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ORIGINAL ARTICLE
Attention-deficit/hyperactivity disorder symptoms and dietary
habits in adulthood: A large population-based twin study
in Sweden
Lin Li
1
| Mark J. Taylor
2
| Katarina Bälter
2,3
| Ralf Kuja-Halkola
2
|
Qi Chen
2
| Tor-Arne Hegvik
2,4
| Ashley E. Tate
2
| Zheng Chang
2
|
Alejandro Arias-Vásquez
5
| Catharina A. Hartman
6
| Henrik Larsson
1,2
1
School of Medical Sciences, Örebro
University, Örebro, Sweden
2
Department of Medical Epidemiology and
Biostatistics, Karolinska Institutet, Stockholm,
Sweden
3
Public Health Sciences, Mälardalen
University, Västerås, Sweden
4
Department of Biomedicine, University of
Bergen, Bergen, Norway
5
The Department of Psychiatry & Human
Genetics, Donders Institute for Brain,
Cognition, and Behavior, Radboud University
Medical Center, Nijmegen, Netherlands
6
Department of Psychiatry, University of
Groningen, University Medical Center
Groningen, Groningen, Netherlands
Correspondence
Lin Li, School of Medical Sciences, Örebro
University, Örebro 701 82, Sweden.
Email: lin.li@oru.se
Funding information
European Union's Horizon 2020 research and
innovation programme, Grant/Award Number:
728018; Swedish Research Council, Grant/
Award Number: 2018-02599; the Swedish
Brain Foundation, Grant/Award Number:
FO2018-0273; Vetenskapsrådet, Grant/Award
Number: 2017-00641
Abstract
Associations between adult attention-deficit/hyperactivity disorder (ADHD) symptoms
and dietary habits have not been well established and the underlying mechanisms remain
unclear. We explored these associations using a Swedish population-based twin study
with 17,999 individuals aged 20–47 years. We estimated correlations between inatten-
tion and hyperactivity/impulsivity with dietary habits and fitted twin models to deter-
mine the genetic and environmental contributions. Dietary habits were defined as
(a) consumption of food groups, (b) consumption of food items rich in particular macro-
nutrients, and (c) healthy and unhealthy dietary patterns. At the phenotypic level, inat-
tention was positively correlated with seafood, high-fat, high-sugar, high-protein food
consumptions, and unhealthy dietary pattern, with correlation coefficients ranging from
0.03 (95%CI: 0.01, 0.05) to 0.13 (95% CI: 0.11, 0.15). Inattention was negatively corre-
lated with fruits, vegetables consumptions and healthy dietary pattern, with correlation
coefficients ranging from −0.06 (95%CI: −0.08, −0.04) to −0.07 (95%CI: −0.09, −0.05).
Hyperactivity/impulsivity and dietary habits showed similar but weaker patterns com-
pared to inattention. All associations remained stable across age, sex and socioeconomic
status. Nonshared environmental effects contributed substantially to the correlations of
inattention (56–60%) and hyperactivity/impulsivity (63–80%) with dietary habits. The
highest and lowest genetic correlations were between inattention and high-sugar food
(r
A
= .16, 95% CI: 0.07, 0.25), and between hyperactivity/impulsivity and unhealthy
dietary pattern (r
A
= .05, 95% CI: −0.05, 0.14), respectively. We found phenotypic and
etiological overlap between ADHD and dietary habits, although these associations were
weak. Our findings contribute to a better understanding of common etiological path-
ways between ADHD symptoms and various dietary habits.
KEYWORDS
ADHD, adults, dietary habits, genetic correlation, phenotypic correlation, twin study
Received: 17 April 2020 Revised: 14 September 2020 Accepted: 22 September 2020
DOI: 10.1002/ajmg.b.32825
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2020 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics published by Wiley Periodicals LLC.
Am J Med Genet. 2020;1–11. wileyonlinelibrary.com/journal/ajmgb 1
1|INTRODUCTION
Attention-deficit/hyperactivity disorder (ADHD) is a neu-
rodevelopmental disorder with an approximate prevalence of 5% in
children and adolescents and 2.5% in adults (Stephen V. Faraone
et al., 2015; Sayal, Prasad, Daley, Ford, & Coghill, 2018; Simon,
Czobor, Balint, Meszaros, & Bitter, 2009). ADHD is associated with
many adverse outcomes across development (Asherson, Buitelaar,
Faraone, & Rohde, 2016; Stephen V. Faraone et al., 2015). The patho-
physiological mechanisms underlying ADHD are unclear and its diag-
nostics and treatment remain challenging (Sharma & Couture, 2014).
Dietary habits, a modifiable lifestyle factor, play a fundamental
role in brain development, physiology, and functioning (Brandt, 2019).
In the two most recent systematic review and meta-analyses, ADHD
was reported to be positively associated with unhealthy dietary habits
(characterized by high consumption of refined sugar and saturated
fat), and negatively associated with healthy dietary habits (high con-
sumption of fruits and vegetables) among children and adolescents
(Del-Ponte, Quinte, Cruz, Grellert, & Santos, 2019; Shareghfarid,
Sangsefidi, Salehi-Abargouei, & Hosseinzadeh, 2020). However, only
two available studies with adult samples were found with conflicting
results. Weissenberger et al. (2018) reported an association between
ADHD symptoms and higher consumption of sweets, while Holton,
Johnstone, Brandley, and Nigg (2019) found nutrient intake was not
associated with diagnosed ADHD.
Due to the lack of evidence from longitudinal studies, previous
studies have failed to explain the exact direction of the associations
and underlying mechanisms. Diet has been suggested as a potential
intervention or treatment of ADHD for many years. According to
double-blind placebo-controlled evidence, a few-foods diet (a diet
based on rice, lamb, lettuce, pears, and water) seems to be the most
promising dietary interventions for a reduction in ADHD symptoms in
children (Pelsser, Frankena, Toorman, & Rodrigues Pereira, 2017).
Although the underlying biological basis remains unclear, recent gut-
brain axis research has shown that human gut microbiota responds rap-
idly to dietary changes and produces neurochemicals comparable to
those produced by the brain (David et al., 2014; Lyte, 2013; Suez
et al., 2014). On the other hand, twin studies have suggested that the
two core symptom domains of ADHD-inattention and hyperactivity/
impulsivity- share genetic factors, but also that the two dimensions also
have significant unique genetic underpinnings (Greven, Rijsdijk, &
Plomin, 2011; McLoughlin, Ronald, Kuntsi, Asherson, & Plomin, 2007).
Therefore, it is possible that the two subdomains of ADHD may con-
tribute to having an unhealthy or less-balanced dietary habit via sepa-
rated, but related, genetic factors (Samuele Cortese et al., 2008;
Samuele Cortese et al., 2015; Nigg et al., 2016). The core symptoms of
inattention, poor planning, and self-regulation deficits, may cause diffi-
culties in adhering to a regular eating pattern, favoring abnormal eating
behaviors (Samuele Cortese et al., 2008; Nigg et al., 2016). In contrast,
deficient inhibitory control and delay aversion, which are expressions
of the hyperactivity/impulsivity component of ADHD may translate
into impulsive eating of highly palatable foods or having no patience to
eat vegetables, which are less rewarding than high-caloric foods
(Samuele Cortese et al., 2008; Mian et al., 2019; Nigg et al., 2016).
However, none of the previous studies on children, adolescents, and
adults has distinguished between the inattentive and hyperactivity/
impulsivity components of ADHD. Consequently, it is not clear whether
the dimensions of ADHD may specifically be associated with various
dietary habits (Samuele Cortese et al., 2008; Nigg et al., 2016).
An important alternative explanation of the association between
ADHD symptoms and dietary habits is that these two traits may share
some etiological factors. ADHD is strongly influenced by genetic fac-
tors, with heritability estimates (ℎ
2
) between 70 and 80% in both chil-
dren and adults (Asherson & Gurling, 2012; S. V. Faraone et al., 2005;
Larsson, Chang, D'Onofrio, & Lichtenstein, 2014), and there are strong
genetic links between ADHD diagnoses and sub-threshold variations
in ADHD traits, thus supporting a dimensional model of ADHD
(Larsson, Anckarsater, Rastam, Chang, & Lichtenstein, 2012; Taylor
et al., 2019). Diet composition is also reported to be moderately heri-
table, with h
2
estimates between 27 and 70% (Hasselbalch, Heitmann,
Kyvik, & Sorensen, 2008; Meddens et al., 2018; Wade, Milner, &
Krondl, 1981). Emerging support for the hypothesis that these two
traits may share some etiological factors has been provided by a
recent genome-wide association study of ADHD that reported signifi-
cant genetic correlations between ADHD and several metabolic traits
(genetic correlation, r
g
= .22–.30) (Demontis et al., 2018). An advanced
understanding of the associations between ADHD and dietary habits
has important clinical and public health implications, because dietary
habits may also explain the link between ADHD to health-related out-
comes such as metabolic syndromes (e.g., obesity)(Samuele Cortese
et al., 2015; S. Cortese & Tessari, 2017).
The aims of this study were three-fold. First, we aimed to identify
and quantify the associations between ADHD symptom dimensions
and different dietary habits in adults. Second, given that ADHD was
more common among young people, males and those with socioeco-
nomic disadvantages (Stephen V. Faraone et al., 2015; Rowland
et al., 2018), and healthy dietary habits were also reported to be
related to age, sex, and SES (Johansson, Thelle, Solvoll, Bjørneboe, &
Drevon, 1999), we further explored the association patterns among
different age, sex, and SES groups. Third, we aimed to investigate the
relative contribution of genetic and environmental factors to the asso-
ciations between adult ADHD symptom dimensions and different die-
tary habits, and test for a causal association between these
phenotypes by using a large population-based twin sample in Sweden.
2|METHODS AND MATERIALS
2.1 |Participants
In May 2005, a total of 42,582 Swedish twins born between 1959 and
1985 who survived their first birthday, were identified from the popula-
tion based Swedish Twin Register (Lichtenstein et al., 2006). Of the tar-
get population, 25,364 (59.6%) individuals participated in the Study of
Twin Adults: Genes and Environment (STAGE). Participants were given
a web-based survey, which included 1,300 questions, in 34 sections,
2LI ET AL.
regarding lifestyle, mental, and physical health. Up to three reminders
were sent out to nonresponders, and participants were offered a tele-
phone interview if they preferred this over the web-based survey.
Questionnaire data and age information were available for 24,872 indi-
viduals. The response rate for ADHD symptoms and Food Frequency
Questionnaire (FFQ) was 71.84% (n= 17,867) and 36.8% (n= 9,156),
respectively. This resulted in 17,999 individuals who provided informa-
tion about either ADHD symptoms and/or FFQ, who were included in
the analyses (Figure S1). Twins who responded to the FFQ question-
naire were more likely to be female (53.83%), have lower SES (50.26%)
and higher ADHD symptoms scores compared to nonrespondents. No
statistically significant differences were found across age and zygosity
groups (Table S1).
All participants provided informed consent. The project has been
reviewed and approved by the Regional Ethics Committee at the
Karolinska Institutet, Stockholm, Sweden (DNR: 03–224).
2.2 |Measures
2.2.1 |ADHD symptoms
Self-reported ADHD symptoms were obtained via nine inattention
items and nine hyperactivity/impulsivity items covering the 18 DSM-
IV symptoms of ADHD (Larsson et al., 2013). Each item had a three-
point answer format (0 = “No,”1=“Yes, to some extent,”2=“Yes”).
The items were summed up to create two subscales of ADHD
symptoms-inattention and hyperactivity/impulsivity. Both inattention
and hyperactivity/impulsivity showed good internal consistency, and
Cronbach's αwas .79 and 0.77, respectively. A validation study on our
ADHD instrument can be found elsewhere (Larsson et al., 2013).
2.2.2 |Dietary habits
Habitual dietary intake was assessed using a food frequency question-
naire (FFQ), comprising of 94 food items. For each food item, the partici-
pants indicated their average consumption frequency over the past year
by selecting one of the following frequency categories: never, 1–3
times/month, 1–2 times/week, 3–4 times/week, 5–6times/week,
1 time/day, 2 times/day, 3 times/day. The frequency for each food item
was converted into number of servings per day. Dietary habits were
expressed in three ways (a) consumption of food groups (fruits, vegeta-
bles, dairy, meat, and seafood), (b) consumption of food items rich in a
particular macronutrient (high-fat food, high-carbohydrate food, high-
sugar food,and high-protein food),and (c) healthy dietarypatterns (fruits,
vegetables, fish, and white meat) and unhealthy dietary patterns (food
with high proportions of refined sugar and saturated fat) respectively, in
line with previous studies on the associations between ADHD and die-
tary (Del-Ponte, Quinte, Cruz, Grellert, & Santos, 2019). Detailed infor-
mation about the definitions of dietary habits can be found in Table S2.
The scores of each dietary habit were calculated as the total daily intake
frequency of each food group.
2.2.3 |Sociodemographic measures
Participants were between 20 and 47 years of age at the time of
assessment and categorized into three groups (20–29, 30–39, and
40–47 years). Socioeconomic status (SES; Larsson, Dilshad, Lichten-
stein, & Barker, 2011) of the participants was indicated by their occu-
pational status: (I) unskilled and semiskilled workers, (II) skilled
workers/assistant nonmanual employees, (III) intermediate nonmanual
collar workers, and (IV) employed and self-employed professionals,
higher civil servants, and executives. Higher SES was defined as those
with occupational status in Class II, III, or IV.
2.2.4 |Zygosity
Standard physical similarity questions that have previously been vali-
dated through genotyping were used to establish zygosity. Individuals
from both complete (n= 10,876) and incomplete (n= 7,123) twin pairs
were included in the twin analyses.
2.3 |Data analysis
The distributions of all main variables were positively skewed (range
of skewness: 0.84–4.56) and therefore log-transformed before analy-
sis (range of transformed skewness: −0.70 to 0.64) (Table S3).
2.4 |Phenotypic analyses
At the phenotypic level, mean differences in ADHD symptoms and
dietary intake across sex, age, and SES groups were estimated using
linear mixed effect models in SAS version 9.4 (SAS Institute Inc., Cary,
NC), which allowed us to account for the dependent nature of the
twin observations. Partial pairwise Pearson's correlation coefficients
were estimated to quantify the associations between ADHD symp-
toms and different diets. As the two core symptom dimensions of
ADHD, inattention, and hyperactivity/impulsivity, were highly corre-
lated with each other, we further tested whether the correlations
between one ADHD symptom dimension and dietary habits could be
ascribed to the other ADHD symptom dimension. To test the associa-
tion patterns among different age, sex, and SES groups, we further
conducted exploratory analyses by stratifying on these factors.
2.5 |Genetically informative analyses
The twin method relies on the different levels of genetic relatedness
between monozygotic twins (MZ), who are genetically identical, and
dizygotic twins (DZ), who share on average 50% of their polymorphic
genetic variation. This information was used to decompose the vari-
ance of each phenotype and the covariation between phenotypes into
additive genetic factors (A), dominant genetic factors (D),
LI ET AL.3
environmental factors shared by twin pairs (C) and nonshared or
unique environmental factors (E), including measurement error
(Rijsdijk & Sham, 2002). As the C and D components cannot be esti-
mated in the same model using the classical twin design, we fitted
ADE and ACE models separately and then compared goodness-of-fit
of the two models. A greater intraclass correlation (ICC) is expected in
MZ twins than in DZ twins if there are genetic influences on a trait.
Similarly, in the cross-twin cross-trait correlations (CTCT), where we
estimated the correlations of ADHD symptoms of twin 1 and the die-
tary habits of twin 2, higher CTCT in MZ twins than in DZ twins indi-
cate genetic influences on the covariation between traits.
Structural equation modeling in the statistical software R version
3.6.1, with the OpenMx package (2.14.11), was used to conduct uni-
variate and bivariate analysis based on raw data (Neale et al., 2016).
Maximum-likelihood model-fitting, which allowed handling of missing
data and inclusion of incomplete twin pairs in models, was performed
on each phenotype. In the univariate and bivariate analyses, we first
fitted a fully saturated model to estimate the means, variances, and
covariances in observed data. Several sub-models were fitted to test
assumptions (equating means and variances across twin order, zygos-
ity, and sex) by performing likelihood ratio tests comparing the current
nested model with the previous best-fitting model. Akaike Information
Criterion (AIC) was additionally used to assess the fit of each solution.
In the bivariate models, including one ADHD symptom dimension
(inattention or hyperactivity/impulsivity) and one dimension of diets,
we choose to fit an ADE model based on the observation that ICC
and CTCT correlations in MZ twins were consistently more than twice
the size of those in DZ twins. Two sub-models (AE and E models)
were fitted to evaluate whether they would explain the data signifi-
cantly worse than ADE model and test the presence of D and A. The
bivariate models also provide estimates of additive genetic correlation
(r
A
), dominance genetic (r
D
) and nonshared environmental (r
E
) correla-
tions, which vary from −1.0 to +1.0. These statistics indicate to what
extent genetic and environmental influences in one phenotype over-
lap with the other.
To further test the robustness of the nonfamilial overlap and the
causal associations between ADHD symptoms and dietary habits, we
computed the differences of the two ADHD symptom dimensions
between an MZ twin and his/her co-twin. These intrapair differences
in symptoms were then regressed on the MZ twin differences in die-
tary habits. Statistically significant and positive associations (i.e., the
MZ twin with more ADHD symptoms than his/her co-twin also con-
sumes more unhealthy or high-sugar food than his/her co-twin) would
point toward a causal effect.
3|RESULTS
3.1 |Phenotypic associations
Descriptive statistics are presented in Table 1. Figure 1 shows the corre-
lations between ADHD symptom dimensions and different dietary
habits. Inattention was positively associated with high consumption of
seafood, high-fat food, high-sugar food, high-protein food, and
unhealthy food, with correlations ranging from 0.03 (95% CI: 0.01, 0.05)
for seafood to 0.13 (95% CI: 0.11, 0.15) for high-sugar food. There were
negative correlations between inattention and fruits, vegetables, and
healthy food intake; the range of the correlations was from −0.06 (95%
CI: −0.08, −0.04) for vegetables and healthy food to −0.07 (95% CI:
−0.09, −0.05) for fruits. The correlations between inattention and high
consumption of dairy, meat, and high-carbohydrate food were even
weaker, ranging from 0.001 to 0.02. Hyperactivity/impulsivity and die-
tary habits showed similar correlation patterns to inattention, but the
magnitude was smaller, with positive correlations ranging between 0.03
(95% CI: 0.01, 0.05) for high-fat food and 0.09 (95% CI: 0.06, 0.11) for
high-sugar food, and negative correlations ranging from −0.02 (95% CI:
−0.04, −0.01) for vegetables to −0.03 (95% CI: −0.05, −0.01) for fruits.
For both inattention and hyperactivity/impulsivity, the strongest correla-
tions with dietary habits were consistently observed for high-sugar food
and unhealthy food.
3.2 |Sensitivity analysis
The correlation patterns between ADHD symptoms and different die-
tary habits were stable across age, sex, and SES. See Figures S2, S3,
S4 and Table S4.
As is shown in Table 2, the correlations between inattention and
different dietary habits were similar, after adjusting for hyperactivity/
impulsivity in the partial pairwise Pearson's correlation models. The
strongest correlations with inattention were still found in high-sugar
food (r= .10, 95%CI: 0.08, 0.12) and unhealthy food (r= .09, 95%CI:
0.07, 0.11). In contrast, the correlations between hyperactivity/impul-
sivity and different dietary habits were largely attenuated after control-
ling for inattention. The only statistically significant correlations with
hyperactivity/impulsivity, after adjusting for inattention were found in
in high-sugar food (r= .03, 95% CI: 0.01, 0.06) and unhealthy food
(r= .04, 95% CI: 0.01, 0.06), but the associations were very weak.
3.3 |Genetically informative associations
Based on the phenotypic correlations, and to maximize statistical
power, the twin analyses focused on ADHD symptoms dimensions,
high-sugar food and unhealthy dietary habits, and were collapsed
across age, sex, and SES.
Twin correlations for each trait and CTCT correlations are shown
in Table 3. We found higher CTCT correlations in MZ twins than in
DZ twins, indicating genetic influences on the covariance between
ADHD symptoms and unhealthy diets. Nonshared environmental
influences were also evident because MZ CTCT correlations were
smaller than the phenotypic correlations.
The univariate model-fitting results are displayed in Table S5.
Table S6 provides the bivariate model fitting results of ADHD symp-
tom dimensions and dietary habits. The parameter estimates of the
ADE models and AE models were presented in Table S7 and Table 4,
4LI ET AL.
TABLE 1 Means and standard deviations (SD) for ADHD scales and total frequency
a
of various diets, by age, sex and SES group
Age Sex SES
20–29 (n = 5,969) 30–39 (n = 6,781) 40–47 (n = 5,249) Male (n = 7,216) Female (n = 10,783) Low (n = 3,146) High (n = 10,269)
IA 2.16 ± 2.15 1.90 ± 2.05 1.84 ± 2.04 2.02 ± 2.08 1.92 ± 2.09 2.22 ± 2.19 1.80 ± 1.99
HI 2.15 ± 2.11 1.98 ± 2.07 1.80 ± 2.02 1.98 ± 2.09 1.99 ± 2.06 2.10 ± 2.14 1.89 ± 2.02
Food groups (n = 2,939–3,116) (n = 3,299–3,370) (n = 2,626–2,680) (n = 3,297–3,352) (n = 5,567–5,813) (n = 1,697–1745) (n = 4,969–5,112)
Fruits 1.39 ± 1.26 1.54 ± 1.32 1.61 ± 1.41 1.16 ± 1.08 1.71 ± 1.42 1.38 ± 1.32 1.59 ± 1.34
Vegetables 2.66 ± 1.98 3.08 ± 2.20 3.33 ± 2.24 2.57 ± 1.87 3.26 ± 2.27 2.74 ± 2.07 3.20 ± 2.16
Dairy 7.18 ± 4.41 7.03 ± 4.11 6.82 ± 4.19 7.66 ± 4.56 6.65 ± 3.99 7.42 ± 4.63 6.84 ± 4.01
Meat 1.37 ± 0.90 1.45 ± 0.83 1.44 ± 0.91 1.55 ± 1.00 1.35 ± 0.79 1.43 ± 0.84 1.43 ± 0.83
Seafood 0.47 ± 0.42 0.54 ± 0.46 0.59 ± 0.48 0.56 ± 0.48 0.52 ± 0.44 0.52 ± 0.46 0.54 ± 0.43
Rich in macro nutrients (n = 3,107–3,117) (n = 3,364–3,371) (n = 2,672–2,685) (n = 3,345–3,357) (n = 5,798–5,815) (n = 1739–1747) (n = 5,102–5,114)
High in fat 2.70 ± 1.93 2.84 ± 1.83 3.01 ± 1.85 3.20 ± 1.98 2.63 ± 1.79 2.99 ± 2.01 2.82 ± 1.80
High in carbohydrates 10.87 ± 4.56 11.45 ± 4.60 11.94 ± 4.50 11.46 ± 4.67 11.36 ± 4.52 11.32 ± 4.85 11.51 ± 4.41
High in sugar 2.98 ± 2.28 2.72 ± 2.14 2.52 ± 2.08 3.22 ± 2.43 2.48 ± 1.97 3.05 ± 2.41 2.54 ± 1.99
High in protein 9.42 ± 4.94 9.35 ± 4.55 9.13 ± 4.58 10.20 ± 5.12 8.80 ± 4.35 9.73 ± 5.15 9.12 ± 4.41
Dietary patterns (n = 3,108–3,110) (n = 3,367–3,368) (n = 2,674–2,679) (n = 3,348–3,353) (n = 2,801–5,804) (n = 1740–1744) (n = 5,103–5,106)
Unhealthy dietary pattern 3.92 ± 2.52 3.83 ± 2.27 3.66 ± 2.24* 4.37 ± 2.63 3.49 ± 2.11* 4.07 ± 2.56 3.66 ± 2.16*
Healthy dietary pattern 5.58 ± 3.36 6.33 ± 3.70 6.76 ± 3.70* 5.40 ± 3.14 6.67 ± 3.80* 5.72 ± 3.44 6.55 ± 3.64*
Abbreviations: HI, hyperactivity/impulsivity; IA, inattention.
a
Times or servings/day.
LI ET AL.5
respectively. The broad-sense heritability (A + D) was 37% for inatten-
tion and hyperactivity/impulsivity. For dietary habits, the heritability
was estimated as 38% (95% CI: 33, 44) for high consumption of high-
sugar food and 36% (95% CI: 31, 42) for unhealthy dietary habits. AE
models provided the best fit to the data on ADHD symptom dimen-
sions and dietary habits. Genetic correlations, except for hyperactivity/
impulsivity and unhealthy food, were statistically significant, ranging
from 0.09 (95% CI: 0.002, 0.19) for hyperactivity/impulsivity and high-
sugar food to 0.16 (95% CI: 0.07, 0.25) for inattention and high-sugar
food. All nonshared environmental correlations were statistically
significant. The bivariate heritability estimates (the fraction of pheno-
typic covariance explained by genetic influences) were 44% (95% CI:
18, 70), 40% (95% CI: 10, 69), and 37% (95% CI: 1, 71) for inattention
and high-sugar food, inattention and unhealthy dietary pattern, and
hyperactivity/impulsivity and high-sugar food, respectively.
In the MZ twin intrapair differences model (Table 5), correlations
of the intrapair differences in ADHD symptoms and the intrapair dif-
ferences in dietary habits, except for hyperactivity/impulsivity and
high-sugar food, were statistically significant, ranging from 0.08 (95%
CI: 0.01, 0.15) to 0.13 (95% CI: 0.05, 0.20). Therefore, in a genetically
FIGURE 1 The correlations with 95% confidence intervals between ADHD trait dimensions and different dietary habits (adjusted the
relatedness of individuals by using Partial pairwise Pearson's correlation analysis), N= 8,731 [Color figure can be viewed at
wileyonlinelibrary.com]
TABLE 2 The correlations with 95%
confidence intervals between ADHD trait
dimensions and different dietary habits,
adjusted for relatedness of individuals
and the other trait in each model,
N= 8,731
IA
a
HI
b
Food groups Fruits −0.06 (−0.08,-0.04) 0.00 (−0.02,0.02)
Vegetables −0.06 (−0.08,-0.04) 0.01 (−0.01,0.03)
Dairy 0.01 (−0.01,0.03) 0.02 (−0.00,0.04)
Meat 0.00 (−0.02,0.03) 0.01 (−0.01,0.03)
Seafood 0.02 (−0.00,0.04) 0.02 (0.00,0.05)
Rich in macro nutrients High in fat 0.05 (0.03,0.07) 0.01 (−0.02,0.03)
High in carbohydrates −0.00 (−0.03,0.02) 0.00 (−0.02,0.02)
High in sugar 0.10 (0.08,0.12) 0.03 (0.01,0.06)
High in protein 0.02 (−0.00,0.04) 0.03 (0.00,0.05)
Dietary patterns Unhealthy dietary pattern 0.09 (0.07,0.11) 0.04 (0.01,0.06)
Healthy dietary pattern −0.06 (−0.08,-0.04) 0.01 (−0.01,0.03)
Abbreviations: HI, Hyperactivity/impulsivity; IA, Inattention.
a
Adjusted hyperactivity-impulsivity for the correlations between inattention and dietary habits.
b
Adjusted inattention for the correlations between hyperactivity-impulsivity and dietary habits.
6LI ET AL.
identical twin pair, the twin who has more ADHD symptoms is likely
to also consumes more high sugar and unhealthy food than
his/her twin.
4|DISCUSSION
In this nationwide population-based sample of adult twins, we identi-
fied positive associations between self-reported trait dimensions of
ADHD and intake of seafood, high-fat food, high-sugar food, high-
protein food, and an unhealthy dietary pattern, and negative associa-
tions with consumption of fruits, vegetables, and a healthy dietary
pattern. However, all of the associations are small in magnitude. These
associations were stronger for inattention compared to hyperactivity/
impulsivity. This pattern of associations was also reflected at the etio-
logical level, where we found a slightly stronger genetic correlation of
inattention with dietary habits than of hyperactivity/impulsivity with
dietary habits. Nonshared environmental influences also contributed
TABLE 3 Intraclass correlations and
cross-twin cross-trait correlations with
95% confidence intervals for ADHD trait
dimensions and dietary habits
Intraclass correlations
IA HI High-sugar food Unhealthy dietary pattern
MZ 0.38 (0.33, 0.40) 0.39 (0.33, 0.40) 0.42 (0.33, 0.45) 0.42 (0.33, 0.45)
DZ 0.11 (0.07, 0.15) 0.14 (0.10, 0.18) 0.16 (0.09, 0.22) 0.16 (0.09, 0.23)
Cross-twin cross-trait correlations
IA-high-sugar
IA-unhealthy
dietary pattern HI-high-sugar
HI-unhealthy
dietary pattern
MZ 0.06 (0.00, 0.11) 0.05 (0.00, 0.11) 0.04 (−0.02, 0.09) 0.02(−0.04, 0.07)
DZ 0.00 (−0.05, 0.05) 0.03 (−0.02, 0.08) 0.02 (−0.03, 0.07) −0.01(−0.06, 0.04)
Abbreviations: IA: Inattention, HI: Hyperactivity/impulsivity.
TABLE 4 Estimates of genetic and environmental effect (95% confidence intervals) from bivariate AE models
AEr
A
r
E
r
P
Bivariate A Bivariate E
IA and high-sugar food
IA 0.34 (0.30, 0.37) 0.66 (0.63, 0.70) .16 (.07, .25) .11 (.06, .16) .13 (.11, .15) 0.44 (0.18, 0.70) 0.56 (0.30, 0.82)
High-sugar food 0.38 (0.33.0.43) 0.62 (0.56, 0.67)
IA and unhealthy
dietary pattern
IA 0.34 (0.30, 0.37) 0.66 (0.63, 0.70) .13 (.03, .22) .10 (.05, .15) .11 (.09, .13) 0.40 (0.10, 0.70) 0.60 (0.30, 0.90)
Unhealthy food 0.36 (0.31, 0.42) 0.64 (0.58, 0.69)
HI and high-sugar food
HI 0.35 (0.32, 0.38) 0.65 (0.62, 0.68) .09 (.002, .19) .09 (.04, .15) .09 (.07, .11) 0.37 (0.01, 0.71) 0.63 (0.30, 0.99)
High-sugar food 0.38 (0.33, 0.44) 0.62 (0.56, 0.67)
HI and unhealthy
dietary pattern
HI 0.35 (0.32, 0.38) 0.65 (0.62, 0.68) .05 (−.05, .14) .11 (.06, .16) .09 (.07, .11) 0.20 (−0.21, 0.56) 0.80 (0.44, 1.20)
Unhealthy food 0.36 (0.31, 0.42) 0.64 (0.58, 0.69)
Note: Bivariate A (bivariate heritability) refers to the amount of covariance between the two phenotypes explained by A, similarly for E.
Abbreviations: A, additive genetic factors; D, dominant genetic factors; E, nonshared environmental factors; HI, hyperactivity/impulsivity; IA, inattention.
TABLE 5 Results from MZ Twin
intrapair differences model Differences (Twin1-Twin2) NCorrelation 95%ci p-value
IA High-sugar food 708 0.13 (0.05, 0.20) <.01
IA Unhealthy food 709 0.12 (0.04, 0,19) <.01
HI High-sugar food 711 0.06 (−0.01, 0.14) .09
HI Unhealthy food 712 0.08 (0.01, 0.15) .04
Abbreviations: HI, hyperactivity/impulsivity; IA, inattention.
LI ET AL.7
to the overlap between ADHD symptom dimensions and consump-
tion of high-sugar food and unhealthy dietary pattern. However,
shared environmental influences probably contributed relatively little
to the associations between ADHD symptoms and dietary habits. Our
findings contribute to a better understanding of common etiological
pathways between ADHD symptoms and various dietary habits.
At the phenotypic level, our results are in line with previous study
from adults associating elevated levels of self-reported ADHD symp-
toms with higher consumption of sweet food and lower consumption
of vegetables and fruits (Weissenberger et al., 2018). However, the
case–control study with 51 young adults (aged 18–25) suggested that
nutrient intake was not associated with ADHD, but the conclusion
was limited by the small sample size leading to low statistical power
(Holton et al., 2019). Our results are also consistent with prior studies
based on children and adolescents (Del-Ponte, Quinte, Cruz,
Grellert, & Santos, 2019), which may indicate that associations
between ADHD symptoms and dietary habits are stable across the
lifespan. We further found that all associations were consistent by
age, sex, and SES. Given the chronic nature of ADHD related prob-
lems, people with ADHD are most likely exposed to unhealthy dietary
factors across a substantial period of time, which may in part explain
the well-established increased risk for a variety of psychiatric and
somatic morbidities (Weissenberger et al., 2017).
Our findings extend the previous literature in four important
ways. First, the current study is the first to identify a dimension-
specific overlap between ADHD and different dietary habits, with a
stronger correlation of inattention with dietary habits than hyperactiv-
ity/impulsivity with dietary habits. Support for a dimension specific
association has also been observed for ADHD symptoms and binge-
eating behavior in adults (Capusan et al., 2017). One explanation for
the minimal contribution of hyperactivity/impulsivity on dietary habits
in adults is that hyperactive/impulsive symptoms tend to decrease at
a higher rate with age compared with inattention symptoms (Willcutt
et al., 2012). Future genomic studies on the association between
ADHD and dietary habits may benefit from including information
about ADHD symptom dimensions and/or subtypes.
Second, our results suggest the association between ADHD
symptoms and different dietary habits is in part explained by shared
genetic factors. Part of the genetic overlap may reflect genetic risk
variants with general effects cutting across boundaries between neu-
ropsychiatric traits and nutrition-related or metabolic problems
(Demontis et al., 2018; Meddens et al., 2018; Watson et al., 2019).
Another more specific mechanism may involve the addictive potential
of highly palatable foods (such as sweet, fatty, and salty foods). It is
well established that the genetic liability of ADHD is in part shared
with the genetic liability of several addiction disorders, such as alco-
holism (Edwards & Kendler, 2012), pathological gambling (Comings
et al., 1999), internet and videogame addiction (Weinstein &
Weizman, 2012), and substance abuse (Zheng Chang, Lichtenstein, &
Larsson, 2012). Our findings may therefore indicate that shared
genetic factors of ADHD symptoms and dietary habits may partly
reflect a common genetic pathway for different addictive behaviors.
Typically, substance use disorders are explained by impulsivity
(de Wit, 2009), although, in line with previous studies on other addic-
tive behaviors (Burke, Loeber, White, Stouthamer-Loeber, &
Pardini, 2007; Wang, Yao, Zhou, Liu, & Lv, 2017) reported attention
problems may potentially play a potent role more broadly in addictive
behaviors beyond diet.
Third, nonshared environmental factors also contributed substan-
tially to the overlap between ADHD symptoms and different dietary
habits. Research has not previously focused on such factors and how
they possibly contribute to the co-occurrence of ADHD symptoms
and different dietary habits. Some studies have reported that screen
time and level of physical activity are associated with ADHD symp-
toms, and there is also support for a link with suggested dietary habits
(Mian et al., 2019; Rios-Hernandez, Alda, Farran-Codina, Ferreira-Gar-
cia, & Izquierdo-Pulido, 2017), but whether such factors explain the
nonshared environmental overlap between ADHD symptom dimen-
sions and dietary habits remains to investigated. Future research on
nonshared environmental risk factors may not only aid in our under-
standing of the association between diet, ADHD and related lifestyle
factors and disorders, but also help identify novel prevention and
intervention target.
Fourth, although the associations between ADHD symptoms and
dietary habits could be explained by common genetic or environmen-
tal determinants, the significant genetic and nonshared environmental
correlations, and significant MZ twin intrapair differences also pro-
vided support for a potential causal link between inattention and die-
tary habits. However, due to the similarity of the etiology of the two
traits, we were unable to further test the test the direction of causa-
tion in the current cross-sectional twin data. Recently, a cohort study
in Dutch children was the first to test bidirectional associations
between ADHD symptoms and diets, indicating that children's ADHD
symptoms predicted poor diet in later life, but that diet quality was
not an independent predictor of later ADHD symptoms (Mian
et al., 2019). Therefore, future longitudinal studies (e.g., cross-lagged
models) and various study designs (e.g., Mendelian randomization) are
needed to replicate this finding in children and adolescents, and to
explore the directions of effect in adults.
4.1 |Strengths and limitations
Our study has several strengths, including but not limited to, a very
large adult twin sample, focus on two separate ADHD trait dimen-
sions and extensive dietary phenotyping as well as an exploration of
potential modifying effects of age, sex, and SES.
Several limitations should be considered. First, the associations
between ADHD symptoms and dietary habits might be biased toward
null as it is known that self-report among adults underestimates
ADHD-symptoms compared to reports from other informants (Brikell,
Kuja-Halkola, & Larsson, 2015; Merwood et al., 2013). Consequently,
the heritability estimates for self-reported ADHD symptoms in adults
were consistent with prior research using self-ratings (S. V. Faraone &
Larsson, 2019; Larsson et al., 2013), but substantially lower than those
based on multiple raters or a clinical ADHD diagnosis (Brikell
8LI ET AL.
et al., 2015; Z. Chang, Lichtenstein, Asherson, & Larsson, 2013; Lar-
sson et al., 2014). Future research needs to resolve if observed pheno-
typic and genetic associations are stronger using other assessments of
ADHD and dietary habits (e.g., 24-hr calls, food records, or macronu-
trients assessment). Second, the response rate in our population-
based twin sample was moderate (59.6%), and FFQ as a voluntary
section had a lower response rate of 36.8%. A previous study from
our team (Larsson et al., 2013) explored differences between partici-
pants and nonparticipants in the STAGE sample, and revealed that
females with higher SES and lower levels of behavioral problems were
more likely to participate in the STAGE questionnaires. In contrast,
when comparing STAGE participants that responded to both the
ADHD questionnaire and the FFQ with those that only responded to
the ADHD questionnaire, we found that females with lower SES and
higher levels of behavior problems were more likely to respond to
both. Thus, our phenotypic results may not generalize to the general
population. However, a similar response rate was found in DZ and MZ
twins, which indicates that bias due to nonrandom missing probably
had little impact on the estimates of the relative contribution of
genetic and environmental influences.
Overall, we have found evidence for both phenotypic and genetic
correlations between ADHD symptoms and unhealthy dietary habits
in adulthood. Future longitudinal studies with various assessments of
ADHD and dietary habits are needed to explore the casual associa-
tions between ADHD symptoms and dietary habits in adults.
ACKNOWLEDGMENTS
We acknowledge The Swedish Twin Registry for access to data. The
Swedish Twin Registry is managed by Karolinska Institutet and
receives funding through the Swedish Research Council under the
grant No. 2017-00641. This project has also received funding from
the Swedish Research Council (2018-02599), the Swedish Brain
Foundation (FO2018-0273) and the European Union's Horizon 2020
research and innovation programme under grant agreement 728018.
The funders had no role in the design and conduct of the study; col-
lection, management, analysis, and interpretation of the data; prepara-
tion, review, or approval of the manuscript; and decision to submit the
manuscript for publication.
CONFLICT OF INTEREST
Dr. Larsson has served as a speaker for Evolan and Shire and has
received research grants from Shire; all outside the submitted work.
The remaining authors declare that they have no conflict of interest.
AUTHOR CONTRIBUTIONS
Lin Li, Katarina Bälter, and Henrik Larsson were responsible for the
study concept and design. Lin Li performed the analyses under the
supervision of Mark J. Taylor. Tor-Arne Hegvik contributed to all the
visualizations. Lin Li drafted the manuscript. Ralf Kuja-Halkola, Qi
Chen, Tor-Arne Hegvik, Ashley E. Tate, Zheng Chang, Alejandro Arias-
Vásquez and Catharina A Hartman gave inputs regarding the overall
interpretation of the method and results and provided critical revision
of the manuscript. All authors critically reviewed content and
approved the final version for publication.
DATA AVAILABILITY STATEMENT
Research data are not shared.
ETHICS STATEMENT
The authors assert that all procedures contributing to this work com-
ply with the ethical standards of the relevant national and institutional
committees on human experimentation and with the Helsinki Declara-
tion of 1975, as revised in 2008.
ORCID
Lin Li https://orcid.org/0000-0002-7946-4574
Ralf Kuja-Halkola https://orcid.org/0000-0002-3765-2067
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of this article.
How to cite this article: Li L, Taylor MJ, Bälter K, et al.
Attention-deficit/hyperactivity disorder symptoms and dietary
habits in adulthood: A large population-based twin study in
Sweden. Am J Med Genet Part B. 2020;1–11. https://doi.org/
10.1002/ajmg.b.32825
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