed are the disease conditions subgrouped for meta-regression analyses purposes are: multiple sclerosis (MS); motor neurone disease (MND); pain (neuropathic pain, rheumatoid arthritis), cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting), diabetes mellitus, chronic obstructive pulmonary disease (COPD), healthy controls (HC), levodopa-induced dyskinesia in Parkinson disease (PD). Fig N in Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig O in (a) All-cause adverse events (AEs); (b) Treatment-related AEs; (c) All-cause serious adverse events (AAEs); (d) Treatment-related SAEs; (e) AE-related withdrawals; (f) deaths. Fig P in S1 Text. THC dose-related withdrawals in THC:CBD studies. Fig Q in S1 Text. CBD dose-related all-cause AEs in THC:CBD studies. Fig R in S1 Text. Forest plot of all-cause adverse events: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig S in S1 Text. Forest plot of treatment-related adverse events: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig T in S1 Text. Forest plot of all-cause serious adverse events: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig U in S1 Text. Forest plot of treatment-related serious adverse events: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig V in S1 Text. Forest plot of all withdrawals: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Mean Age (yrs)” and “Withdrawals (n)” columns refer to the values in active and control intervention arms, respectively. The conditions listed are the disease conditions subgrouped for meta-regression analyses purposes are: multiple sclerosis (MS); motor neurone disease (MND); pain (neuropathic pain, rheumatoid arthritis), cancer (cancer or chemotherapy-related anorexia, pain or nausea/vomiting), diabetes mellitus, chronic obstructive pulmonary disease (COPD), healthy controls (HC), levodopa-induced dyskinesia in Parkinson disease (PD). Fig W in S1 Text. Forest plot of all deaths: THC:CBD studies (participants with ≥50 years of age). Numbers under the “Subjects (n)” column refer to analysed participants from the active and control intervention arms, respectively. CE refers to cannabis extract. Fig X in S1 Text. Funnel plot for all-cause adverse events (AEs): CBD studies.

(DOCX)

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9 Oct 2020

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Dear Dr. Bhattacharyya,

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Comments from Academic Editor:

1. Page 4: The authors write "THC is addictive(4)...". Please use a more specific word or set of words in lieu of the word "addictive" -- are the authors referring to physiological dependence? Psychological dependence? Both? Compulsive use despite negative consequences? Something else?

2. Page 4: Same as above with the statement "CBD is non-addictive(5)..."

3. Page 6: As with R2, I have questions about the focus of this review to studies of "older adults", defined as studies with "mean age >=50 years". This inclusion criterion is based on an individual-level variable, ie, the authors did not restrict their meta-analysis to studies in which the age cutoff for participation was age >=50 years. Thus, many of the studies included in this meta-analysis involve much younger individuals. For example, the study by Curtis et al. 2009 had a mean age of 52 years (SD, 9.5 years), thus assuming the distribution of ages was normal we would expect approximately 16% of participants were <42 years of age. The study by Lane et al. 1991 appears to include study participants with an age range of 22-64 years. There are other examples. The authors seem to be aware of this distinction, given that in the Supplementary Appendix they write (for example): "These results are also consistent with a prospective observational study of 901 people above 65 years of age (74.5 ± 7.5 years)..."-- in that sentence it is clear that the study restricted enrollment to people >65 years of age and that the mean age of study participants was also >65 years. Yet on Page 14 the authors cite their specific focus on older-age people to distinguish their meta-analysis from two previously published meta-analyses (refs #15 and #16). The JAMA paper does not AFAIK report mean ages, but in the CMAJ paper, the mean ages for most of the included studies appear to be roughly in the same range as those included in the current meta-analysis.

To fix this, I think the authors need to do the following:

a) I think the authors should consider restricting this meta-analysis to studies where age >=50 years was one of the inclusion criteria.

If the authors decide not to do the above, and provide reasonable justification for not doing so, then I think the authors need to do the following:

b) Make it clear in the Methods and Discussion that their inclusion criterion was *mean* age >=50 years. There are places in the manuscript where this is not always clear. For example, on page 15, the authors write: "Our use of a lower cut-off age of 50 years to define the target population..." This sentence somewhat obscures the limitation here. A more precisely worded sentence would be something like "We included studies in which the mean age of study participants was >=50 years (although the studies included many participants who were <50 years of age), and this cutoff differs from the conventional threshold of 65 years..."

c) The tables should split the rows into two categories: "Trials in which enrollment was restricted to age >=50 years" and "Trials in which enrollment was not restricted to age >=50 years, but the mean age of study participants was >=50 years".

d) If adequately powered, subgroup meta-analyses for the two categories above should be conducted (and reported in the main text if substantively different from the primary analysis; or perhaps reported in an Appendix if the pattern of findings is largely similar to the primary analysis). If subgroup analyses are not possible, the Results text should at least identify the number of RCTs and patients in which enrollment was restricted to age >=50 years.

e) A column should be added to the tables showing the age cutoff for enrollment (eg., >18 years, >40 years, "none", etc)

f) As suggested by R2, the Results text should identify the number of RCTs and patients in which enrollment was restricted to age >=65 years, and/or >=75 years. (If there were no trials in which

g) The same modifications to the tables should be done for the Appendix Table 1. For example Appendix Tables 2A and 2B should report the N (%) of studies that were restricted to age >=50 years and age >=65 years.

4. Page 8: The authors report that the mean age of treatment-arm study participants was "mean± SD: 44.39±190.52 person-years". How did they calculate the pooled mean age here? This detail is not reported in the Methods. They report pooled mean ages later on in the manuscript as well, eg page 11, Appendix Table 2A, etc.

5. On a related note, the authors report other pooled mean estimates (eg., Appendix Table 2A, pooled mean duration of study in weeks)-- how are these estimated?

6. Figures 2A-2G: Figure 2A reports very high levels of heterogeneity (I2>73%), but Figures 2B-2F report no heterogeneity at all (I2=0%). Is this a typo? The patterning of the figures does not seem consistent with I2's of 0%.

a) Given the high levels of heterogeneity (reported in Figure 2A at least), I would suggest including prediction intervals:

https://link.springer.com/article/10.1007/s10654-012-9738-y

b) The same modifications should be done for Appendix Figure 1A & Appendix Figures 8A-8F.

c) I think the authors should also include a more detailed discussion of the heterogeneity, which is only mentioned in passing on page 15. Some would argue that such a high degree of heterogeneity would suggest _not_ pooling the data using meta-analysis:

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2758470

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2758468

7. Figures 2G & 3G: The studies are only identified with numbers, but on first glance it is not clear what these numbers are referring to. These numbers are not listed in the other tables, obscuring the studies. I don't see the studies identified until I scrutinize the footnotes to Figures 2 & 3.

a) The row headers for Figures 2G & 3G should instead identify the study the same way they are identifed in the other figures (eg, "Notcutt et al., 2012"). If the authors are worried about horizontal space, I would suggest formatting the figure in landscape mode.

b) In addition, the column headers for Figures 2G & 3G would benefit by simply stating the domain names (eg, "random sequence generation") instead of the abbreviation (eg., "D1"). Yes, the footnote does connect "D1" to "random sequence generation" but it is an additional cognitive step for readers. If the authors are worried about horizontal space in the column headers, they could orient the text at 90 degrees or 45 degrees.

c) The same modifications should be done for Appendix Figure 1B.

8. Appendix Table 3B & Table 4: The authors report a pooled estimate for the "immune system disorder" subgroup but the k=1-- is this a typo?

Comments from the reviewers:

*** Reviewer #1:

I confine my remarks to statistical aspects of this paper. These were generally fine. I have one request and some comments on the figures.

First, please give an effect size whereever you give a p value (e.g. you don't do this in a couple spots on p. 11).

For the forest plots, I would combine the active and control colums (e.g 21/21) and then abbreviate the drug (D M, T, etc( to save space. Then expand teh actual graph portion. I also wonder how the numbers at teh bottom (0.02, 0.14 ...) were chosen rather than something more standard like 0.01, 0.05 etc.

But, generally, very well done

Peter Flom

*** Reviewer #2:

This is an excellent systematic review and meta-analysis on the safety and tolerability of natural and synthetic cannabinoids in older adults.

Minor comments:

Since life expectancy have increased considerably over the past and people in the age of 50 years are nowadays commonly rather fit, the reader wonders why this age was chosen for the identification of older adults? The authors briefly address this in their discussion, however, one wonders whether other factors might have determined their choice of this age (≥ 50 years), e.g. that they would not have collected enough RCTs for this meta-analysis. Thus, they could give a little more transparency whether they would have recruited enough RCTs and patients if the cut-off in age ≥ 65 years would have been chosen.

For this reason, they should clearly state the age ≥ 50 years in their final conclusions (in the abstract and end of discussion) and state that results of this meta-analysis might be different in the age group of ≥ 65 years or even ≥ 75 years.

It is somewhat mysterious that the THC:CBD combination but not THC or CBD alone significantly increased the risk of AE-related withdrawals and the authors do not give a good explanation. Might this result be out of pure chance? The authors should give some explanation for this phenomenon?

In their supplemental discussion the authors state "Firstly, Whiting reported odds ratios and Wang et al reported relative risk, wherein we have reported IRR which takes into consideration person-years of treatment and hence is more meaningful." The authors should address this more thoroughly, explain the differences and clinical consequences in the estimated risk to the reader in the discussion of the manuscript and not only in the supplemental material.

*** Reviewer #3:

Thank you for the opportunity to review this manuscript. The authors make a sound case for the need to determine the safety and tolerability of CBMs among older populations. My read of the review process and analysis seems appropriate, however I would encourage having a statistical expert in meta-analysis techniques to also review this paper. My comments are primarily around the positioning of the review, and how the results are contextualised.

On page 5, there is reference made to growing evidence that THC and CBD may have 'opposing effects on certain organ systems' - this should be elaborated on, both in terms of outlining what this means, and what its consequences are for patients who may use different forms of CBMs. It is also noted on page 4 that 'THC is addictive' while CBD is 'non-addictive' - I think there are many who would contest this statement. THC has largely been known as the cannabinoid that psychoactive, however there is extensive literature that maintains that cannabinoids are not addictive. If this is a statement that the authors hold, there should be a great discussion and engagement with why particular strains of cannabinoids are addictive. Giving more context to both the opposing effects on certain organ systems, as well as the debate over the different natures of CBD vs THC would help to better foreground the rationale for hypothesis i.

While it RCTs are privileged in meta-analysis reviews, it would be good if the authors could also make reference to the amount of literature that is being published such as open-label trials, n-of-1 studies, as well as observational studies where CBMs and their safety are being observed. While these methods certainly don't have the same level of rigour or control, it is worth noting, particularly in terms of how these studies align (or disagree) with the results presented in this review.

On page 6, it would be useful to give a brief overview of the broader subgroups of disease conditions. Also, if possible and to provide context, it would be useful at some point in the article to outline what the common treatments (particularly pharmaceutical) are used for these conditions, and whether treatment side effects differ greatly compared to those reported in this review.

It would be useful at the beginning of the discussion to re-state the hypotheses, and more clearly align the findings of the review to each of them. As noted, there were some findings that were contrary to predictions, and while there is some speculation for the cause of this difference, more literature could be drawn on to make sense of the finding.

There are a number of sentences throughout that read as incomplete or require a re-write that could be part of the revision process.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

25 Nov 2020

Submitted filename: Response to reviewers 25.11.2020, final.docx

Click here for additional data file.

9 Dec 2020

Dear Dr. Bhattacharyya,

Thank you very much for re-submitting your manuscript "Safety and tolerability of natural and synthetic cannabinoids in older adults: a systematic review and meta-analysis" (PMEDICINE-D-20-04864R2) for consideration at PLOS Medicine.

I have discussed the paper with our academic editor and it was also seen again by two reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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------------------------------------------------------------

Requests from Editors:

Were there any departures from the study protocol?

Please consider adapting the title from "... older adults" to "... adults aged over 50 years".

We would suggest also specifying the relevant age range in the first subsection of your abstract.

Please make that "data were" in your abstract.

Please adapt your abstract so that the summary of limitations is restricted to a single sentence, beginning "Study limitations include ..." or similar and falling at the end of the "Methods and findings" subsection. Please do not include mitigating arguments in this sentence.

Please adapt the author summary so that one or two items use the active voice, e.g. "We analysed pooled data ...". Please address "that that".

Please trim and reorganize the author summary so that it consists of up to 3 points per subsection, with each point consisting of no more than 1-2 short sentences.

Early in the Discussion section, please adapt "We predicted ..." to "We hypothesized ..." or similar. You mention "predictions" elsewhere in the same section of the ms, and in your Introduction, and we suggest rewording here too.

Please revisit "gender", used in the Discussion section and perhaps elsewhere, substituting "sex" where appropriate.

Noting references 30 & 67, for example, please ensure that information on competing interests and so on is removed from all relevant citations.

Please adapt the title of fig 1 to "Study disposition" or similar.

RevMan permitting, please avoid "p=0.000" in fig 2a.

Please remove the draft paper included as an attachment.

Comments from Reviewers:

*** Reviewer #1:

The authors have addressed my concerns and I now recommend publication.

Peter Flom

*** Reviewer #2:

Excellent revision of manscript. No more comments!

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

11 Dec 2020

Submitted filename: Author Response_revision 2.docx

Click here for additional data file.

15 Dec 2020

Dear Dr Bhattacharyya,

On behalf of my colleagues and the Academic Editor, Dr Tsai, I am pleased to inform you that we have agreed to publish your manuscript "Safety and tolerability of natural and synthetic cannabinoids in adults aged over 50 years: a systematic review and meta-analysis" (PMEDICINE-D-20-04864R3) in PLOS Medicine.

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Senior Editor, PLOS Medicine

rturner@plos.org