TY - JOUR
AU - Kim, Junghoon
AB - Abstract Background Clinical exercise interventions show that combining moderate to vigorous intensity physical activity (MVPA) and muscle strengthening exercise (MSE) has more favourable cardiometabolic health benefits than engaging in only one mode of physical activity. However, few studies have examined these associations among community-based samples and none among Asian adults. Methods This cross-sectional analysis included 9120 participants aged 20–80 years from the 2014–2015 Korea National Health and Nutritional Examination Survey. Fasting blood samples were analysed for adverse cardiometabolic biomarkers (e.g. high glucose/glycohaemoglobin/triglycerides) and MVPA and MSE were assessed using validated questionnaires. Poisson regression models examined the association between the individual and total number of adverse biomarkers across categories of MVPA–MSE guideline adherence (met neither (reference); met MSE only; met MVPA only; met both) and prevalence ratios are reported adjusted for covariates (e.g. age, education, smoking, waist circumference and sitting time). Results The mean ± SD age was 46.2 ± 16.3 years and 50.3% of the participants were women. Compared with meeting neither guideline, meeting both guidelines had the lowest prevalence ratios for four out of eight individual adverse cardiometabolic biomarkers. In a sex-stratified analysis of men, only meeting both guidelines had a lower prevalence ratio for ≥4 adverse biomarkers (prevalence ratio 0.67; 95% confidence interval 0.53–0.85). For women, the prevalence ratio for ≥4 adverse biomarkers was lowest among those meeting both guidelines (prevalence ratio 0.46; 95% confidence interval 0.31–0.66), followed by MSE only (prevalence ratio 0.65; 95% confidence interval 0.42–0.96) and MVPA only (prevalence ratio 0.78; 95% confidence interval 0.65–0.92). Conclusions Among a large sample of Korean adults, concurrent MVPA–MSE was independently associated with favourable cardiometabolic outcomes. This study supports public health action to promote adherence to MVPA–MSE guidelines to enhance cardiovascular health among Korean adults. Physical activity, epidemiology, strength training, cardiovascular disease, exercise Introduction Cardiometabolic diseases – such as ischaemic heart disease, diabetes and stroke – are among the leading global causes of morbidity and mortality.1 Elevated cardiometabolic biomarkers (e.g. hyperglycaemia, dyslipidaemia, hypercholesterolaemia and hypertension) are key risk factors associated with cardiometabolic diseases2 and their associated complications (e.g. nephropathy, neuropathy and peripheral vascular disease).3 Physical inactivity is a key lifestyle risk factor for cardiometabolic diseases1 and elevated cardiometabolic biomarkers.4,5 Meta-analyses of epidemiological studies show that physical inactivity, typically operationalised as ‘insufficient’ moderate to vigorous intensity physical activity (MVPA; e.g. walking, running or cycling), independently increases the risk of incident coronary artery disease,6 type 2 diabetes,6 stroke7 and all-cause mortality.8 Although physical activity public health recommendations have historically solely emphasised MVPA,9 muscle strengthening exercise (MSE; e.g. weight/resistance training) has been added over the last decade.10 Systematic reviews of short duration (typically 6–12 week) controlled clinical exercise trials demonstrate that compared with one type of activity alone, a combination of MVPA and MSE has favourable effects on cardiometabolic biomarkers, including insulin sensitivity,11 lipid metabolism12 and chronic inflammation.13 The World Health Organization’s 2010 report Global Recommendations on Physical Activity for Health states that adults aged 18–64 years should participate in ≥150 minutes/week of MVPA and MSE involving the major muscle groups on two or more days a week.10 Despite concurrent MVPA–MSE being globally recommended, few epidemiological studies have examined the cardiometabolic consequences of combining these two physical activity components.14 To our knowledge, only two cross-sectional studies from the National Health and Nutritional Examination Survey in the USA have examined concurrent MVPA–MSE and its associations with cardiometabolic health. In brief, when compared with adherence to MVPA or MSE guidelines alone, meeting the combined MVPA–MSE guidelines had more favourable associations with metabolic syndrome14 and multi-morbidity (defined as having ≥1 chronic disease).15 Although not examining cardiometabolic biomarkers, recent prospective studies have identified an independent reduced mortality risk associated with concurrent MVPA–MSE. Among a sample of about 80,000 UK adults, meeting both guidelines was associated with an lower risk of all-cause mortality compared with meeting the MVPA or MSE guideline alone.16 Data from the US National Health Interview Survey showed that when contrasted with meeting MVPA or MSE recommendations only, meeting both recommendations was independently associated with a decreased risk of all-cause mortality among about 14,000 participants surviving cancer for 3+ years.17 The strong clinical and emerging epidemiological evidence suggests that concurrent MVPA–MSE is independently associated with optimum cardiometabolic health14,15 and a reduced risk of mortality.16,17 However, as the current evidence focuses on Western countries (e.g. the USA and UK) with predominately white populations, it is unclear whether these beneficial health outcomes are generalizable globally. Moreover, given that the association between cardiovascular disease risk factors may differ by ethnicity,18,19 it is important to examine these associations among populations form other regions such as Asia. The aim of this study was to examine the associations between different combinations of physical activity guideline adherence – in terms of MVPA and MSE – and cardiometabolic biomarkers among a large sample of Korean adults. Methods Participants Data were obtained from the Korea National Health and Nutritional Examination Survey 2014–2015 (KNHANES 2014–2015). Initiated in 1998, KNHANES is a periodic cross-sectional survey of nationally representative samples from the civilian, non-institutionalized population of the Republic of Korea (hereafter Korea), conducted with the purpose of assessing the health and nutritional status of the population.20 A full description of the background and scope of KNHANES is available elsewhere.21 The KNHANES surveys were approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention and all the participants provided written informed consent (IRB number: 2013-07 CON-03-4C, 2013-12EXP-03-5C). To facilitate the recruitment of a representative sample of the Korean population, KNHANES uses a stratified multi-stage probability sampling design, where participants are selected from sampling units based on geographical area, age and sex, identified using household registries. The KNHANES 2014–2015 had a response rate of 76.1%. The initial KNHANES 2014–2015 sample included 11,752 adults aged 20–80 years. Participants were excluded if they had missing data on: MVPA and MSE (13.5%); demographic information (0.1%); or had fasted for <8 hours or had missing data on their fasting period (2.5%). The final sample in the present study was 9120 (Figure 1). Figure 1. Open in new tabDownload slide KNHANES 2014–2015 participant flow diagram. Physical activity assessments The Global Physical Activity Questionnaire (GPAQ) was used to assess MVPA.22 The GPAQ has acceptable reliability (Spearman’s ρ = 0.67–0.81) and concurrent validity (Spearman’s ρ = 0.45–0.65).21 Consistent with previous studies, only MVPA during recreational activities and for transportation purposes were included in our MVPA estimates.23 Participants reported their frequency (days) and duration (hours/minutes) of MVPA in a typical week and were asked to report only activities that were for ‘at least 10 minutes continuously’.22 Activities were classified into vigorous (8 metabolic equivalents (METs)) or moderate (4 METs) intensity.24 Using a standardized approach,22 we calculated the total amount of MVPA in a typical week by summing the reported time spent on transportation and recreational activities. According to the Korean public health recommendations,25 the sample was dichotomized as meeting the MVPA guidelines (≥150 moderate intensity minutes/week or ≥75 vigorous intensity minutes/week or an equivalent combination of both) or not meeting the MVPA guidelines (not meeting these criteria). To assess MSE, respondents were asked the following question: ‘Over the past 7 days, did you do any physical activities specifically designed to strengthen your muscles such as lifting weights, push-ups, or sit-ups?’. A similar item has shown acceptable reliability (Cohen’s κ = 0.85–0.92)26 and convergent validity (using ≥2 sessions/week against prevalence of metabolic syndrome).14 Those who reported ‘yes’ were asked to report their MSE frequency (times/week). Based on the Korean guidelines,25 MSE levels were dichotomized as meeting the MSE guidelines (≥2 sessions/week) or not meeting the MSE guidelines (not meeting these criteria). Physical activity classification categories To examine the associations between patterns of MVPA–MSE and adverse cardiometabolic biomarkers, each respondent was categorised into one of four groups: met neither (MVPA 0–149 minutes/week and MSE 0–1 sessions/week); MSE only (MSE ≥2 sessions/week and MVPA 0–149 minutes/week); MVPA only (MVPA ≥150 minutes/week and MSE 0–1 sessions/week); and met both (MVPA ≥150 minutes/week and MSE ≥2 sessions/week). Cardiometabolic biomarkers All data collected during KNHANES were collected by trained researchers using a consistent methodology. Cardiometabolic biomarkers included fasting glucose (mg/dL), glycosylated haemoglobin (%), insulin (mIU/ml), total cholesterol (mg/dL), high-density lipoprotein (HDL) cholesterol (mg/dL), triglycerides (mg/dL), systolic blood pressure (mmHg) and diastolic blood pressure (mmHg). All biological sample testing was performed with COBAS 8000 C702 (Roche, Mannheim, Germany).20 Triglycerides and total cholesterol were assessed by implementing the enzymatic method. Fasting glucose level and glycosylated haemoglobin were assessed via the hexokinase method.20 Blood pressure was assessed by taking three readings using a mercury sphygmomanometer after five minutes of seated rest. Mean systolic blood pressure and diastolic blood pressure were expressed as the mean of the last two measurements. A detailed description of the data collection processes used in KNHANES 2014–2015 is available elsewhere.20 Classification of adverse cardiometabolic biomarkers Established cut-points were used to classify adverse cardiometabolic biomarkers,27–29 similar to those used among a large sample of Korean adults.30 These were: high fasting glucose ≥100 mg/dL; high glycosylated haemoglobin ≥6.5%; high insulin ≥25 mIU/L; high total cholesterol ≥190.0 mg/dL; low HDL cholesterol <40 mg/dL L in men, <50 mg/dL in women; high triglycerides ≥150 mg/dL; high systolic blood pressure ≥130 mm Hg; and high diastolic blood pressure ≥85 mm Hg. Consistent with previous studies, we report individual adverse cardiometabolic biomarkers and the total number of adverse biomarkers (0 to ≤4).18,31,32 Covariates Information was collected on sociodemographic and lifestyle-related factors using standardized survey items. Household income was categorized into quartiles. Education level was categorized into elementary school, middle school, high school and college. Smoking status was categorized into never smoked, former smoker and current smoker. Alcohol consumption was categorized into never, <1 time/week, 2–4 times/week and >4 times/week. Body mass index (BMI) was treated as a continuous variable and was calculated from objectively measured height (m) and weight (kg). Self-rated health status was categorized into very good, good, neither good nor bad, bad or very bad. Sleep duration was treated as a continuous variable and was reported in hours and minutes and presented as mean values. Energy intake was assessed via a standard questionnaire and energy intake per day was expressed as mean kilocalories/day (kcal) and was treated as a continuous variable. Time spent sitting was assessed by a single item and included time spent at work, at home, during transportation, during coursework and during leisure time (e.g. time spent sitting at a desk, visiting friends, reading, or sitting or lying down to watch TV). Based on a cut-off point from a previous study,33 participants were categorized as either low sitting (<480 min/day) or high sitting (≥8 hours/day). Waist circumference was treated as a continuous variable and was measured to the nearest 0.1 cm during exhalation by measuring tape (SECA 200, SECA) at the mid-axillary line on the horizontal plane mid-point between the inferior costal margin and the iliac crest. Statistical analysis Analyses were conducted using the Complex Samples module of SPSS version 22 (SPSS Inc., Chicago, IL, USA). For each respondent, weighting factors were constructed to represent the Korean population by accounting for the complex survey design, survey non-response and post-stratification.20 These weighting factors were based on the converse of selection probabilities and the converse of response rates were adapted by adjusting these to be age- and sex-specific Korean populations (post-stratification).20 Descriptive statistics were used to describe the profile of the sample according to the physical activity category classification (independent variables) across proportions of adverse biomarkers (dependent variables) and covariates. Given the binary nature of adverse cardiometabolic biomarkers (yes versus no), we used Poisson regression models with robust error variance to examine the associations between physical activity categories and the individual and total number of adverse biomarkers. Prevalence ratios are reported adjusted for selected covariates in our fitted binary Poisson regression models. For our analysis, we used those not meeting either the MVPA or MSE guidelines (meet neither) as the reference group. In addition, because adverse cardiometabolic biomarkers are likely to differ by sex,34 we also present a sex-stratified analysis (Appendix 1). In cross-sectional epidemiological studies, presenting adjusted prevalence ratios is considered a more robust statistical approach than the traditionally used logistic regression reporting odds ratios.35,36 Before conducting our analytical models, we tested for multicollinearity among potential covariates and adverse biomarkers using tests for χ2 and the variance inflation factor (VIF), with VIF ≥ 2 indicating multicollinearity.37 Three pairs of covariates had VIFs ≥ 2: education and household income; smoking and alcohol; and waist circumference and BMI. After excluding household income, alcohol and BMI, the VIFs ranged from 1.02 to 1.94. Results Sample characteristics Data were available for 9120 respondents. Over half (50.3%) were women and the mean age was 46.2 years (SE 1.7 years). Overall, 44.1% met neither, 6.0% met MSE only, 33.4% met MVPA only and 15.4% met both guidelines (Table 1). Table 1. Sample characteristics and individual and total number of adverse cardiometabolic biomarkers according to categories of physical activity guideline adherencea among Korean adults in the Korea National Health and Nutrition Examination Survey (2014–2015). . Physical activity guideline adherencea . Participant characteristics . Met neither . Met muscle strengthening only . Met MVPA only . Met both . No. of participants (%b; SE) 4350 (44.4; 0.7) 600 (6.3; 0.3) 2962 (33.8; 0.7) 1208 (15.4; 0.5) Percentageb (SE) male sex 43.3 (0.8) 65.5 (2.1) 45.3 (1.1) 69.4 (1.4) Mean (SE)c age (years) 48.1 (0.4) 49.5 (0.8) 45.0 (0.4) 41.9 (0.5) Percentageb (SE) with lowest educational level 20.7 (0.9) 14.3 (1.5) 12.4 (0.7) 5.9 (0.7) Percentageb (SE) in lowest quartile of household income 16.1 (0.8) 10.6 (1.4) 12.9 (0.8) 8.8 (1.0) Percentageb (SE) current smoker 23.4 (0.8) 21.5 (2.2) 19.9 (1.0) 24.3 (1.5) Percentageb (SE) highest alcohol consumption 8.0 (0.5) 6.7 (1.1) 6.4 (0.6) 5.9 (0.7) Percentageb (SE) sedentary behaviour (≥8 hours/day sitting) 51.8 (1.1) 48.4 (2.5) 47.6 (1.3) 49.0 (1.7) Mean (SE)c sleepb (hours/day) 6.8 (0.03) 6.7 (0.07) 6.7 (0.03) 6.7 (0.04) Percentageb (SE) self-rated health 4.4 (0.4) 2.7 (0.8) 2.0 (0.3) 1.1 (0.3) Mean (SE)c energy intake per dayd (kcal) 2040.2 (18.8) 2282.0 (61.1) 2118.2 (24.9) 2395.3 (45.4) Mean (SE)c waist circumferenced (cm) 81.9 (0.2) 83.3 (0.5) 81.3 (0.3) 81.6 (0.3) Individual adverse cardiometabolic biomarkerse Weighted percentage (95% CI) High fasting glucose (≥100 mg/dL) 33.3 (31.5–35.1) 33.4 (28.8–38.3) 28.7 (26.6–30.9) 25.5 (22.8–28.3) High glycohaemoglobin (≥6.5%) 7.9 (7.0–8.8) 9.2 (6.9–12.2) 6.7 (5.8–7.7) 6.4 (5.0–8.0) High insulin (≥25 mIU/mL) 2.5 (1.8–3.3) 1.9 (0.7–4.9) 1.4 (0.8–2.5) 1.3 (0.6–3.0) High total cholesterol (≥190.0 mg/dL) 47.5 (45.8–49.6) 44.7 (39.7–49.7) 46.6 (44.2–49.0) 44.1 (40.5–47.6) Low high-density lipoprotein cholesterol (<40 mg/dL men; <50 mg/dL women) 35.4 (33.7–37.2) 29.7 (25.6–34.2) 30.7 (28.7–32.7) 21.5 (18.9–24.3) High triglycerides (≥150 mg/dL) 31.1 (29.1–32.9) 30.6 (26.5–35.0) 28.9 (26.7–31.1) 24.4 (21.6–27.5) Systolic blood pressure (≥130 mmHg) 20.1 (18.5–21.7) 24.5 (20.7–28.8) 16.6 (15.1–18.3) 16.7 (14.5–19.2) Diastolic blood pressure (≥85 mmHg) 17.1 (15.7–18.6) 22.9 (19.1–27.2) 17.4 (15.7–19.2) 16.9 (14.6–19.6) Total number of adverse cardiometabolic biomarkers 0 21.2 (19.7–22.8) 21.2 (17.6–25.4) 25.0 (23.1–27.0) 31.3 (28.1–34.7) 1 27.3 (25.7–29.0) 25.6 (21.6–30.1) 28.9 (27.0–30.9) 27.7 (24.9–30.8) 2 19.2 (18.0–20.6) 22.2 (18.5–26.3) 18.4 (16.9–20.1) 17.5 (15.0–20.4) 3 15.4 (14.1–16.7) 13.9 (11.7–17.4) 15.1 (13.7–16.7) 13.8 (11.5–16.4)  ≥4 16.9 (15.4–18.4) 17.0 (13.5–21.2) 12.5 (11.2–14.1) 9.6 (7.9–11.8) . Physical activity guideline adherencea . Participant characteristics . Met neither . Met muscle strengthening only . Met MVPA only . Met both . No. of participants (%b; SE) 4350 (44.4; 0.7) 600 (6.3; 0.3) 2962 (33.8; 0.7) 1208 (15.4; 0.5) Percentageb (SE) male sex 43.3 (0.8) 65.5 (2.1) 45.3 (1.1) 69.4 (1.4) Mean (SE)c age (years) 48.1 (0.4) 49.5 (0.8) 45.0 (0.4) 41.9 (0.5) Percentageb (SE) with lowest educational level 20.7 (0.9) 14.3 (1.5) 12.4 (0.7) 5.9 (0.7) Percentageb (SE) in lowest quartile of household income 16.1 (0.8) 10.6 (1.4) 12.9 (0.8) 8.8 (1.0) Percentageb (SE) current smoker 23.4 (0.8) 21.5 (2.2) 19.9 (1.0) 24.3 (1.5) Percentageb (SE) highest alcohol consumption 8.0 (0.5) 6.7 (1.1) 6.4 (0.6) 5.9 (0.7) Percentageb (SE) sedentary behaviour (≥8 hours/day sitting) 51.8 (1.1) 48.4 (2.5) 47.6 (1.3) 49.0 (1.7) Mean (SE)c sleepb (hours/day) 6.8 (0.03) 6.7 (0.07) 6.7 (0.03) 6.7 (0.04) Percentageb (SE) self-rated health 4.4 (0.4) 2.7 (0.8) 2.0 (0.3) 1.1 (0.3) Mean (SE)c energy intake per dayd (kcal) 2040.2 (18.8) 2282.0 (61.1) 2118.2 (24.9) 2395.3 (45.4) Mean (SE)c waist circumferenced (cm) 81.9 (0.2) 83.3 (0.5) 81.3 (0.3) 81.6 (0.3) Individual adverse cardiometabolic biomarkerse Weighted percentage (95% CI) High fasting glucose (≥100 mg/dL) 33.3 (31.5–35.1) 33.4 (28.8–38.3) 28.7 (26.6–30.9) 25.5 (22.8–28.3) High glycohaemoglobin (≥6.5%) 7.9 (7.0–8.8) 9.2 (6.9–12.2) 6.7 (5.8–7.7) 6.4 (5.0–8.0) High insulin (≥25 mIU/mL) 2.5 (1.8–3.3) 1.9 (0.7–4.9) 1.4 (0.8–2.5) 1.3 (0.6–3.0) High total cholesterol (≥190.0 mg/dL) 47.5 (45.8–49.6) 44.7 (39.7–49.7) 46.6 (44.2–49.0) 44.1 (40.5–47.6) Low high-density lipoprotein cholesterol (<40 mg/dL men; <50 mg/dL women) 35.4 (33.7–37.2) 29.7 (25.6–34.2) 30.7 (28.7–32.7) 21.5 (18.9–24.3) High triglycerides (≥150 mg/dL) 31.1 (29.1–32.9) 30.6 (26.5–35.0) 28.9 (26.7–31.1) 24.4 (21.6–27.5) Systolic blood pressure (≥130 mmHg) 20.1 (18.5–21.7) 24.5 (20.7–28.8) 16.6 (15.1–18.3) 16.7 (14.5–19.2) Diastolic blood pressure (≥85 mmHg) 17.1 (15.7–18.6) 22.9 (19.1–27.2) 17.4 (15.7–19.2) 16.9 (14.6–19.6) Total number of adverse cardiometabolic biomarkers 0 21.2 (19.7–22.8) 21.2 (17.6–25.4) 25.0 (23.1–27.0) 31.3 (28.1–34.7) 1 27.3 (25.7–29.0) 25.6 (21.6–30.1) 28.9 (27.0–30.9) 27.7 (24.9–30.8) 2 19.2 (18.0–20.6) 22.2 (18.5–26.3) 18.4 (16.9–20.1) 17.5 (15.0–20.4) 3 15.4 (14.1–16.7) 13.9 (11.7–17.4) 15.1 (13.7–16.7) 13.8 (11.5–16.4)  ≥4 16.9 (15.4–18.4) 17.0 (13.5–21.2) 12.5 (11.2–14.1) 9.6 (7.9–11.8) a Physical activity guideline adherence: Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Weighted percentage with standard error (SE). c Weighted mean with SE. d Missing data for covariates, n (% of total sample): sleep, 4 (0.04%); energy intake per day, 912 (10.0%); waist circumference, 3 (0.03%). e Missing data for cardiometabolic biomarkers, n (% of total sample): fasting glucose, 603 (6.3); glycohaemoglobin, 641 (8.9); insulin, 4952 (52.1); total cholesterol, 603 (6.3); high-density lipoprotein cholesterol, 831 (8.7); triglycerides, 603 (6.2); systolic/diastolic blood pressure, 21 (0.2). Open in new tab Table 1. Sample characteristics and individual and total number of adverse cardiometabolic biomarkers according to categories of physical activity guideline adherencea among Korean adults in the Korea National Health and Nutrition Examination Survey (2014–2015). . Physical activity guideline adherencea . Participant characteristics . Met neither . Met muscle strengthening only . Met MVPA only . Met both . No. of participants (%b; SE) 4350 (44.4; 0.7) 600 (6.3; 0.3) 2962 (33.8; 0.7) 1208 (15.4; 0.5) Percentageb (SE) male sex 43.3 (0.8) 65.5 (2.1) 45.3 (1.1) 69.4 (1.4) Mean (SE)c age (years) 48.1 (0.4) 49.5 (0.8) 45.0 (0.4) 41.9 (0.5) Percentageb (SE) with lowest educational level 20.7 (0.9) 14.3 (1.5) 12.4 (0.7) 5.9 (0.7) Percentageb (SE) in lowest quartile of household income 16.1 (0.8) 10.6 (1.4) 12.9 (0.8) 8.8 (1.0) Percentageb (SE) current smoker 23.4 (0.8) 21.5 (2.2) 19.9 (1.0) 24.3 (1.5) Percentageb (SE) highest alcohol consumption 8.0 (0.5) 6.7 (1.1) 6.4 (0.6) 5.9 (0.7) Percentageb (SE) sedentary behaviour (≥8 hours/day sitting) 51.8 (1.1) 48.4 (2.5) 47.6 (1.3) 49.0 (1.7) Mean (SE)c sleepb (hours/day) 6.8 (0.03) 6.7 (0.07) 6.7 (0.03) 6.7 (0.04) Percentageb (SE) self-rated health 4.4 (0.4) 2.7 (0.8) 2.0 (0.3) 1.1 (0.3) Mean (SE)c energy intake per dayd (kcal) 2040.2 (18.8) 2282.0 (61.1) 2118.2 (24.9) 2395.3 (45.4) Mean (SE)c waist circumferenced (cm) 81.9 (0.2) 83.3 (0.5) 81.3 (0.3) 81.6 (0.3) Individual adverse cardiometabolic biomarkerse Weighted percentage (95% CI) High fasting glucose (≥100 mg/dL) 33.3 (31.5–35.1) 33.4 (28.8–38.3) 28.7 (26.6–30.9) 25.5 (22.8–28.3) High glycohaemoglobin (≥6.5%) 7.9 (7.0–8.8) 9.2 (6.9–12.2) 6.7 (5.8–7.7) 6.4 (5.0–8.0) High insulin (≥25 mIU/mL) 2.5 (1.8–3.3) 1.9 (0.7–4.9) 1.4 (0.8–2.5) 1.3 (0.6–3.0) High total cholesterol (≥190.0 mg/dL) 47.5 (45.8–49.6) 44.7 (39.7–49.7) 46.6 (44.2–49.0) 44.1 (40.5–47.6) Low high-density lipoprotein cholesterol (<40 mg/dL men; <50 mg/dL women) 35.4 (33.7–37.2) 29.7 (25.6–34.2) 30.7 (28.7–32.7) 21.5 (18.9–24.3) High triglycerides (≥150 mg/dL) 31.1 (29.1–32.9) 30.6 (26.5–35.0) 28.9 (26.7–31.1) 24.4 (21.6–27.5) Systolic blood pressure (≥130 mmHg) 20.1 (18.5–21.7) 24.5 (20.7–28.8) 16.6 (15.1–18.3) 16.7 (14.5–19.2) Diastolic blood pressure (≥85 mmHg) 17.1 (15.7–18.6) 22.9 (19.1–27.2) 17.4 (15.7–19.2) 16.9 (14.6–19.6) Total number of adverse cardiometabolic biomarkers 0 21.2 (19.7–22.8) 21.2 (17.6–25.4) 25.0 (23.1–27.0) 31.3 (28.1–34.7) 1 27.3 (25.7–29.0) 25.6 (21.6–30.1) 28.9 (27.0–30.9) 27.7 (24.9–30.8) 2 19.2 (18.0–20.6) 22.2 (18.5–26.3) 18.4 (16.9–20.1) 17.5 (15.0–20.4) 3 15.4 (14.1–16.7) 13.9 (11.7–17.4) 15.1 (13.7–16.7) 13.8 (11.5–16.4)  ≥4 16.9 (15.4–18.4) 17.0 (13.5–21.2) 12.5 (11.2–14.1) 9.6 (7.9–11.8) . Physical activity guideline adherencea . Participant characteristics . Met neither . Met muscle strengthening only . Met MVPA only . Met both . No. of participants (%b; SE) 4350 (44.4; 0.7) 600 (6.3; 0.3) 2962 (33.8; 0.7) 1208 (15.4; 0.5) Percentageb (SE) male sex 43.3 (0.8) 65.5 (2.1) 45.3 (1.1) 69.4 (1.4) Mean (SE)c age (years) 48.1 (0.4) 49.5 (0.8) 45.0 (0.4) 41.9 (0.5) Percentageb (SE) with lowest educational level 20.7 (0.9) 14.3 (1.5) 12.4 (0.7) 5.9 (0.7) Percentageb (SE) in lowest quartile of household income 16.1 (0.8) 10.6 (1.4) 12.9 (0.8) 8.8 (1.0) Percentageb (SE) current smoker 23.4 (0.8) 21.5 (2.2) 19.9 (1.0) 24.3 (1.5) Percentageb (SE) highest alcohol consumption 8.0 (0.5) 6.7 (1.1) 6.4 (0.6) 5.9 (0.7) Percentageb (SE) sedentary behaviour (≥8 hours/day sitting) 51.8 (1.1) 48.4 (2.5) 47.6 (1.3) 49.0 (1.7) Mean (SE)c sleepb (hours/day) 6.8 (0.03) 6.7 (0.07) 6.7 (0.03) 6.7 (0.04) Percentageb (SE) self-rated health 4.4 (0.4) 2.7 (0.8) 2.0 (0.3) 1.1 (0.3) Mean (SE)c energy intake per dayd (kcal) 2040.2 (18.8) 2282.0 (61.1) 2118.2 (24.9) 2395.3 (45.4) Mean (SE)c waist circumferenced (cm) 81.9 (0.2) 83.3 (0.5) 81.3 (0.3) 81.6 (0.3) Individual adverse cardiometabolic biomarkerse Weighted percentage (95% CI) High fasting glucose (≥100 mg/dL) 33.3 (31.5–35.1) 33.4 (28.8–38.3) 28.7 (26.6–30.9) 25.5 (22.8–28.3) High glycohaemoglobin (≥6.5%) 7.9 (7.0–8.8) 9.2 (6.9–12.2) 6.7 (5.8–7.7) 6.4 (5.0–8.0) High insulin (≥25 mIU/mL) 2.5 (1.8–3.3) 1.9 (0.7–4.9) 1.4 (0.8–2.5) 1.3 (0.6–3.0) High total cholesterol (≥190.0 mg/dL) 47.5 (45.8–49.6) 44.7 (39.7–49.7) 46.6 (44.2–49.0) 44.1 (40.5–47.6) Low high-density lipoprotein cholesterol (<40 mg/dL men; <50 mg/dL women) 35.4 (33.7–37.2) 29.7 (25.6–34.2) 30.7 (28.7–32.7) 21.5 (18.9–24.3) High triglycerides (≥150 mg/dL) 31.1 (29.1–32.9) 30.6 (26.5–35.0) 28.9 (26.7–31.1) 24.4 (21.6–27.5) Systolic blood pressure (≥130 mmHg) 20.1 (18.5–21.7) 24.5 (20.7–28.8) 16.6 (15.1–18.3) 16.7 (14.5–19.2) Diastolic blood pressure (≥85 mmHg) 17.1 (15.7–18.6) 22.9 (19.1–27.2) 17.4 (15.7–19.2) 16.9 (14.6–19.6) Total number of adverse cardiometabolic biomarkers 0 21.2 (19.7–22.8) 21.2 (17.6–25.4) 25.0 (23.1–27.0) 31.3 (28.1–34.7) 1 27.3 (25.7–29.0) 25.6 (21.6–30.1) 28.9 (27.0–30.9) 27.7 (24.9–30.8) 2 19.2 (18.0–20.6) 22.2 (18.5–26.3) 18.4 (16.9–20.1) 17.5 (15.0–20.4) 3 15.4 (14.1–16.7) 13.9 (11.7–17.4) 15.1 (13.7–16.7) 13.8 (11.5–16.4)  ≥4 16.9 (15.4–18.4) 17.0 (13.5–21.2) 12.5 (11.2–14.1) 9.6 (7.9–11.8) a Physical activity guideline adherence: Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Weighted percentage with standard error (SE). c Weighted mean with SE. d Missing data for covariates, n (% of total sample): sleep, 4 (0.04%); energy intake per day, 912 (10.0%); waist circumference, 3 (0.03%). e Missing data for cardiometabolic biomarkers, n (% of total sample): fasting glucose, 603 (6.3); glycohaemoglobin, 641 (8.9); insulin, 4952 (52.1); total cholesterol, 603 (6.3); high-density lipoprotein cholesterol, 831 (8.7); triglycerides, 603 (6.2); systolic/diastolic blood pressure, 21 (0.2). Open in new tab For individual adverse cardiometabolic biomarkers, when compared with other physical activity categories, there was a pattern for the highest percentages to be among those who met the MSE guideline only and met neither groups. For a total of ≥4 adverse cardiometabolic biomarkers, the lowest prevalence was 9.6% (95% confidence interval [CI] 7.9–11.8) among those who meet both guidelines and the highest was 17.0% (95% CI 13.5–21.2) among those meeting the MSE guideline only. Adverse cardiometabolic biomarkers The unadjusted and adjusted prevalence ratios (APR) for individual adverse cardiometabolic biomarkers and for having ≥3 and ≥4 adverse biomarkers across of physical activity categories (reference group met neither) are shown in Tables 2 and 3, respectively (Forest plot represents APRs). Table 2. Prevalence ratios of individual adverse cardiometabolic biomarkers according to levels of adherencea to physical activity guidelines and among adults from the Korea National Health and Nutrition Examination Survey (2014–2015). Individual adverse cardiometabolic biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . High fasting glucose Met neither 1 1 MSE only 1.02 (0.88–1.17) 0.99 (0.85–1.15) MVPA only 0.87 (0.80–0.95) 0.86 (0.79–0.94) Met both 0.83 (0.74–0.93) 0.82 (0.72–0.93) High glycohaemoglobin Met neither 1 1 MSE only 1.02 (0.77–1.33) 1.00 (0.75–1.32) MVPA only 0.84 (0.72–0.98) 0.80 (0.68–0.94) Met both 0.78 (0.62–0.97) 0.75 (0.59–0.95) High insulin Met neither 1 1 MSE only 0.80 (0.31–1.73) 0.82 (0.28–1.89) MVPA only 0.51 (0.28–0.88) 0.48 (0.24–0.89) Met both 0.49 (0.20–1.01) 0.53 (0.20–1.16) High total cholesterol Met neither 1 1 MSE only 0.93 (0.81–1.05) 0.92 (0.80–1.05) MVPA only 0.99 (0.93–1.06) 1.01 (0.94–1.09) Met both 0.94 (0.85–1.03) 0.94 (0.85–1.04) Low high-density lipoprotein cholesterol Met neither 1 1 MSE only 0.86 (0.73–0.99) 0.86 (0.73–1.00) MVPA only 0.87 (0.80–0.94) 0.86 (0.79–0.94) Met both 0.58 (0.51–0.66) 0.59 (0.51–0.67) High triglycerides Met neither 1 1 MSE only 0.92 (0.78–1.08) 0.91 (0.77–1.08) MVPA only 0.90 (0.82–0.98) 0.91 (0.82–1.00) Met both 0.81 (0.72–0.92) 0.80 (0.70–0.92) High systolic blood pressure Met neither 1 1 MSE only 1.18 (1.00–1.38) 1.16 (0.97–1.36) MVPA only 0.83 (0.75–0.92) 0.83 (0.75–0.92) Met both 0.86 (0.74–0.98) 0.83 (0.71–0.95) High diastolic blood pressure Met neither 1 1 MSE only 1.27 (1.04–1.53) 1.25 (1.02–1.53) MVPA only 0.99 (0.88–1.11) 1.00 (0.88–1.13) Met both 0.99 (0.85–1.16) 0.92 (0.77–1.10) Individual adverse cardiometabolic biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . High fasting glucose Met neither 1 1 MSE only 1.02 (0.88–1.17) 0.99 (0.85–1.15) MVPA only 0.87 (0.80–0.95) 0.86 (0.79–0.94) Met both 0.83 (0.74–0.93) 0.82 (0.72–0.93) High glycohaemoglobin Met neither 1 1 MSE only 1.02 (0.77–1.33) 1.00 (0.75–1.32) MVPA only 0.84 (0.72–0.98) 0.80 (0.68–0.94) Met both 0.78 (0.62–0.97) 0.75 (0.59–0.95) High insulin Met neither 1 1 MSE only 0.80 (0.31–1.73) 0.82 (0.28–1.89) MVPA only 0.51 (0.28–0.88) 0.48 (0.24–0.89) Met both 0.49 (0.20–1.01) 0.53 (0.20–1.16) High total cholesterol Met neither 1 1 MSE only 0.93 (0.81–1.05) 0.92 (0.80–1.05) MVPA only 0.99 (0.93–1.06) 1.01 (0.94–1.09) Met both 0.94 (0.85–1.03) 0.94 (0.85–1.04) Low high-density lipoprotein cholesterol Met neither 1 1 MSE only 0.86 (0.73–0.99) 0.86 (0.73–1.00) MVPA only 0.87 (0.80–0.94) 0.86 (0.79–0.94) Met both 0.58 (0.51–0.66) 0.59 (0.51–0.67) High triglycerides Met neither 1 1 MSE only 0.92 (0.78–1.08) 0.91 (0.77–1.08) MVPA only 0.90 (0.82–0.98) 0.91 (0.82–1.00) Met both 0.81 (0.72–0.92) 0.80 (0.70–0.92) High systolic blood pressure Met neither 1 1 MSE only 1.18 (1.00–1.38) 1.16 (0.97–1.36) MVPA only 0.83 (0.75–0.92) 0.83 (0.75–0.92) Met both 0.86 (0.74–0.98) 0.83 (0.71–0.95) High diastolic blood pressure Met neither 1 1 MSE only 1.27 (1.04–1.53) 1.25 (1.02–1.53) MVPA only 0.99 (0.88–1.11) 1.00 (0.88–1.13) Met both 0.99 (0.85–1.16) 0.92 (0.77–1.10) a Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Prevalence ratio calculated using Poisson regression with a robust error variance. c Prevalence ratio adjusted for sex, age, education, smoking, sedentary behaviour, sleep duration, self-rated health, energy intake and waist circumference. Open in new tab Table 2. Prevalence ratios of individual adverse cardiometabolic biomarkers according to levels of adherencea to physical activity guidelines and among adults from the Korea National Health and Nutrition Examination Survey (2014–2015). Individual adverse cardiometabolic biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . High fasting glucose Met neither 1 1 MSE only 1.02 (0.88–1.17) 0.99 (0.85–1.15) MVPA only 0.87 (0.80–0.95) 0.86 (0.79–0.94) Met both 0.83 (0.74–0.93) 0.82 (0.72–0.93) High glycohaemoglobin Met neither 1 1 MSE only 1.02 (0.77–1.33) 1.00 (0.75–1.32) MVPA only 0.84 (0.72–0.98) 0.80 (0.68–0.94) Met both 0.78 (0.62–0.97) 0.75 (0.59–0.95) High insulin Met neither 1 1 MSE only 0.80 (0.31–1.73) 0.82 (0.28–1.89) MVPA only 0.51 (0.28–0.88) 0.48 (0.24–0.89) Met both 0.49 (0.20–1.01) 0.53 (0.20–1.16) High total cholesterol Met neither 1 1 MSE only 0.93 (0.81–1.05) 0.92 (0.80–1.05) MVPA only 0.99 (0.93–1.06) 1.01 (0.94–1.09) Met both 0.94 (0.85–1.03) 0.94 (0.85–1.04) Low high-density lipoprotein cholesterol Met neither 1 1 MSE only 0.86 (0.73–0.99) 0.86 (0.73–1.00) MVPA only 0.87 (0.80–0.94) 0.86 (0.79–0.94) Met both 0.58 (0.51–0.66) 0.59 (0.51–0.67) High triglycerides Met neither 1 1 MSE only 0.92 (0.78–1.08) 0.91 (0.77–1.08) MVPA only 0.90 (0.82–0.98) 0.91 (0.82–1.00) Met both 0.81 (0.72–0.92) 0.80 (0.70–0.92) High systolic blood pressure Met neither 1 1 MSE only 1.18 (1.00–1.38) 1.16 (0.97–1.36) MVPA only 0.83 (0.75–0.92) 0.83 (0.75–0.92) Met both 0.86 (0.74–0.98) 0.83 (0.71–0.95) High diastolic blood pressure Met neither 1 1 MSE only 1.27 (1.04–1.53) 1.25 (1.02–1.53) MVPA only 0.99 (0.88–1.11) 1.00 (0.88–1.13) Met both 0.99 (0.85–1.16) 0.92 (0.77–1.10) Individual adverse cardiometabolic biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . High fasting glucose Met neither 1 1 MSE only 1.02 (0.88–1.17) 0.99 (0.85–1.15) MVPA only 0.87 (0.80–0.95) 0.86 (0.79–0.94) Met both 0.83 (0.74–0.93) 0.82 (0.72–0.93) High glycohaemoglobin Met neither 1 1 MSE only 1.02 (0.77–1.33) 1.00 (0.75–1.32) MVPA only 0.84 (0.72–0.98) 0.80 (0.68–0.94) Met both 0.78 (0.62–0.97) 0.75 (0.59–0.95) High insulin Met neither 1 1 MSE only 0.80 (0.31–1.73) 0.82 (0.28–1.89) MVPA only 0.51 (0.28–0.88) 0.48 (0.24–0.89) Met both 0.49 (0.20–1.01) 0.53 (0.20–1.16) High total cholesterol Met neither 1 1 MSE only 0.93 (0.81–1.05) 0.92 (0.80–1.05) MVPA only 0.99 (0.93–1.06) 1.01 (0.94–1.09) Met both 0.94 (0.85–1.03) 0.94 (0.85–1.04) Low high-density lipoprotein cholesterol Met neither 1 1 MSE only 0.86 (0.73–0.99) 0.86 (0.73–1.00) MVPA only 0.87 (0.80–0.94) 0.86 (0.79–0.94) Met both 0.58 (0.51–0.66) 0.59 (0.51–0.67) High triglycerides Met neither 1 1 MSE only 0.92 (0.78–1.08) 0.91 (0.77–1.08) MVPA only 0.90 (0.82–0.98) 0.91 (0.82–1.00) Met both 0.81 (0.72–0.92) 0.80 (0.70–0.92) High systolic blood pressure Met neither 1 1 MSE only 1.18 (1.00–1.38) 1.16 (0.97–1.36) MVPA only 0.83 (0.75–0.92) 0.83 (0.75–0.92) Met both 0.86 (0.74–0.98) 0.83 (0.71–0.95) High diastolic blood pressure Met neither 1 1 MSE only 1.27 (1.04–1.53) 1.25 (1.02–1.53) MVPA only 0.99 (0.88–1.11) 1.00 (0.88–1.13) Met both 0.99 (0.85–1.16) 0.92 (0.77–1.10) a Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Prevalence ratio calculated using Poisson regression with a robust error variance. c Prevalence ratio adjusted for sex, age, education, smoking, sedentary behaviour, sleep duration, self-rated health, energy intake and waist circumference. Open in new tab Table 3. Prevalence ratios of total adverse cardiometabolic biomarkers according to levels of physical activity guideline adherencea and among Korean adults in the Korea National Health and Nutrition Examination Survey (2014–2015). Total number of adverse biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . ≥3d Met neither 1 1 MSE only 1.00 (0.81–1.22) 0.99 (0.79–1.22) MVPA only 0.92 (0.82–1.03) 0.91 (0.82–1.02) Met both 0.74 (0.62–0.87) 0.75 (0.62–0.89) ≥4e Met neither 1 1 MSE only 0.98 (0.79–1.19) 0.93 (0.74–1.15) MVPA only 0.80 (0.71–0.90) 0.80 (0.71–0.91) Met both 0.68 (0.57–0.81) 0.64 (0.53–0.78) Total number of adverse biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . ≥3d Met neither 1 1 MSE only 1.00 (0.81–1.22) 0.99 (0.79–1.22) MVPA only 0.92 (0.82–1.03) 0.91 (0.82–1.02) Met both 0.74 (0.62–0.87) 0.75 (0.62–0.89) ≥4e Met neither 1 1 MSE only 0.98 (0.79–1.19) 0.93 (0.74–1.15) MVPA only 0.80 (0.71–0.90) 0.80 (0.71–0.91) Met both 0.68 (0.57–0.81) 0.64 (0.53–0.78) a Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Prevalence ratio calculated using Poisson regression with a robust error variance. c Prevalence ratio adjusted for sex, age, education, smoking, sedentary behaviour, sleep duration, self-rated health, energy intake and waist circumference. d Reference ≤2 adverse biomarkers. e Reference ≤3 adverse biomarkers. Open in new tab Table 3. Prevalence ratios of total adverse cardiometabolic biomarkers according to levels of physical activity guideline adherencea and among Korean adults in the Korea National Health and Nutrition Examination Survey (2014–2015). Total number of adverse biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . ≥3d Met neither 1 1 MSE only 1.00 (0.81–1.22) 0.99 (0.79–1.22) MVPA only 0.92 (0.82–1.03) 0.91 (0.82–1.02) Met both 0.74 (0.62–0.87) 0.75 (0.62–0.89) ≥4e Met neither 1 1 MSE only 0.98 (0.79–1.19) 0.93 (0.74–1.15) MVPA only 0.80 (0.71–0.90) 0.80 (0.71–0.91) Met both 0.68 (0.57–0.81) 0.64 (0.53–0.78) Total number of adverse biomarkers . Physical activity guideline adherencea . Unadjusted prevalence ratiob (95% CI) . Adjusted prevalence ratioc (95% CI) . . ≥3d Met neither 1 1 MSE only 1.00 (0.81–1.22) 0.99 (0.79–1.22) MVPA only 0.92 (0.82–1.03) 0.91 (0.82–1.02) Met both 0.74 (0.62–0.87) 0.75 (0.62–0.89) ≥4e Met neither 1 1 MSE only 0.98 (0.79–1.19) 0.93 (0.74–1.15) MVPA only 0.80 (0.71–0.90) 0.80 (0.71–0.91) Met both 0.68 (0.57–0.81) 0.64 (0.53–0.78) a Physical activity levels: met neither, MVPA 0–149 minutes/week, MSE 0–1 sessions/week; MSE only, MSE ≥2 sessions/week and MVPA 0–149 minutes/week; MVPA only, MVPA ≥150 minutes/week and MSE 0–1 sessions/week; and met both, MVPA ≥150 minutes/week and MSE ≥2 sessions/week. b Prevalence ratio calculated using Poisson regression with a robust error variance. c Prevalence ratio adjusted for sex, age, education, smoking, sedentary behaviour, sleep duration, self-rated health, energy intake and waist circumference. d Reference ≤2 adverse biomarkers. e Reference ≤3 adverse biomarkers. Open in new tab Individual adverse cardiometabolic biomarkers After adjusting for cofounders (sex, age, education, smoking, sleep and waist circumference), compared with other physical activity categories, meeting both guidelines was significantly associated with the lowest APRs for four of eight individual adverse biomarkers, including high fasting glucose, high glycohaemoglobin, low HDL cholesterol and high triglycerides. For high insulin, compared with meeting neither, meeting the MVPA guideline only was associated a significantly lower APR (0.48). Compared with those who met neither guideline, no statistically significant difference across physical activity categories was identified for high total cholesterol. For high diastolic blood pressure, compared with meeting neither, meeting the MSE guideline only was associated with a 25% significant increased APR. Mutually, meeting the MVPA only and meeting both guidelines resulted in similar significantly lower APRs for high systolic blood pressure. In the sex-stratified analysis, the APRs for the individual adverse cardiometabolic biomarkers were generally concordant across physical activity categories for men and women (Appendix 1). Total number of adverse cardiometabolic biomarkers Compared with meeting neither, having ≥3 adverse biomarkers (reference category ≤2), meeting both guidelines had a significantly lower prevalence ratio (APR = 0.75) (Table 3). For having ≥4 adverse biomarkers (reference category ≤3), compared with meeting neither, meeting both guidelines (APR = 0.64) and meeting MVPA only (APR = 0.80) had significantly lower prevalence ratios. In a sex-stratified analysis (Figure 2), among men, for the prevalence of ≥4 adverse biomarkers (reference category ≤3 adverse biomarkers), compared with meeting neither, only meeting both guidelines was associated with significantly lower prevalence ratios (APR = 0.67). For women, compared with meeting neither guideline, all other physical activity categories had significantly lower prevalence ratios. The APR was lowest among those meeting both guidelines (APR = 0.46), followed by those meeting MSE only (APR = 0.65) and those meeting MVPA only (APR = 0.78) (Appendix 1). Figure 2. Open in new tabDownload slide Adjusteda prevalence ratios (95% confidence interval) for having ≥4 adverse cardiometabolic biomarkers (reference ≤3) according to categories of physical activity guideline adherenceb among Korean adults in the Korea National Health and Nutrition Examination Survey (2014–2015) overall and by sex. Discussion We are the first to describe the associations between categories of MVPA and MSE guideline adherence with adverse cardiometabolic biomarkers among a large sample of East Asian adults. The key finding was that meeting both guidelines, when compared with meeting neither, MVPA only or MSE only, was independently associated with a more favourable cardiometabolic biomarker profile. These findings suggest that concurrent MVPA–MSE is independently associated with optimum cardiometabolic health among adult Korean populations. Our findings add to the existing Korean research reporting on the associations between behaviours related to physical activity with indicators of adult cardiometabolic health. A recent population-representative cross-sectional study by Kim et al.38 showed that insufficient self-reported MVPA was independently associated with increased odds of central obesity and reported cardiovascular diseases (e.g. stroke, myocardial infarction and chronic renal disease). Park et al.30 identified that being in the highest quartile of daily sitting time was associated with increased odds of high diastolic BP and low HDL cholesterol. However, importantly, these studies do not report the cardiometabolic consequences of meeting the MSE guideline and concurrent MVPA–MSE guidelines, which are both components of the Korean physical activity guidelines.25 Moreover, these studies examined a smaller number of cardiometabolic biomarkers than reported in our study. Compared with cross-sectional studies in the UK and USA, which include predominately white participants, we showed comparable beneficial cardiometabolic health outcomes for meeting both MVPA–MSE guidelines.14,15 Moreover, our findings support the emerging evidence from longitudinal studies suggesting that meeting both MVPA–MSE guidelines is associated with lower mortality risks compared with meeting one guideline.13,16 Several systemic reviews of clinical exercise studies have shown that, compared with one activity mode alone, concurrent MVPA–MSE has favourable effects on insulin sensitivity,11,39 lipid/glucose metabolism,12 40 and improved athletic performance.41 These positive physiological consequences are likely to be associated with favourable health outcomes among those who meet both the MVPA–MSE guidelines in the present study. We showed that meeting the MSE guideline only (≥2 sessions/week and MVPA 0–149 minutes/week) was not associated with a lower prevalence ratio of having ≥4 adverse cardiometabolic biomarkers; in fact, it was associated with a higher prevalence ratio for high diastolic blood pressure. These findings might be explained by the clinical evidence suggesting that high levels/intensity of MSE independently increases the risk of hypertension42 and arterial stiffness.43 Possible physiological mechanisms explaining this may be that high levels of MSE have the potential to lead to an increase sympathetic nervous system activity, resulting in increased arterial stiffness by inducing chronic restraint on the arterial wall44 and extreme increases in arterial BP, with some data reporting levels of about 320/250 mmHg.45 Each of these pathophysiological consequences is likely to be detrimental to cardiovascular health. A prospective study has identified a reverse J‐shaped non-linear association between weekly minutes spent in MSE and all‐cause mortality among older women after adjusting for MVPA and other potential cofounders (age, sex and education).46 Collectively, these studies and our results suggest that high levels of MSE, without sufficient MVPA, may not be necessarily health-enhancing. By contrast, meeting the MSE guideline only has been associated with a reduced mortality risk16,47,48 and, among cross-sectional studies, favourable cardiometabolic biomarker profiles,14,49 after adjusting for MVPA and confounders (e.g. age, sex, education and BMI). To address these inconsistencies, prospective studies with repeated MSE and MVPA measures are needed to delineate the associations between MSE and CVD. This will provide a more nuanced understanding of adherence to different combinations of physical activity guidelines and their implications for cardiovascular health. In a sex-stratified analysis, among men, compared with other physical activity categories, only meeting both guidelines resulted in a significantly lower prevalence ratio of having ≥4 adverse cardiometabolic biomarkers. By contrast, among women, all three other physical activity categories were associated with significantly lower prevalence ratios, with the concurrent MVPA–MSE having the lowest adjusted prevalence ratio. Given the cross-sectional design, we are cautious in drawing conclusions from these sex-specific findings. Nevertheless, our findings suggest that health promotion strategies may wish to emphasize the importance of concurrent MVPA–MSE for cardiometabolic health among Korean men. Only 15.4% of Korean adults meet the combined MVPA–MSE guidelines. This prevalence estimate is almost identical to that observed in Australian adults (15.0%),33 but lower than those from USA (20.6%)50 and the UK (26.5%).51 Consistent across these studies, meeting the combined guidelines was associated with a younger age, male sex, higher education/income and better self-rated health.33,50,51 From a health promotion perspective, it should be acknowledged that MVPA and MSE are complex behaviours, each with unique behavioural influences. For example, according the American College of Sports Medicine,52 optimum/safe MSE progression requires access to some basic equipment (hand-held weights, dumb-bells), self-efficacy/motor skill proficiency (e.g. to perform squats, push-ups and lunges) and an understanding of specific terminology (e.g. repetitions, sets and progressive overload). Although it could be argued that MVPA may require comparatively less knowledge, exercise proficiency and specialized equipment, participation in aerobic activities requires exercise intention/motivation, as well as social and physical environmental support (e.g. social/peer support and access to recreation facilities/locations).10 These health promotion complexities underscore the need for multiple and simultaneous strategies to promote and support population level MVPA–MSE, similar to those proposed in social ecological theories of community health promotion.53 Strengths and limitations The strengths of this study include the use of a nationally representative sample of East Asian adults and comprehensive adjustment of covariates in the analysis. The use of standardized recruitment, data/biological sample collection and data reduction processes makes it possible to compare our findings with future KNHANES data collection and similar studies. This study has several limitations. First, the cross-sectional design limits inferences of causality for the main outcomes and therefore the findings need to be interpreted with caution. Future longitudinal studies could better determine the temporal order of the association between MVPA–MSE guideline adherence and adverse cardiometabolic biomarkers. Second, the use of self-reported measures of MVPA, which may have resulted in recall bias (e.g. social desirability or under-/over-reporting). Notwithstanding significant logistical issues (e.g. cost and high participant burden), future studies might include device-based physical activity assessments, such as accelerometers, to potentially improve the validity of MVPA estimates, as well as objective assessments of aerobic fitness (e.g. VO2 maximal oxygen uptake), muscle strength (e.g. isokinetic dynamometry) and body composition (e.g. dual-energy X-ray absorptiometry/bioelectrical impedance). For MSE, however, there is currently no alternative to self-reported assessments and these are routinely used in the surveillance of physical activity.33,50,51 These assessment instruments have shown evidence of acceptable reliability26 and convergent validity.14 The lack of information on the participants’ use of medication is also a limitation. For example, a recent review showed that exercise combined with statins is effective in improving insulin sensitivity and reducing chronic inflammation.54 Third, the fact that 13.5% of the sample did not report their MVPA–MSE levels is likely to influence the findings. For example, it is possible that those who did not report their physical activity levels are among the most physically inactive participants, which, in turn, can affect the results in ways that are not straightforward to predict. Conclusions Among a large sample of Korean adults, meeting both MVPA and MSE guidelines was independently associated with a reduced prevalence of multiple adverse cardiometabolic biomarkers. However, from a population cardiometabolic health perspective, it was concerning that >85% of Korean adults did not meet both guidelines. Findings from our study support public health actions to promote concurrent MVPA–MSE uptake and adherence to improve cardiovascular health at the population level. Acknowledgements We thank all participants in the Korea National Health and Nutritional Examination Survey (KNHANES) 2014–2015. Author contribution JB, DD and JK conceptualized the study. JK accessed and interpreted the data. JB and DD designed the analysis. JB and AK analysed the data. JB, DD, AK, ES, SB, AK and JK contributed to the writing of the manuscript. All authors approved the final article. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. JK is supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Korean Ministry of Education (2017 R1D1A1B03035192). DD is supported by a Heart Foundation Future Leader Fellowship (#101234), Australia. References 1 World Health Organization . Global health risks: Mortality and burden of disease attributable to selected major risks , Geneva : World Health Organization , 2009 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 2 Vasan RS . Biomarkers of cardiovascular disease . Circulation 2006 ; 113 : 2335 – 2362 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Association AD . Diagnosis and classification of diabetes mellitus . Diabetes Care 2010 ; 33 : S62 – S62 . Google Scholar Crossref Search ADS PubMed WorldCat 4 Arsenault BJ , Cote M, Cartier A, et al. Effect of exercise training on cardiometabolic risk markers among sedentary, but metabolically healthy overweight or obese post-menopausal women with elevated blood pressure . Atherosclerosis 2009 ; 207 : 530 – 533 . Google Scholar Crossref Search ADS PubMed WorldCat 5 Lin X , Zhang X, Guo J, et al. Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: A systematic review and meta‐analysis of randomized controlled trials . Journal of the American Heart Association 2015 ; 4 : e002014 – e002014 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 6 Sattelmair J , Pertman J, Ding EL, et al. Dose response between physical activity and risk of coronary heart disease: A meta-analysis . Circulation 2011 ; 124 : 789 – 795 . Google Scholar Crossref Search ADS PubMed WorldCat 7 Kyu HH , Bachman VF, Alexander LT, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: Systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013 . BMJ 2016 ; 354 : i3857 – i3857 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Lee IM , Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy . Lancet 2012 ; 380 : 219 – 229 . Google Scholar Crossref Search ADS PubMed WorldCat 9 Oja P , Titze S. Physical activity recommendations for public health: Development and policy context . EPMA J 2011 ; 2 : 253 – 259 . Google Scholar Crossref Search ADS PubMed WorldCat 10 Bauman AE , Reis RS, Sallis JF, et al. Correlates of physical activity: Why are some people physically active and others not? Lancet 2012 ; 380 : 258 – 271 . Google Scholar Crossref Search ADS PubMed WorldCat 11 Bird SR , Hawley JA. Update on the effects of physical activity on insulin sensitivity in humans . BMJ Open Sport Exerc Med 2017 ; 2 : e000143 – e000143 . Google Scholar Crossref Search ADS PubMed WorldCat 12 Mann S , Beedie C, Jimenez A. Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: Review, synthesis and recommendations . Sports Med 2014 ; 44 : 211 – 221 . Google Scholar Crossref Search ADS PubMed WorldCat 13 Ihalainen JK , Schumann M, Eklund D, et al. Combined aerobic and resistance training decreases inflammation markers in healthy men . Scand J Med Sci Sports 2018 ; 28 : 40 – 47 . Google Scholar Crossref Search ADS PubMed WorldCat 14 Dankel SJ , Loenneke JP, Loprinzi PD. The individual, joint, and additive interaction associations of aerobic-based physical activity and muscle strengthening activities on metabolic syndrome . Int J Behav Med 2016 ; 23 : 707 – 713 . Google Scholar Crossref Search ADS PubMed WorldCat 15 Dankel SJ , Loenneke JP, Loprinzi PD. Combined associations of muscle-strengthening activities and accelerometer-assessed physical activity on multimorbidity: Findings from NHANES . Am J Health Promot 2017 ; 31 : 274 – 277 . Google Scholar Crossref Search ADS PubMed WorldCat 16 Stamatakis E , Lee IM, Bennie JA, et al. Does Strength-Promoting Exercise Confer Unique Health Benefits? A Pooled Analysis of Data on 11 Population Cohorts With All-Cause, Cancer, and Cardiovascular Mortality Endpoints . Am J Epidemiol 2018 ; 187 : 1102 – 1112 . Google Scholar Crossref Search ADS PubMed WorldCat 17 Tarasenko YN , Linder DF, Miller EA. Muscle-strengthening and aerobic activities and mortality among 3+ year cancer survivors in the U.S . Cancer Causes Control 2018 ; 29 : 475 – 484 . Google Scholar Crossref Search ADS PubMed WorldCat 18 Jin K , Gullick J, Neubeck L, et al. Acculturation is associated with higher prevalence of cardiovascular disease risk-factors among Chinese immigrants in Australia: Evidence from a large population-based cohort . Eur J Prevent Cardiol 2017 ; 24 : 2000 – 2008 . Google Scholar Crossref Search ADS WorldCat 19 Kurian AK , Cardarelli KM. Racial and ethnic differences in cardiovascular disease risk factors: A systematic review . Ethn Dis 2007 ; 17 : 143 – 152 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 20 Kweon S , Kim Y, Jang MJ, et al. Data resource profile: The Korea National Health and Nutrition Examination Survey (KNHANES) . Int J Epidemiol 2014 ; 43 : 69 – 77 . Google Scholar Crossref Search ADS PubMed WorldCat 21 Bull FC , Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): Nine country reliability and validity study . J Phys Act Health 2009 ; 6 : 790 – 804 . Google Scholar Crossref Search ADS PubMed WorldCat 22 World Health Organization. Global physical activity questionnaire: Analysis guide 2006. Geneva: World Health Organization, www.who.int/ncds/surveillance/steps/resources/GPAQ_Analysis_Guide.pdf (2006) . 23 Hallal PC , Andersen LB, Bull FC, et al. Global physical activity levels: Surveillance progress, pitfalls, and prospects . Lancet 2012 ; 380 : 247 – 257 . Google Scholar Crossref Search ADS PubMed WorldCat 24 Ainsworth BE , Haskell WL, Herrmann SD, et al. 2011 Compendium of physical activities: A second update of codes and MET values . Med Sci Sports Exerc 2011 ; 43 : 1575 – 1581 . Google Scholar Crossref Search ADS PubMed WorldCat 25 Korean Ministry of Health and Welfare. The physical activity guide for Koreans, 2013. Seoul: The Ministry of Health and Welfare, www.donghyang.or.kr/board/filedown.asp?down_num=100& (2013) . 26 Yore MM , Ham SA, Ainsworth BE, et al. Reliability and validity of the instrument used in BRFSS to assess physical activity . Med Sci Sports Exerc 2007 ; 39 : 1267 – 1274 . Google Scholar Crossref Search ADS PubMed WorldCat 27 Grundy SM , Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement . Circulation 2005 ; 112 : 2735 – 2752 . Google Scholar Crossref Search ADS PubMed WorldCat 28 World Health Organization . Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus , Geneva : World Health Organization , 2011 . Google Scholar PubMed OpenURL Placeholder Text Google Preview WorldCat COPAC 29 World Health Organization . Global health observatory data repository , Geneva : World Health Organization , 2011 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 30 Park JH , Joh HK, Lee GS, et al. Association between sedentary time and cardiovascular risk factors in Korean adults . Korean J Fam Med 2018 ; 39 : 29 – 36 . Google Scholar Crossref Search ADS PubMed WorldCat 31 Stamatakis E . Association between sedentary behaviour and cardiometabolic risk factors in older adults . J Aging Phys Act 2012 ; 20 : S301 – S301 . Google Scholar OpenURL Placeholder Text WorldCat 32 Stamatakis E , Hamer M, Tilling K, et al. Sedentary time in relation to cardio-metabolic risk factors: Differential associations for self-report vs accelerometry in working age adults . Int J Epidemiol 2012 ; 41 : 1328 – 1337 . Google Scholar Crossref Search ADS PubMed WorldCat 33 Bennie JA , Pedisic Z, van Uffelen JG, et al. The descriptive epidemiology of total physical activity, muscle-strengthening exercises and sedentary behaviour among Australian adults – results from the National Nutrition and Physical Activity Survey . BMC Public Health 2016 ; 16 : 73 – 73 . Google Scholar Crossref Search ADS PubMed WorldCat 34 Lew J , Sanghavi M, Ayers CR, et al. Sex-based differences in cardiometabolic biomarkersclinical perspective . Circulation 2017 ; 135 : 544 – 555 . Google Scholar Crossref Search ADS PubMed WorldCat 35 Coutinho LM , Scazufca M, Menezes PR. Methods for estimating prevalence ratios in cross-sectional studies . Rev Saude Publica 2008 ; 42 : 992 – 998 . Google Scholar Crossref Search ADS PubMed WorldCat 36 Bastos LS , Oliveira Rde V, Velasque Lde S. Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies . Cad Saude publica 2015 ; 31 : 487 – 495 . Google Scholar Crossref Search ADS PubMed WorldCat 37 Cohen J , Cohen P, West SG, et al. Applied multiple regression/correlation analysis for the behavioral sciences , London : Routledge , 2013 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 38 Kim G-S , Im E, Rhee J-H. Association of physical activity on body composition, cardiometabolic risk factors, and prevalence of cardiovascular disease in the Korean population (from the fifth Korea national health and nutrition examination survey, 2008–2011) . BMC Public Health 2017 ; 17 : 275 – 275 . Google Scholar Crossref Search ADS PubMed WorldCat 39 Mann S , Beedie C, Balducci S, et al. Changes in insulin sensitivity in response to different modalities of exercise: A review of the evidence . Diabetes Metab Res Rev 2014 ; 30 : 257 – 268 . Google Scholar Crossref Search ADS PubMed WorldCat 40 Booth FW , Roberts CK, Thyfault JP, et al. Role of inactivity in chronic diseases: Evolutionary insight and pathophysiological mechanisms . Physiol Rev 2017 ; 97 : 1351 – 1402 . Google Scholar Crossref Search ADS PubMed WorldCat 41 Coffey VG , Hawley JA. Concurrent exercise training: Do opposites distract? J Physiol 2017 ; 595 : 2883 – 2896 . Google Scholar Crossref Search ADS PubMed WorldCat 42 Karpinos AR , Roumie CL, Nian H, et al. High prevalence of hypertension among collegiate football athletes . Circ Cardiovasc Qual Outcomes 2013 ; 6 : 716 – 723 . Google Scholar Crossref Search ADS PubMed WorldCat 43 Miyachi M . Effects of resistance training on arterial stiffness: A meta-analysis . Br J Sports Med 2013 ; 47 : 393 – 396 . Google Scholar Crossref Search ADS PubMed WorldCat 44 Raastad T , Glomsheller T, Bjøro T, et al. Changes in human skeletal muscle contractility and hormone status during 2 weeks of heavy strength training . Eur J Appl Physiol 2001 ; 84 : 54 – 63 . Google Scholar Crossref Search ADS PubMed WorldCat 45 Jones NL , McCartney N, McComas AJ, et al. Human muscle power , Champaign, IL : Human Kinetics , 1986 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 46 Kamada M , Shiroma EJ, Buring JE, et al. Strength training and all‐cause, cardiovascular disease, and cancer mortality in older women: A cohort study . J Am Heart Assoc 2017 ; 6 : e007677 – e007677 . Google Scholar Crossref Search ADS PubMed WorldCat 47 Dankel SJ , Loenneke JP, Loprinzi PD. Dose-dependent association between muscle-strengthening activities and all-cause mortality: Prospective cohort study among a national sample of adults in the USA . Arch Cardiovasc Dis 2016 ; 109 : 626 – 633 . Google Scholar Crossref Search ADS PubMed WorldCat 48 Kraschnewski JL , Sciamanna CN, Poger JM, et al. Is strength training associated with mortality benefits? A 15 year cohort study of US older adults . Prev Med 2016 ; 87 : 121 – 127 . Google Scholar Crossref Search ADS PubMed WorldCat 49 Cheng YJ , Gregg EW, De Rekeneire N, et al. Muscle-strengthening activity and its association with insulin sensitivity . Diabetes Care 2007 ; 30 : 2264 – 2270 . Google Scholar Crossref Search ADS PubMed WorldCat 50 Centers for Disease Control and Prevention . Adult participation in aerobic and muscle-strengthening physical activities—United States, 2011 . MMWR Morb Mortal Wkly Rep 2013 ; 62 : 326 – 330 . PubMed OpenURL Placeholder Text WorldCat 51 UK Health and Social Care Information Centre . The health survey for England , London : Health and Social Care Information Centre , 2012 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 52 ACSM. American College of Sports Medicine position stand . Progression models in resistance training for healthy adults . Med Sci Sports Exerc 2009 ; 41 : 687 – 708 . Crossref Search ADS PubMed WorldCat 53 Stokols D . Translating social ecological theory into guidelines for community health promotion . Am J Health Promot 1996 ; 10 : 282 – 298 . Google Scholar Crossref Search ADS PubMed WorldCat 54 Gui YJ , Liao CX, Liu Q, et al. Efficacy and safety of statins and exercise combination therapy compared to statin monotherapy in patients with dyslipidaemia: A systematic review and meta-analysis . Eur J Prev Cardiol 2017 ; 24 : 907 – 916 . Google Scholar Crossref Search ADS PubMed WorldCat © The European Society of Cardiology 2020 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The European Society of Cardiology 2020
TI - Run, lift, or both? Associations between concurrent aerobic–muscle strengthening exercise with adverse cardiometabolic biomarkers among Korean adults
JO - European Journal of Preventive Cardiology
DO - 10.1177/2047487318817899
DA - 2020-05-01
UR - https://www.deepdyve.com/lp/oxford-university-press/run-lift-or-both-associations-between-concurrent-aerobic-muscle-djFzJF9MqQ
SP - 738
EP - 748
VL - 27
IS - 7
DP - DeepDyve
ER -