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Cardiovascular and metabolic risk
Association between personality, lifestyle behaviors, and cardiovascular diseases in type 2 diabetes mellitus: a population-based cohort study of UK Biobank data
  1. Chan Soon Park1,
  2. Jaewon Choi2,3,
  3. Soongu Kwak1,
  4. Seung-Pyo Lee1,4,
  5. Hyung-Kwan Kim1,4,
  6. Yong-Jin Kim1,4,
  7. Soo Heon Kwak2,5,
  8. Jun-Bean Park1,4
  1. 1 Cardiovascular Center, Seoul National University Hospital, Seoul, Republic of Korea
  2. 2 Division of Data Science Research, Seoul National University Hospital, Seoul, Republic of Korea
  3. 3 Department of Biomedical Sciences, Seoul National University Graduate School, Seoul National University Hospital, Seoul, Republic of Korea
  4. 4 Division of Cardiology, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
  5. 5 Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul National University College of Medicine & Seoul National University Hospital, Seoul, Republic of Korea
  1. Correspondence to Dr Jun-Bean Park; nanumy1{at}snu.ac.kr

Abstract

Introduction Various strategies aim to better assess risks and refine prevention for patients with type 2 diabetes mellitus (T2DM), who vary in cardiovascular disease (CVD) risk. However, the prognostic value of personality and its association with lifestyle factors remain elusive.

Research design and methods We identified 8794 patients with T2DM from the UK Biobank database between 2006 and 2010 and followed them up until the end of 2021. We assessed personality traits using the Big Five proxies derived from UK Biobank data: sociability, warmth, diligence, curiosity, and nervousness. Healthy lifestyle behaviors were determined from information about obesity, smoking status, and physical activity. The primary outcome was a composite of incident CVD, including myocardial infarction (MI), ischemic stroke (IS), atrial fibrillation (AF), and heart failure (HF).

Results During a median follow-up of 13.6 years, a total of 2110 patients experienced CVDs. Among personality traits, diligence was significantly associated with a reduced risk of primary and secondary outcomes. The adjusted HRs with 95% CIs were: composite CVD, 0.93 (0.89–0.97); MI 0.90 (0.82–1.00); IS 0.83 (0.74–0.94); AF 0.92 (0.85–0.98); HF 0.84 (0.76–0.91). Healthy lifestyle behaviors significantly reduced the risk of composite CVDs in groups with high and low diligence. The findings of a structural equation model showed that diligence directly affected the risk of the primary outcome or indirectly by modifying lifestyle behaviors.

Conclusion This study revealed which personality traits can influence CVD risk during T2DM and how patients might benefit from adopting healthy lifestyle behaviors in relation to personality.

  • Diabetes Mellitus, Type 2
  • Life Style

Data availability statement

Data are available upon reasonable request. The data used in the present research are available via a direct application to the UK Biobank (http://www.ukbiobank.ac.uk/register-apply/) and this research was conducted with approved access to UK Biobank data under application ID 91312. All other data supporting the research findings are available within the article and its supplementary information files and from the corresponding author upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjdrc-2024-004244

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • Personality encompasses individual differences in cognitive processes, emotionality, and behaviors and affects the initiation, maintenance, or change in health-related behaviors.

    WHAT THIS STUDY ADDS

    • Among five personality traits, high diligence independently correlated with a significantly lower risk of composite and individual cardiovascular diseases (CVDs) (myocardial infarction, ischemic stroke, atrial fibrillation, and heart failure).

    • The proportion of patients with healthy lifestyle behaviors increased as the diligence score increased, and patients with healthy lifestyle behaviors in high and low diligence subgroups had better cardiovascular outcomes than those with unhealthy lifestyle behaviors

    • Diligence had direct and indirect prognostic implications for CVDs through lifestyle behaviors.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • This study revealed which personality traits can influence CVD risk during the course of type 2 diabetes mellitus (T2DM) and how patients might benefit from adopting healthy lifestyle behaviors in relation to personality traits.

    • These findings highlight the importance of assessing personality traits for cardiovascular risk prediction and modifying lifestyle factors as preventive strategies for patients with T2DM.

    Introduction

    The estimated prevalence of diabetes mellitus (DM) is ~1 per 11 adults worldwide, with the number of individuals with DM more than tripling between 1980 and 2014.1 2 Over 90% of individuals with DM have type 2 (T2DM),3 4 which imposes substantial risks of developing cardiovascular diseases (CVDs).5 This has positioned T2DM globally as one of the most prevalent and critical risk factors for CVDs. Although extensive efforts have been dedicated to reducing cardiovascular morbidity and mortality in patients with T2DM,6 the burden of CVD in this population remains unacceptably high.5 This highlights an urgent need for more tailored risk prediction and management of patients to improve the clinical outcomes of T2DM.

    In addition to pharmacological treatment,6 non-pharmacological approaches play pivotal roles in improving glycemic control and clinical outcomes.7 Educational interventions encouraging the maintenance of healthy lifestyle behaviors are vital components of non-pharmacological management.8–11 Personality encompasses individual differences in cognitive processes, emotionality, and behaviors.12–14 Therefore, it is not particularly surprising that personality affects the initiation, maintenance, or change in health-related behaviors, either voluntarily or induced by educational intervention. One of the personality dimensions, conscientiousness, is associated positively with beneficial health-related behaviors and negatively with high-risk health-related behaviors.14 This suggested that the contribution of personality traits to the risk of adverse clinical events is important among high-risk populations, in whom the role of health-related behaviors is important. The role of personality traits in disease prognosis has been investigated based on this theoretical background for a few decades.15 16 Among several taxonomies proposed to describe personality, the Five-Factor (‘Big Five’) model has gained widespread acceptance for assessing psychological disposition.17 18 This model includes five traits that capture clinically meaningful individual variations in personality and comprise extraversion, agreeableness, conscientiousness, openness, and neuroticism. Since data on direct measures of these Big Five personality traits are not available in the UK Biobank, proxies to match all facets of the Big Five personality traits were created using self-reported information on mental health, psychological factors, and social support. These personality proxies include sociability, warmth, diligence, curiosity, and nervousness, which respectively represent extraversion, agreeableness, conscientiousness, openness, and neuroticism. The impact of personality traits has been assessed on various CVDs in the general population using proxies for the Big Five personality traits. For example, previous studies showed that diligence and sociability were protective against myocardial infarction (MI) and stroke in the general population.19 20 Neuroticism was also found to be associated with the risk of atrial fibrillation (AF) in the general population.21 High openness might have protective impacts on the risks of coronary heart disease (CHD).22 However, despite the high risk of cardiovascular complications in individuals with T2DM, data about the prognostic implications of personality traits for CVD are scant. Furthermore, considering that optimal care of T2DM demands adequate adherence to healthy lifestyle behaviors and medications, the impact of personality traits on CVD might be profound in people with T2DM. Therefore, addressing the question regarding the clinical significance of personality traits in patients with T2DM would enhance our understanding of potential factors influencing the risk of CVD and provide novel perspectives for T2DM management.

    In this study, we aimed to determine associations between the Big Five personality traits and CVDs, including MI, ischemic stroke (IS), AF, and heart failure (HF) by exploring data from the UK Biobank. We also hypothesized that health-related lifestyle behaviors could act as a mediator of the association between personality traits and CVDs. To test this hypothesis, we further assessed relationships between personality traits with the most significant association with the risk of CVDs and lifestyle behaviors, as well as the risk of incident CVDs.

    Methods

    Ethical statement and data availability

    The need for informed consent was waived as anonymized data were used. The data underlying this article will be shared on reasonable request with the corresponding author through the approval and oversight of the UK Biobank.

    Data source and study population

    The UK Biobank is a large-scale prospective cohort study of ~500 000 volunteers between 2006 and 2010.23 It contains individual demographic, clinical, and genetic information.

    Figure 1 shows a flowchart of the study. We identified 20 118 persons with previously or newly diagnosed T2DM between 2006 and 2010 based on the previously reported algorithm.24 The exclusion criteria comprised a history of MI, IS, AF, and/or HF (n=3506), incomplete responses to questionnaires assessing personality traits (n=4938), or missing data (n=2880). The final sample comprised 8794 participants. The index date was defined as the baseline presentation to the UK Biobank. We also identified 8794 individuals without diabetes who were 1-to-1 matched to patients with T2DM based on age, sex, and body mass index (BMI) to compare the two populations.

    Figure 1
    Figure 1

    Schema of study population. AF, atrial fibrillation; HF, heart failure; IS, ischemic stroke; MI, myocardial infarction; T2DM, type 2 diabetes mellitus.

    Definitions of personality, study endpoint, and covariates

    We assessed baseline personality traits using data about Big Five proxies derived from self-reported questionnaires in the UK Biobank. The proxies consisted of questions that were validated for their similarities to an established personality model (the Big Five Inventory).25 There are four or five questions for each of the personality traits: four questions for sociability, diligence, and curiosity, and five questions for warmth and nervousness. The total score for each of the five personality traits is determined by the number of questions related to each trait, with each question being worth 1 point. Online supplemental table 1 shows that personality traits were scored on a scale from 0 to 4 (sociability, diligence, and curiosity) or from 0 to 5 (warmth and nervousness) based on questionnaire responses.19 20 For analysis to evaluate the joint effect of personality traits and healthy lifestyles on the prediction of CVD risk, we dichotomized personality traits according to the scores; individuals with scores ranging from 0 to 2 or 3 or more for each personality trait were categorized as groups with low and high scores, respectively. The primary outcome of interest was a composite of an initial diagnosis of the CVDs, MI, IS, AF, and HF, all of which were defined based on the International Classification of Diseases codes in the hospital inpatient data and death register records and self-reported medical conditions by participants (online supplemental table 2). Secondary outcomes were the individual components of the primary outcome. The follow-up duration was defined as the interval between the index date and the occurrence of each CVD or 31 December 2021 for those who were censored.

    Supplemental material

    Demographic and anthropometric measurements, systolic and diastolic blood pressure, as well as the medical histories of the participants were obtained from the UK Biobank dataset. A healthy body weight was defined as a BMI <30 kg/m2 according to the previous reports.26–29 Data regarding smoking status were obtained by questionnaire or interview, and self-reported data on physical activity was collected using the short form of the International Physical Activity Questionnaire at recruitment between 2006 and 2010. We selected the following healthy lifestyle factors: never smoked, BMI <30 kg/m2, and regular moderate-to-vigorous physical activity, at least twice per week.26 Individuals with zero or one and with two or three healthy lifestyle factors were classified as having unhealthy or healthy lifestyle behaviors, respectively. Comorbidities are defined in online supplemental table 2.

    Statistical analysis

    Data are presented as numbers and frequencies for categorical variables and as means±SD. Categorical variables were compared using χ2 or Fisher exact tests as appropriate. Continuous variables between two and ≥three groups were compared using unpaired Student t-tests and one-way analysis of variance, respectively. The incidence rate (IR) was calculated as the number of events per 1000 person-years (PY). Multivariate Cox proportional hazard regression models were used to estimate HRs and their corresponding 95% CIs to assess associations between each of the personality trait and CVDs. In these analyses, each personality trait was treated as a continuous variable. Since diligence demonstrated significant prognostic implications for CVD risks, we additionally analyzed the associations between diligence, lifestyle behaviors, and CVDs. For these analyses, we categorized the patients into four groups according to the combination of high and low diligence scores and healthy and unhealthy lifestyle behaviors. When we calculated adjusted HR for an individual component of personality traits, we included age, sex, history of hypertension, dyslipidemia, chronic kidney disease, cancer, and other components of personality traits as covariates. Values with a two-sided p<0.05 were considered statistically significant. We further elucidated whether personality traits exert their effects directly on the risk of CVDs or indirectly by modifying lifestyle behaviors, using a structural equation model (SEM) analysis that enables measurements of the direct and indirect effects of variables.30 The SEM was fitted using the maximum likelihood estimation method. The goodness of fitness was evaluated using the comparative fit index, root mean square error of approximation, and standardized root mean square residual. All data were statistically analyzed using R V. 4.2.1 (The R Foundation for Statistical Computing, Vienna, Austria).

    Results

    Clinical characteristics of the study population

    We obtained data about 8794 individuals with T2DM from the UK Biobank database. Table 1 shows their baseline characteristics. Over 50% of the patients reported healthy lifestyle behaviors. The mean personality trait scores were 2.7, 3.6, 2.4, 2.1, and 1.7 for sociability, warmth, diligence, curiosity, and nervousness, respectively. The data of baseline characteristics of matched individuals without T2DM are presented as online supplemental table 3. Briefly, individuals without T2DM had higher scores in sociability, warmth, and curiosity and lower scores in diligence and nervousness compared with those with T2DM.

    View this table:
    Table 1

    Clinical characteristics of the study subjects

    Online supplemental tables 4-8 show the baseline characteristics according to each personality trait score. Lifestyle behaviors, namely obesity, smoking history, and regular physical activity, significantly differed among the scores for each personality trait.

    Associations between CVDs and personality traits

    Table 2 shows the numbers of events and IRs for the study endpoints. During a median follow-up period of 13.6 years (IQR, 10.5–14.6), 2110 incidents of the primary outcome (composite of MI, IS, AF, or HF) occurred, with an IR of 19.91 per 1,000 PY. With regard to the individual secondary outcomes, 599 MI, 431 IS, 1202 AF, and 817 HF were observed, corresponding to IRs of 5.25, 3.75, 10.85, and 7.17 per 1000 PY, respectively. Table 2 summarizes the crude and adjusted HRs for CVD, which were obtained by treating personality trait scores as continuous variables. Univariate analysis showed that diligence was significantly associated with a lower risk of the primary outcome (HR, 0.93; 95% CI, 0.89‒0.97). This remained significant after adjusting for covariates (HR, 0.91; 95% CI, 0.86‒0.96). Sociability was not significantly associated with the primary outcome in the univariate analysis (HR, 0.99; 95% CI, 0.94‒1.03), but became significantly associated in the adjusted analysis (HR, 0.94; 95% CI, 0.89‒0.99). Other personality traits, including warmth, curiosity, and nervousness, were not significantly associated with the primary outcome in either univariate or multivariate analyses. Assessment of associations between personality traits and secondary outcomes (table 2) significantly associated diligence with reductions in all individual CVD events in the univariable (MI: HR, 0.90; 95% CI, 0.82‒0.97; IS: HR, 0.88; 95% CI, 0.80‒0.97; AF: HR, 0.95; 95% CI, 0.90‒1.00; HF: HR, 0.86; 95% CI, 0.80‒0.92) and multivariable analyses (MI: HR, 0.90; 95% CI, 0.82‒1.00; IS: HR, 0.83; 95% CI, 0.74‒0.94; AF: HR, 0.92; 95% CI, 0.85‒0.98; HF: HR, 0.84; 95% CI, 0.76‒0.91). Sociability was independently associated with reductions in MI (HR, 0.91; 95% CI, 0.82‒1.00) and HF (HR, 0.92; 95% CI, 0.84‒0.99). Other personality traits were not significantly associated with the secondary outcomes. In contrast, no significant association was found between any personality trait and the primary outcome among individuals without T2DM. With regard to secondary outcomes, diligence was significantly associated with only MI and HF (MI: HR, 0.95; 95% CI, 0.80‒1.11; HF: HR, 0.83; 95% CI, 0.73‒0.95), whereas sociability was not significantly associated with any of the secondary outcomes in the multivariable analysis (online supplemental table 9).

    View this table:
    Table 2

    Cardiovascular diseases according to personality traits

    Figure 2 shows the IRs and adjusted HRs for the associations between each personality trait and any CVD, which were obtained by treating the personality trait score as a categorical variable, with a reference category score of 0. The risk of composite CVD events gradually decreased as diligence scores increased. The risk was significantly lower among patients with the highest diligence scores than those in the reference category. The risk of composite CVD events did not significantly differ according to other personality traits, including warmth, curiosity, and nervousness. The results of the secondary outcomes were largely consistent with those generated using personal traits as continuous variables (online supplemental figure 1).

    Figure 2
    Figure 2

    Risk for CVDs according to personality traits. The risk of CVDs (composite of newly diagnosed myocardial infarction, ischemic stroke, atrial fibrillation, and heart failure) is shown according to personality traits of sociability, warmth, diligence, curiosity, and nervousness. Individuals who scored zero served as reference group for all analyses. HRs with 95% CIs are shown as dot and whisker plots after adjusting for covariates. Incidence rates are represented by bars. Covariates include age, sex, history of hypertension, dyslipidemia, chronic kidney disease, cancer, and other personality traits, except personal traits of interest. CVD, cardiovascular disease.

    Implications for healthy lifestyle behaviors on personality and CVDs

    A clear and significant positive association was found between diligence scores and healthy lifestyles (figure 3A and online supplemental table 6). Specifically, as the diligence score increased, the proportion of individuals with regular physical activity, healthy body weight, and never smoking, and consequently that of those having healthy lifestyle behaviors gradually increased. To explore the joint effect of diligence status and healthy lifestyles for the prediction of the risk of CVDs, we classified the patients based on diligence scores and the number of healthy lifestyle factors: high diligence and healthy lifestyle behaviors group (n=2603), high diligence and unhealthy lifestyle behaviors group (n=1673), low diligence and healthy lifestyle behaviors group (n=2338), and low diligence and unhealthy lifestyle behaviors group (n=2180). Multivariate Cox analysis showed that the risk of composite CVD was lower in the healthy lifestyle behaviors group than in the unhealthy lifestyle behaviors group, regardless of their diligence scores (figure 3B). The results of the secondary outcomes were similar (online supplemental figure 2).

    Figure 3
    Figure 3

    Relationships between personality traits, and lifestyle behaviors, as well as CVDs. (A) The proportion of patients who never smoked, had a healthy body weight, regularly participated in physical activity, and had a healthy lifestyle is shown according to diligence scores. The line graph indicates each healthy lifestyle behavior and the bar graph indicates healthy lifestyle profile defined as having two or three healthy lifestyle behaviors. (B) Risk for composite cardiovascular diseases according to diligence scores and lifestyle behaviors. Unhealthy lifestyle behaviors and poor diligence are both associated with high risks of CVDs. (C) Structural equation model of relationships between personality traits and unhealthy lifestyle behaviors as well as CVDs. Standardized path coefficients are shown on each path as effect estimates with statistical significance. Solid lines, significant paths; dashed lines, non-significant paths. Red lines represent diligence. Unhealthy lifestyle behaviors were assessed as scores ranging from 0 to 3, with 1 point each for the following conditions: obesity, smoking, and no regular physical activity. AF, atrial fibrillation; HF, heart failure; IS, ischemic stroke; MI, myocardial infarction.

    Figure 3C shows an SEM diagram of associations between personality traits, and each of the lifestyle behaviors, and CVDs. Specifically, the model included a direct path from each personality trait to CVDs and an indirect path to CVDs via lifestyle behaviors. Assessment of the goodness-of-fit parameters indicated an adequate fit for this model (online supplemental table 10). The paths were significant from healthy lifestyle behaviors to CVDs, from diligence to CVDs, and from diligence to healthy lifestyle behaviors with coefficients of −0.086 (p<0.001),–0.037 (p=0.003), and 0.100 (p<0.001), respectively. However, the path from sociability to healthy lifestyle behaviors was significant (coefficient, 0.123; p<0.001), whereas the direct path from sociability to CVD did not show statistical significance (coefficient=0.010, p=0.406) while the path from sociability to healthy lifestyle behaviors was significant (coefficient=0.123, p<0.001). Detailed results are also provided in online supplemental table 11.

    Discussion

    This study of a cohort using the UK Biobank comprehensively examined associations between personality and each of the lifestyle behaviors, and CVDs in individuals with T2DM. The key findings were as follows: (1) among five personality traits, high diligence independently correlated with a significantly lower risk of composite and individual CVDs (MI, IS, AF, and HF); (2) as the diligence score increased, the proportion of patients with healthy lifestyle behaviors increased; (3) individuals with healthy lifestyle behaviors in high and low diligence subgroups had better cardiovascular outcomes than those with unhealthy lifestyle behaviors; (4) diligence had direct and indirect prognostic implications for CVD through lifestyle behaviors.

    Current clinical guidelines for patients with T2DM recommend managing body weight, quitting smoking, engaging in regular physical activity, and promoting emotional well-being.7 Identifying risk factors is important for developing prevention and intervention strategies to improve care for individuals with T2DM. DM-related excessive mortality has decreased in the USA due to improved control of these traditional risk factors, but T2DM is still associated with an unacceptable increase in cardiovascular mortality, up to 18%, in the contemporary era.31 Cardiovascular complications in patients with T2DM, namely, MI, IS, AF, and HF, remain leading causes of mortality, which imposes a significant burden on patients, healthcare systems, and societies.1 2 5 Optimally managed patients with T2DM have a 21% higher risk of hospitalization due to the composite of CHD, stroke, or HF than patients without diabetes.32 These findings suggest that more effort should be directed towards further reducing cardiovascular complications in patients with T2DM. Therefore, numerous studies have aimed to identify novel clinical or social characteristics related to the risk of CVDs that can guide clinicians in providing optimal care for patients with T2DM. Among several potential characteristics, personality has gained considerable attention because of its established association with individual cognition, emotions, and behaviors13 14 that can influence adherence to healthy lifestyles and medication. A previous study using the Big Five model also suggested that assessing personality is a key factor in personalizing preventive strategies.33 The findings showed that measures of personality traits in a young general population predicted a range of health outcomes in midlife, after adjusting for established risk factors, such as smoking, obesity, current or past medical conditions, socioeconomic status, education, and family medical history.33 The effect sizes of personality traits in that study were notably similar to other traditional risk factors, including smoking and socioeconomic status. Given the importance of risk factor control and medication adherence in managing patients with T2DM,34 35 we speculated that personality and clinical prognosis might be significantly associated in this population. Therefore, we explored the implications of personality traits on cardiovascular outcomes in patients with T2DM and found that conscientiousness, as indicated by the diligence scores of proxies for the Big Five model, had strong and consistent associations with composite and individual CVDs. We also found that the effect of diligence on the risk of CVDs was mediated by lifestyle behaviors, which further supports our speculation.

    Diligence, a proxy for conscientiousness, is characterized by resourceful, disciplined, and organized behavior, which is reflected in several traits such as orderliness and industriousness. Individuals with diligence are less likely to smoke and are more likely to engage in regular physical activity.36–38 We also found negative associations between diligence scores and smoking as well as obesity and a positive association with regular physical activity in patients with T2DM (figure 3A and online supplemental table 6), which substantiated previous findings. Moreover, diligence is related to better compliance with medication regimens,38 39 suggesting that diligence could be used as a screening tool for identifying patients at high risk of poor medication compliance. This is particularly important in the management of DM because medication non-compliance is prevalent and associated with adverse outcomes.35 40 41 Since we did not directly evaluate associations between personality traits and medication compliance, further studies are needed to determine whether and which personality traits are predictors of medication compliance among patients with chronic diseases including DM.

    Individuals are often confused about the implications of T2DM at the time of their diagnosis,41 which might hamper appropriate self-management activities, including lifestyle modifications. Educational and counseling interventions can effectively overcome this by providing information and emotional support so that patients with T2DM can become active participants in their care.42 43 However, the availability of healthcare professionals and the resources required to deliver diabetes education and counseling are significantly limited. Therefore, identifying subpopulations of patients at higher risk for unhealthy lifestyle behaviors and medication non-compliance and those who are likely to benefit more from targeted interventions is essential to maximizing the cost-effectiveness of this preventive approach. Taken together, our findings suggest that personality traits should be assessed to improve risk stratification in patients with T2DM. Specifically, the risk of CVDs was significantly higher in patients with T2DM with low diligence scores and more prevalent unhealthy lifestyle behaviors compared with those who had high diligence scores. Importantly, as in patients with high diligence scores, healthy lifestyle factors were associated with a reduced risk of CVDs in those with low diligence scores. These findings suggested that this subgroup of patients with T2DM could serve as a good target population for lifestyle modification interventions.

    Our study has several strengths. While a link between personality traits and CVDs has been determined in the general population,19 20 22 we are the first to do so in patients with T2DM who face a heightened risk of CVDs,5 using the UK Biobank data, which offers a large sample size of patients with T2DM with available information on personality. In this study, we could provide evidence that personalities are associated with the risk of CVDs in individuals with T2DM who may benefit from the intervention to promote healthy lifestyle behaviors. We investigated joint associations between diligence scores and lifestyle factors and the risk of CVDs and found the benefits of healthy lifestyles among patients with high and low diligence scores. Since individuals with low diligence scores were more susceptible to unhealthy lifestyle behaviors, the benefits of educating and intervening to support healthy lifestyle behaviors may be more substantial among this subpopulation of patients with T2DM. Simple screening tools, such as case-finding questionnaires, can be used to identify patients with T2DM with low diligence and, in turn, to provide more intensive educational and counseling interventions to support healthy lifestyle behaviors in this subpopulation. Further studies are needed to determine whether a more personalized approach based on the assessment of personality traits is superior, or at least complementary, to conventional population-based approaches to CVD prevention in individuals with T2DM. Mediation analysis using the SEM also revealed that the association between diligence and CVDs was partly mediated through lifestyle behaviors, providing an understanding of which type of preventive intervention might be beneficial for managing patients with T2DM and low diligence scores. Taken together, our study underscores the importance of assessing personality traits in patients with T2DM as a risk stratification method and developing effective preventive strategies to help them initiate and maintain healthy lifestyle changes. This study also has several limitations. First, the possibility of unmeasured confounders remains due to the observational cohort design, even though the study was large and well-controlled. For example, psychological factors, such as depression and anxiety, are well-known for their associations with both personality traits and CVDs, but we could not comprehensively evaluate the role of these factors. However, indexes for depression and anxiety, including the Patient Health Questionnaire-9 (PHQ-9) for depressive disorder and the Generalized Anxiety Disorder-7 (GAD-7) for anxiety disorder, were available for 2381 participants in our study population. We found that PHQ-9 was significantly associated with the risks of composite CVD events, MI, and HF but not with those of IS and atrial AF, while there was no significant association between GAD-7 and any study outcomes (online supplemental table 12). Further research is needed to confirm and clarify the interactions among psychological factors, personality traits, and CVD risk. Second, the data from the UK Biobank cohort predominantly contained samples of Caucasian European ancestry. Further investigation is needed to determine whether our findings can be generalized to other ethnicities and races. Third, we could not analyze other lifestyle factors, such as alcohol consumption, diet quality, and sleep patterns, because much information about them was unavailable. Further studies are warranted to understand whether and how these factors influence the association between personality traits and CVD risk. Fourth, we were unable to assess rates of treatment compliance among patients with T2DM, which should provide insights into the relationship between personality traits and CVDs.44

    Conclusion

    Among personality traits, a lower diligence level was significantly associated with a higher risk of CVDs, including MI, IS, AF, and HF, in patients with T2DM. Unhealthy lifestyle behaviors were more prevalent in patients with low than high diligence scores. However, healthy lifestyle factors had a protective association with CVDs in both groups of patients. These findings highlight the importance of assessing personality traits for cardiovascular risk prediction and modifying lifestyle factors as preventive strategies for patients with T2DM.

    Transparency

    J-BP affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned have been explained.

    Dissemination to participants and related patient and public communities

    There are no plans to disseminate the results to the individual study participants or the relevant patient community. Since we used deidentified data in this study, we had no direct contact information of the individual study participants for disseminating the study results.

    Data availability statement

    Data are available upon reasonable request. The data used in the present research are available via a direct application to the UK Biobank (http://www.ukbiobank.ac.uk/register-apply/) and this research was conducted with approved access to UK Biobank data under application ID 91312. All other data supporting the research findings are available within the article and its supplementary information files and from the corresponding author upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. E-2301-111-1396), which proceeded according to the principles of the Declaration of Helsinki (2013 amendment). All participants in that study provided written informed consent and the UK Biobank received ethical approval from the National Health Service Research Ethics Service (reference 11/NW/0382).

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    Footnotes

    • CSP and JC contributed equally.

    • Contributors CSP contributed to the conception and design of the work, data interpretation and analysis, and drafting of the manuscript. J-BP is the guarantor of the manuscript and contributed to the conception, design, data acquisition, and interpretation, and critical revision of the manuscript. JC and SHK contributed to the data acquisition, interpretation, and analysis. SK, S-PL, H-KK, and Y-JK contributed to the conception and design of the work and critically revised the manuscript.

    • Funding This work was supported by the National Research Foundation of Korea of the Ministry of Science and ICT (NRF-2020R1C1C1010890).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.