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ABSTRACT Depression and metabolic syndrome (MetS) are correlated, leading to an increased healthcare burden and decreased productivity. We aimed to investigate the association between

MetS-related factors and depression using a health checkup and claims database. Individuals aged 18–75 years who underwent health examinations between 2014 and 2019 were enrolled in the

study. Among 76,277 participants, “ever” and “incident” antidepressant users exhibited worse metabolic profiles and were more likely to be prescribed hypnotics and anxiolytics than “never”

users. In a nested case–control study with a 1:10 ratio of incident users to controls, MetS was associated with incident antidepressant use (odds ratio, 1.53 [95% confidence interval

1.24–1.88]) adjusted for lifestyle information obtained from a self-administered questionnaire, medical history, and medications. Other metabolic traits also showed significant associations:

body mass index (1.04 [1.02–1.06]), abdominal circumference per 10 cm (1.17 [1.08–1.27]), high blood pressure (1.17 [1.00–1.37]), glucose intolerance (1.29 [1.05–1.58]), and dyslipidemia

(1.27 [1.08–1.51]). A bodyweight increase > 10 kg from age 20 years (1.46 [1.25–1.70]) was also significantly associated with incident antidepressant use. In conclusion, metabolic

abnormalities were associated with incident antidepressant use and can be useful in identifying populations at high risk of depression. SIMILAR CONTENT BEING VIEWED BY OTHERS INCREASED ODDS

OF METABOLIC SYNDROME AMONG ADULTS WITH DEPRESSIVE SYMPTOMS OR ANTIDEPRESSANT USE Article Open access 27 February 2025 ASSOCIATION BETWEEN DEPRESSION AND DIABETES AMONG AMERICAN ADULTS USING

NHANES DATA FROM 2005 TO 2020 Article Open access 12 November 2024 EXPLORING THE RELATIONSHIP BETWEEN THE URIC ACID TO HIGH-DENSITY LIPOPROTEIN CHOLESTEROL RATIO AND DEPRESSION: A

CROSS-SECTIONAL STUDY FROM NHANES Article Open access 30 December 2024 INTRODUCTION Depression is one of the most common mental illnesses affecting working-age adults and is a major public

health problem worldwide1. Depression is associated with increased mortality in the general population and among persons with comorbidities2,3,4,5. A growing body of evidence demonstrates

that depression is associated with an increased risk of physical disease, including diabetes mellitus6,7 and cardiovascular disease (CVD)2,8,9,10. Furthermore, mental disorders including

depression account for approximately 20–50% of disability benefits across the Organisation for Economic Co-operation and Development countries11 and have been found to significantly reduce

the ability of people to work by increasing other disabilities, including physical ailments12,13. Thus, depression not only has the potential to increase net healthcare costs for the

treatment of various physical illnesses in addition to depression, but can also cause presenteeism and absenteeism, which potentially lead to lost productive time in the workplace14,15.

Various comorbidities, such as cancer, stroke, and diabetes mellitus, are associated with the incidence of depression16,17,18. A systematic review revealed that depression is two to three

times more likely to occur in people with multimorbidity than in those without multimorbidity or those with no chronic physical condition. Metabolic syndrome (MetS) is a pre-symptomatic

state and is defined as a combination of abdominal or visceral obesity, hypertension, dyslipidemia, and glucose dysregulation, and it has a diagnostic significance in predicting subsequent

coronary artery disease, metabolic diseases, and certain cancers19,20. Previous studies showed that even MetS is associated with the development of depression21,22. However, few longitudinal

studies have demonstrated the association between MetS and depression, and knowledge regarding the association between lifestyle and depression remains limited. In Japan, universal health

screening and health guidance were initiated in the early 2000s with the main objective of detecting high-risk populations for CVD23. Since then, the word “_metabo_” has been coined in

Japan, and this health checkup has become more widespread and promoted. However, one study showed that the effectiveness of prevention of CVD and improvement of anthropometric metrics was

limited24. Although this is different from the original purpose of this health checkup, if the data from this checkup can be used to show an association between MetS and depression, it may

be possible to identify people at high risk of developing depression and add new significance to universal health screening and health guidance. Therefore, this study aimed to examine the

association between MetS-related factors and the development of depression after adjustment for various factors such as medication, prior hospitalization, and lifestyle. We used health

insurance claims and health checkup data of corporate insurance beneficiaries, who are representative of the major workforce in Japan. These data covered medical costs, hospitalizations,

disease codes, and prescription medicines, allowing us to adjust for comprehensive factors related to the medical interventions received by study participants. RESULTS BASELINE

CHARACTERISTICS OF THE OVERALL STUDY POPULATION Individuals aged 18–75 years who underwent health examinations between April 1, 2014, and March 31, 2019, were enrolled in the study. The flow

diagram shows the study population and overall study design (Fig. 1). Participants with the following characteristics were excluded: a lookback period < 730 days (n = 33,299); end-stage

kidney disease (n = 49); use of second generation antipsychotics or lithium (n = 512); missing information on the following metabolic traits: body mass index (BMI), blood pressure (BP), or

abdominal circumference (AC); missing laboratory data on hemoglobin A1c (HbA1c), fasting blood glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), or low-density

lipoprotein cholesterol (LDL-c); or missing questionnaire on bodyweight change from age 20 (n = 33,736). In the descriptive cohort, 76,277 individuals were eligible for the study, including

2051 individuals who had ever used or were currently using antidepressants, 74,226 who had never used antidepressants, and 941 incident users of antidepressants after baseline. The baseline

characteristics of “ever”, “never”, and “incident” users of antidepressants in the descriptive cohort are shown in Table 1. Ever users had a higher proportion of diabetes mellitus and

dyslipidemia cases than the other users. Ever and incident users had a high proportion of MetS cases. Questionnaires on lifestyle revealed that never users were less likely to experience

bodyweight increase and poor sleep and more likely to be physically active. Late supper, skipping breakfast, and eating speed were not clinically different across the groups. The

questionnaire on bodyweight change showed that ever and incident users were more likely to experience bodyweight increase from the age of 20. Regarding alcohol-drinking habits, never users

had a lower proportion of occasional or daily drinkers than the others. Regarding medication, incident users were more likely to be prescribed hypnotics and analgesics than never users;

furthermore, ever users were considerably more likely to be prescribed hypnotics than incident users (Fig. 2a). Regarding hospitalization records, ever and incident users were more likely to

be hospitalized, especially cancer-related hospitalization (Fig. 2b). NESTED CASE–CONTROL STUDY TO EXAMINE THE FACTORS ASSOCIATED WITH INCIDENT ANTIDEPRESSANT USE After excluding ever users

of antidepressants from the descriptive cohort, we performed a nested case–control study to examine the factors associated with incident antidepressant use. The nested case–control design,

also known as risk-set sampling, is a type of study design that identifies controls from a group of people who are “at risk” at the index date of the case25. Herein, we defined the day of

the first filling of an antidepressant prescription as the index date. For each case, up to 10 controls were selected, and they were selected from subjects of the same sex and age ± 3 years;

hence, we finally analyzed the data of 10,915 individuals, including 941 incident users and 9254 controls. The baseline characteristics are compared in Table 2. Incident users had a higher

proportion of MetS cases and hypnotic and analgesic users, and they were more likely to experience body weight increase from age 20 and any hospitalization and cancer-related

hospitalizations prior to the index date. Table 3 shows the results of both univariable and multivariable conditional logistic regression analyses to examine associations with incident

antidepressant use (case). MetS was significantly associated with the incident antidepressant use in Model 3 (adjusted odds ratio, 1.53 [95% confidence interval 1.24–1.88]). Lifestyle,

eating speed, poor sleep, and drinking habits were significantly associated with the outcome. Hospitalization due to cancer and use of hypnotics and anxiolytics were also significantly

associated with the outcome. Regarding other metabolic traits, BMI, AC, high BP, glucose intolerance, dyslipidemia, a > 10 kg increase in bodyweight from age 20, and the number of

metabolic components were also significantly associated with antidepressant initiation (Table 4). Here, the number of metabolic components was defined as the number of cases with high BP,

glucose intolerance, and dyslipidemia. As a sensitivity analysis, the same analysis as in Tables 2 and 3 was performed for the lookback period of 1 year; data from 14,337 individuals,

including 1330 affected individuals and 13,307 controls, were analyzed (Supplementary Fig. 1). Similar results were obtained in Supplementary Table 1 and 2. DISCUSSION This study clearly

demonstrated that MetS was associated with the incident use of antidepressants after adjustment for various medical and lifestyle factors using large-scale real-world data from health

insurance claims and health checkups in Japan. The major finding of the present study was that MetS, other metabolic abnormalities, and pre-symptomatic conditions, including BMI, AC, high

BP, glucose intolerance, and dyslipidemia, were associated with antidepressant initiation. We also showed that bodyweight increase of > 10 kg from the age of 20 years was associated with

the incident use of antidepressants. MetS and its components were associated with the incident use of antidepressants independent of lifestyle, cancer-related or CVD-related

hospitalizations, and medications. We also demonstrated a dose–response association between the number of metabolic components and incident antidepressant use. These findings were of

particular interest in that the sum of pre-symptomatic conditions alone was associated with the development of depression despite the absence of a need for disability acceptance or problems

with life dysfunction. This study allowed us to add more significance to health checkups: for screening individuals at high risk of depression. In Japan, a survey showed that the lifetime

prevalence and 12-month prevalence of major depressive disorder were 6.1% and 2.2%, respectively, and that the proportion of individuals receiving any type of treatment was 38.7%, which was

lower than that in many other high-income countries26. The survey found that over 70% of people have moderate or severe depression, yet the low percentage receiving appropriate treatment is

problematic. Now that this study showed that MetS is a risk factor for depression, we expect that health checkups and specified health guidance will have a new role as screening sessions for

depression and will increase public awareness of the opportunity for early consultation and early appropriate treatment for depression. The pathways from MetS to depression could be

biological or social27, a phenomenon that can be explained by the physiological consequences of obesity, including higher inflammation28,29 and the psychological/social consequences of MetS

or obesity. However, most of the health-related behaviors assessed by the questionnaire, such as exercise habits, late supper, skipping breakfast, and smoking, were not significantly

different between the cases and controls. Notably, information on physical activity or exercise habits was collected using self-administered questionnaires rather than quantitative methods.

More detailed research is required to investigate physical activity and exercise habits more quantitatively and accurately through a quantitative physical activity using biometric sensors

and detailed questionnaires on exercise habits. Regarding psychosocial factors, physical and mental stress can be common causes of MetS and depression. The study subjects were corporate

insurance beneficiaries comprising corporate employees and their dependents, and mental stress is potentially caused by the work itself, human relationships in the workplace, and family

issues in this population. Physical stress may be caused by shift work. These not only trigger depression, but can also result in obesity and MetS through overeating, sleep disorders, and

effects on various metabolic systems. However, this study did not include information such as work shifts and work-related stress, and further studies are required to identify modifiable

factors and identify solutions to overcome both health issues. We also observed interesting findings regarding lifestyle. First, we found that alcohol consumption is potentially protective

against depression. Several previous studies have shown consistent results regarding the protective effect of alcohol against depression30,31,32,33,34. Although our results did not show a

clear benefit of daily physical activity and modifiable lifestyle factors including sleep and exercise habits as potential candidates for intervention in depression and MetS. Daily exercise

habits are particularly suitable lifestyle interventions, as a large body of evidence demonstrates the effectiveness of exercise on depression and sleep disorders35,36,37,38. Regarding

prescription medicine, the use of hypnotics, anxiolytics, and NSAIDs at baseline was associated with incident antidepressant use. Studies have shown that insomnia is associated with

depression and anxiety disorders39,40. However, depression is often underdiagnosed in primary-care settings or for older patients41, and such medications might have been prescribed as

supportive care for patients with symptoms and complaints related to depression but not formally diagnosed. Hence, individuals with underdiagnosed and undertreated depression may be hidden

among those who have been prescribed these drugs. Finally, a history of hospitalization for malignant diseases was also associated with incident antidepressant use. However, the association

was attenuated after adjusting for medication information. This may be due to the mediation effects of anxiety and insomnia on the association. Previous studies have demonstrated that

anxiety, depression, and insomnia are associated with cancer as well as with increased adverse outcomes, including mortality and psychosocial problems in cancer survivors42,43,44,45. The

present study reaffirms the importance of psychological care for those who experience cancer-related hospitalization. This study had certain limitations. First, the present study was

observational and did not demonstrate any causality. However, our results revealed various medical and lifestyle factors associated with depression, which we believe provide valuable

insights into workplace mental and physical health. Second, we classified depression based on specific, prescribed classes of antidepressants, that is, selective serotonin reuptake

inhibitors (SSRIs), serotonin-noradrenalin reuptake inhibitors (SNRIs), or noradrenergic and specific serotonin antidepressants (NaSSAs), thus potentially leading to misclassification bias

since those who were treated with other classes of medication or those who had not been treated with medication were not diagnosed with depression. This type of misclassification is

unavoidable in large-scale database studies. However, the main purpose of this study was to explore and generate hypotheses for future research. Therefore, sufficient findings have been

generated from this analysis. Third, there was a concern regarding selection bias. Those who were taking sick leave due to depression might not have undergone health checkups in the

workplace. However, in our country, the number of individuals who undergo health checkups is high in the working generations. Finally, most of the lifestyle information was derived from

self-administered questionnaires, thus potentially undermining accuracy and objectivity. Furthermore, recall bias may have existed when filling out the questionnaire. We may consider using

activity monitoring46, such as gyrometers, to gain objective personal activity information in future studies. In conclusion, MetS and other metabolic abnormalities were associated with

incident antidepressant use in working-age individuals. This study also allowed us to add more significance to health checkups: for screening individuals at high risk of depression.

Lifestyle intervention could be the subsequent step in reducing both mental and physical burdens among individuals in their prime. METHODS DATA SOURCE AND ETHICAL ISSUES The data source

comprised anonymized, processed receipt information and medical checkup data provided by health insurance associations in Japan contracted with PREVENT Co., Ltd, stored at the company. The

present study analyzed data from the administrative claims-based database that included information on 134,677 individuals who underwent health examinations at least once and were under

health insurance coverage between April, 2014 and March, 2019, targeted at corporate employees and their dependents in Japan. All data was extracted and processed on November 17, 2020. We

conducted this study in accordance with the guidelines of the Declaration of Helsinki. The institutional ethics committee of Nagoya University Graduate School of Medicine formally approved

this study (Approval No. 2020-0142). The Ethical Review Committee for Observational Research is an officially approved and registered organization (No. 15000226). Since the study data were

provided anonymously, and the study participants did not receive any intervention, informed consent for study participation was waived by ethics committee of Nagoya University Graduate

School of Medicine. DEFINITIONS We obtained the following information from checkup data: sex, age, BMI, BP, and AC data; laboratory data on HbA1c, fasting blood glucose, TG, HDL-c, and

LDL-c; smoking and alcohol habits; and lifestyle and behavior, including weight changes, exercise habits, physical activity, walking speed, eating speed and habits, and poor sleep. Blood

tests and physical measurements such as BMI, BP, and AC were performed at the nearest local clinic, hospital, or health screening center for each subject, using standard procedures. A

habitual cigarette smoker was defined as a person who smoked a total of over 100 cigarettes or for over six months and has smoked in the last month. Exercise habits were defined as

exercising to sweat lightly for > 30 min per session, twice weekly, for over a year. Physical activity was defined as walking or performing an equivalent amount of physical activity for 

> 1 h per day. Fast walking speed was defined as faster speed than that of almost the same age and of the same gender. Eating speed was categorized into the following three categories:

“quicker” than others, “normal,” and “late.” Late supper was defined as eating supper 2 h before bedtime more than three times a week. Skipping breakfast was defined as skipping breakfast

more than three times a week. Bodyweight change was defined as a > 10 kg increase in body weight from the age of 20 years. We defined the following medicines or their combination as

antidepressants: SSRI, SNRI, and NaSSA. We did not include other types of classical antidepressants, including tricyclic or tetracyclic antidepressants, or serotonin antagonists and reuptake

inhibitors, since they have been used for purposes other than depression treatment. We defined “ever” users as individuals whose last antidepressant prescription filling was earlier than 30

 days after the baseline date and “never” users as those who had never been prescribed an antidepressant within 30 days after the baseline date (Fig. 3). “Incident” users were defined as

individuals who had not been prescribed any antidepressants for at least the past 2 years (730 days) and who were initiated on antidepressants > 30 days after the baseline date. In a

prior study, the lookback period was set as 1 year47. However, the study population comprised youths who were 5 to 20 years of age. Considering the lifelong recurrence of major depressive

disorder, we set the lookback period as 2 years48. For sensitivity analysis, we set the lookback period as 1 year and performed the same analysis. Additionally, we obtained baseline data on

medication history other than antidepressants, disease name, hospitalization, and procedure from the claims data prior to 30 days after the baseline. A positive medication history was

defined as at least one prescription filling during the study period. Data on antihypertensive agents, antidiabetic agents, statins, hypnotics, and NSAIDs were collected using the World

Health Organization anatomical therapeutic chemical classification codes (Supplementary Table 2). Detailed information on drug code combinations representing these drugs is available on

GitHub (https://github.com/PREVENT-Inc/MyscopeMasterList/tree/master/nagoya-u/medical_conditions_and_depression). Hospitalization information was also obtained from claims data prior to 30 

days after the baseline date. Hospitalizations due to specific causes were defined using a combination of the International Statistical Classification of Diseases and Related Health Problems

(ICD)-10 codes in the hospitalization information of medical claims data. Herein, we classified hospitalizations into the following categories: any cause, CVD, congestive heart failure,

cancer, and psychotic disorders. CVD-related hospitalization was defined as having any of the following diseases: acute myocardial infarction, congestive heart failure, and cerebrovascular

disease. The combinations of ICD-10 codes have been described in a previous study by Quan et al.49 Hospitalizations due to mental disorders were classified based on the ICD-10 classification

of mental and behavioral disorders50. STATISTICAL ANALYSIS For between-group comparisons, categorical variables are expressed as numbers and percentages, and continuous variables as the

median (interquartile range) or mean (standard deviation). In the nested case–control study, a between-group comparison was performed in a fashion similar to the comparison of the overall

study participants. Univariable and multivariable conditional logistic regression was used to examine the factors associated with the incident prescription of antidepressants. Covariates

included age; sex; smoking and drinking habits; physical activity; poor sleep; the use of hypnotics, anxiolytics, and NSAIDs; and hospitalizations due to CVD and cancer. Hospitalization

records were utilized in a time-updated manner, implying that hospitalization records were collected not only from baseline but also from data prior to the index dates. Two-sided statistical

significance was set at P < 0.05. All analyses were performed using Stata version 17.0 software (StataCorp, TX, USA). DATA AVAILABILITY The data used in this study is the property of

PREVENT Co., Ltd. and the purpose of use is restricted by PREVENT’s policy on data utilization. The data used in this study was also used under contract and is not available to the public.

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Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines_ (1992). Download references ACKNOWLEDGEMENTS The authors deeply acknowledge all the participants of the present study.

The authors also acknowledge Editage for providing editorial and publication support. FUNDING This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Advanced Medicine, Nagoya University Hospital, 65 Tsuruma-cho, Showa-ku, Nagoya, Aichi,

464-8550, Japan Takahiro Imaizumi & Masahiko Ando * Department of Nephrology, Nagoya University Graduate School of Medicine, 65 Tsuruma-cho, Showa-ku, Nagoya, Aichi, 464-8550, Japan

Takahiro Imaizumi & Shoichi Maruyama * Prevent Co., Ltd., Nagoya, Japan Takuya Toda, Daisuke Sakurai & Yuta Hagiwara * Hidamari Kokoro Clinic, Ama, Japan Michitaka Maekawa *

Innovative Research Center for Preventive Medical Engineering, Nagoya University, Nagoya, Japan Yasuko Yoshida Authors * Takahiro Imaizumi View author publications You can also search for

this author inPubMed Google Scholar * Takuya Toda View author publications You can also search for this author inPubMed Google Scholar * Michitaka Maekawa View author publications You can

also search for this author inPubMed Google Scholar * Daisuke Sakurai View author publications You can also search for this author inPubMed Google Scholar * Yuta Hagiwara View author

publications You can also search for this author inPubMed Google Scholar * Yasuko Yoshida View author publications You can also search for this author inPubMed Google Scholar * Masahiko Ando

View author publications You can also search for this author inPubMed Google Scholar * Shoichi Maruyama View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS T.I. analyzed the data and wrote the paper; T.T. and D.S. prepared the data; M.M., Y.H., Y.Y., M.A., and S.M. interpreted the results. CORRESPONDING AUTHORS Correspondence to

Takahiro Imaizumi or Shoichi Maruyama. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature

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Maekawa, M. _et al._ Identifying high-risk population of depression: association between metabolic syndrome and depression using a health checkup and claims database. _Sci Rep_ 12, 18577

(2022). https://doi.org/10.1038/s41598-022-22048-9 Download citation * Received: 27 January 2022 * Accepted: 07 October 2022 * Published: 03 November 2022 * DOI:

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