Skip to main content

The relationship between lifestyle risk factors and depression in Korean older adults: a moderating effect of gender

Abstract

Background

Little is known regarding the role of gender as a possible modulator in determining the associations between lifestyle risk factors and depression in older adults.

Objectives

This study examined whether gender modulates the relationship between depression and lifestyle risk factors in Korean adults aged 65 years and older (n = 3700).

Methods

Data were obtained from the 2016 and 2018 Korea National Health and Examination Survey. The primary outcome was depression, assessed with the patient health questionnaire-9. As exposures, smoking habits, at-risk alcohol consumption, and physical inactivity were assessed with a standardized questionnaire. In addition, mean adequacy ratio (MAR) as an indicator of overall nutritional inadequacy was assessed with dietary intakes of macro- and micronutrients.

Results

In men only, either two or three and more risk factors were significantly associated with higher depression risk (OR (95% confidence interval, CI) = 2.886 (1.003–8.299) and OR (95% CI) = 3.109 (1.064–9.097), respectively). In women only, either two or three and more risk factors were also significantly associated with higher depression risk (OR (95% CI) = 1.505 (1.067–2.124) and OR (95% CI) = 2.828 (1.527–5.239), respectively). In particular, the presence of smoking habits and MAR were the major determinants of depression (OR (95% CI) = 1.835 (1.09–3.10) and OR (95% CI) = 1.585 (1.125–2.233), respectively) in women only. Finally, a moderation analysis with the Hayes PROCESS Macro showed a significant moderating effect of gender (β (95% CI) = 0.633 (0.206 ~ 1.060)) on the relationship between risk factors and depression. In addition, the slope of the relationship was much steeper in women than in men.

Conclusion

Current findings suggest that lifestyle risk factors are more closely associated with depression risk in women than in men.

Peer Review reports

Introduction

Depression is a common mental disorder resulting in serious physical and mental health problems worldwide [1, 2]. In South Korea, the annual prevalence of major depressive disorder was 1.7, 2.5, and 3.1% in 2001, 2006, and 2011, respectively [3]. The prevalence is lower than in high-income countries (5.5%) and low-to-middle income countries (5.9%) but is rising steadily [4]. Furthermore, statistics in 2017 showed that South Korea has the highest suicide rate (at 23 deaths per 100,000 persons) among Organization for Economic Co-operation and Development countries [5] (https://data.oecd.org/healthstat/suicide-rates.htm). The total economic burden of depression was estimated to be 4049 million USD in 2005 [6]. By 2012, this figure had skyrocketed to 1.331 billion USD [7].

Etiologically, it is well established that depression is significantly associated with various lifestyle risk factors, including smoking [8], heavy alcohol consumption [9], physical inactivity [10], and unhealthy diet [11]. Depression is also associated with health conditions [12,13,14] and low socioeconomic status [15]. The relationships between lifestyle risk factors and the prevalence and incidence of depression have been also demonstrated in previous studies. For example, in a 7-year follow-up of the Komo-Ise cohort study, Tanaka et al. [16] showed that heavy alcohol consumption and physical inactivity were associated with a higher prevalence of depression in Japanese men, and obesity, inadequate sleep, and smoking were associated with a higher prevalence of depression in Japanese women. In an international cohort study of patients with muscle sclerosis, Taylor et al. [17] showed that healthy lifestyles, including moderate alcohol intake, being a non-smoker, diet quality, no meat or dairy intake, vitamin D supplementation, omega 3 supplement use, regular exercise, and meditation at baseline, were associated with lower incidences of depression.

Likewise, the findings from previous studies showed the relationships of depression with lifestyle risk factors in Korean populations. Interesting enough, women are more likely to suffer from depression [18, 19] and have a higher prevalence of depression than men [20]. Furthermore, an active or a passive healthy lifestyle compared to unhealthy lifestyle was associated with a lower risk of depressive symptoms in women, but such a relationship was not observed in men [21], implying that gender may play a role in determining the relationship between lifestyle risk factors and depression [22].

To the best of our knowledge, however, little is known regarding the moderating effect of gender on the relationships between lifestyle risk factors and depression in Korean populations. Therefore, we hypothesized that gender plays as a moderator in determining the relationship between modifiable lifestyle risk factors, including smoking, alcohol consumption, physical inactivity, and nutritional inadequacy, and depression risk in Korean geriatric populations. This study investigates whether gender moderates the association between lifestyle risk factors and depression in Korean older adults.

Methods and materials

Data source and study participants

The data used in the current study was obtained from the six and seven Korea National Health and Examination Survey (KNHANES) in 2041, 2016, and 2018, a nationwide survey designed to assess health and nutritional status in the Korean population. Initially, we selected all participants aged 1 year and older (n = 23,692) of the KNHANES VI and VII. We then excluded participants aged 64 years and younger (n = 18,821). Among the remaining 4871 participants, Additionally, we excluded those without PHQ-9 data (n = 662) and covariates and others (n = 509). Consequently, the 3700 participants aged 65 years and older who had all the parameters were included in final data analyses (Fig. 1). The Korea Centers for Disease Control and Prevention institutional review board reviewed and approved the KNHANES VII surveys (2018-01-03-P-A) in accordance with the Declaration of Helsinki. Informed consent forms were obtained from all the participants herein.

Fig. 1
figure1

Flow chart of selection for study participants

Patient health Questionnaire-9 (PHQ-9)

The PHQ-9 is a self-reported version of the primary care evaluation of mental disorders (PRIME-MD) diagnostic instrument for major depressive disorders [23]. The Korean version of the PHQ-9 was downloaded from the PHQ website (https://www.phqscreeners.com/) and was used to match each of 9 items with criteria from the Diagnostic and Statistical Manual of Mental Disorders according to scores ranging from “0” (not at all) to “3” (nearly every day). Subjects were classified based on the severity of depressive symptoms (none, 0; minimal, 1–4; mild, 5–9; moderate, 10–14; moderately severe, 15–19; or severe, 20–27) [23]. In the current study, a PHQ-9 score of ≥10 was defined as having major depressive disorders (MDD). The cutoff value of 10 for screening MDD using the Korean version of PHQ-9 was previously tested and validated [24].

Lifestyle risk factors

Lifestyle risk factors measured in the study, including current or past smoking, at-risk alcohol consumption, physical inactivity, and overall nutritional inadequacy, are modifiable behaviors and exposures that can raise or lower a person’s risk of diseases [25]. In addition, those lifestyle risk factors are the key components of the Healthy Promotion Programs by the Korea Ministry of Health and Welfare (https://www.khealth.or.kr/) and associated with depression and/or depressive symptoms in Korean populations [26,27,28,29].

With regard to smoking, respondents were classified as never smokers and past or current smokers. At-risk alcohol intake was defined as having seven or more (five or more for women) glasses of alcohol per occasion per two or more times per week [30]. Physical inactivity was defined as not participating in at least 150 min of moderate physical activity (PA) per week or 75 min of vigorous PA or a combination of moderate and vigorous PA (https://www.who.int/news-room/fact-sheets/detail/physical-activity).

With respect to overall nutritional inadequacy, dietary intake of macronutrients (i.e., carbohydrates, fats, and proteins) and micronutrients (i.e., vitamins A and C, thiamine, riboflavin, niacin, phosphorous, calcium, and iron) was assessed with a 24-h (h) recall method. Trained interviewers conducted computer-assisted personal interviews to assess all food items ingested during the previous 24 h. Mean adequacy ratio (MAR), which represents a population’s overall nutritional adequacy, was then calculated by using the following equation: MAR = (sum of NAR / number of nutrients) × 100. The nutrient adequacy ratio (NAR) represents an individual’s intake of a nutrient as a percentage of the age- and gender-specific recommended dietary allowance (RDA) for that nutrient [31] accessed on 24 May 2021. For respondents, MAR was dichotomized as adequacy (100% and higher) or inadequacy (less than 100%) based on the age- and gender-specific RDA for macro- and micronutrients. A score of 1 was given for each lifestyle risk factors. Number of lifestyle risk factors was defined as a total score of the risk factors.

Covariates

The covariates used in this study were age (continuous), gender (categorical: male or female), body mass index (quantitative), education (categorical: elementary school or lower, middle or high school, university or higher), income (quantitative), and marital status (categorical: yes or no). Body mass index (BMI) was calculated as weight divided by height squared (kg/m2).

Statistics

Normality of data distribution was confirmed with quantile-quantile (Q-Q) plotting, and the absence of multicollinearity was assessed by a variance of inflation factor (VIF). Descriptive statistics between men and women in measured parameters were performed with Student’s t-tests and chi-squared tests for continuous and categorical variables, respectively, and they are presented as mean ± standard deviations (SDs) and numbers (n) and percentages (%), respectively.

Multivariate logistic regression was performed to estimate odds ratios (ORs) and 95% confidence intervals (CI) of MDD with a PHQ-9 score of > 10 (categorical) by lifestyle risk factors-based subgroups (categorical) before and after adjustments for the covariates. For the classification of lifestyle risk factors, 0 and 1 risk factors were collapsed and classified as < 1 risk factor because of a small proportion of 0 risk factor (2.4% in total/1.9% in men and 2.9% in women). Likewise, 3 and 4 risk factors were collapsed and classified as > 3 risk factors because the proportion of 4 risk factors was 1.7% in total (3.6% in men and 0.2% in women). Consequently, lifestyle risk factors-based subgroups were classified as < 1 risk factor or 2 risk factors or > 3 risk factors.

Finally, we examined the moderating effect (or the interaction effect) of gender (moderator, W) on the relationship between number of lifestyle risk factors (continuous, X) and PHQ-9 score (continuous, Y), as shown in Fig. 2, with the Hayes PROCESS Macro using ordinary least squares (OLS) regression. For moderation analysis, gender was dummy coded (i.e., 0 = male and 1 = female), and lifestyle risk factors and PHQ-9 scores were treated as continuous variables. After centering number of lifestyle risk factors and PHQ-9 score and computing the interaction (i.e., X × W) term [32], the predictor and the interaction were entered into a simultaneous regression model. Age, alcohol consumption, sleeping time, body mass index, income, education level, marital status, residence area, and comorbidities were controlled in the model. The model was also tested with bias-corrected bootstrapping (n = 5000) and 95% confidence intervals (CI). If a 95% bootstrapped CI does not include zero, it indicates the relationship is statistically significant. All other statistical significances were tested at p = 0.05 by using statistical software PASW SPSS WIN 27.0 (SPSS Inc., Chicago, IL).

Fig. 2
figure2

A hypothetical model of moderation analysis

Results

Table 1 presents the descriptive statistics of the study participants. Men had lower BMI (p <  0.001) but had higher incomes (p <  0.001), higher education levels (p <  0.001), and higher rates of past/current smoking (p <  0.001), at-risk alcohol consumption (p <  0.001), and physical inactivity (p <  0.001) than women. Men also had lower rates of overall nutritional inadequacy (p < 0.001), lower PHQ-9 scores (p < 0.001), lower prevalence of depression (p < 0.001), and lower numbers of comorbidities (p < 0.001) than women.

Table 1 Descriptive statistics of study participants

Table 2 presents the associations between individual lifestyle risk factors and depression. In total, smoking was associated with greater odds of depression (OR (95% CI) = 1.531 (0.986 2.316)). In addition, overall nutritional inadequacy was also associated with greater odds of depression (OR (95% CI) = 1.559 (1.170–2.078)) even after adjustments for age, gender, BMI, income, education, marital status, residence area, and number of comorbidities. Gender-specific analysis showed that smoking was significantly associated with greater odds of depression among women (OR (95% CI) = 1.835 (1.09–3.10)) but not among men (OR (95%CI) = 1.14 (0.60–2.17)). Likewise, overall nutritional inadequacy was significantly associated with greater odds of depression among women (OR (95% CI) = 1.585 (1.125–2.233)) but not among men (OR (95% CI) = 1.518 (0.897–2.570)).

Table 2 Odds ratios (ORs) and 95% confidence intervals (CIs) of depression according to individual lifestyle risk factors

Table 3 represents depression risk stratified by number of lifestyle risk factors. In total, two risk factors and three and more risk factors were significantly associated with increased depression risk (OR (95% CI) = 1.786 (1.308–2.440) and OR (95% CI) = 2.979 (1.903–4.632), respectively) in comparison to one or zero risk factor (OR = 1). The increased ORs for two risk factors and three and more risk factors remained statistically significant (OR (95% CI) = 1.620 (1.174–2.236) and OR (95% CI) = 2.341(1.473–3.719), respectively) even after adjustments for age, gender, BMI, income, education, marital status, residence area, and number of comorbidities. In men only, two risk factors and three and more risk factors were significantly associated with increased depression risk (OR (95% CI) = 3.446 (1.208–9.829) and OR (95% CI) = 4.510 (1.580–12.875), respectively) in comparison to one or zero risk factor (OR = 1). The increased ORs for two risk factors and three and more risk factors remained statistically significant (OR (95% CI) = 2.886 (1.003–8.299) and OR (95% CI) = 3.109 (1.064–9.097), respectively) even after adjustments for age, BMI, income, education, marital status, residence area, and number of comorbidities. In women only, two risk factors and three and more risk factors were significantly associated with increased depression risk (OR (95% CI) = 1.634 (1.174–2.273) and OR (95% CI) = 3.363 (1.842–6.139), respectively) in comparison to one or zero risk factor (OR = 1). The increased ORs for two risk factors and three and more risk factors remained statistically significant (OR (95% CI) = 1.505 (1.067–2.124) and OR (95% CI) = 2.828 (1.527–5.239), respectively) even after adjustments for all the covariates.

Table 3 Odds ratios (ORs) and 95% confidence intervals (CIs) of depression according to lifestyle risk factors-based subgroups

Table 4 represents the moderating analysis of gender for the relationship between depression and lifestyle risk factors. There was a significant interaction between gender and risk factors on depression (β (95% CI) = 0.480 (0.173 ~ 0.787)). The moderating effect of gender remained significant (β (95% CI) = 0.509 (0.204 ~ 0.814)) even after adjustments for the covariates. Since the interaction term (X × W) in the model was significant, we wanted to probe the interaction to better interpret the moderating effect of gender on the relationship between lifestyle risk factors and PHQ-9 score. The slope of PHQ-9 score on lifestyle risk factors was much steeper in female gender than in male gender, as depicted in Fig. 3.

Table 4 Relationship of PHQ-9 score with lifestyle risk factors: a moderating effect of gender
Fig. 3
figure3

The moderating effect of gender in the relationship between lifestyle risk factors (i.e., smoking, physical inactivity, and overall nutritional inadequacy) depression risk in Korean older adults

Discussion

This cross-sectional and population-based study examines gender differences in the relationships between lifestyle risk factors (i.e., smoking habits, at-risk alcohol consumption, physical inactivity, and overall nutritional inadequacy) and depression in Korean older adults. Results show that regardless of gender, accumulation of lifestyle risk factors is significantly associated with depression. In addition, smoking and overall nutritional inadequacy are the major determinants of depression risk in women only. Novel to this study is that the slope of the relationship between the four risk factors and depression is much steeper in women than in men, implying that women might be more vulnerable to depression associated with lifestyle risk factors.

The findings of the current study are in line with findings from previous studies. For example, in a population-based study involving 3300 Chinese adults (1973 women) aged 35–74 years, Cui et al. [33] examined the associations between health conditions, lifestyle factors, and depression. That study finds that diabetes (both pre-diabetes and previously diagnosed diabetes) and physical activity were independent determinants of depression among male participants, while waist circumference was an independent determinant of depression among female participants. In a study comprised of 665 Japanese adults (347 women) aged 65 years and older, Katsumata et al. [34] examined the relative contributions of four risk domains including demographic characteristics, health and disability, stress, and social networks for depression and found that stress was the major risk domain for men, while health and disability status were the major risk domains for women. Chang et al. [35] examined the relationships between lifestyle risk factors and depressive symptoms in 1020 Taiwanese community-dwelling older adults and found that regular exercise and the regular consumption of vegetables were associated with a lower risk of depressive symptoms in men, while regular exercise was significantly associated with low depressive symptoms in women.

Gender differences in depression and/or depressive symptoms have been found in Korean populations. For example, Jang et al. [36] examined gender differences in depressive symptoms in a sample of 230 older Korean-American immigrants and found a significant interaction between gender and chronic conditions on depressive symptoms. Specifically, women with chronic conditions had a higher prevalence of depressive symptoms. Roh et al. [37] examined the relationship of depression and cognitive impairment with the risk of falling in 7150 Korean older adults (4147 women) who submitted complete responses to a three-year follow-up in the Living Profiles of Older People Survey in Korea, finding that depression was more closely associated with the risk of falling in men than in women. In a population-based study involving 7554 Korean adults aged 45–74 years (60% women), Lee et al. [38] examined the relationship between depressive symptoms and carotid atherosclerosis. That research shows that women with depressive symptoms were more vulnerable to the risk of carotid atherosclerosis than women without depressive symptoms, with no such relationship observed in men. By conducting a secondary analysis of data (1938 men and 2404 women aged 40 years) obtained from the 2010–2014 Korean National Health and Nutrition Examination Survey, Ra and Kim [39] showed that depression was significantly associated with greater waist circumference and higher triglycerides in women but not in men. By analyzing data (n = 5103) obtained from the seventh Korea National Health and Nutrition Examination Survey 2018, Lee and Kim [40] showed that dietary habits such as skipping lunch, practicing a meal frequency of two times per day, and practicing a lunch frequency of 3-4times per week were significant predictors of depression risk in women. These findings imply that unhealthy dietary patterns are a potential contributor to gender differences in the relationship between lifestyle risk factors and depression. Considering the cross-sectional nature of previous studies including the current one, however, gender difference in the relationship should be further tested and confirmed in a cause-and-effect manner in a well-designed longitudinal cohort study.

Several explanations can be given for the gender differences observed in the current study. First, the high odds of women to depression associated with lifestyle risk factors may reflect health consequences of the loss or reduction of antidepressant effects of estrogen due to menopause [41], female-specific reproductive events (i.e., perimenstrual changes, pregnancy, postpartum periods, and menopause [42], and women-specific risk variants in the genetic architecture of depression [43]. Second, unhealthy eating behaviors in women may lead to overall nutritional inadequacy, potentially contributing to increased depression risk [44]. However, a bidirectional relationship between dietary behaviors and depression is also possible; depressed persons are likely to have unhealthy dietary patterns, resulting in undernutrition and/or malnutrition [45]. Caution is necessary in interpreting directionality in the relationship between overall nutritional inadequacy and depression observed herein. Third, health inequity associated with low socioeconomic status (SES) and demographic factors may also contribute to the higher susceptibility of women to chronic illness and to mental illness [15]. Lastly, Smoking may play as another mediator in determining the relationship between lifestyle risk factors and depression. By analyzing data from the participants aged 91 years and older (n = 31,814) of the 2008–2012 Korean National Health and Nutrition Examination Survey (KNHANES), Kim et al. [46] showed that current female smokers were more likely to have a depressive episode, suicidal ideation and attempts, and psychiatric counselling compared to current male smokers. Consequently, it cannot be ruled out the possibility that gender difference in the relationship may reflect gender-specific smoking rate and its relationship with mental health. Taken together, although the exact mechanisms underlying the gender differences observed in the current study are unclear, it is likely that the abovementioned factors (either singularly or accumulatively) may be intertwined.

This study has some limitations. First, although the PHQ-9 score-based assessment of MDD was previously tested and validated in Korean elderly populations [24], the instrument is a subjective screening test and not cover all depression criteria, especially functional impairment. Therefore, an additional item of assessing depression-specific functional impairments should be supplemented so that making type II error or false negative can be minimized in a future study. Second, the cross-sectional nature of the study limits any cause-and-effect explanation for the current findings. Third, a bidirectional relationship between depression and lifestyle risk factors seems possible. That is, accumulation of lifestyle risk factors may contribute to increased depression risk, and vice versa, and this remains to be further investigated in a future study. Fourth, it is possible that the modulating effects of gender on the relationship between lifestyle risk factors and depression differ by certain confounders such as age and socioeconomic status, and this remains to be further addressed in a future research. Lastly, the data used in the current study was obtained on the basis of binary gender categories so that we could not address any non-binary gender effect(s) on the study outcomes.

Despite limitations, this study also has strengths. First, it is a population-based study with a relatively high response rate and large sample size. Second, to the best of our knowledge, this study is the first to report that lifestyle risk factors may play a role for gender difference in the prevalence of depression.

Conclusion

This population-based study finds that 1) lifestyle risk factors, especially smoking and overall nutritional inadequacy, are significantly associated with depression risk in women but not in men and 2) the slope of the relationship between the risk factors and depression is much steeper in women than in men, implying the importance of implementing gender-specific interventions for at-risk populations.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. 1.

    Ge T, Fan J, Yang W, Cui R, Li B. Leptin in depression: a potential therapeutic target. Cell Death Dis. 2018;9:1096.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Ren X, Yu S, Dong W, Yin P, Xu X, Zhou M. Burden of depression in China, 1990-2017: findings from the global burden of disease study 2017. J Affect Disord. 2020;268:95–101.

    PubMed  Google Scholar 

  3. 3.

    Kim GE, Jo MW, Shin YW. Increased prevalence of depression in South Korea from 2002 to 2013. Sci Rep. 2020;10:16979.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Bromet E, Andrade LH, Hwang I, Sampson NA, Alonso J, de Girolamo G, et al. Cross-national epidemiology of DSM-IV major depressive episode. BMC Med. 2011;9:90.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Lee SU, Park JI, Lee S, Oh IH, Choi JM, Oh CM. Changing trends in suicide rates in South Korea from 1993 to 2016: a descriptive study. BMJ Open. 2018;8:e023144.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Chang SM, Hong JP, Cho MJ. Economic burden of depression in South Korea. Soc Psychiatry Psychiatr Epidemiol. 2012;47:683–9.

    PubMed  Google Scholar 

  7. 7.

    Shin D, Kim NW, Kim MJ, Rhee SJ, Park CHK, Kim H, et al. Cost analysis of depression using the national insurance system in South Korea: a comparison of depression and treatment-resistant depression. BMC Health Serv Res. 2020;20:286.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    He Q, Yang L, Shi S, Gao J, Tao M, Zhang K, et al. Smoking and major depressive disorder in Chinese women. PLoS One. 2014;9:e106287.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Manninen L, Poikolainen K, Vartiainen E, Laatikainen T. Heavy drinking occasions and depression. Alcohol Alcohol. 2006;41:293–9.

    PubMed  Google Scholar 

  10. 10.

    Meng X, D'Arcy C. The projected effect of increasing physical activity on reducing the prevalence of common mental disorders among Canadian men and women: a national population-based community study. Prev Med. 2013;56:59–63.

    PubMed  Google Scholar 

  11. 11.

    Khosravi M, Sotoudeh G, Majdzadeh R, Nejati S, Darabi S, Raisi F, et al. Healthy and unhealthy dietary patterns are related to depression: a case-control study. Psychiatry Investig. 2015;12:434–42.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Sandoval Garrido FA, Tamiya N, Lloyd-Sherlock P, Noguchi H. Relation of depression with health behaviors and social conditions of dependent community-dwelling older persons in the republic of Chile. Int Psychogeriatr. 2016;28:2029–43.

    PubMed  Google Scholar 

  13. 13.

    Clayborne ZM, Colman I. Associations between depression and health behaviour change: findings from 8 cycles of the Canadian community health survey. Can J Psychiatr. 2019;64:30–8.

    Google Scholar 

  14. 14.

    Nguyen HA, Anderson CAM, Miracle CM, Rifkin DE. The association between depression, perceived health status, and quality of life among individuals with chronic kidney disease: an analysis of the national health and nutrition examination survey 2011-2012. Nephron. 2017;136:127–35.

    PubMed  Google Scholar 

  15. 15.

    Freeman A, Tyrovolas S, Koyanagi A, Chatterji S, Leonardi M, Ayuso-Mateos JL, et al. The role of socio-economic status in depression: results from the COURAGE (aging survey in Europe). BMC Public Health. 2016;16:1098.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Tanaka H, Sasazawa Y, Suzuki S, Nakazawa M, Koyama H. Health status and lifestyle factors as predictors of depression in middle-aged and elderly Japanese adults: a seven-year follow-up of the Komo-Ise cohort study. BMC Psychiatry. 2011;11:20.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Taylor KL, Simpson S Jr, Jelinek GA, Neate SL, De Livera AM, Brown CR, et al. Longitudinal associations of modifiable lifestyle factors with positive depression-screen over 2.5-years in an international cohort of people living with multiple sclerosis. Front. Psychiatry. 2018;9:526.

    Google Scholar 

  18. 18.

    Albert PR. Why is depression more prevalent in women? J Psychiatry Neurosci. 2015;40:219–21.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Young MA, Fogg LF, Scheftner WA, Keller MB, Fawcett JA. Sex differences in the lifetime prevalence of depression: does varying the diagnostic criteria reduce the female/male ratio? J Affect Disord. 1990;18:187–92.

    CAS  PubMed  Google Scholar 

  20. 20.

    Roh S, Lee SU, Soh M, Ryu V, Kim H, Jang JW, et al. Mental health services and R&D in South Korea. Int J Ment Heal Syst. 2016;10:45.

    Google Scholar 

  21. 21.

    Park Y, Kim S. Gender differences in healthy lifestyle clusters and their relationship with depressive symptoms among middle-aged and older adults in Korea. Korean J Health Educ Promot. 2016;33(1):1–12.

    Google Scholar 

  22. 22.

    Oh DH, Kim SA, Lee HY, Seo JY, Choi BY, Nam JH. Prevalence and correlates of depressive symptoms in Korean adults: results of a 2009 Korean community health survey. J Korean Med Sci. 2013;28:128–35.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Lee S, Huh Y, Kim J, Hang C. Finding optimal cut off points of the Korean version of the patient health questionnaire-9 (PHQ-9) for screening depressive disorders. Mood Emot. 2014;12:32–6.

    Google Scholar 

  25. 25.

    Ng R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020;49:113–30.

    PubMed  Google Scholar 

  26. 26.

    Jang BN, Lee HJ, Joo JH, Park EC, Jang SI. Association between health behaviours and depression: findings from a national cross-sectional study in South Korea. BMC Psychiatry. 2020;20:238.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Cho Y, Lee JK, Kim DH, Park JH, Choi M, et al. Factors associated with quality of life in patients with depression: a nationwide population-based study. PLoS One. 2019;14:e0219455.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Cho J, Jin Y, Kang H. Weight status, physical activity, and depression in Korean older adults. J Epidemiol. 2018;28:292–9.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Song J, Koh T, Park O, Kwon D, Kang S, Kwak K, et al. Association between depression and cardiovascular disease risk in general population of Korea: results from the Korea National Health and nutrition examination survey, 2016. Ann Occup Environ Med. 2019;31:e10.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Shin C, Kim Y, Park S, Yoon S, Ko YH, Kim YK, et al. Prevalence and associated factors of depression in general population of Korea: results from the Korea national health and nutrition examination survey, 2014. J Korean Med Sci. 2017;32:1861–9.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Project I: Data 4Diets:Buliding Blocks for Diet-related Food Security Analysis. Tufs Univerity, Boston, MA, 2018. inddex.nutrition.tufts.edu/data 4diets. accessed July 10, 2021.

  32. 32.

    Aiken LS, West SG. Multiple regression: testing and interpreting interactions: Sage Publications, Inc.; 1991.

    Google Scholar 

  33. 33.

    Cui N, Cui J, Xu X, Aslam B, Bai L, Li D, et al. Health conditions, lifestyle factors and depression in adults in Qingdao, China: a cross-sectional study. Front Psychiatry. 2021;12:508810.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Katsumata Y, Arai A, Ishida K, Tomimori M, Denda K, Tamashiro H. Gender differences in the contributions of risk factors to depressive symptoms among the elderly persons dwelling in a community, Japan. Int J Geriatr Psychiatry. 2005;20:1084–9.

    PubMed  Google Scholar 

  35. 35.

    Chang S-H, Chen M-C, Chien N-H, Lin T-Y, Chang Y-Y. Gender differences in the relationship of lifestyle and depressive symptoms among Taiwanese older people. Collegian. 2016;23:249–55.

    Google Scholar 

  36. 36.

    Jang Y, Kim G, Chiriboga DA. Gender differences in depressive symptoms among older Korean American immigrants. Soc Work Public Health. 2011;26:96–109.

    PubMed  Google Scholar 

  37. 37.

    Roh HW, Lee DE, Lee Y, Son SJ, Hong CH. Gender differences in the effect of depression and cognitive impairment on risk of falls among community-dwelling older adults. J Affect Disord. 2021;282:504–10.

    PubMed  Google Scholar 

  38. 38.

    Lee Y-H, Shin M-H, Choi J-S, Nam H-S, Jeong S-K, Park K-S, et al. Gender differences in the association between depressive symptoms and carotid atherosclerosis among middle-aged and older Koreans: the namwon study. J Korean Med Sci. 2014;29:1507–13.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Ra JS, Kim HS. Sex-based association between depression and metabolic syndrome in Korean middle-aged and older adults. Osong Public Health Res Perspect. 2017;8:130.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Lee E, Kim J-M. Association between depression and eating behavior factors in Korean adults: the Korea National Health and nutrition examination survey 2018. J Nutr Health. 2021;54:152–64.

    Google Scholar 

  41. 41.

    Llaneza P, Garcia-Portilla MP, Llaneza-Suarez D, Armott B, Perez-Lopez FR. Depressive disorders and the menopause transition. Maturitas. 2012;71:120–30.

    PubMed  Google Scholar 

  42. 42.

    Soares CN, Zitek B. Reproductive hormone sensitivity and risk for depression across the female life cycle: a continuum of vulnerability? J Psychiatry Neurosci. 2008;33:331–43.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Kang HJ, Park Y, Yoo KH, Kim KT, Kim ES, Kim JW, et al. Sex differences in the genetic architecture of depression. Sci Rep. 2020;10:9927.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Lai JS, Hiles S, Bisquera A, Hure AJ, McEvoy M, Attia J. A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr. 2014;99:181–97.

    CAS  PubMed  Google Scholar 

  45. 45.

    Kshatriya GK, Acharya SK. Gender disparities in the prevalence of undernutrition and the higher risk among the Young women of Indian tribes. PLoS One. 2016;11:e0158308.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Kim SM, Jung JW, Park IW, Ahn CM, Kim YI, Yoo KH, et al. Gender differences in relations of smoking status, depression, and suicidality in Korea: findings from the Korea national health and nutrition examination survey 2008-2012. Psychiatry Investig. 2016;13:239–46.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

Not applicable.

Funding

Not applicable.

Author information

Affiliations

Authors

Contributions

SK involved in the conception and study design and contributed to data collection, data analysis and interpretation, preparation of the manuscript. SK reviewed and approved the final manuscript.

Corresponding author

Correspondence to Shinuk Kim.

Ethics declarations

Ethics approval and consent to participate

The Korea Centers for Disease Control and Prevention institutional review board reviewed and approved the KNHANES VII surveys (2018-01-03-P-A) in accordance with the Declaration of Helsinki. Informed consent form was obtained prior to data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kim, S. The relationship between lifestyle risk factors and depression in Korean older adults: a moderating effect of gender. BMC Geriatr 22, 24 (2022). https://doi.org/10.1186/s12877-021-02729-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-021-02729-2

Keywords

  • Gender
  • Behavioral risk factors
  • Depression
  • Koreans
  • Nutritional inadequacy