Association between Healthy Lifestyle Factors and Metabolic Syndrome Risk: A Prospective Analysis of the Korean Genome and Epidemiology Study
Article information
Abstract
Background
To investigate the association of adherence to five modifiable lifestyle factors (limiting alcohol, physical activity, limiting smoking, favorable diet quality, and adequate sleep) with metabolic syndrome (MetS) risk in Korean adults.
Methods
Health Examinees Study data were used, and 41,368 participants aged 40 to 69 years were included. Cox proportional hazards regression analyses assessed the associations of individual and combined healthy lifestyle factors (32 and 16 lifestyle profiles in men and women.
Results
During a median 4.2-year follow-up, 6,213 participants were newly diagnosed with MetS. Adherence to more healthy lifestyle factors (4–5 vs. 0–1) could lower MetS risk by 28% and 12% in men and women (hazard ratio [HR], 0.72; 95% confidence interval [CI], 0.63 to 0.83 in men; HR, 0.88; 95% CI, 0.78 to 0.99 in women). Each additional healthy lifestyle could reduce the risk of MetS by 10% and 6% in men and women. The pooled analysis yielded similar results based on similar numbers of healthy lifestyle factors, the risk of MetS decreased as the number of healthy lifestyle factors increased.
Conclusion
Adherence to more healthy lifestyle factors was inversely associated with MetS risk. These findings highlight the importance of limiting drinking in managing MetS. Future research should consider the synergistic effects of emerging lifestyle factors, such as sleep duration, on chronic disease development, while focusing on the effects of traditional lifestyle factors.
Highlights
• Combining healthy lifestyle factors enhances MetS prevention and management.
• This underscores the importance of alcohol awareness in MetS management.
• Sleep duration is crucial for MetS risk, alongside traditional risk factors.
INTRODUCTION
Metabolic syndrome (MetS) is a complex and multifaceted condition characterized by a combination of frequently observed metabolic disorders, including abdominal obesity, high blood pressure, elevated blood glucose levels, and dyslipidemia. This cluster of conditions significantly elevates the risk for several common chronic diseases, such as cardiovascular disease, type 2 diabetes mellitus, certain types of cancer, and even premature death [1,2]. According to the ‘2021 Metabolic Syndrome Fact Sheet,’ MetS affects approximately 27.7% of adults over the age of 30, with prevalence surging to 45.3% among adults aged 65 and older [3]. The high and rising prevalence of MetS has placed an enormous strain on socioeconomic systems, public health infrastructure, and healthcare resources, particularly in the Asia-Pacific region [4].
Due to the complexity of the pathophysiology of MetS and the irreversibility present in its etiology, no drugs can eradicate or even reduce it. In addition, genetic factors, overnutrition and increased sedentary lifestyle are thought to be important causes of increased MetS. As modifiable conditions, healthy lifestyles are legitimately outlined as preventable measures [5].
The existing literature basically investigated the relationship between the common traditional lifestyle factors and the risk of MetS, consistently demonstrating that more healthy lifestyle factors could reduce the risk of MetS [6,7]. Furthermore, various studies have focused on independently considering the relationship between individual lifestyle factor and risk of MetS, and growing evidence focuses on the effect of emerging lifestyle factors, such as sleep duration on the risk of MetS [8,9]. However, few studies have combined emerging lifestyle factors with traditional lifestyle factors to consider the combined effects on risk of MetS [10].
Moreover, due to individual differences, different combinations of healthy behaviors may impact MetS risk in unique ways. Remarkably, no study has yet analyzed the association of the 32 (men) and 16 (women) lifestyle profiles, considering a range of emerging and traditional factors.
To address these knowledge gaps, this cohort study aimed to investigate the combination effects of traditional lifestyle factors (including no smoking, no drinking, healthy diet, and physical activity), alongside emerging factor (sleep duration). By analyzing diverse lifestyle profiles, this study seeks to provide a comprehensive perspective on MetS prevention, promoting a holistic approach that encourages optimal lifestyle practices for reducing the incidence of MetS.
METHODS
Study population
The Health Examinees (HEXA) Study is a large prospective cohort study that recruited more than 170,000 participants between 2004 and 2013 in 38 general hospitals and health examinations in Korea. The HEXA study was approved by the local Institutional Review Board (IRB) of the ethics committee of the Korean Genome and Epidemiology Study of the Korea National Institute of Health (IRB No. 1041078-20230628-HR174). Written informed consent was obtained from all participants. In this study, we included 64,625 participants aged from 40 to 69 and completed the follow-up survey between 2012 and 2016. After following the exclusion criteria to delete the participants who did not meet this study, 13,139 men and 28,229 women were included in this study (Supplementary Fig. 1).
Assessment of exposure
Data about five healthy lifestyle factors including four traditional factors and sleep duration were obtained from baseline questionnaires and anthropometric tests. Details on definition of healthy lifestyle factors and lifestyle profile were described in Supplementary Methods. We adopted five healthy lifestyle factors to construct an operationalized scoring scale, with one point scored for adherence to a healthy lifestyle and zero points otherwise (Supplementary Table 1). In addition, based on the combinations of four or five dichotomized lifestyle factors, we constructed 32 lifestyle profiles for men and 16 lifestyle profiles for women.
Outcome ascertainment
MetS is defined as meeting three or more out of five of the following criteria according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III): (1) waist circumference (WC) ≥90 for men and ≥80 cm for women (the cut off of WC was modified according to the Asian guidelines) [11]; (2) triglycerides ≥150 mg/dL or pharmacological treatment; (3) high-density lipoprotein cholesterol ≤40 for men and ≤50 mg/dL for women; (4) systolic blood pressure (SBP) ≥130 or diastolic blood pressure (DBP) ≥85 mm Hg or pharmacological treatment; and (5) fasting plasma glucose (FPG) ≥100 mg/dL or pharmacological treatment [12,13].
Assessment of other covariates
We assessed other potential covariates including age, total energy intake, education level, income level, marriage status, history of chronic disease (hyperlipidemia, hypertension, diabetes, stroke, transient ischemic attacks, angina, and myocardial infarction), parity, age of first menstrual period, age at first birth, menopausal status (premenopausal and postmenopausal women), age of menopause, were obtained from self-administrated questionnaires. Education level was categorized into three groups: less than middle school, high school graduate or equivalent, and college or above. Income levels were divided into less than 3 million won per month and ≥3 million won per month. Marriage status was divided into three groups: unmarried/cofactorating, divorced/separated/widowed, and married. History of chronic disease was divided into two groups: had one of history of common chronic disease or not. Parity was divided into three groups: never given birth, gave birth to one baby, gave birth to two babies, gave birth ≥3 babies, and less than 10 babies. The age of the first menstrual period was categorized into less than 15 and ≥15 years old. Age at first birth was categorized into less than 25 and ≥25 years old. Age of menopause was categorized into less than 50 and ≥50 years old. If was missing value exist, the value will be imputed by multiple imputation methods [14].
Statistical analysis
For each participant, person-years of follow-up were calculated from baseline (2004–2013) to the follow-up survey (2012–2016). The baseline characteristics of all participants were described across different number of optimal lifestyle factors. Categories variables were reported as numbers and percentages, and continuous variables were reported as mean and standard errors. After assessing the proportional hazards assumption by conducting proportional and Schönfeld tests [15], Cox proportional hazard regression analyses were used to assess hazard ratio (HR) and 95% confidence interval (CI) for the associations of individual and combined healthy lifestyle factors with risk of MetS. In addition, the associations between 32 (men) and 16 (women) lifestyle profiles and risk of MetS were assessed and pooled analyses on HRs from the same number of optimal lifestyle factors were further conducted. The Metan command for STATA (StataCorp., College Station, TX, USA) was performed to obtain a pooled estimated of the HR from the same number of healthy lifestyle factors.
Multivariable adjusted models included age, total energy intake, education level, income level, marriage status, history of chronic disease (hyperlipidemia, hypertension, diabetes, stroke, transient ischemic attacks, angina, myocardial infarction), and some variables were adjusted for women only (parity, age at first birth, menopausal status, age of menopause, age of first menstrual period). All data analyses were performed with SAS 9.4 and STATA MP17.0 statistical software. All statistical significance was set at two-sided, with P values <0.05.
RESULTS
Population characteristic
The baseline characteristics of included participants are shown in Table 1. Of the 41,368 participants, 3,648 (27.76%) men adhered to four or five lifestyle factors, and 3,443 (12.20%) women adhered to four healthy lifestyle factors. Irrespective of gender differences, similar associations were found in age, household income, metabolism-related biomarkers, and total energy intake. Compared with those in the lowest score group, participants with higher healthy lifestyle scores tended to be older, had higher household income and lower total cholesterol, triglycerides. In terms of body mass index, WC, low-density lipoprotein cholesterol, FPG, SBP, DBP, no associations were found in men. For nutrient intake, participants with higher scores more often reported lower total energy intake, lower percentage of energy from fat, percentage of energy from protein, and higher percentage of energy from carbohydrate. However, gender differences were noted in education levels. For men, education level showed a positive association, with more healthy lifestyle factors tended to have higher education level. In contrast, for women, a negative association was observed.
General characteristics of associations between combination healthy lifestyle factors and metabolic syndrome risk
Association of combined healthy lifestyle factors with metabolic syndrome risk
Over the median 4.2 years follow-up period, 6,213 cases were diagnosed as MetS. As shown in Table 2, in the multivariable model hazard analysis, adherence to four or more healthy lifestyle factors was associated with a 28% and 12% lower risks of MetS in men and women (men [HR, 0.72; 95% CI, 0.63 to 0.83] and women [HR, 0.88; 95% CI, 0.78 to 0.99]), compared with participants who adherence to 0–1 healthy lifestyle factors. In the continuous analysis, similar results were observed, each one-point increment of healthy lifestyle score could lower almost 10% and 6% risks of MetS in men and women (men [HR, 0.90; 95% CI, 0.86 to 0.93] and women [HR, 0.94; 95% CI, 0.91 to 0.97]). In addition, similar result was validated again in the pooled analysis, as the number of healthy lifestyle factors increases, a downward trend in the risk of MetS (Figs. 1 and 2).
HRs of associations between combination of healthy lifestyle factors and metabolic syndrome risk
Hazard ratios (HRs) for associations between adherence to 32 lifestyle profile and metabolic syndrome risk and pooled analyses for associations between healthy lifestyle factors and metabolic syndrome risk in men. Adjustment model: age, total energy, education, income level, marriage status, history of chronic disease (hyperlipidemia, hypertension, diabetes, stroke, transient ischemic attacks, angina, myocardial infarction). Yes means maintaining a healthy lifestyle. The red dotted lines represent the HR values of the pooled results for different numbers of optimal lifestyle factors. CI, confidence interval.
Hazard ratios (HRs) for associations between adherence to 16 lifestyle profile and metabolic syndrome risk and pooled analyses for associations between healthy lifestyle factors and metabolic syndrome risk in women. Adjustment model: age, total energy, education, income level, marriage status, history of chronic disease (hyperlipidemia, hypertension, diabetes, stroke, transient ischemic attacks, angina, myocardial infarction). Yes means maintaining a healthy lifestyle. The red dotted lines represent the HR values of the pooled results for different numbers of optimal lifestyle factors. CI, confidence interval.
Association of individual healthy lifestyle factor with metabolic syndrome risk
Considering the effect of individual healthy lifestyle factor on the risk of MetS, similar results were obtained, are presented in Supplementary Table 2. For drink status, negative association was found between limiting drinking and risk of MetS (men [HR, 0.80; 95% CI, 0.73 to 0.88] and women [HR, 0.85; 95% CI, 0.79 to 0.91]). Similar association was observed between limit smoking and risk of MetS (men: HR, 0.72; 95% CI, 0.66 to 0.79). For diet, protective effects were observed (men [HR, 0.90; 95% CI, 0.82 to 0.99] and women [HR, 0.92; 95% CI, 0.86 to 0.99]). For physical activity and sleep duration, no associations were found.
In addition, we further classified lifestyle factors into three or four groups to investigate the associations between them (Supplementary Table 3). For alcohol consumption, compared with the reference group (nondrinkers), the most significantly adverse effects were observed in participants who engaged in heavy drinking (men [HR, 1.63; 95% CI, 1.40 to 1.90] and women [HR, 1.71; 95% CI, 1.36 to 2.14]). For smoke status, regardless of whether participants were past or current smoker may higher the risk of MetS (past smoker vs. never smoker: HR, 1.25; 95% CI, 1.13 to 1.39; current smoker vs. never smoker: HR, 1.58; 95% CI, 1.42 to 1.77).
Association of healthy lifestyle profiles with metabolic syndrome risk
Considering the independent and pooled effects of different lifestyle profiles on the risk of MetS, results are shown in Figs. 1 and 2. Compared with those without any healthy lifestyle factors, given the same number of healthy lifestyle factors, the most significant protective effects were seen in the combination of limit drink and other healthy lifestyle factors. As for participants who adhered to two healthy lifestyle factors, the strongest protective effect in men was observed for those who limited both smoking and drinking (HR, 0.49; 95% CI, 0.28 to 0.84); In women, participants who physcially active and had a good diet quality had the strongest proctive effect (HR, 0.74; 95% CI, 0.57 to 0.95). Among participants who adhered to three healthy lifestyle factors, the most protective effect was observed in participants who adhered to limit drinking, limit smoking, and physically active (HR, 0.49; 95% CI, 0.28 to 0.85 in men; in women, who adhered to limit drinking, physically active, and had a good quality HR, 0.69; 95% CI, 0.55 to 0.85). Among participants who adhered to four healthy lifestyle factors, the protective effect was observed in men who adhered to profile 29 (limit drinking, limit smoking, physically active, and good diet quality: HR, 0.68; 95% CI, 0.46 to 0.99), and who adhered to profile 30 (limit drinking, limit smoking, good diet quality, and sleep 6–8 hours: HR, 0.65; 95% CI, 0.47 to 0.90).
DISCUSSION
Our overall data showed that adhering to more healthy lifestyle factors could reduce the risk of MetS among Korean adults. It was found that among four traditional healthy lifestyle factors and sleep duration, limiting drinking was the largest protective factor. If this healthy lifestyle factor was ensured to be maintained, regardless of whether other healthy lifestyle factors were adhered to, the risk of MetS may be better reduced than other combined patterns that adherence to the same number of healthy lifestyle factors.
Consistent with prior results, our data highlighted the protective effect of combined traditional healthy lifestyle factors (noncurrent drinker, noncurrent smoker, and good diet quality) on MetS risk [10,16,17]. Notably, this study was the first prospective study to consider the association between a combination of five healthy lifestyle factors (including adequacy sleep duration) and risk of MetS and indicated a protective association between them, which provided new ideas for the formulation of health guidelines.
For individual lifestyle factors, smoking status, and diet quality were also consistent with previous findings [18-20]. For smoke status, plethora of studies elucidated the harmful effects of smoking on MetS [21,22]. Not surprisingly, our results supported this conclusion, showing that never smokers or past smokers have a 27% reduced risk of MetS compared with current smokers. In clinical studies, smoking has been shown to cause insulin resistance, increase triglyceride levels, and may even lead to impaired fasting glucose by damaging β-cells in the pancreas [23].
For diet quality, as was found in our study, good diet quality could reduce the risk of MetS by almost 10%. The diet quality index used in this study recommended daily consumption of whole grains, fruits and vegetables, and limited intake of sodium, cholesterol, and unsaturated fatty acids, based on the Korean dietary guidelines factors [24]. The protective effect of these ingredients on MetS has been widely verified [25,26]. From a biological perspective, dietary intervention may manage MetS by engaging the modulation of the gut microbiome and reducing inflammation [26].
To date, there is no consensus on the associations of drinking status and alcohol consumption with MetS risk. The results of this study were consistent with previous research, that drinking alcohol, even in light drinking, might increase the risk of MetS [19]. Research was conducted by Kim et al. [27], found that alcohol consumption was associated with an increased risk of MetS. However, difference results were found on alcohol consumption, light drinking was inversely associated with higher risk of MetS, whereas moderate and heavy drinking were not related to risk of MetS [27]. Additionally, there was also substantial evidence that light or even moderate drinking may reduce MetS risk [28]. Therefore, more future studies are needed to cautiously draw uniform results.
Prior research has found an inverse association between physical activity and MetS risk [29,30]. However, we found no association between exercise and MetS risk, which was partly consistent with cross sectional studies based on Koreans [31] and Spanish [10] and Hispanics/Latino [32]. One potential explanation for these results was synergistic effects of sedentary time. This research only considered weekly exercise time, without considering the negative effects of prolonged sedentary time. Even very physically active participants may have prolonged sedentary time. A randomized controlled trial recruited 54 subjects demonstrated a positive association between increased sedentary time and increased risk of MetS among physically active subjects [33]. The second explanation may be due to individual differences in exercise intensity, duration, and frequency.
For sleep duration, previous studies have shown that there was a U-shaped relationship between sleep duration and MetS risk, which was partly consistent with our results [34]. We did not observe significance when we simply divided sleep duration into two groups (adequate sleep duration and short or long sleep duration). However, when we subdivided it into short sleep duration, appropriate sleep duration, and long sleep duration, found that compared with adequate sleep duration, short sleep duration increases the risk of MetS [18,35]. A Mendelian randomization analyzes study was conducted by China team, included 335,727 participants from UK biobank was in line with our data [35]. Mechanisms underpinning short sleep patterns lead to changes in hypothalamic control, inducing overactivation of the sympathetic nervous system, resulting in changes in the ratio of ghrelin and leptin [36].
The lifestyle profile conducted from four traditional healthy lifestyle factors and sleep duration was the innovation of this article. It was the first study to consider the effect of different lifestyle combination patterns on risk of MetS. As expected, the findings confirmed that adhering to a greater number of healthy lifestyle factors was associated with a lower risk of MetS compared to those not following any healthy lifestyle factors. Notably, limiting alcohol intake showed the most significant protective effect among the factors. Participants who adhered to limit drinking were found a relatively stronger protective effect than those who adhered to other health combinations when adhering to the same number of healthy lifestyle. However, the mechanisms underlying this effect remain unclear, and more prospective studies or clinical trials are needed to prove it.
There are several strengths in this study. This was the first cohort study to investigate the association of four traditional and one emerging lifestyle factors with MetS risk, with large sample size. Second, when calculating alcohol consumption, this study distinguished the alcohol contribution of different types, increasing the credibility of the correlation. Furthermore, this study considered the complex synergies between foods and nutrients using validated dietary indices rather than single foods and nutrients. The biggest drawback of this study was that lifestyle factors were only obtained at baseline and did not consider follow-up change. To further develop a more comprehensive understanding of the change of lifestyle habits that may have an impact on outcomes, it was recommended that future studies increase the consideration of multiple time point measurements. In addition, the constructed lifestyle factor scores were simply dichotomized, ignoring the results error caused by differences in associations. Meanwhile, due to the results of lifestyle factors were obtained through semi-self-report, this may lead results bias. Furthermore, about physical activity definition, it was simply defined based on weekly exercise volume without considering the impact of sedentary time on the outcome.
In summary, adherence to more healthy lifestyle factors were inversely associated with MetS risk. Notably, limiting drinking exerts a more positive effect on reducing MetS risk than other healthy lifestyle factors, and it yields similar results when combined with other healthy lifestyle factors. This study helps increase awareness of limiting drinking when managing MetS. Furthermore, the findings advocate for increased focus on sleep duration, while considering traditional lifestyle factors.
SUPPLEMENTARY MATERIALS
Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2024.0427.
Operationalization of healthy lifestyle factors recommendation
HRs for metabolic syndrome risk according to dichotomous lifestyle factor scores
HRs for metabolic syndrome risk according to individual lifestyle factors
Flow chart of selection of participants. HEXA, Health Examinees.
Notes
CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.
AUTHOR CONTRIBUTIONS
Conception or design: all authors.
Acquisition, analysis, or interpretation of data: J.F.
Drafting the work or revising: all authors.
Final approval of the manuscript: all authors.
FUNDING
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government through the Ministry of Science and ICT (MSIT) (grant number: 2022R1F1A1074279).
ACKNOWLEDGMENTS
None