# High Intelligence is Not Associated with a Greater Propensity for Mental Health Disorders

Camille Michèle Williams, Hugo Peyre, Ghislaine Labouret, Judicael Fassaya, Adoración Guzmán García, Nicolas Gauvrit, Franck Ramus

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## To cite this version:

Camille Michèle Williams, Hugo Peyre, Ghislaine Labouret, Judicael Fassaya, Adoración Guzmán García, et al.. High Intelligence is Not Associated with a Greater Propensity for Mental Health Disorders. European Psychiatry, inPress, pp.1-27. 10.1192/j.eurpsy.2022.2343. hal-03860469

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<figure><img src="image_2.png" /></figure>
**High Intelligence is Not Associated with a Greater Propensity for Mental Health**
**Disorders**

Camille Michèle Williams¹, Hugo Peyre¹,²,³, Ghislaine Labouret¹, Judicael Fassaya¹, Adoración Guzmán García¹, Nicolas Gauvrit⁴, & Franck Ramus¹

¹ Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France

² INSERM UMR 1141, Paris Diderot University, Paris, France.

³ Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France.

⁴ Human and Artificial Cognition Lab, Ecole Pratique des Hautes Etudes, Paris, France.

*CORRESPONDING AUTHOR: Camille Williams, LSCP, Département d'Etudes Cognitives, École Normale Supérieure, 29 rue d'Ulm, 75005 Paris, France. Email: williams.m.camille@gmail.com.

**Acknowledgments** This work received support under the program "Investissements d'Avenir" launched by the French Government and implemented by l'Agence Nationale de la recherche (ANR) with the references ANR-17-EURE-0017 and ANR-10-IDEX-0001-02 PSL. This research has been conducted using the UK Biobank Resource. Declarations of interest: none.
Preregistration: https://osf.io/cywd6/?view_only=fa9f5091de124d96be3eb1a55a4e7f01.

**Conflict of interest** - On behalf of all authors, the corresponding author states that there is no conflict of interest.

**Data/Code Availability** - This research has been conducted using data from UK Biobank, a major biomedical database (http://www.ukbiobank.ac.uk/). Restrictions apply to the availability of these data, which were used under license for this study: application 46007.

Preregistration, code, and supplemental tables and data are available on OSF:

This peer-reviewed article has been accepted for publication but not yet copyedited or typeset, and so may be subject to change during the production process. The article is considered published and may be cited using its DOI.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
29 https://osf.io/cywd6/?view_only=fa9f5091de124d96be3eb1a55a4e7f01. Preprint:

30 https://doi.org/10.1101/2022.05.26.22275621

31 **Ethics Approval Statement**- All participants provided informed consent (“Resources tab” at
32 https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=200). All procedures involving human
33 subjects/patients were approved by the Research Ethics Committee (reference 11/NW/0382)
34 and the present study was conducted based on application 46 007.

35

36 Abstract

37

38 **Background:** Studies reporting that highly intelligent individuals have more mental health
39 disorders often have sampling bias, no or inadequate control group, or insufficient sample size.
40 We addressed these caveats by examining the difference in the prevalence of mental health
41 disorders between individuals with high and average general intelligence (g-factor) in the UK
42 Biobank.

43

44 **Methods:** Participants with general intelligence (g-factor) scores standardized relative to the
45 same-age UK population, were divided into 2 groups: a high g-factor group (g-factor 2 SD
46 above the UK mean; N=16,137) and an average g-factor group (g-factor within 2 SD of the UK
47 mean; N=236,273). Using self-report questionnaires and medical diagnoses, we examined
48 group differences in prevalence across 32 phenotypes, including mental health disorders,
49 trauma, allergies, and other traits.

50

51 **Results:** High and average g-factor groups differed across 15/32 phenotypes and did not
52 depend on sex and/or age. Individuals with high g-factors had less general anxiety (OR=0.69,
53 95%CI[0.64;0.74]) and PTSD (OR=0.67, 95%CI[0.61;0.74))), were less neurotic (β=-0.12,
54 95%CI[-0.15;-0.10]), less socially isolated (OR=0.85, 95%CI[0.80;0.90]), and were less likely
55 to have experienced childhood stressors and abuse, adulthood stressors, or catastrophic trauma
56 (OR=0.69-0.90). They did not differ in any other mental health disorder or trait. However, they
57 generally had more allergies (e.g., eczema; OR=1.13-1.33).

58

59 **Conclusions:** The present study provides robust evidence that highly intelligent individuals
60 have no more mental health disorders than the average population. High intelligence even
61 appears as a protective factor for general anxiety and PTSD.

62
63 **Word count:** 240

64

65 **Key words:** Intelligence; Psychopathology; Post-Traumatic Stress Disorder; Anxiety;

66 Allergies

67
68 **Table of Contents**

69 **1. Introduction** *Error! Bookmark not defined.*

70 **2. Methods** *Error! Bookmark not defined.*

71 **2.1. Participants** 7

72 **2.2. G-factor Groups** 8

73 **2.4. Phenotypes** 8

74 **2.5. Age** *Error! Bookmark not defined.*

75 **2.6. Statistical Analyses** 11

76 **3. Results** 12

77 **3.1. Differences in the Prevalence of Phenotypes between Participants with High and Average**

78 **CA** 12

79 **3.1.1. Mental Health Disorders** 12

80 **3.1.2. Somatic Phenotypes** 15

81 **3.1.3. Trauma** 15

82 **3.1.4. Traits** 15

83 **3.2. Phenotypes with group differences between both the high and average and the low and**
**average g-factor groups** 15

85 **4. Discussion** 17

86

87
## 1. Introduction

Intelligence – the ability to learn, reason, and solve problems (Arvey et al., 1994) – is associated with greater physical health and longevity. And yet, some researchers report that highly intelligent individuals are at a higher risk of developing mental health and somatic disorders (Karpinski et al., 2018; Kermarrec et al., 2020; Lancon et al., 2015; Smith et al., 2015). The majority of studies that report a negative effect of intelligence on mental and somatic health, however, often suffer from sampling bias, the lack of a control group, or insufficient sample size (Gauvrit, 2014; Martin et al., 2010). The present paper addresses these caveats by examining the difference in the prevalence of health and somatic disorders and other traits between the highly intelligent (2 sd above mean) and averagely intelligent (within 2 sd of the mean) individuals of the UK Biobank (N ≈ 261,500).

The notion of general intelligence (g) stems from the positive correlation in performance across most cognitive tests (Spearman, 1904). It is formally defined as the common source of variance underlying performance across a wide range of tests and is usually computed using factor analysis. Intelligence Quotient (IQ) reflects a person's average performance across cognitive tests relative to a representative sample of the same-age national population. Henceforth, IQ and g will be used interchangeably and as shortcuts for general intelligence scores.

Over the last decades, intelligence has proved to be a strong predictor of education (Deary et al., 2007), socio-economic success (Schmidt & Hunter, 2004; Strenze, 2007), and health outcomes (Calvin et al., 2017;Twig et al., 2018; Wraw et al., 2018; Wrulich et al., 2014). For instance, having a childhood IQ one standard deviation (sd) above the mean decreases one's risk of accidents and developing heart, respiratory, and digestive disease by 20-25% (Wraw et al., 2018; Wrulich et al., 2014). And yet, whether intelligence also serves as a protective factor for mental health disorders is still subject to debate. While some postulate that high intelligence serves as a protective factor for several mental and somatic disorders (Burdick et al., 2009; Ghaffari et al., 2014, 2014; Koenen et al., 2009, 2009; Sjölund et al., 2015), others suggest that high intelligence is a risk factor for these phenotypes (Kanazawa, 2010; Karpinski et al., 2018; Lancon et al., 2015; Smith et al., 2015, 2015; White & Batty, 2012; K. M. Williams et al., 2017) or that the effects of intelligence vary across mental health phenotypes (Wraw et al., 2016).

The most recent study examining the prevalence of mental health and somatic (i.e., allergies, asthma, immunodeficiencies) disorders in highly intelligent individuals reported that high IQ was a risk factor for affective disorders, neurodevelopmental disorders, and diseases
related to the immune system (Karpinski et al., 2018). However, the study suffers from
sampling bias because participants were recruited from the American Mensa Ltd. – a society
open to individuals that at some point scored in the top 2% on a verified intelligence test
(N=3,715). Since IQ tests are typically administered to children when parents or teachers notice
behavioral problems or by individuals experiencing stereotypical characteristics associated
with IQ, selecting individuals from a sample of individuals who actively decided to take an IQ
test or become members of a highly intelligent society may exacerbate the correlation between
having a high IQ and mental health disorders and/or behavioral problems (Gauvrit, 2014;
Martin et al., 2010). The present paper thus aims to address these limitations.

We investigated the difference in prevalence between individuals with high (2 sd above
the population mean) and average (within 2 sd from the population mean) general intelligence
scores (g-factor scores; Williams et al., 2022) in the UK Biobank across mental health
disorders, somatic disorders, and certain traits. We examined group differences in the
prevalence of available mental health and somatic disorders in the UK Biobank, as well as
phenotypes that are thought to differ in prevalence in highly intelligent individuals, such as
subjective well-being phenotypes (e.g., well-being, social isolation; Li & Kanazawa, 2016;
Zettergren & Bergman, 2014), myopia (Czepita et al., 2008), chronotype (Ujma et al., 2020),
and trauma (McGuire & Jackson, 2020). Finally, we included sexual behaviors because studies
have shown heterogeneous results concerning the relationship between sexual behaviors and
IQ (Rahman et al., 2012; Rowe, 2002). As a point of comparison, we report differences in
prevalence across phenotypes between the average and low (2 sd under the population mean)
g-factor groups as exploratory analyses.
## 2. Methods

Analyses were conducted using R (R Core Team, 2022). R Packages are listed in S3 and the preregistration, code, and supplements are available here: https://osf.io/cywd6/?view_only=fa9f5091de124d96be3eb1a55a4e7f01).

### 2.1. Participants

Participants were taken from the UKBiobank, an open-access large prospective study with phenotypic, genotypic, and neuroimaging data from more than 500,000 participants recruited between 2006 and 2011 at 40 to 69 years old (Sudlow et al., 2015). All participants provided informed consent ("Resources tab" at https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=200). All procedures involving human subjects/patients were approved by the Research Ethics Committee (reference 11/NW/0382). The present study was conducted based on the UK Biobank application 46 007.

### 2.2. G-factor

We defined our g-factor groups using g-factor scores from a previous paper that are relative to the UK population (C. M. Williams et al., 2022). In brief, we calculated g-factor scores for individuals that completed at least one of the UK Biobank cognitive tests.

UK Biobank participants completed a variety of cognitive tests upon each of their visits to the UKBiobank assessment center (Category 100026) and online (Category 116), including: Fluid Intelligence, Matrix Pattern Completion, Tower Rearranging, Numeric Memory, Pairs Matching, Symbol Digit Substitution, Reaction Time, and Trail Making tests (Section S1.1). Most tests were completed at the first visit and we used the score of the first instance of completion if the test was completed several times.

Although the UK Biobank cognitive battery contains adapted versions of commonly-used tests and tests designed for the UK Biobank, the g-factor from well-validated tests correlates highly with the g-factor from the UK Biobank cognitive tests (r= 0.83; Fawns-Ritchie & Deary, 2020).

Because UK Biobank participants tend to live in less socioeconomically deprived areas and are healthier than the UK population (Fry et al., 2017), we expected more than 2% of UK participants in the highly intelligent group (aka individuals with g-factor > 2 sd from the UK mean). To accurately identify highly intelligent individuals in the UK Biobank, we created a g-factor that would be relative to the UK mean.

To adjust for sampling bias, we calculated weights for each UK Biobank participant based on their country of residence, sex, age range, occupation status, and occupation SOC group
179 using public the 2001 UK census data. We then adjusted for age with a semiparametric
180 continuous norming method that considers weights (Lenhard et al., 2016).

181 From the age-standardized cognitive tests that are relative to the UK population, we
182 extracted a g-factor from a single-factor model. We then computed the g-factor scores'
183 weighted mean and sd, using the census weighting factors. Finally, we standardized each
184 person's g-factor score with the weighted sample mean and weighted sample sd for a general
185 population mean of 0 and sd of 1.

186 We estimated the quality of the g-factor based on the combination of completed tests,
187 and we selected participants who had a reliable estimate of the g-factor (correlation with full g
188 factor > 0.7; N= 261,701 participants; Section S1.2). Participants were excluded when there
189 was a mismatch between the self-reported (field 31) and genetic sex (field 22001). Self-
190 reported sex was coded -0.5 for males and 0.5 for females.

## 2.3. G-factor Groups

192 We created 3 g-factor groups: a high g-factor group (g-factor 2 sd above the population
193 mean), a low g-factor group (g-factor 2 sd below the population mean), and an average g-factor
194 group (g-factor within 2 sd from the population mean). About 90% of individuals were in the
195 average g-factor group (236,273/261,701), 6.2% in the high g-factor group (16,137/261,701),
196 and 3.6% in the low g-factor group (9,291/261,701; sex differences in Section S1.2).

## 2.4. Phenotypes

198 To maximize the number of participants with a diagnosis, each phenotype was created
199 by combining questions and diagnoses from mental health professionals and self-reported
200 questionnaires (Davis et al., 2020; Guggenheim et al., 2015; Smith et al., 2013; Tables SA,
201 Section 1.3). We examined 32 phenotypes using one or several binary, ordinal, or continuous
202 variables (Table SA; Table SB1-B2).
<table><caption>**Table 1.** Phenotypic Prevalence in the UK Biobank across and by Sexes</caption><thead><tr><th rowspan="3">Phenotypes</th><th colspan="3">Both Sexes</th><th colspan="3">Men</th><th colspan="3">Women</th></tr><tr><th colspan="2">Without</th><th rowspan="2">With</th><th colspan="2">Without</th><th rowspan="2">With</th><th colspan="2">Without</th><th rowspan="2">With</th></tr><tr><th>N</th><th>N</th><th>%</th><th>N</th><th>N</th><th>%</th><th>N</th><th>N</th></tr></thead><tbody><tr><td>Eating Disorder</td><td>124,092</td><td>1,489</td><td>1.19</td><td>54,488</td><td>116</td><td>0.21</td><td>69,604</td><td>1,373</td><td>1.93</td></tr><tr><td>Anorexia</td><td>124,886</td><td>750</td><td>0.6</td><td>54,583</td><td>35</td><td>0.06</td><td>70,303</td><td>715</td><td>1.01</td></tr><tr><td>Anxiety</td><td>69,555</td><td>12,337</td><td>15.06</td><td>34,437</td><td>4,219</td><td>10.91</td><td>35,118</td><td>8,118</td><td>18.78</td></tr><tr><td>Social Anxiety</td><td>117,099</td><td>1,412</td><td>1.19</td><td>50,835</td><td>654</td><td>1.27</td><td>66,264</td><td>758</td><td>1.13</td></tr><tr><td>Depression Ever</td><td>22,872</td><td>11,556</td><td>33.57</td><td>11,678</td><td>3,765</td><td>24.38</td><td>11,194</td><td>7,791</td><td>41.04</td></tr><tr><td>Probable Depression</td><td>86,282</td><td>30,995</td><td>26.43</td><td>43,254</td><td>10,970</td><td>20.23</td><td>43,028</td><td>20,025</td><td>31.76</td></tr><tr><td>Bipolar Disorder</td><td>30,035</td><td>226</td><td>0.75</td><td>14,494</td><td>108</td><td>0.74</td><td>15,541</td><td>118</td><td>0.75</td></tr><tr><td>Schizophrenia</td><td>125,387</td><td>194</td><td>0.15</td><td>54,497</td><td>107</td><td>0.2</td><td>70,890</td><td>87</td><td>0.12</td></tr><tr><td>Self-Harm Thought or Action</td><td>220,343</td><td>41,079</td><td>15.71</td><td>103,443</td><td>15,215</td><td>12.82</td><td>116,900</td><td>25,864</td><td>18.12</td></tr><tr><td>Post-Traumatic Stress Disorder</td><td>117,290</td><td>7,820</td><td>6.25</td><td>51,826</td><td>2,626</td><td>4.82</td><td>65,464</td><td>5,194</td><td>7.35</td></tr><tr><td>Obsessive-Compulsive Disorder</td><td>156,192</td><td>1,268</td><td>0.81</td><td>67,781</td><td>400</td><td>0.59</td><td>88,411</td><td>606</td><td>0.68</td></tr><tr><td>Alcohol Hazardous Substance Use or Dependence</td><td>117,083</td><td>6,591</td><td>5.33</td><td>49,594</td><td>4,072</td><td>7.59</td><td>67,489</td><td>2,519</td><td>3.6</td></tr><tr><td>Other Hazardous Substance Use or Dependence</td><td>258,625</td><td>2,760</td><td>1.06</td><td>117,119</td><td>1,520</td><td>1.28</td><td>141,506</td><td>1,240</td><td>0.87</td></tr><tr><td>Cannabis Use Ever</td><td>97,040</td><td>28,375</td><td>22.62</td><td>40,330</td><td>14,195</td><td>26.03</td><td>56,710</td><td>14,180</td><td>20</td></tr><tr><td>General Allergy</td><td>73,287</td><td>29,550</td><td>28.73</td><td>32,990</td><td>12,538</td><td>27.54</td><td>40,297</td><td>17,012</td><td>29.68</td></tr><tr><td>Asthma</td><td>86,918</td><td>15,919</td><td>15.48</td><td>38,860</td><td>6,668</td><td>14.65</td><td>48,058</td><td>9,251</td><td>16.14</td></tr><tr><td>Hay Fever Rhinitis</td><td>450,094</td><td>51,200</td><td>10.21</td><td>205,755</td><td>22,680</td><td>9.93</td><td>244,339</td><td>28,520</td><td>10.45</td></tr><tr><td>Other Allergies</td><td>489,588</td><td>11,682</td><td>2.33</td><td>224,543</td><td>3,880</td><td>1.7</td><td>265,045</td><td>7,802</td><td>2.86</td></tr><tr><td>Myopia</td><td>76,190</td><td>33,881</td><td>30.78</td><td>34,734</td><td>15,489</td><td>30.8</td><td>41,456</td><td>18,392</td><td>30.7</td></tr><tr><td>Eczema</td><td>252,947</td><td>8,448</td><td>3.23</td><td>114,743</td><td>3,902</td><td>3.29</td><td>138,204</td><td>4,546</td><td>3.18</td></tr><tr><td>Insomnia</td><td>57,376</td><td>204,142</td><td>78.06</td><td>32,753</td><td>85,948</td><td>72.41</td><td>24,623</td><td>118,194</td><td>82.76</td></tr><tr><td>Loneliness</td><td>452,782</td><td>23,105</td><td>4.86</td><td>204,356</td><td>11,782</td><td>5.45</td><td>248,426</td><td>11,323</td><td>4.36</td></tr><tr><td>Social Isolation</td><td>444,466</td><td>45,053</td><td>9.2</td><td>201,014</td><td>21,856</td><td>9.81</td><td>243,452</td><td>23,197</td><td>8.7</td></tr></tbody></table>
<table><tr><td>Chronotype (More Evening)</td><td>145,500</td><td>87,667</td><td>37.6</td><td>63,275</td><td>39,521</td><td>38.45</td><td>82,225</td><td>48,146</td><td>36.93</td></tr><tr><td>Same-sex sexual behavior</td><td>15,757</td><td>432,740</td><td>96.49</td><td>8,873</td><td>197,539</td><td>95.7</td><td>6,884</td><td>235,201</td><td>97.16</td></tr><tr><td>Adulthood Abuse</td><td>20,479</td><td>20,479</td><td>16.32</td><td>4,701</td><td>4,701</td><td>8.62</td><td>15,778</td><td>15,778</td><td>22.25</td></tr><tr><td>Adulthood Stressors</td><td>51,167</td><td>51,167</td><td>40.91</td><td>21,289</td><td>21,289</td><td>39.12</td><td>29,878</td><td>29,878</td><td>42.28</td></tr><tr><td>Catastrophic Trauma</td><td>63,893</td><td>63,893</td><td>50.88</td><td>30,748</td><td>30,748</td><td>56.31</td><td>33,145</td><td>33,145</td><td>46.7</td></tr><tr><td>Childhood Abuse</td><td>14,060</td><td>14,060</td><td>11.2</td><td>5,387</td><td>5,387</td><td>9.87</td><td>8,673</td><td>8,673</td><td>12.22</td></tr><tr><td>Childhood Stressors</td><td>42,779</td><td>42,779</td><td>34.07</td><td>17,770</td><td>17,770</td><td>32.54</td><td>25,009</td><td>25,009</td><td>35.24</td></tr></table>

N.B. Probable Depression and Cannabis Use are ordinal but here reflect the prevalence of ever meeting the criteria regardless of severity.

204
205
Since the age of onset of disorders was not available for all participants and/or
measures, we used the maximum age at which an individual provided the most recent measure
of a disorder to approximate lifetime prevalence (Section S1.4).

## 2.6. Statistical Analyses

To reduce the number of statistical tests when examining group differences across
phenotypes, we first examined age and sex effects and interactions on each phenotype (Section
S1.5.1). If age and sex's main effects or interactions did not predict a phenotype (p > 0.05),
they were excluded from the g-factor group analyses. Equation 1 corresponds to the model with
all possible predictors (f corresponds to the logit function for binary and ordinal phenotypes
and to the linear function for continuous phenotypes).

Participants were included in the g-factor group analyses if they had a g-factor measure
and answered all of the questions used to create that phenotype, except when the same question
was asked several times. If participants responded as having the phenotype across any of the
questions, they were marked as having the phenotype, otherwise, they were marked as not
having the phenotype.

Age was mean-centered in ordinal and binary regressions, while age and continuous
phenotypes were mean-centered and divided by 1sd in linear regressions to report standardized
betas.

Equation 1:

$$
\begin{align*}
f(\text{Phenotype}) &= \beta_0 + \beta_1 * \text{age} + \beta_2 * \text{age}^2 + \beta_3 * \text{sex} + \beta_4 * \text{CA}_{\text{Group}} + \beta_5 \\
&\quad * \text{age} * \text{sex} + \beta_6 * \text{age}^2 * \text{sex} + \beta_7 * \text{sex} * \text{CA}_{\text{Group}} + \beta_8 * \text{age} \\
&\quad * \text{CA}_{\text{Group}} + \beta_9 * \text{age}^2 * \text{CA}_{\text{Group}} + \beta_{10} * \text{age} * \text{sex} * \text{CA}_{\text{Group}} + \beta_{11} \\
&\quad * \text{age}^2 * \text{sex} * \text{CA}_{\text{Group}}
\end{align*}
$$

We report results with the g-factor group predictor that survived a Bonferroni correction
for multiple comparisons of 0.05 divided by the number of coefficients in the equation with the
g-factor group term times the number of coefficients of interest times the number of
investigated phenotypes (e.g., 0.05/ (6*32) for equation 1). Since the analyses on the low g-
factor group were exploratory, we applied the same multiple comparison correction as for the
high g-factor group (e.g., 0.05/ (6*32) for equation 1). Odd Ratios (ORs) reflect the odds that
a person will have a phenotype or disorder given that they are highly compared to averagely
intelligent and were calculated as the exponent of the regression estimate.

238
239 **3. Results**

240 The model assumptions of low-powered phenotypes with few cases in the UK Biobank
241 (including, Anorexia, Eating Disorder, Schizophrenia, Bipolar Disorder, OCD, Social Anxiety,
242 and Other Hazardous Substance Use) were not met and should be interpreted with caution
243 (Supplemental Files S9-40). We additionally ran the analyses with the g-factor scores instead
244 of the g-factor group and found consistent results with our group analyses (S2.4, Figures S5-8
245 and Supplemental Files S9-S40).

246 **3.1. Differences in the Prevalence of Phenotypes between Participants with High and**
247 Average CA

248 Sex and age effects across phenotypes are described in S2.1. and in Supplemental Tables
249 SC. In Supplemental Tables SD, we provide the phenotypic prevalence by g-factor group and
250 the mean and sd of the g-factor, Townsend Index, birth year, number of children, and number
251 of individuals in the household of each g-factor group by diagnosis to describe the investigated
252 populations. Regression results are reported in Supplemental Tables SE.

253 Across the 32 phenotypes, the prevalence differed between the high g-factor and average
254 g-factor groups in 15 phenotypes (47%) and between the low g-factor and average g-factor
255 groups in 12 phenotypes (38%; Figure 1). Phenotypic differences between the Low and
256 Average g-factor are discussed in S2.3. There were no significant interactions in the analyses
257 comparing the average to the high g-factor groups.

258 **3.1.1. Mental Health Disorders**

259 Compared to individuals in the average g-factor group, there was a 33% decrease in the
260 odds of suffering from Post-Traumatic Stress Disorder (OR=0.67, 95%CI[0.61;0.74]) and a
261 31% decrease in the odds of having general anxiety (OR=0.69, 95%CI[0.64;0.74])) in the high
262 g-factor group (Table 2). There was no significant difference across other mental health
263 disorders (Figure 1).

264
<figure><img src="image_3.png" /></figure>

265

266

267

268

269

270

271

272

**Figure 1.** *Group Differences in Prevalence between High and Average and Low and Average g-factor Groups across Phenotypes and Scores.* OCD: Obsessive-Compulsive Disorder; PTSD: Post-Traumatic Stress Disorder. Correction for multiple comparisons varies by phenotype. See supplemental tables standard error (SE) for p-value thresholds for multiple comparison corrections. High g-factor: participants with a g-factor score 2SD above the mean. Low g-factor: participants with a g-factor score 2SD under the mean. Average g-factor: participants with a g-factor score between + or – 2SD from the mean.
273

<table><caption>**Table 2.** Phenotypes that differ in Prevalence between Average and High General Intelligence Groups.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">Estimate</th><th rowspan="2">SE</th><th rowspan="2">OR</th><th rowspan="2">t/z</th><th rowspan="2">p</th><th colspan="3">Average g-Factor</th><th colspan="3">High g-Factor</th></tr><tr><th>Control</th><th>Cases</th><th>%</th><th>Control</th><th>Cases</th><th>%</th></tr></thead><tbody><tr><td rowspan="6">High > Average</td><td>Other Allergies</td><td>0.28</td><td>0.06</td><td>1.33</td><td>4.50</td><td>6.71E-06</td><td>229,995</td><td>6,012</td><td>2.55</td><td>15627</td><td>500</td><td>3.10</td></tr><tr><td>Eczema</td><td>0.22</td><td>0.06</td><td>1.25</td><td>3.96</td><td>7.61E-05</td><td>228,451</td><td>7,556</td><td>3.20</td><td>15472</td><td>655</td><td>4.06</td></tr><tr><td>Cannabis Use Ever</td><td>0.22</td><td>0.03</td><td>1.25</td><td>7.06</td><td>1.64E-12</td><td>86,279</td><td>24,836</td><td>22.35</td><td>8092</td><td>3,088</td><td>27.62</td></tr><tr><td>Same-Sex Behavior</td><td>0.21</td><td>0.05</td><td>1.23</td><td>3.89</td><td>1.02E-04</td><td>209,239</td><td>8,741</td><td>4.01</td><td>14659</td><td>752</td><td>4.88</td></tr><tr><td>Chronotype (E vs M)</td><td>0.14</td><td>0.02</td><td>1.15</td><td>5.99</td><td>2.04E-09</td><td>132,775</td><td>82,172</td><td>38.23</td><td>8483</td><td>6,092</td><td>41.80</td></tr><tr><td>General Allergy</td><td>0.12</td><td>0.03</td><td>1.13</td><td>4.00</td><td>6.29E-05</td><td>64,788</td><td>25,833</td><td>28.51</td><td>6851</td><td>3,149</td><td>31.49</td></tr><tr><td rowspan="6">High < Average</td><td>Myopia</td><td>0.66</td><td>0.10</td><td>1.93</td><td>6.49</td><td>8.79E-11</td><td>70,191</td><td>30,851</td><td>30.53</td><td>2903</td><td>2,281</td><td>44.00</td></tr><tr><td>Trauma Catastrophic</td><td>-0.10</td><td>0.03</td><td>0.90</td><td>-3.70</td><td>2.20E-04</td><td>54,472</td><td>56,797</td><td>51.04</td><td>5640</td><td>5,554</td><td>49.62</td></tr><tr><td>Social Isolation</td><td>-0.16</td><td>0.04</td><td>0.85</td><td>-4.28</td><td>1.83E-05</td><td>210,478</td><td>23,110</td><td>9.89</td><td>14634</td><td>1,414</td><td>8.81</td></tr><tr><td>Trauma Childhood Stressors</td><td>-0.27</td><td>0.03</td><td>0.76</td><td>-8.92</td><td>4.62E-19</td><td>73,178</td><td>38,076</td><td>34.22</td><td>8141</td><td>3,053</td><td>27.27</td></tr><tr><td>Trauma Adulthood Stressors</td><td>-0.37</td><td>0.03</td><td>0.69</td><td>-12.43</td><td>1.83E-35</td><td>65,178</td><td>45,654</td><td>41.19</td><td>7608</td><td>3,565</td><td>31.91</td></tr><tr><td>General Anxiety</td><td>-0.37</td><td>0.05</td><td>0.69</td><td>-7.18</td><td>7.00E-13</td><td>61,127</td><td>11,118</td><td>15.39</td><td>6925</td><td>847</td><td>10.90</td></tr><tr><td rowspan="4"></td><td>Post-Traumatic Stress Disorder</td><td>-0.40</td><td>0.07</td><td>0.67</td><td>-5.77</td><td>7.70E-09</td><td>103,826</td><td>7,022</td><td>6.33</td><td>10730</td><td>441</td><td>3.95</td></tr><tr><td>Trauma Childhood Abuse</td><td>-0.48</td><td>0.05</td><td>0.62</td><td>-9.18</td><td>4.14E-20</td><td>98,544</td><td>12,688</td><td>11.41</td><td>10374</td><td>819</td><td>7.32</td></tr><tr><td>Neuroticism Score</td><td>-0.12</td><td>0.01</td><td></td><td>-10.17</td><td>2.87E-24</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

274

N.B. G-Factor: General Intelligence. High g-Factor: participants with a g-factor score 2SD above the mean. Average g-Factor: participants with a g-factor score between + or – 2SD from the mean. Neuroticism estimate is a standardized beta. Cannabis Use: Never used vs. used at least once.

275

276

E: Evening, M: Morning. Cannabis Use: never used versus used at least once.
## 3.1.2. Somatic Phenotypes

Compared to individuals in the average g-factor group, the odds of having some type of allergy increased by 13% for individuals in the high g-factor group (OR=1.13, 95%CI[1.08;1.18]). This was explained by their greater propensity to having eczema and other allergies (e.g., food; respectively 1.25 and 1.33 times more likely), which were included in the general allergy diagnosis (Table 2). The odds of being myopic increased by 93% in the high g-factor group (OR=1.93, 95%CI[1.82;2.04]) and this remained significant when controlling for educational attainment (OR=1.75; S2.2).

## 3.1.3. Trauma

Compared to individuals in the average g-factor group, the odds of experiencing catastrophic trauma, adulthood stressors, childhood abuse, and childhood stressors decreased by 10% (OR=0.90, 95%CI[0.87;0.94]), 31% (OR=0.69, 95%CI[0.66;0.72]), 38% (OR=0.62, 95%CI[0.58;0.67]), and 24% (OR=0.76, 95%CI[0.73;0.79]; Table 2) in the high g-factor group, respectively.

## 3.1.4. Traits

Compared to individuals in the average g-factor group, the odds of feeling more socially isolated decreased by 15% in the high g-factor group (OR=0.85, 95%CI[0.80;0.90]), whereas the odds of having an evening-like chronotype, ever engaging in same-sex behavior, and ever using cannabis increased by 15% (OR=1.15, 95%CI[1.11;1.19]), 23% (OR=1.23, 95%CI[1.14;1.33]), and 25% (OR=1.25, 95%CI[1.20;1.31]) respectively in the high g-factor group. Fig S4 shows that there are more individuals with a higher-than-average g-factor that have used cannabis 1 to 100 times and that over 100 times there are no group differences. The high g-factor group had a lower neuroticism score than individuals in the average g-factor group ($\beta$=-0.12, SE=0.01; Table 2).

## 3.2. Phenotypes with group differences between both the high and average and the low and average g-factor groups

Childhood stressors, childhood abuse, adulthood stressors, PTsd, and social isolation were more prevalent in the low compared to the average g-factor group (Table S2) and were more prevalent in the average compared to the high g-factor group (Table 2), suggesting that the prevalence of these phenotypes decreases with an increasing g-factor. The low g-factor group had a higher neuroticism score than the average g-factor group ($\beta$=0.22, SE=0.02), which had a higher neuroticism score than the high g-factor group. The odds of ever trying cannabis and having an evening-like chronotype respectively decreased by 41% (OR=0.59,
310 95%CI[0.53;0.65]) and 14% (OR=0.86, 95%CI[0.82;0.90]) in the low compared to the average

311 g-factor group.

312
4. Discussion

We examined differences in the prevalence of mental health disorders, somatic and
certain traits between individuals with high (2 sd above mean) and average g-factor scores
(within 2 sd of the mean) in the UK Biobank (N ≈ 7,266 - 252,249). We contrasted these results
with differences observed between individuals with low and average g-factor scores.

We found that the high g-factor group did not have more mental health disorders than
the average g-factor group in general and that they were less likely to have general anxiety and
PTsd. Individuals with higher intelligence were also less likely to have experienced trauma and
stressors, except for adulthood abuse, which may be part of the explanation for the previous
finding. The high g-factor group was also less neurotic and felt less socially isolated. In
contrast, the low g-factor group was more neurotic, felt more socially isolated, and had a greater
prevalence of trauma, stressors, and PTsd than the average g-factor group, suggesting that the
prevalence of these phenotypes decreases with increasing intelligence. Among the few somatic
disorders that were examined, we found that individuals with high intelligence were more
myopic and had more allergies, although they had a lower prevalence of hay fever rhinitis, and
asthma. Individuals with high intelligence were also more likely to present certain traits, such
as having an afternoon-evening chronotype, ever tried cannabis, and ever engaged in same-sex
behavior, whereas the low g-factor group was less likely to have ever tried cannabis and
engaged in same-sex behavior than the average g-factor group. There were no differences
between groups in the prevalence of insomnia.

The result of the present study contradicts several studies that reported an increased risk
for various psychiatric disorders in individuals with high intelligence (e.g., Karpinski et al.,
2018; Smith et al., 2015). These studies were generally based on small samples and suffered
from major sampling bias or a lack of a control group (Gauvrit, 2014; Martin et al., 2010). We
find that high intelligence is not a risk factor for psychiatric disorders and even a protective
factor for general anxiety. Higher intelligence was associated with a decrease in trauma
exposure, and consequently PTsd. This is consistent with previous findings (McGuire &
Jackson, 2020) and with the association of childhood trauma with lower intelligence (Enlow et
al., 2012).

With regard to somatic disorders, we replicate the increased risk of allergies in
individuals with high intelligence (Benbow, 1985; Karpinski et al., 2018). One possible
explanation for this association is that allergies and intelligence share neural correlates
(Takeuchi et al., 2018). Another possibility is that more intelligent individuals with a higher g-
factor live in more urban areas (Jokela, 2014), where allergies are more prevalent (Schröder et
al., 2015), or that individuals with high intelligence are more aware of allergic symptoms and have better access to health care. However, the prevalence between groups did not differ across all allergies (e.g., Asthma, Hay Fever Rhinitis).

In line with a previous literature review (Czepita et al., 2008), the risk of myopia was greater for individuals with high intelligence. While near-work activities (e.g., reading and computer use) seem to be a risk factor for myopia (Huang et al., 2015; Mutti et al., 2002), this association appears to be distinct from that of higher intelligence and education level (K. M. Williams et al., 2017). Although additional years of education contribute to an increase in the risk of developing myopia (Mountjoy et al., 2018), most of the evidence points toward shared genetic factors between intelligence and myopia (K. M. Williams et al., 2017), which is consistent with our observation that the risk of myopia associated with a high g-factor only slightly decreased when adjusting for educational attainment.

Our results indicate more afternoon-evening chronotypes in individuals with high intelligence than in individuals with average intelligence, which could be explained by differences in the work schedules of the different g-factor groups (Ujma et al., 2020). In line with a previous study (Rahman et al., 2012), we find that individuals with high intelligence are more likely to ever have engaged in same-sex behavior. Although consistent with previous studies (Rahman et al., 2012; Willmott & Brierley, 1984), our measure reflects sexual exploration rather than sexual orientation. We also found an association between ever trying cannabis and intelligence, but this was only true for individuals who consumed cannabis less than 101 times in their lifetime, suggesting that this measure also reflects a tendency to explore. One possibility is that individuals with higher intelligence, which is positively correlated with the “Openness to Experience” personality trait (r=0.30; Stankov, 2018), may be more likely to seek out new experiences and explore alternative behaviors than the average.

This paper was first limited in its ability to study neurodevelopmental disorders and some psychotic disorders, which were absent or had few cases in the UK Biobank. We nonetheless investigated phenotypic differences in prevalence between g-factor groups across a greater number of psychiatric disorders than previously done (Brown et al., 2021; Karpinski et al., 2018) and examined additional phenotypes, including traumatic experiences.

Second, the prevalence of some psychiatric disorders and traits differs from the general population (Davis et al., 2020) because the UK Biobank has a "healthy volunteer" bias (Fry et al., 2017). However, this should not affect the validity of the group comparisons because the phenotypes were designed with the same criterion between high and average IQ groups.
Third, while some data may not be missing at random, our participants are more representative of highly intelligent individuals in the general population than those from previous Mensa studies, which did not have a control group and examined individuals that had to take or decided to take an IQ test and join a society for the highly intelligent.

Fourth, although we used self-report diagnoses, which are less precise than professional diagnoses, UK Biobank participants self-reporting any psychiatric diagnosis appear to have an elevated risk of any symptom-based outcome (Davis et al., 2019). By using professional and probable diagnoses with validated short mental health tests, we included individuals with mental health disorders that have not sought or received mental health assistance (Kessler et al., 2007; Newson et al., 2011).

Fifth, the prevalence of psychiatric disorders and traits between g-factor groups may differ across the lifespan. However, we were interested in lifetime prevalence, making the UK Biobank, a prospective aging study, a good candidate for the question at hand.

Therefore, while this paper is not without limitations, we provide a large scaled analysis of the difference in the prevalence of numerous phenotypes, including mental health and somatic disorders, across individuals of varying intelligence. We find that high intelligence is not a risk factor for psychiatric disorders and is a protective factor for general anxiety and PTSD. Whereas our results support the advantageous nature of higher intelligence (Brown et al., 2021), general intelligence may still be relevant for psychiatric evaluation: higher intelligence may affect the presentation of symptoms and the available resources for recovery.

400
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