Received: 28 October 2022 DOI: 10.1002/jcv2.12158 RESEARCH REVIEW - Accepted: 16 February 2023 Attention‐deficit/hyperactivity disorder is associated with increased risk of cardiovascular diseases: A systematic review and meta‐analysis Lin Li1,2 | Honghui Yao2 | Le Zhang2 | Miguel Garcia‐Argibay1 | Ebba Du Rietz2 | Isabell Brikell2,3 | Marco Solmi4,5,6,7,8 | 8,9,10,11 12,13,14,15 Samuele Cortese | J. Antoni Ramos‐Quiroga | Marta Ribasés13,14,15,16 | Zheng Chang2 | Henrik Larsson1,2 Correspondence Lin Li, Department of Medical Epidemiology Abstract and Biostatistics, Karolinska Institutet, Nobels Attention‐deficit/hyperactivity disorder (ADHD) often co‐occurs with other psy- väg 12A, SE‐17177, Stockholm, Sweden. Email: lin.li@ki.se chiatric and physical diseases. However, available evidence on associations between ADHD and cardiovascular diseases (CVDs) is mixed. To systematically review, Funding information quantitatively synthesize, and appraise available evidence on the link between The Swedish Society for Medical Research (SSMF), Grant/Award Number: PD20‐0036; ADHD with CVDs, we searched relevant articles in PubMed, Embase, PsycINFO, and Vetenskapsrådet; Hjärnfonden; Web of Science from inception to May 1, 2022. Study quality was assessed by using Forskningsrådet om Hälsa, Arbetsliv och Välfärd; Karolinska Institutet; Swedish Brain the Newcastle‐Ottawa Scale, and random‐effects model meta‐analyses were per- Foundation, Grant/Award Number: FO2021‐ formed. A total of 18,391,169 (ADHD: n = 421,224) individuals from 11 studies 0115; European Union’s Horizon 2020 research and innovation programme, Grant/ were included in our systematic review and 8,196,648 (ADHD = 332,619) in- Award Numbers: 667302, 965381 dividuals from five studies were included in the main meta‐analysis of adjusted estimates. Pooled estimates showed that ADHD was significantly associated with an increased risk of CVDs in analyses based on adjusted effect size (odds ratio (OR) = 1.96; 95% confidence interval (CI) = 1.19–2.23, Q = 140.74, PQ < 0.001, I2 = 97.2%). When restricted among adults, the heterogeneity declined to null (OR = 1.73; 95% CI = 1.14–2.62, Q = 6.28, PQ = 0.10, I2 = 6.28%), suggesting age might be the main source of heterogeneity. In subgroup analyses, we found increased risk of CVDs associated with ADHD across age groups, type of CVDs, and data sources. This systematic review and meta‐analyses indicate that ADHD is associated with increased risk for CVDs, but further studies with various study designs are warranted to advance the understanding of the underlying mechanisms for the observed association between ADHD and CVDs. Additional research is also needed to resolve the role of ADHD medications which remains unclear due to the limited number of primary studies exploring this issue. KEYWORDS attention‐deficit/hyperactivity disorder, cardiovascular diseases, meta‐analysis, observational studies, systematic review Lin Li, Honghui Yao and Le Zhang contributed equally to this work. - This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, pro- vided the original work is properly cited. © 2023 The Authors. JCPP Advances published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health. JCPP Advances. 2023;3:e12158. wileyonlinelibrary.com/journal/jcv2 1 of 14 https://doi.org/10.1002/jcv2.12158 2 of 14 - LI ET AL. INTRODUCTION Key Points Cardiovascular diseases (CVDs) are the leading cause of morbidity, � Evidence on the associations between ADHD and CVDs mortality, and rising health care costs worldwide (Mensah is mixed. et al., 2019; Vos et al., 2020). Cardiovascular diseases caused an � Individuals with ADHD were nearly twice as likely to estimated 18.6 million deaths in 2019 worldwide, corresponding to develop at least one CVD, compare with non‐ADHD. almost 400 million years of life lost and another 34.4 million years � The observed strength of associations was largely com- lived with disability (Roth et al., 2020). It is therefore a public health parable to estimates of associations between severe priority to gain further insight into the factors that contribute to mental illness and CVDs. CVDs in order to guide prevention and clinical practice. Besides � More studies are needed to explore the role of ADHD traditional CVD risk factors, such as overweight/obesity, diabetes, medications. and smoking, concerns around the risk for CVDs in individuals with psychiatric disorders are growing (Correll et al., 2017, 2022; Ever- son‐Rose & Lewis, 2005; Pedersen et al., 2017; Solmi et al., 2021). Attention‐deficit/hyperactivity disorder (ADHD) is one of the METHODS most prevalent psychiatric disorders, affecting around 5% of children and 2.5% of adults (Davidson, 2007; Faraone et al., 2015). It is The study was conducted and reported following the Preferred characterized by developmentally inappropriate, pervasive and Reporting Items for Systematic Reviews and Meta‐Analyses guidelines impairing inattention and/or hyperactivity‐impulsivity. In addition to (Boutron et al., 2021) and Conducting Systematic Reviews and Meta‐ the core clinical symptoms of ADHD, co‐occurring poor mental and Analyses of Observational Studies of Etiology (Dekkers et al., 2019). physical comorbidities are prevalent in individuals with ADHD This protocol was registered in the International Prospective Register (Angold et al., 1999). However, compared with the extensive of Systematic Reviews (PROSPERO: CRD42021274367) (Lin Li research of psychiatric comorbidities in ADHD (Gnanavel et al., 2021). et al., 2019), physical comorbidities (Arrondo et al., 2022), such as CVDs, have received less attention, particularly among adults (Instanes et al., 2018). Search strategy There are several reasons why individuals with ADHD could be at increased risk for CVDs. First, previous research suggests that the A systematic search for observational studies was conducted in link between ADHD and CVDs is biologically plausible via immune PubMed, Embase, PsycINFO, and Web of Science databases, up to system abnormalities (Fernández‐Ruiz, 2016; Hoekstra, 2019), neu- May 1, 2021, with no language and article type restrictions. We used romodulator dysregulation (Ahmad Banday & Lokhandwala, 2006; various combinations of the following keywords “cardiovascular dis- Misener et al., 2004), and dysregulation of the hypothalamic‐ ease”, “coronary heart disease”, “heart disease”, “sudden death”, pituitary‐adrenal (HPA) axis (Corominas et al., 2012; Jokinen & “ischemic heart disease”, “hypertension”, “cerebrovascular disease”, Nordström, 2009). Second, individuals with ADHD are at increased “stroke”, “transient ischemic attack”, “attention‐deficit hyperactivity risk for unhealthy lifestyle factors (e.g., smoking, obesity, poor disorder”, “central nervous system stimulants” and “observational physical activity) (Cook et al., 2014; Cortese et al., 2015; van study”. The complete search strategy is presented in Table S1. In Amsterdam et al., 2018), which are all well‐established risk factors addition, we performed manual searches through the reference lists for CVDs (Dwivedi et al., 2020; Mons et al., 2015; Wilmot of original publications and reviewed articles to identify further et al., 2012). Third, previous studies indicate that several psychiatric pertinent studies. comorbidities of ADHD, for example, depression (Hare et al., 2014; Nicholson et al., 2006), substance use disorders (Aldridge et al., 2018; Roerecke & Rehm, 2014), schizophrenia (Hennekens et al., 2005), Inclusion criteria bipolar disorder (Swartz & Fagiolini, 2012), and anxiety disorder (Tully et al., 2016), are associated with CVDs. Fourth, even though We included all types of observational (cross‐sectional or prospec- findings are inconclusive, there has been a long‐standing concern tive) studies providing data on the strength of the association be- around a potential increased risk of cardiovascular events due to the tween ADHD and CVDs in children, adolescents, or adults. Eligible use of stimulant medications to treat ADHD (Liu et al., 2019; Zhang definitions of ADHD were as follows: (a) a categorical diagnosis of et al., 2022). However, the available epidemiological findings are ADHD according to the DSM‐III, DSM‐III‐R, DSM‐IV, DSM‐IV‐TR, mixed, despite the potential hypotheses for an increased risk of DSM‐5, ICD‐9, or ICD‐10; (b) ADHD‐medication prescriptions as a CVDs in ADHD. proxy for ADHD diagnosis; (c) ADHD symptoms based on value The aim of the current study was to conduct a systematic review above cut‐off on a validated self/parents/teacher‐reported ADHD and meta‐analysis to provide a quantitative summary of observa- questionnaire; (d) ADHD diagnosed via a structured psychiatric tional studies on the associations between ADHD and CVDs. A sec- interview or positive answer by self/parents/teachers to the question ond aim was to evaluate the impact of study design and confounder ‘Did your doctor ever tell you that you/the child have ADHD?’ Titles, adjustment (e.g., sociodemographic factors, traditional risk factors for abstracts, and full text of included studies were screened indepen- CVDs, and use of medications) on the observed associations, in order dently by two authors. Discrepancies were resolved through dis- to provide insights into underlying mechanisms. cussion with a senior investigator. ADHD IS ASSOCIATED WITH INCREASED RISK OF CVDS - 3 of 14 The main outcome was the maximally adjusted odds ratio (OR), Heterogeneity across studies was described by Cochran's Q test risk ratio, and hazard ratio, with their corresponding 95% confidence and the inconsistency index (I2). If significant heterogeneity was interval (CI) ratio expressing the association between ADHD and detected by the Q test, we considered I2 values greater than 75% as CVDs. The secondary outcome was the unadjusted estimate of the high heterogeneity (Higgins & Thompson, 2002). Restricted associations. When sufficient data (i.e., sample size, prevalence of maximum likelihood method was used to estimate between‐study ADHD, CVDs) was available, crude ORs were manually calculated or variability, with Hartung‐Knapp‐Sidik‐Jonkman CI for the summary obtained by contacting the original authors, if not reported in the effect. To evaluate each study's effect on the overall effect size, a original paper. The choice of primary and secondary outcomes was leave‐one‐out analysis was also conducted. The publication bias was made because adjusted estimates are more informative and poten- first assessed through visual inspection of the funnel plot and then tially less prone to biased derived from the selection of the partici- tested quantitatively with Egger's test. All analyses were performed pants (Cortese et al., 2022). with Stata 16.0 (StataCorp L.P., College Station, TX). Data extraction RESULTS The following information was extracted from each study for the Study characteristics qualitative and quantitative synthesis: name of the first author, year of publication, sample size, data source, study country, age and sex The study selection process is shown in Figure 1, and the list of for participants, study design, years of original data collection, the excluded articles (with reasons) after the full‐text screen is presented definition of ADHD and CVDs, effect size (ORs, RRs and HRs) and in Table S2. Table 1 shows the main characteristics of the 11 original information on confounding adjustment. We extracted both adjusted studies included in the systematic review (Akmatov et al., 2019; and unadjusted ratios if available, and we used the maximally Chen et al., 2018; Du Rietz et al., 2021; Fuemmeler et al., 2011; adjusted ratios in the analyses. Unadjusted effect sizes were calcu- Grisaru et al., 2018; Li et al., 2022; Nilgün et al., 2018; Ramos Ola- lated based on the information provided in the paper when neces- zagasti et al., 2013; Semeijn et al., 2013; Spencer et al., 2014; Xu sary. Two authors conducted the data extraction process separately, et al., 2022). Among the 18,391,169 participants from six countries, a and any disagreements were resolved by discussing with a third total of 421,214 individuals were with ADHD. The age of participants investigator. ranged from 5 to 81 years old, and 9,889,671 (53.8%) participants The quality assessment of eligible studies was performed using were men. The publication years were between 2011 and 2022. The the Newcastle‐Ottawa Scale (NOS) by two independent authors, and most common study design included in the meta‐analysis were cross‐ the discrepancies were solved by consensus. This 9‐star scale con- sectional (n = 4) (Akmatov et al., 2019; Chen et al., 2018; Semeijn sists of three parts: selection of participants and the measurement of et al., 2013; Xu et al., 2022), cohort (n = 4) (Du Rietz et al., 2021; exposure (4 stars), comparability (2 stars), and assessment of out- Fuemmeler et al., 2011; Li et al., 2022; Ramos Olazagasti et al., 2013), comes and adequate follow‐up (3 stars) (Wells et al., 2000). A higher followed by case‐control (n = 3) (Grisaru et al., 2018; Nilgün score on the NOS represents a higher‐quality study, and generally, a et al., 2018; Spencer et al., 2014) studies. These studies were con- score of 0–3, 4–6, or 7–9 is regarded as low‐, moderate‐, or high‐ ducted in the United States, Sweden, Germany, Turkey, Netherlands, quality, respectively (Wells et al., 2000). and Canada. Of the 11 studies, five (Akmatov et al., 2019; Chen et al., 2018; Du Rietz et al., 2021; Fuemmeler et al., 2011; Li et al., 2022) obtained data from healthcare/insurance registries, five Statistical analysis (Grisaru et al., 2018; Nilgün et al., 2018; Ramos Olazagasti et al., 2013; Semeijn et al., 2013; Spencer et al., 2014) obtained data We first described the characteristics of the included studies and from research samples, and one (Xu et al., 2022) used data from a various confounding adjustment strategies to provide an overall specific national cohort. Therefore, register‐based or electronic picture of the current evidence. Meta‐analyses were conducted health care databases comprising large numbers of participants were using random‐effects models in order to take into account het- the most commonly used data source for the studied associations. erogeneity between studies, and the results were summarized in The definition of ADHD varied across studies, self/teacher/parent‐ forest plots. When there were several studies from the same reported ADHD symptoms (Fuemmeler et al., 2011; Ramos Olaza- population, only the one with the largest sample size was included gasti et al., 2013; Semeijn et al., 2013; Spencer et al., 2014; Xu in the meta‐analysis to avoid overrepresentation bias (Li et al., 2022) were the most widely used measurements, followed by et al., 2020). ORs from logistic regression and HRs from Cox electronic health records based on codes in the ICD codes or the regression were combined because they closely approximate each DSM‐IV (Akmatov et al., 2019; Chen et al., 2018; Du Rietz other (D'Agostino et al., 1990). We then meta‐analyzed adjusted et al., 2021; Li et al., 2022; Nilgün et al., 2018). With regards to the and unadjusted ORs across all studies. Additionally, several sub- CVD outcomes, various measurements were used, including CVD groups and sensitivity analyses were conducted to investigate diagnosis based on ICD codes and several specific recommendations/ whether the main results were robust across age groups (adults and guidance for hypertension (e.g. Recommendations of the American children separately), specific types of CVD (hypertension and other Heart Association). The most commonly studied CVD outcomes were types of CVDs), and different data sources (registers and research hypertension (Akmatov et al., 2019; Chen et al., 2018; Du Rietz samples). et al., 2021; Fuemmeler et al., 2011; Grisaru et al., 2018; Nilgün 4 of 14 - LI ET AL. F I G U R E 1 Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow diagram for inclusion of the studies examining the association between Attention‐deficit/hyperactivity disorder (ADHD) and cardiovascular diseases (CVDs). et al., 2018) and any type of CVDs (Li et al., 2022; Ramos Olazagasti score of each item. Generally, most studies used well‐defined expo- et al., 2013; Semeijn et al., 2013; Spencer et al., 2014; Xu et al., 2022). sures and outcomes, but some included studies with one or no star in As shown in Table 2, seven (Akmatov et al., 2019; Chen relation to ‘Comparability’ did not adjust for sex, age, and other et al., 2018; Du Rietz et al., 2021; Fuemmeler et al., 2011; Li sociodemographic characteristics. et al., 2022; Semeijn et al., 2013; Xu et al., 2022) studies (64%) accounted for potential confounders by adjusting for a number of measured covariates. However, inadequate adjustment for con- Meta‐analysis founding was commonly found among included studies. Three studies (Chen et al., 2018; Du Rietz et al., 2021; Semeijn et al., 2013) only In the main analysis, we first explored the associations between ADHD adjusted for age and gender, while the other three studies and CVDs among seven studies that provided adjusted effect sizes (Fuemmeler et al., 2011; Li et al., 2022; Xu et al., 2022) also included (Akmatov et al., 2019; Chen et al., 2018; Du Rietz et al., 2021; sociodemographic characteristics (e.g., ethnicity, education), psychi- Fuemmeler et al., 2011; Li et al., 2022; Ramos Olazagasti et al., 2013; atric comorbidities, lifestyle factors (e.g., smoking, alcohol use, and Xu et al., 2022). The three Swedish register‐based studies (Chen physical activity), and metabolic conditions (e.g., obesity, type 2 dia- et al., 2018; Du Rietz et al., 2021; Li et al., 2022) involved similar betes, and hyperlipidemia). However, these lifestyle factors, psychi- population, therefore only the largest one was included in the main atric comorbidities, and metabolic conditions are more likely analysis (Chen et al., 2018). A total of 8,196,648 (ADHD = 332,619) mediators in the pathways linking ADHD to CVDs (Figure 2) rather individuals from five studies were included in the main meta‐analysis than confounders. The potential role of familial factors was only of adjusted estimates. We found that ADHD was associated with a explored in one Swedish study (Du Rietz et al., 2021). Of particular significantly increased risk of CVDs (pooled OR = 1.96; 95% CI = 1.19– importance, only one study examined the contribution of ADHD 2.23) (Figure 3). However, heterogeneity was high and significant medication to the association between ADHD and CVDs in a sensi- (Q = 140.74, PQ < 0.001, I2 = 97.2%) and the between‐study standard tivity analysis, and suggested null effect of ADHD medication (Li variance was not high (tau2 is 0.09, 95%CI = 0.03–0.44) but with wide et al., 2022). CI. The effect size was robust in the leave‐one‐out sensitivity analysis The quality scores based on the NOS ranged from 2 to 9 stars (Figure S1), and the effect size was not driven by one single study. (Median: 5), suggesting an overall moderate quality of the included There was no evidence of publication bias for the primary outcomes studies. As shown in Table S3, the number of stars represented the (Egger test: p = 0.81) (Figures S2 and S3). TABLE 1 Overview of studies included in the systematic review. Exposure Outcomes Data sources and NOS year of original Sample N (CVD)/N N (CVD)/N Male Mean age or Age at Study Country data collection Study design size (ADHD) (NON‐ADHD) (%) age range ADHD definition assessment Definition Diseases Akmatov Germany Insurance claims Cross‐sectional 2,586,620 4837/ 16,519/ 75.6 5–14 ICD‐10 5–14 ICD‐10 Hypertension 7 (2019)a data, 2017 258,662 2,327,958 Chen Sweden National medical Cross‐sectional 5,551,807 2072/ 247,870/ 50.8 40.55 � 13.49; ICD‐9 and 10 18–64 ICD‐8, 9 and 10 Hypertension 6 (2018)a registers, 61,129 5,490,678 18–64 1968–2013 Du Rietz Sweden National medical Cohort (follow 4,789,799 NA/61,960 NA/4,727,839 51.0 18–81 ICD‐9 and 10, N/A ICD‐8, 9 and 10 Hypertension, ischaemic 8 (2021) registers, for ADHD heart disease, 1932–2013 47 years) medication pulmonary disease, atrial prescription fibrillation, heart failure, ADHD IS ASSOCIATED WITH INCREASED RISK OF CVDS stroke, peripheral vascular disease Fuemmeler United National Cohort (follow 11,015 46/262 NA/10,753 51.0 28.80 � 0.12 Self‐report N/A 7th report of the joint Hypertension 7 (2011)a States longitudinal for national committee on study of 33 years) prevention detection, adolescent evaluation, and health, 1995– treatment of high blood 2009 pressure: SBP ≥160 mm Hg and DBP ≥100 Grisaru Canada Research sample, Case‐control 3804 3/55 53/3749 51.1 6–19 Medical record 6–19 The fourth report on the Hypertension 2 (2018) 2007–2013 diagnosis evaluation and treatment of high blood pressure in children and adolescents Li (2022) Sweden National medical Cohort (follow 5,389,519 663/37,027 62,089/ 48.9 38.44 � 12.32; ICD‐9 and 10 3–73 ICD‐8, 9 and 10 CVD 9 registers, 11.8 years) 5,352,492 18–73 1941–2013 Nilgün Turkey Research sample, Case‐control 177 10/77 9/100 24.3 8.95 � 2.68; 5– DSM‐IV N/A Recommendations of the Hypertension 3 (2019) 2012 15 American heart association Olazagasti United Research sample Cohort 271 37/135 37/136 100.0 41.4 � 2.9 Teacher and 41.4 � 2.9 N/A CVD 3 (2013) States parent rating Semeijn Netherlands Research sample, Cross‐sectional 231 7/23 67/208 40.7 71.6 � 7.7 Semi‐structured N/A Self‐report CVD 5 (2013)a 2008–2009 diagnostic - interview (Continues) 5 of 14 6 of 14 - LI ET AL. When we repeated the analysis among six studies (Akmatov NOS et al., 2019; Fuemmeler et al., 2011; Grisaru et al., 2018; Nilgün 5 5 et al., 2018; Ramos Olazagasti et al., 2013; Spencer et al., 2014) with unadjusted associations (five of six estimates were manually calcu- Coronary heart disease lated), a positive association was found for CVDs in individuals with ADHD compared to controls (OR = 1.48, 95% CI = 0.97–2.26; Heart attack Q = 188.57, PQ < 0.001, I2 = 97.3%) but this was not statistically Diseases All CVD significant (Figure S4). Stroke Table 3 summarizes the results from subgroup and sensitivity analyses based on adjusted estimates. First, in the analysis limited to adults, the association between ADHD and CVDs was significant among six studies (pooled OR = 1.73; 95% CI = 1.14–2.62) without significant heterogeneity (Q = 6.28, PQ = 0.10, I2 = 6.28%), while the only study in children reported a stronger association between Self‐report Self‐report Outcomes assessment Definition ADHD and CVD (hypertension as main outcome) than adults (OR = 3.26; 95% CI = 3.00–3.55; Qbetween‐group = 86.74, p < 0.0001). As age was a main factor affecting the heterogeneity of the results, we restricted the following sensitivity analyses on type of CVDs and Age at data sources among adults. Second, among four studies with adjusted N/A N/A data on specific types of CVDs, we found that ADHD was associated with a higher risk of hypertension (OR = 1.55; 95% CI = 0.75–3.21, ADHD definition The adult ADHD (ASRS) v1.1 reportscale Q = 5.89, PQ = 0.06, I2 = 66.0%) and other CVDs (OR = 1.73; 95% symptom checklist Self‐report CI = 1.19–2.68, Q = 3.20, PQ = 0.20, I2 = 37.7%), even though the Exposure self‐ association between ADHD and Hypertension was not statistically significant. Mean age or 47.7 18 or older age range 45.5 31 � 11 DISCUSSION The meta‐analysis provided evidence that ADHD was significantly Male (NON‐ADHD) (%) associated with an increased risk of CVDs, after adjusting for po- tential confounders. However, due to the limited number of available 7650/55,938 N (CVD)/N studies and lack of information in the original studies, it is currently 1/100 unclear whether these associations can be explained by other con- founders. Therefore, there is a critical need for future studies on this N (CVD)/N understudied topic to advance the understanding of the underlying 256/1790 (ADHD) mechanism for the observed association between ADHD and CVDs, 1/98 especially the role of ADHD medications and other mediating factors, such as mental comorbid conditions (i.e. substance use disorders or Sample Cross‐sectional 57,728 depression). A better understanding of major mediating factors may 198 size inform clinical guidelines about how to intervene on CVDs in ADHD. Compared with evidence from meta‐analysis for well‐established Study design Case‐control risk factors for CVDs (i.e., smoking, obesity, physical activity/seden- tary behavior, diabetes, dyslipidemia and sleep disorders) among adults, the observed magnitude of association between ADHD and CVDs was slightly smaller than the magnitude of association for Data sources and survey, 2007 Research sample year of original National health data collection sedentary behavior (Wilmot et al., 2012), diabetes (Garcia‐Argibay Included in the adjusted meta‐analysis. and 2012 interview et al., 2023), and smoking (Mons et al., 2015), but stronger than the magnitude for dyslipidemia (Sniderman et al., 2011), obesity (Dwivedi et al., 2020) and sleep disorders (Li et al., 2014) (Table 4). When T A B L E 1 (Continued) comparing with other psychiatric disorders, the observed strength of States States Country associations between ADHD and CVDs is largely similar to estimates United United of associations of schizophrenia (Lambert et al., 2022) and substance use disorders (Gan et al., 2021) with CVDs, but stronger than stress‐ Xu (2021)a (2014) related disorder (Song et al., 2019), depression (Emdin et al., 2016), Spencer Study bipolar disorder (Lambert et al., 2022), and anxiety disorders (Ta- ble 4). (Batelaan et al., 2016) Therefore, it is important to call for a TABLE 2 Adjusted covariates in studies of Attention‐deficit/hyperactivity disorder (ADHD) and cardiovascular diseases (CVDs). Covariates adjusted in each included study Age/Year Region/race/ Education Metabolic Family Other psychiatric Psychiatric Family history Physical Study Gender of birth ethnicity achieved conditionsa income Depression comorbiditiesb Smoking Alcohol medications of CVD activity BMI ADHD IS ASSOCIATED WITH INCREASED RISK OF CVDS Akmatov (2019) � � � Chen (2018) � � Du Rietz (2021) � � Fuemmeler (2011) � � � � � � � � � Grisaru (2018) N/A Li (2022) � � � � � � � � � � Nilgün (2019) N/A Olazagasti (2013) N/A Semeijn (2013) � � Spencer (2014) N/A Xu (2021) � � � � � � � a Metabolic conditions include obesity, type 2 diabetes, dyslipidemia. b Other psychiatric comorbidities include anxiety disorder, autism spectrum disorder, bipolar disorder, conduct disorder, depressive disorder, eating disorder, intellectual disability, personality disorder, schizophrenia and substance use disorder. - 7 of 14 8 of 14 - LI ET AL. enhanced clinical awareness of cardiovascular risk among adults with further explore the underlying mechanisms. Support for a potential ADHD. causal association of ADHD with coronary heart disease (Leppert In the original studies, the type and number of possible con- et al., 2021) and stroke (Du et al., 2022) has been observed in a founding factors adjusted for varied across studies, and few studies recent Mendelian Randomization study. In addition, to identify crit- provided a clear rationale for their covariate selection, which is a ical intervention targets, more research is also needed on potential common problem in contemporary observational research on mental mediating factors, such as ADHD medication use, other psychiatric health (Larsson, 2022). Therefore, the significant adjusted association comorbidities and metabolic diseases. should also be interpreted with caution. In addition, inadequate Heterogeneity was high and significant for the main analyses, adjustment for confounding was found in most studies (e.g., only four indicating that the pooled OR cannot appropriately summarize re- studies (Akmatov et al., 2019; Fuemmeler et al., 2011; Li et al., 2022; sults from all the included individuals' studies, but it has limited effect Xu et al., 2022) considered other potential confounders except for on the conclusion of a positive association between ADHD and CVDs age and sex), and only one of the included studies adjusted for ADHD (Cortese et al., 2018; Imrey, 2020). When we restricted the analyses medication (Li et al., 2022), which may contribute to increased risk of to different age group (children and adults), the associations CVDs (Habel et al., 2011; Levin et al., 2018). Only one of the previous remained stable, but the degree of heterogeneity substantially studies used a sibling comparison design (Du Rietz et al., 2021) and decreased to null, suggesting that age might be an important factor suggested that the observed associations could also be partly affecting the heterogeneity of the results. explained by shared familial factors. Therefore, it is currently unclear Using subgroup meta‐analyses, we clarified the differences in whether any of the observed association reflect a causal effect or associations between ADHD and different types of CVD outcomes. confounding. Clearly, more research using different designs, such as The available studies indicate that individuals with ADHD had a 73% within‐family analysis or instrumental variable design, are needed to higher risk of a broad range of CVDs than those without ADHD, and systematically adjust for a broad set of possible confounders and the strength of the associations was more pronounced for cardiac arrest, hemorrhagic stroke, and peripheral vascular disease/arterio- sclerosis (Li et al., 2022). However, research from independent samples is needed to replicate their findings. On the other hand, TABLE 3 Summary of results of subgroups analyses. Number Study of studies Pooled ORs Q PQ I2 Adults 4 1.73 (1.14–2.62) 6.28 0.1 6.28% Children 1 3.26 (3.00–3.55) N/A N/A N/A Adults: Hypertension 3 1.55 (0.75–3.21) 5.89 0.06 66.0% F I G U R E 2 Casual diagram representing the potential pathways Other CVDs 3 1.73 (1.19–2.68) 3.2 0.201 37.7% of the association Attention‐deficit/hyperactivity disorder (ADHD) and cardiovascular diseases (CVDs). Abbreviation: PQ, p‐value associated to the Q statistic of heterogeneity. F I G U R E 3 Forest plot of all studies describing associations between Attention‐deficit/hyperactivity disorder (ADHD) and cardiovascular diseases (CVDs) with adjusted estimates. ADHD IS ASSOCIATED WITH INCREASED RISK OF CVDS - 9 of 14 T A B L E 4 The magnitude of associations between Attention‐deficit/hyperactivity disorder (ADHD), well‐established risk factors for cardiovascular diseases (CVDs), and psychiatric disorders with CVDs among adults. OR/HR/RR (95% CI) Type of evidence ADHD 1.73 (1.14–2.62) Meta‐analysis of observational studies Well‐established risk factors for CVD Sedentary behavior (Wilmot et al., 2012) 2.47 (1.44–4.24) Meta‐analysis of observational studies Diabetes (Garcia‐Argibay et al., 2023) 2.29 (1.48–3.55) Meta‐analysis of observational studies Smoking (Mons et al., 2015) 2.07 (1.82–2.36) Meta‐analysis of observational studies Dyslipidemia (Sniderman et al., 2011) 1.43 (1.35–1.51) Meta‐analysis of observational studies Obesity (Dwivedi et al., 2020) 1.43 (1.33–1.54) Meta‐analysis of observational studies Sleep disorders (Li et al., 2014) 1.33 (1.13–1.57) Meta‐analysis of observational studies Psychiatry comorbidities Schizophrenia (Lambert et al., 2022) 1.91 (1.52–2.41) Meta‐analysis of observational studies Substance use disorder (Gan et al., 2021) 1.70 (1.6 ˗ 1.9) Cohort study Stress‐related disorders (Song et al., 2019) 1.64 (1.45–1.84) Cohort study Depression (Emdin et al., 2016) 1.64 (0.84, 3.19) Meta‐analysis of observational studies Bipolar disorder (Lambert et al., 2022) 1.61 (1.34–1.94) Meta‐analysis of observational studies Anxiety disorders (Batelaan et al., 2016) 1.52 (1.36–1.71) Meta‐analysis of observational studies previous studies indicating that ADHD‐related traits such as impul- Future perspective sivity, hostility, and time urgency/impatience, are associated with increased risk for hypertension (Fuemmeler et al., 2011; Yan Compared with other psychiatric disorders (e.g., schizophrenia, major et al., 2003). Consistently, we also found a potential elevated risk of depression), the risk of CVDs in individuals with ADHD is largely hypertension among adults with ADHD, but this was not statistically understudied. A number of important research questions, therefore, significant. Therefore, more studies are needed to further explore the need to be addressed in future research. First, except for studies with associations between ADHD and specific types of CVDs. a specific focus on hypertension, the most commonly used measure of CVDs was a broad category, encompassing a wide range of circula- tory system diseases. Future studies are needed to explore the as- Strengths and limitations sociations between ADHD and specific types of CVDs, which is critical to enabling risk reductions via targeted intervention and This is the first systematic review and meta‐analysis, with 421,224 preventative efforts. Second, future research should also consider individuals having ADHD, to assess the relationship between ADHD careful adjustments for a wide range of possible confounders of the and CVDs. Additionally, we also conducted sensitivity and subgroup observed associations between ADHD and CVDs, including but not analyses to further evaluate the findings from the main analyses. limited to early life risk factors (e.g., preterm birth (Bavineni However, our results should be considered in the context of some et al., 2019; Franz et al., 2018) and birth weight (Franz et al., 2018; limitations. First, we reported unadjusted ORs as secondary out- Mohseni et al., 2020)) and socioeconomic status (Clark et al., 2009; comes, but most (83.3%) of the unadjusted ORs were from manually Russell et al., 2016), as well as unmeasured familial factors (Du Rietz calculated effect sizes based on available information in the original et al., 2021). To further identify critical intervention targets, future studies, as these studies did not generate unadjusted effect size of research also needs to explore the role of potential mediating factors, the association between ADHD and CVDs. Second, we attempted to such as smoking (Ambrose & Barua, 2004; McClernon & Kol- reduce publication bias by including both published and unpublished lins, 2008), sleep problems (Fan et al., 2019; Owens, 2005), metabolic studies, but bias cannot be ruled out completely. Third, most of the conditions (Landau & Pinhas‐Hamiel, 2019) (e.g., obesity (Powell‐ included studies were conducted in Europe and the U.S., which Wiley et al., 2021), types 2 diabetes mellitus (Einarson et al., 2018), limits the generalizability of the findings to other populations across dyslipidemia (Hedayatnia et al., 2020), and in particular obesity given the world. Therefore, more studies are needed to examine the as- that a recent Mendelian Randomization study suggested that obesity sociations between ADHD and CVDs using samples from different may mediate the causal association between ADHD and coronary settings and regions. Fourth, the definition and measurements of heart disease (Leppert et al., 2021). Third, only one study has ADHD and CVDs from original studies varied substantially. Due to explored the role of psychiatric comorbidities and the use of psy- the limited number of included studies, it is not possible to assess chotropic medications in the studied associations. This is an impor- the associations among different definitions, which need to be tant limitation given that ADHD is frequently comorbid with other evaluated in future systematic reviews and meta‐analyses on this psychiatric disorders (e.g., mood disorder and substance use disor- topic. der), and these conditions and related medications are in turn 10 of 14 - LI ET AL. associated with increased risk of CVDs (Li et al., 2022; Vancampfort A U T HO R C O NT R I B U TI O NS et al., 2015). Future studies should examine to what extent the Lin Li: Conceptualization, Data curation, Formal analysis, Method- observed associations between ADHD and CVDs could be explained ology, Software, Validation, Visualization, Writing – original draft, by ADHD medications, other psychiatric disorders and related med- Writing – review & editing. Honghui Yao: Data curation, Method- ications. Fourth, research is also needed to test the potential sex‐ and ology, Writing – review & editing. Le Zhang: Data curation, Meth- age differences in the associations of ADHD with CVDs, which is odology, Writing – review & editing. Miguel Garcia‐Argibay: helpful for risk stratification and individualized treatment recom- Conceptualization, Data curation, Methodology, Writing – review & mendations in clinical practice. Taken together, more studies on this editing. Ebba Du Rietz: Funding acquisition, Writing – review & topic are needed, especially using different study designs, such as editing. Marco Solmi: Methodology, Writing – review & editing. matched‐cohort studies (Du Rietz et al., 2021; Fuemmeler Samuele Cortese: Methodology, Writing – review & editing. J. et al., 2011), genetically‐informed studies (e.g., sibling comparison Antoni Ramos‐Quiroga: Methodology, Writing – review & editing. studies and Mendelian Randomization studies) (Leppert et al., 2021) Marta Ribasés: Methodology, Writing – review & editing. Zheng and advanced statistical methods (e.g. propensity score methods) to Chang: Conceptualization, Funding acquisition, Software, Supervi- account for confounding. sion, Writing – review & editing. Henrik Larsson: Conceptualization, Funding acquisition, Supervision, Writing – review & editing. CONCLUSION AC K NO W L ED G E M EN T S The project has received funding from the European Union's Horizon This systematic review and meta‐analysis suggest a significant posi- 2020 research and innovation program under grant agreements No. tive association between ADHD and CVDs. More efforts are needed 667302 and 965,381. HL acknowledge financial support from the to this substantially understudied research field. In particular, medi- Swedish Research Council (2018‐02599) and the Swedish Brain ation effects by psychiatric comorbidities and related medications, as Foundation (FO2021‐0115). ZC was supported by grants from the well as the causal mechanisms underlying the association, deserve Swedish Council for Health, Working Life and Welfare (2019‐00176). further attention because of their important public health EDR was supported by grant 2019‐01172 from the Swedish Research implications. Council for Health, Working Life, and Welfare (FORTE), grant PD20‐ 0036 from the Swedish Society for Medical Research (SSMF), and A F F I LI A T IO N S Funds from the Strategic Research Program in Epidemiology at 1 School of Medical Sciences, Örebro University, Örebro, Sweden Karolinska Institutet and Fredrik & Ingrid Thurings Stiftelse. 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden CO N F L I C T S O F I N TE R ES T S T A TE M E N T 3 Department of Biomedicine, Aarhus University, Aarhus, Denmark Dr Larsson has served as a speaker for Medice, Evolan Pharma and 4 Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada Shire/Takeda and has received research grants from Shire/Takeda; 5 Department of Mental Health, The Ottawa Hospital, Ottawa, Ontario, all outside the submitted work. He is Editor‐in‐Chief of JCPP Ad- Canada vances. Ebba Du Rietz has served as a speaker for Shire Sweden AB 6 Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program outside the submitted work. Dr. Solmi received honoraria/has been University of Ottawa, Ottawa, Ontario, Canada a consultant for Angelini, Lundbeck, Otsuka. He is a joint editor of 7 Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, JCPP Advances. S Cortese declares honoraria and reimbursement Berlin, Germany for travel and accommodation expenses for lectures from the 8 Centre for Innovation in Mental Health‐Developmental Lab, School of following non‐profit associations: Association for Child and Psychology, University of Southampton, Southampton, UK 9 Adolescent Central Health, Canadian ADHD Alliance Resource, Solent NHS Trust, Southampton, UK 10 British Association of Pharmacology, and from Healthcare Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, Hampshire, USA Convention for educational activity on ADHD. He serves on the 11 Editorial Advisory Board for JCPP Advances. Dr. J. Antoni was on Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK the speakers' bureau and/or acted as consultant for Janssen‐Cilag, 12 Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Novartis, Shire, Takeda, Bial, Shionogi, Sincrolab, Novartis, BMS, Barcelona, Barcelona, Spain Medice, Rubió, Uriach, Technofarma and Raffo in the last 3 years. 13 Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, He also received travel awards (air tickets + hotel) for taking part Spain in psychiatric meetings from Janssen‐Cilag, Rubió, Shire, Takeda, 14 Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Shionogi, Bial and Medice. The Department of Psychiatry chaired by Spain him received unrestricted educational and research support from 15 Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, the following companies in the last 3 years: Janssen‐ Cilag, Shire, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain Oryzon, Roche, Psious, and Rubió. No other disclosures were re- 16 ported. Z Chang serves on the Editorial Advisory Board for JCPP Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain Advances. ADHD IS ASSOCIATED WITH INCREASED RISK OF CVDS - 11 of 14 O PE N R E SE AR C H B A D G ES attention‐deficit/hyperactivity disorder: A population‐based cross‐ sectional study. PLoS One, 13(9), e0204516. https://doi.org/10. 1371/journal.pone.0204516 Clark, A. M., DesMeules, M., Luo, W., Duncan, A. S., & Wielgosz, A. (2009). 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