Skip to main content
Download PDF

Association between sedentary behavior and chronic kidney disease in Korean adults

BMC Public Health volume 23, Article number: 306 (2023) Cite this article

Abstract

Background

Chronic kidney disease (CKD) is a significant health care burden, with a worldwide prevalence of approximately 11%. The general population spends over 50% of the awake time sedentary activities. However, to the best of our knowledge, no study has evaluated the association between sedentary time and CKD, with a focus on both kidney damage and kidney function, in the South Korean population. Accordingly, the present study aimed to address this gap in the knowledge.

Method

We used data from the 8th Korea National Health and Nutrition Examination Survey. The analysis included 9,534 participants, especially excluded those who had been diagnosed with kidney disease or who were currently undergoing treatment. Sedentary behavior was self-reported by the participants. An estimated glomerular filtration rate (eGFR) and/or albuminuria were used as measures for detection of CKD according to the guidelines of the Kidney Disease Improving Global Outcomes. We analyzed the data using multiple logistic regression.

Results

Among the women, the risk of CKD was significantly greater among those who sat for ≥ 12 h/d relative to those who sat for < 6 h/d, after adjusting for physical activity and other covariates (odds ratio [OR]: 1.45, 95% confidence interval [CI]: 1.01–2.06). Similarly, among those who sat over 12 h/d, those who engaged in low levels of physical activity had a higher risk of CKD than those who engaged in high levels of activity (OR: 1.65, 95% CI: 1.04–2.61). No statistically significant results were found for men.

Conclusion

Excessive sedentary behavior was associated with an increased risk of CKD, especially albuminuria, regardless of the level of physical activity, only in women. These findings emphasize the importance of avoiding excessive sitting for a long time and increasing overall physical activity levels.

Peer Review reports

Introduction

The global prevalence of chronic kidney disease (CKD) is increasing, and it has become a serious public health problem worldwide [1]. In the United States, the reported prevalence of CKD among adults is 11.5%, and up to 40% among people aged ≥ 70 years [2]. CKD is characterized by progressive deterioration of kidney function. In addition, functional abnormalities are inferred by the glomerular filtration rate (GFR), whereas structural abnormalities are inferred from markers of kidney damage, including albuminuria. Both decreased GFR and albuminuria are independent risk factors for many manifestations of CKD, including stroke, heart failure, and coronary heart disease [3, 4]. According to a previous study, individuals with CKD have a higher risk of developing cardiovascular disease (CVD) than those with normal kidney function [5]. Therefore, since CKD shares many risk factors with CVD, including hypertension, diabetes, and obesity, it is important to identify the factors that contribute to the increased propensity for reduction in the eGFR and development of albuminuria.

It has been reported that the general population spends over 50% of the awake time engaging in sedentary activities [6]. The decrease in physical activity and continued increase in sedentary lifestyles owing to the development of transportation and the widespread availability of the Internet are gradually becoming global problems. According to the National Health and Nutrition Examination Survey(NHNES), in the USA, from 2003 to 2006, the average daily sitting time was 7.3–9.3 h, and, older people spent more time sitting than average [7]. In South Korea, it has been reported that adults spend > 7 h sitting down after waking up [8]. Sedentary time has a significant effect on health, and individuals who use more screen-based entertainment have a higher risk of developing cardiovascular diseases [9]. Furthermore, studies have reported that sedentary behavior is associated with cardiometabolic disease and mortality independent of physical activity [10]. A meta-analysis of nine prospective studies showed a nonlinear association between sedentary behavior and CVD events, with an increased risk associated with sitting > 10/d despite the presence of physical activity [10,11,12,13].

Similarly, previous studies demonstrated that prolonged sedentary behavior is associated with CKD development [14, 15], and most of the studies were conducted in Western countries [11, 16, 17], and to the best of our knowledge, no study has evaluated the association between sedentary time, kidney damage, and kidney function in the Korean population. According to a scoping review that self-reported sitting time in 29 countries in worldwide, Korea was the second-longest sedentary country among the countries participating in the study. In particular, the sitting time was longer than all Western countries [18]; Therefore, the purpose of this study was to investigate the association between time spent in sedentary behavior and the risk of CKD in the Korean population.

Methods

Data

Data were obtained from the 2019 and 2020 of the Korean National Health and Nutrition Examination Survey (KNHANES). The KNHANES is a cross-sectional nationwide survey conducted by the Korean Center for Disease Control and Prevention [19]. The survey is conducted in non-institutionalized Korean civilians throughout 192 regions to monitor trends in health risk factors and the prevalence of major chronic diseases, to evaluate the health and nutritional status of Koreans, and to provide relevant data for the development and evaluation of health policies and programs in Korea [19]. The KNHANES recruits a nationally representative sample of the South Korean population, using a complex and multistage clustered probability design.

Participants

Since the variable of albuminuria was examined in the KNHANES from 2019 onwards, the current study used data from the 2019 and 2020 of the KNHANES. The data of 15,469 participants were examined. The exclusion criteria were aged < 19 years (n = 2,730), menstruation and pregnancy at the time of survey (n = 474), diagnosis of kidney disease or current treatment (n = 127), and missing data (n = 2,604). Finally, the study included 9,534 participants (4,491 men and 5,043 women).

Variables

The main variable of interest was the participant’s sedentary behavior. The total weekday sedentary behavior was measured by the response to the following question, based on the format of the International Physical Activity Questionnaire [20]: “How much time do you typically spend sitting or lying down in a day?” Question about engagement with activities such as working at a desk or computer, reading books, writing, watching television, using the Internet, listening to music, etc. The participants’ responses were divided into the following four categories using quartiles [21]: 1st, < 6 h; 2nd, 6–8.9 h; 3rd, 9–11.9 h; 4th, ≥ 12 h/d).

The dependent variable was the prevalence of CKD. According to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, CKD is defined as the presence of moderately-to-severely decreased kidney function and/or kidney damage [22]. Reduced kidney function was identified by the eGFR, and kidney damage was identified by the presence of microalbuminuria [23]. The eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) formula, Korean version, (eGFR < 60 mL/min/1.73 m2) [24]. Urinary albumin-creatinine ratio (UACR) of ≥ 17 in men or ≥ 25 mg/g in women indicated the presence of microalbuminuria [25].

The following covariates were included in the analyses: demographic factors (sex, age, educational level, and marital status), socioeconomic factors (region, occupation, and family income), and health-related factors (alcohol use and smoking). In addition, adjustments were made for the following variables: physical activity, which was divided into three categories using the metabolic equivalent of task (METS) [26], body mass index (BMI) was categorized into 3 groups according to the World Health Organization and Korean Society for the Study of Obesity standards [27], hypertension [28], and diabetes [29].

Statistical analyses

Owing to sex differences in physical conditions, such as the urine albumin/creatinine ratio being high in women owing to low muscle mass and low levels of excretion of creatinine and urea, all analyses were stratified by sex. Descriptive analysis was performed to examine the distribution of the general characteristics of the study population by using chi-square test. Multiple logistic regression modelling was used to assess the association between sedentary behavior and CKD prevalence after adjusting for measured covariates in the study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to compare the subjects with CKD. SAS (version 9.4M6; SAS Institute Inc., Cary, NC, USA) was used for all statistical analyses.

Result

Table 1 presents the general characteristics of the study population, stratified by sex. Of the 9,534 participants, 4,491 were men and 5,043 were women. Of these, 1,056 individuals, 581 men and 475 women, had CKD. The presence of CKD tended to increase with increasing age, reduction in family income, and increase in education level. Additionally, hypertension, diabetes, and obesity were associated with the prevalence of CKD.

Table 1 General characteristics of the study population
Full size table

Table 2 shows the odds of developing CKD across different levels of sedentary behavior stratified by sex. Among women, long-term sedentary behavior (≥ 12 h/d) was significantly associated with the prevalence of CKD compared to short-term sedentary behavior (< 6 h/d), after adjusting for physical activity and other covariates (OR: 1.45, 95% CI: 1.01–2.06). No statistically significant associations were found among men.

Table 2 Association between CKD and subject demographic
Full size table

The results of the subgroup analysis are shown in Table 3. In the analyses stratified by independent variables, in the group with the highest levels of sedentary behavior, the participants with low physical activity showed a significantly increased risk of CKD (OR: 1.65, 95% CI: 1.04–2.61). A similar trend was observed for participants with hypertension. In addition, among the participants with high family income showed a sharp increase in the risk of CKD (OR: 3.37, 95% CI: 1.19–9.56).

Table 3 Results of subgroup analysis stratified by independent variables
Full size table

Table 4 presents the association of each component of CKD, namely kidney function (estimated glomerular filtration rate) and kidney damage (microalbuminuria) with sedentary behavior, stratified by sex. After adjusting for potential covariates, the overall trend was similar to that observed in Table 2. The results pertaining to men showed no statistically significant association. Among women, those who sat for ≥ 12 h/d, regardless of physical activity level, had a higher risk of albuminuria relative to those who sat for < 6 h/d (OR: 1.45, 95% CI: 1.01–2.08). No statistically significant association was observed for kidney function.

Table 4 Association between sedentary behavior and each components of CKD definition
Full size table

Discussion

This nationwide population-based study found a positive association between long-term sedentary behavior (≥ 12 h/d) and increased risk of CKD, independent of physical activity level, BMI, hypertension, and other confounding variables. In addition, the participants with the low levels of physical activity tended to have an increased risk of CKD. Similarly, among the high physical activity individuals were tended to decreased risk of CKD as the level of sedentary behavior increased, but who sat over the ≥ 12 h/d still increased risk of CKD. Moreover, women with long sitting hours (≥ 12 h/d) had a 1.65-fold higher risk of microalbuminuria than those with short sitting hours (< 6 h/d).

Globally, the obesity epidemic and aging population have led to increasing health burdens of Acute kidney diseases and disorders (AKD) and CKD [30]. According to the World Health Organization, CKD has resulted in 1.2 million deaths and is the 12th leading cause of death worldwide [31]. These results suggest the need for the detection, treatment, and evaluation of early stages of kidney disease to slow progression and prevent complications [32]. Previous studies have shown associations between sedentary behavior, physical activity, and kidney disease [16]. Some of the previous findings were consistent with our findings, especially that long sedentary behavior was associated with a high risk of CKD. Since few muscles are used in a sedentary lifestyle, it affects total blood volume and blood flow circulation, and as endothelial-dependent blood vessel relaxation capacity decreases, vascular endothelial damage due to blood flow resistance increases [33, 34]. Therefore, continued sedentary behavior could increase the risk of CKD owing to problems in blood flow circulation in the kidney or vascular structure [33,34,35].

Regular engagement with physical activity has been reported to have a positive effect on renal vascular resistance by reducing blood pressure and blood sugar levels, independent of other risk factors; the improvement in cardiovascular and endovascular functions through an improved insulin response has a positive effect on the vascular response of the kidney [35, 36]. Moderate and vigorous levels of physical activity are effective in improving kidney function by expanding the renal blood vessels [35]. This may explain our finding that individuals with low levels of physical activity and long periods of sedentary behavior had increased odds of developing CKD.

Our study extends the limited evidence base by suggesting that after adjusting for measured confounding variables, overall sedentary behavior is associated with CKD regardless of the physical activity measured by METS, only in women. According to previous study, they suggested that the higher levels of sedentary behavior, independent of physical activity, are associated with higher risk of CKD. Reasons for the observed gender difference in the relationship between sedentary behavior and CKD are not clear, although several hypotheses have been proposed. Previous report showed that a more positive association between time spent participating in sedentary behavior and pro-inflammatory biomarker levels, such as IL-6 and fibrinogen, in women compared to men [37]. The other study suggested that, although men report higher levels of sedentary behavior than women, men also tend to engage in different patterns of physical activity and that may protect against the effect of excess sedentary behavior [38]. Also, possible that sedentary behavior is associated with gender-specific differences in patterns of other potentially deleterious health behavior such as snacking [39]. Gender-specific differences in the accuracy of self-reported sitting time also potentially could act to dilute the strength of the measured associations in men [40]. Due to the inconsistency between studies evaluating the sex-specific association between sedentary behavior and CKD, more studies are needed to clarify this.

Both eGFR and albuminuria reflect the excretory function of the glomerulus and are the effective measures of kidney disease [32]. Through subgroup analysis, we found that the highest level of sedentary behavior was independently associated with kidney damage, regardless of the level of physical activity engaged in. From a pathophysiological perspective, albuminuria has been used as a biomarker of generalized endothelial dysfunction and capillary dilution [41, 42]. Therefore, our results show that sedentary lifestyle might contribute to the development of generalized endothelial dysfunction and capillary dilution, especially in women. These results are consistent with those of a previous study [23].

A decrease in the eGFR is the first step in the development of kidney failure and a meaningful indicator of CKD [30]. Most previous studies have shown associations between sedentary behavior and eGFR [16, 33]. However, in our study, we cannot find the statistically association. Presumably due to differences in self-reported measurements of physical activity and sedentary behavior or differences in sample size and study population.

Although this study showed that sedentary behavior is associated with the risk of CKD, it has some limitations. First, this study used a cross-sectional data set; thus, we could only determine the association and should beware to investigate the causal relationship between the variables. According to another study, reduced renal function was associated with a significant increase in sitting time [43]. Therefore, a similar observation is likely to be derived in the reverse direction. However, in our study, we attempted to minimize it by excluding those who were diagnosed with kidney disease or who were currently receiving treatment. Because, by physicians, they could affect health behavioral pattern, such as more exercise or reduce sitting time or medication use that can affect kidney disease. However, additional research is needed to accurately infer causality. Second, since the level of sedentary behavior was self-reported, the effect of recall bias could not be eliminated, and the responses might not have been accurate. Third, as we excluded unavailable missing data which accounted for nearly 15% of the original survey, we might have issues with representativeness. In hence, further research should be considered to make up for the missing data. Fourth, eGFR calculated using MDRD formula, but this formulas have some limitations [44]. So other variables that are accurately estimate kidney function, such as cystatin C, should be considered in future studies. Finally, although we adjusted for covariates related to sedentary behavior and chronic kidney disease, the confounding effect cannot be completely excluded because other potential confounding variables might exist such as unhealthy diet or other medication use.

Despite these limitations, this study has several strengths. First, since this study included a nationally representative sample, the results can be generalized to the Korean population. Second, to the best of our knowledge this study was the first to find a positive association between sedentary behavior and CKD, with a focus on both kidney function and kidney damage, in Korean adults.

Conclusion

This study provides the additional evidence by showing that those who spent ≥ 12 h/d in sedentary behavior were at a high risk of developing CKD, only in women, regardless of the level of physical activity they engaged in and other confounding variables. The other components of CKD showed similar associations with sedentary lifestyle, especially when kidney damage was defined on the basis of microalbuminuria. In addition, the association between sedentary behavior and the risk of CKD was modified by the level of physical activity, as measured by METS, where in the group with long-term sedentary behavior (≥ 12 h/d), the risk of CKD was significantly higher in the low-level exercise subgroup than in the high-level exercise group. These findings emphasize the importance of avoiding sitting for a long time and increasing the levels of physical activity.

Availability of data and materials

Data used in this study was from 2019,2020 KNHANES, Raw data as a whole is available to the public, and data can be downloaded from the KNHANES official website (https://knhanes.kdca.go.kr/).

References

  1. Chen TK, Knicely DH, Grams ME. Chronic kidney disease diagnosis and management. JAMA. 2019;322:1294. https://doi.org/10.1001/jama.2019.14745.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Levey AS, Stevens LA, Schmid CH, Zhang Y (Lucy), Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604. https://doi.org/10.7326/0003-4819-150-9-200905050-00006.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375:2073–81. https://doi.org/10.1016/s0140-6736(10)60674-5.

  4. Matsushita K, Ballew SH, Coresh J, Arima H, Ärnlöv J, Cirillo M, et al. Measures of chronic kidney disease and risk of incident peripheral artery disease: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2017;5:718–28. https://doi.org/10.1016/s2213-8587(17)30183-3.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chrysohoou C, Panagiotakos DB, Pitsavos C, Skoumas J, Toutouza M, Papaioannou I, et al. Renal function, cardiovascular disease risk factors’ prevalence and 5-year disease incidence; the role of diet, exercise, lipids and inflammation markers: the ATTICA study. QJM. 2010;103:413–22. https://doi.org/10.1093/qjmed/hcq045.

    Article  CAS  PubMed  Google Scholar 

  6. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of Time Spent in Sedentary Behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167:875–81. https://doi.org/10.1093/aje/kwm390.

    Article  PubMed  Google Scholar 

  7. Healy GN, Wijndaele K, Dunstan DW, Shaw JE, Salmon J, Zimmet PZ, et al. Objectively measured sedentary time, physical activity, and metabolic. Risk. 2008;31:369–71. https://doi.org/10.2337/dc07-1795.

    Article  Google Scholar 

  8. Kim Y. The Korea National Health and Nutrition Examination Survey (KNHANES): current status and challenges. Epidemiol Health. 2014;36:e2014002. https://doi.org/10.4178/epih/e2014002.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Stamatakis E, Hamer M, Dunstan DW. Screen-based entertainment time, all-cause mortality, and cardiovascular events. J Am Coll Cardiol. 2011;57:292–9. https://doi.org/10.1016/j.jacc.2010.05.065.

    Article  PubMed  Google Scholar 

  10. Proper KI, Singh AS, van Mechelen W, Chinapaw MJM. Sedentary behaviors and health outcomes among adults. Am J Prev Med. 2011;40:174–82. https://doi.org/10.1016/j.amepre.2010.10.015.

    Article  PubMed  Google Scholar 

  11. Pandey A, Salahuddin U, Garg S, Ayers C, Kulinski J, Anand V, et al. Continuous dose-response association between sedentary time and risk for cardiovascular disease. JAMA Cardiol. 2016;1:575. https://doi.org/10.1001/jamacardio.2016.1567.

    Article  PubMed  Google Scholar 

  12. Young DR, Hivert M-F, Alhassan S, Camhi SM, Ferguson JF, Katzmarzyk PT, et al. Sedentary behavior and cardiovascular morbidity and mortality: a science advisory from the American Heart Association. Circulation. 2016;134. https://doi.org/10.1161/cir.0000000000000440.

  13. Stamatakis E, Ekelund U, Ding D, Hamer M, Bauman AE, Lee I-M. Is the time right for quantitative public health guidelines on sitting? A narrative review of sedentary behaviour research paradigms and findings. Br J Sports Med. 2019;53:377–82. https://doi.org/10.1136/bjsports-2018-099131.

    Article  PubMed  Google Scholar 

  14. Hawkins M, Newman AB, Madero M, Patel KV, Shlipak MG, Cooper J, et al. TV Watching, but Not Physical Activity, Is Associated With Change in Kidney Function in Older Adults. 2015;12:561–8. https://doi.org/10.1123/jpah.2013-0289.

  15. Kosaki K, Tanahashi K, Matsui M, Akazawa N, Osuka Y, Tanaka K, et al. Sedentary behaviour, physical activity, and renal function in older adults: isotemporal substitution modelling. BMC Nephrol. 2020;21. https://doi.org/10.1186/s12882-020-01869-8.

  16. Bharakhada N, Yates T, Davies MJ, Wilmot EG, Edwardson C, Henson J, et al. Association of sitting time and physical activity with CKD: a cross-sectional study in family practices. Am J Kidney Dis. 2012;60:583–90. https://doi.org/10.1053/j.ajkd.2012.04.024.

    Article  PubMed  Google Scholar 

  17. Volaklis K, Mamadjanov T, Meisinger C. Sedentary behavior and kidney function in adults: a narrative review. Wien Klin Wochenschr. 2021;133:144–52. https://doi.org/10.1007/s00508-020-01673-2.

    Article  PubMed  Google Scholar 

  18. Mclaughlin M, Atkin AJ, Starr L et al. Worldwide surveillance of self-reported sitting time: a scoping review. Int J Behav Nutr Phys Act. 2020. https://doi.org/10.1186/s12966-020-01008-4.

  19. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, et al. Data Resource Profile: The Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014;43:69–77. https://doi.org/10.1093/ije/dyt228.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Rosenberg DE, Bull FC, Marshall AL, Sallis JF, Bauman AE. Assessment of sedentary behavior with the international physical activity questionnaire. 2008;5:S30–44. https://doi.org/10.1123/jpah.5.s1.s30.

  21. Joo JH, Kim HJ, Park EC, Jang SI. Association between sitting time and non-alcoholic fatty liver disease in South Korean population: a cross-sectional study. Lipids Health Dis. 2020;19. https://doi.org/10.1186/s12944-020-01385-6.

  22. Stevens PE. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158:825. https://doi.org/10.7326/0003-4819-158-11-201306040-00007.

    Article  PubMed  Google Scholar 

  23. Martens RJH, van der Berg JD, Stehouwer CDA, Henry RMA, Bosma H, Dagnelie PC, et al. Amount and pattern of physical activity and sedentary behavior are associated with kidney function and kidney damage: the Maastricht Study. PLoS One. 2018;13:e0195306. https://doi.org/10.1371/journal.pone.0195306.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lee CS, Cha R, Lim Y-H, Kim H, Song KH, Gu N, et al. Ethnic coefficients for glomerular filtration rate estimation by the modification of diet in renal disease study equations in the Korean Population. J Korean Med Sci. 2010;25:1616. https://doi.org/10.3346/jkms.2010.25.11.1616.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. K/DOQI Clinical Practice Guidelines on Chronic Kidney DiseaseWork Group and Evidence Review Team Membership. Am J Kidney Dis. 2002;39:S11–2. https://doi.org/10.1016/s0272-6386(02)70084-x.

  26. Chun MY. Validity and reliability of Korean version of international physical activity questionnaire short form in the elderly. Korean J Fam Med. 2012;33:144. https://doi.org/10.4082/kjfm.2012.33.3.144.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kuczmarski RJ, Flegal KM. Criteria for definition of overweight in transition: background and recommendations for the United States. 2000;72:1074–81. https://doi.org/10.1093/ajcn/72.5.1074.

  28. Horowitz B, Miskulin D, Zager P. Epidemiology of hypertension in CKD. Adv Chronic Kidney Dis. 2015;22:88–95. https://doi.org/10.1053/j.ackd.2014.09.004.

    Article  PubMed  Google Scholar 

  29. Pyram R, Kansara A, Banerji MA, Loney-Hutchinson L. Chronic kidney disease and diabetes. Maturitas. 2012;71:94–103. https://doi.org/10.1016/j.maturitas.2011.11.009.

    Article  CAS  PubMed  Google Scholar 

  30. James MT, Levey AS, Tonelli M, et al. Incidence and prognosis of acute kidney diseases and disorders using an integrated approach to laboratory measurements in a universal health care system.  JAMA Netw Open. 2019;2(4):e191795. https://doi.org/10.1001/jamanetworkopen.2019.1795.

  31. Nutritional Anaemias: Tools For Effective Prevention And Control. World Health Organization. 2017. https://apps.who.int/iris/handle/10665/259425. Accessed 1 Nov 2022.

  32. Levey AS, Grams ME, Inker LA. Uses of GFR and Albuminuria Level in Acute and Chronic Kidney Disease. N Engl J Med. 2022;386:2120–8. https://doi.org/10.1056/nejmra2201153.

    Article  CAS  PubMed  Google Scholar 

  33. Hamburg NM, McMackin CJ, Huang AL, Shenouda SM, Widlansky ME, Schulz E, et al. Physical inactivity rapidly induces insulin resistance and microvascular dysfunction in healthy volunteers. ATVB. 2007;27:2650–6. https://doi.org/10.1161/atvbaha.107.153288.

    Article  CAS  Google Scholar 

  34. Schrage WG. Not a search in vein: novel stimulus for vascular dysfunction after simulated microgravity. J Appl Physiol. 2008;104:1257–8. https://doi.org/10.1152/japplphysiol.00174.2008.

    Article  PubMed  Google Scholar 

  35. Jafar TH, Jin A, Koh W-P, Yuan J-M, Chow KY. Physical activity and risk of end-stage kidney disease in the Singapore Chinese Health Study. Nephrology. 2015;20:61–7. https://doi.org/10.1111/nep.12355.

    Article  PubMed  Google Scholar 

  36. Assah FK, Brage S, Ekelund U, Wareham NJ. The association of intensity and overall level of physical activity energy expenditure with a marker of insulin resistance. Diabetologia. 2008;51:1399–407. https://doi.org/10.1007/s00125-008-1033-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bergens O, Nilsson A, Papaioannou KG, Kadi F. Sedentary patterns and systemic inflammation: sex-specific links in older adults. Front Physiol. 2021;12:625950. https://doi.org/10.3389/fphys.2021.625950.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Dunstan DW, Salmon J, Healy GN, Shaw JE, Jolley D, Zimmet PZ, Owen N, AusDiab Steering Committee. Association of television viewing with fasting and 2-h postchallenge plasma glucose levels in adults without diagnosed diabetes. Diabetes Care. 2007;30(3):516–22. https://doi.org/10.2337/dc06-1996. (PMID: 17327314).

    Article  CAS  PubMed  Google Scholar 

  39. Charreire H, Kesse-Guyot E, Bertrais S, Simon C, Chaix B, Weber C, Touvier M, Galan P, Hercberg S, Oppert JM. Associations between dietary patterns, physical activity (leisure-time and occupational) and television viewing in middle-aged French adults. Br J Nutr. 2011;105(6):902–10. https://doi.org/10.1017/S000711451000440X. (Epub 2011 Jan 21 PMID: 21251337).

    Article  CAS  PubMed  Google Scholar 

  40. Yates T, Khunti K, Wilmot EG, Brady E, Webb D, Srinivasan B, Henson J, Talbot D, Davies MJ. Self-reported sitting time and markers of inflammation, insulin resistance, and adiposity. Am J Prev Med. 2012;42(1):1–7. https://doi.org/10.1016/j.amepre.2011.09.022. (PMID: 22176839).

    Article  PubMed  Google Scholar 

  41. Martens RJH, Henry RMA, Houben AJHM, van der Kallen CJH, Kroon AA, Schalkwijk CG, et al. Capillary rarefaction associates with albuminuria: the Maastricht Study. JASN. 2016;27:3748–57. https://doi.org/10.1681/asn.2015111219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Stehouwer CDA, Smulders YM. Microalbuminuria and risk for cardiovascular disease: analysis of potential mechanisms. JASN. 2006;17:2106–11. https://doi.org/10.1681/asn.2005121288.

    Article  CAS  PubMed  Google Scholar 

  43. Glavinovic T, Ferguson T, Komenda P, Rigatto C, Duhamel TA, Tangri N, et al. CKD and sedentary time: results from the Canadian Health Measures Survey. Am J Kidney Dis. 2018;72:529–37. https://doi.org/10.1053/j.ajkd.2018.03.031.

    Article  PubMed  Google Scholar 

  44. Delanaye P, Cohen EP. Formula-based estimates of the GFR: equations variable and uncertain. Nephron Clin Pract. 2008;110:c48-54. https://doi.org/10.1159/000151436.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We express our gratitude to the Korea Centers for Disease Control and Prevention for providing nation-wide survey data. Also appreciate our colleagues at Yonsei University’s Health Research Institute for their all advice on drafting the manuscript.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea

    Ye Seul Jang & Yu Shin Park

  2. Institute of Health Services Research, Yonsei University, Seoul, Republic of Korea

    Ye Seul Jang, Yu Shin Park, Hyunkyu Kim, Kyungduk Hurh, Eun-Cheol Park & Suk-Yong Jang

  3. Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyunkyu Kim

  4. Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyunkyu Kim, Kyungduk Hurh & Eun-Cheol Park

  5. Department of Healthcare Management, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea

    Suk-Yong Jang

Authors
  1. Ye Seul Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Shin Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyunkyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyungduk Hurh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eun-Cheol Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suk-Yong Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jang YS designed the study, collected the data, performed the statistical analysis, and drafted the manuscript. Jang YS, Park YS, Huh KD, Kim HK, Park EC, Jang YS contributed to the discussion. All authors reviewed and edited drafts of the manuscript and approved the final version. Jang YS is the guarantor of this work and, as such, has full access to all study data. Jang YS assumes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have approved the final manuscript.

Corresponding author

Correspondence to Suk-Yong Jang.

Ethics declarations

Ethics approval and consent to participate

This study did not require prior consent or approval from an institutional review board because the KNHANES is a secondary dataset consisting of already de-identified data that are available in the public domain.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jang, Y.S., Park, Y.S., Kim, H. et al. Association between sedentary behavior and chronic kidney disease in Korean adults. BMC Public Health 23, 306 (2023). https://doi.org/10.1186/s12889-022-14929-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-14929-5

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us