Is cannabis associated with bipolar disorder?

Author
Affiliation

Bruno Braga Montezano

Federal University of Rio Grande do Sul

Published

October 10, 2023

Methods

In order to summarize the exposure variables, we used a descriptive table with absolute and relative frequencies of the categorical variables and presented mean with standard deviations and median with minimum and maximum values for numeric variables. The bivariate analyses were performed with Student’s \(t\)-tests, \(\chi\)-squared tests, Fisher’s exact test and Mann-Whitney \(U\) tests depending on the variables distributions.

We built binomial logistic models to assess the effects for bipolar disorder incidence at 22 years old in the studied exposures, controlling for the other variables. Considering multicollinearity in regression analysis can be a problem since variables wouldn’t provide independent or unique information, we used variance inflation factor (VIF) to measure the correlations between the predictors in the model (Fox et al., 1992). We considered a VIF of 4 or greater as threshold to classify a predictor estimate as non-reliable.

All analyses were conducted through scripts written in the R programming language (version 4.3.1). Additional information on the present R session is available at the end of the page.

Results

How is the bipolar disorder (BD) outcome distributed?

Eighty-seven participants (2.3%) were diagnosed with bipolar disorder at follow-up, of which, 76 (2.04%) had a diagnosis for bipolar disorder type I and 11 (0.3%) for bipolar disorder type II.

Firstly, we’ll create the main descriptive table

For the \(p\)-values, chisq.test.no.correct was used for categorical variables with all expected cell counts \(\geq 5\), and fisher.test for categorical variables with any expected cell count \(< 5\).

Characteristic Overall, N = 3,7121 No, N = 3,6251 Yes, N = 871 p-value2
BD subtype <0.001
    NOS 68 (1.8%) 68 (1.9%) 0 (0%)
    Not BD 3,557 (96%) 3,557 (98%) 0 (0%)
    Type 1 76 (2.0%) 0 (0%) 76 (87%)
    Type 2 11 (0.3%) 0 (0%) 11 (13%)
Sex 0.7
    Female 1,968 (53%) 1,920 (53%) 48 (55%)
    Male 1,744 (47%) 1,705 (47%) 39 (45%)
Skin color <0.001
    Non-white 1,263 (36%) 1,219 (36%) 44 (54%)
    White 2,219 (64%) 2,181 (64%) 38 (46%)
    Unknown 230 225 5
Socioeconomic status at 18 years old (1: poorest; 5: wealthiest) 0.022
    1 671 (20%) 645 (19%) 26 (32%)
    2 672 (20%) 655 (20%) 17 (21%)
    3 685 (20%) 668 (20%) 17 (21%)
    4 706 (21%) 695 (21%) 11 (14%)
    5 703 (20%) 693 (21%) 10 (12%)
    Unknown 275 269 6
Physical abuse by parents (at 11 years old) 2,203 (63%) 2,147 (63%) 56 (68%) 0.3
    Unknown 229 224 5
Cannabis use (at 11 years old) 7 (0.2%) 6 (0.2%) 1 (1.3%) 0.15
    Unknown 275 264 11
Cannabis use (at 15 years old) 48 (1.4%) 48 (1.5%) 0 (0%) 0.6
    Unknown 377 367 10
Lifetime cannabis use (at 18 years old) 715 (21%) 690 (21%) 25 (33%) 0.014
    Unknown 382 371 11
Cannabis use frequency (at 18 years old) 0.014
    Never 2,615 (79%) 2,564 (79%) 51 (67%)
    Experimented 377 (11%) 365 (11%) 12 (16%)
    Used in the past 158 (4.7%) 150 (4.6%) 8 (11%)
    Sometimes 110 (3.3%) 109 (3.3%) 1 (1.3%)
    Weekends 17 (0.5%) 17 (0.5%) 0 (0%)
    Daily 53 (1.6%) 49 (1.5%) 4 (5.3%)
    Unknown 382 371 11
Lifetime cocaine use (at 18 years old) 318 (9.6%) 305 (9.4%) 13 (17%) 0.027
    Unknown 384 374 10
1 n (%)
2 Fisher’s exact test; Pearson’s Chi-squared test

Missing data visualization

Are cannabis and cocaine use associated with bipolar disorder?

We fitted a logistic model (estimated using ML) to predict bipolar disorder with lifetime cannabis use at 18 years old (formula: \(BD\) ~ \(cannabis\)). The model’s explanatory power is very weak (Tjur’s \(R^2\) = 1.81e-03). The model’s intercept, corresponding to cannabis = No, is at -3.92 (95% CI [-4.21, -3.65], \(p\) < .001). The effect of cannabis use at 18 [Yes] is statistically significant and positive (\(\beta\) = 0.60, 95% CI [0.10, 1.08], \(p\) = 0.016; \(B\) = 0.60, 95% CI [0.10, 1.08]; OR = 1.82, 95% CI [1.10, 2.93]).

We also fitted a logistic model (estimated using ML) to predict bipolar disorder with lifetime cocaine use at 18 years old (formula: \(BD\) ~ \(cocaine\)). The model’s explanatory power is very weak (Tjur’s \(R^2\) = 1.47e-03). The model’s intercept, corresponding to cocaine = No, is at -3.83 (95% CI [-4.09, -3.59], \(p\) < .001). The effect of cocaine use at 18 [Yes] is statistically significant and positive (\(\beta\) = 0.67, 95% CI [0.02, 1.25], \(p\) = 0.030; \(B\) = 0.67, 95% CI [0.02, 1.25]; OR = 1.96, 95% CI [1.02, 3.49]).

These results are summarized in the tables below.

Crude model of lifetime cannabis use at 18 years old and bipolar disorder onset at 22 years old.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.82 1.10, 2.93 0.016
1 OR = Odds Ratio, CI = Confidence Interval
Crude model of lifetime cocaine use at 18 years old and bipolar disorder onset at 22 years old.
Characteristic OR1 95% CI1 p-value
Lifetime cocaine use
    No — —
    Yes 1.96 1.02, 3.49 0.030
1 OR = Odds Ratio, CI = Confidence Interval

What about adjusted models for lifetime cannabis use at 18 years old?

Since the crude (non-adjusted) model was significant, we may follow it up with an adjusted analysis. The results below are the adjusted model for sex, skin color, and socioeconomic status (asset index quintiles).

Adjusted model for lifetime cannabis use at 18 years old. Adjusted for socioeconomic status, sex, and skin color.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.92 1.16, 3.10 0.009
Socioeconomic status 0.79 0.66, 0.94 0.009
Sex
    Female — —
    Male 0.86 0.53, 1.37 0.5
Skin color
    Non-white — —
    White 0.57 0.36, 0.91 0.020
1 OR = Odds Ratio, CI = Confidence Interval

Then, we added physical abuse by parents and lifetime cocaine use as well in the previous model from the table above.

Adjusted model for lifetime cannabis use at 18 years old. Adjusted for socioeconomic status, sex, skin color, and also physical abuse by parents.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 2.00 1.20, 3.25 0.006
Socioeconomic status 0.81 0.68, 0.96 0.017
Sex
    Female — —
    Male 0.88 0.55, 1.41 0.6
Skin color
    Non-white — —
    White 0.60 0.37, 0.97 0.036
Physical abuse by parents
    No — —
    Yes 1.15 0.71, 1.94 0.6
1 OR = Odds Ratio, CI = Confidence Interval

Finally, we added lifetime cocaine use as well for the last adjusted model.

Adjusted model for lifetime cannabis use at 18 years old. Adjusted for socioeconomic status, sex, skin color, physical abuse by parents, and also lifetime cocaine use.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.79 0.95, 3.19 0.059
Socioeconomic status 0.81 0.68, 0.96 0.017
Sex
    Female — —
    Male 0.87 0.54, 1.40 0.6
Skin color
    Non-white — —
    White 0.60 0.37, 0.96 0.033
Physical abuse by parents
    No — —
    Yes 1.16 0.71, 1.95 0.6
Lifetime cocaine use
    No — —
    Yes 1.35 0.62, 2.86 0.4
1 OR = Odds Ratio, CI = Confidence Interval

In conclusion, lifetime cannabis use just became not significant after the inclusion of lifetime cocaine use in the adjusted model.

Based on previous analyses, we considered to try an approach based on stratification by sex and parental spanking

First, the model stratified by sex. The models are presented in the tables below.

Crude model for lifetime cannabis use at 18 years old in bipolar disorder onset using a male subset.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.99 0.95, 4.03 0.060
1 OR = Odds Ratio, CI = Confidence Interval
Crude model for lifetime cannabis use at 18 years old in bipolar disorder onset using a female subset.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.80 0.88, 3.44 0.088
1 OR = Odds Ratio, CI = Confidence Interval

In the following tables, we present the results on stratification by physical abuse by parents.

Crude model for lifetime cannabis use at 18 years old in bipolar disorder onset using a subset of subjects that were exposed to physical abuse by parents.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.68 0.91, 3.00 0.084
1 OR = Odds Ratio, CI = Confidence Interval
Crude model for lifetime cannabis use at 18 years old in bipolar disorder onset using a subset of subjects that were not exposed to physical abuse by parents.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 2.48 0.99, 5.78 0.041
1 OR = Odds Ratio, CI = Confidence Interval

We also tested for binary logistic regression models with interaction terms between cannabis and sex/physical abuse by parents and there was no significant result, besides the standalone lifetime cannabis use parameter in the physical abuse model. You can check the results in a detailed manner in the tables below.

Logistic regression with interaction term for lifetime cannabis use at 18 years old and sex.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 1.80 0.88, 3.44 0.088
Sex
    Female — —
    Male 0.74 0.41, 1.29 0.3
Lifetime cannabis use * Sex
    Yes * Male 1.10 0.41, 2.99 0.8
1 OR = Odds Ratio, CI = Confidence Interval
Logistic regression with interaction term for lifetime cannabis use at 18 years old and physical abuse by parents.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 2.48 0.99, 5.78 0.041
Physical abuse by parents
    No — —
    Yes 1.41 0.78, 2.68 0.3
Lifetime cannabis use * Physical abuse by parents
    Yes * Yes 0.68 0.24, 2.01 0.5
1 OR = Odds Ratio, CI = Confidence Interval

Can the risk of lifetime cannabis use in bipolar disorder vary depending on the frequency of use?

The effect of cannabis use frequency at 18 [Yes (use on weekends or daily)] is statistically non-significant and positive (OR = 2.68, 95% CI [0.80, 6.72], \(p\) = 0.062).

Does cannabis use at 18 years old mediate the effect of sex, skin color or socioeconomic status in young adults?

After evaluating it in three separate models for each, we did not found any mediation effect of lifetime cannabis use at 18 years old related to the variables. Refer to the author of this document for more details on this analysis.

What about cannabis use at 11 (and 15) years old?

We fitted a logistic model (estimated using ML) to predict bipolar disorder with cannabis use at 11 years old (formula: \(BD\) ~ \(cannabis\ at\ 11\)). The model’s explanatory power is very weak (Tjur’s \(R^2\) = 1.38e-03). The model’s intercept, corresponding to cannabis at 11 = No, is at -3.80 (95% CI [-4.04, -3.58], \(p\) < .001). The effect of cannabis at 11 [Yes] is statistically non-significant and positive (\(\beta\) = 2.01, 95% CI [-0.94, 3.79], \(p\) = 0.064; \(B\) = 2.01, 95% CI [-0.94, 3.79]; OR = 7.46, 95% CI [0.39, 44.38]). The model is summarized at the table below.

Crude model of lifetime cannabis use at 11 years old and bipolar disorder onset at 22 years old.
Characteristic OR1 95% CI1 p-value
Lifetime cannabis use
    No — —
    Yes 7.46 0.39, 44.4 0.064
1 OR = Odds Ratio, CI = Confidence Interval

The lifetime cannabis use at 15 years old is not available to be modelled because there are no subjects with positive instances on the outcome and exposure and the model will not fit.

Count unique values based on outcome (bipolar disorder at 22 years old) and cannabis use at 15 years old.
Outcome Cannabis at 15 n
No No 3210
No Yes 48
No NA 367
Yes No 77
Yes NA 10

What about the effect of having used cocaine or cannabis at age 18?

We built another generalized linear model to estimate whether cannabis or cocaine lifetime use as a unique feature could predict bipolar disorder. The result is reported in the table below.

Crude model of aggregate effect of cocaine and cannabis at 18 years old and bipolar disorder onset at 22 years old.
Characteristic OR1 95% CI1 p-value
agg_sub
    No — —
    Yes 1.95 1.20, 3.12 0.006
1 OR = Odds Ratio, CI = Confidence Interval

Session information for reprodutibility purposes

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