Physical performance and chronic kidney disease development in elderly adults: results from a nationwide cohort study

Results

Baseline characteristics

The baseline characteristics of the subjects according to tertiles of TUG and OLS test scores are shown in Table 1 and Supplementary Table 1, respectively. Among the 30,871 subjects, 15,070 (48.8%) were men. The mean estimated glomerular filtration rate (eGFR) was 84.9 ± 19.2 mL/min/1.73 m². During the study period, more than 72.9% of subjects measured creatinine ≥4 times (Supplementary Figure 1). The median (interquartile range) TUG and OLS test scores were 9 (7-10) and 16 (10-20) seconds, respectively. The subjects in the higher TUG tertile groups tended to be women, have higher BMI, and frequently have a history of smoking. In addition, the proportions of subjects with chronic diseases including diabetes mellitus, hypertension, and cardiovascular disease (CVD) were elevated in tertile groups with higher TUG test scores. The proportions of subjects with dementia and malignancy were comparable among the groups. When the subjects were categorized according to OLS test scores, those in the higher OLS test score groups tended to be men, have lower BMI, and less likely have a history of CVD. Since baseline eGFR was lower in the lower TUG groups than the groups with higher TUG, the correlation between baseline eGFR and physical performance tests were examined using Pearson correlation analysis. The TUG test scores were positively associated with baseline eGFR (r=0.0321, P<.001) while OLS test scores did not show a significant correlation (Supplementary Figure 2).

Table 1. Baseline characteristics according to 3-m timed up and go test.

	3-m timed up and go test tertile
	Overall (N=30,871)	Tertile 1 (n=15,155)	Tertile 2 (n=10,067)	Tertile 3 (n=5649)
TUG test score, s
Mean	9.2 ± 4.1	6.9 ± 1.2	9.6 ± 0.5	14.9 ± 6.3
Median (IQR)	9 (7-10)	7 (6-8)	10 (9-10)	13 (12-15)
OLS test score, s
Mean	16.9 ± 10.4	17.9 ± 10.6	16.6 ± 9.5	14.7 ± 11.1
Median (IQR)	16 (10-20)	18 (11-21)	15 (10-20)	13 (7-20)
Demographic data
Male sex	15,070 (48.8)	7975 (52.6)	4698 (46.7)	2397 (42.5)
Body mass index, kg/m2	24.2 ± 3.0	24.1 ± 2.9	24.2 ± 3.0	24.4 ± 3.1
SBP, mmHg	130.1 ± 16.5	130.2 ± 16.6	129.8 ± 16.3	130.3 ± 16.7
DBP, mmHg	78.8 ± 10.2	78.8 ± 10.3	78.7 ± 10.1	79.1 ± 10.2
Smoking status
Non-smoker	18,818 (61.0)	8893 (58.7)	6262 (62.2)	3663 (64.8)
Ex-smoker	.5946 (19.3)	3191 (21.1)	1833 (18.2)	922 (16.3)
Current smoker	.6107 (19.8)	3071 (20.3)	1972 (19.6)	1064 (18.9)
Drinker	13,533 (43.8)	6947 (45.8)	4319 (42.9)	2267 (40.2)
Comorbidities
Diabetes mellitus	.6951 (22.5)	3240 (21.4)	2292 (22.8)	1419 (25.1)
Hypertension	15,602 (50.5)	7636 (50.4)	5022 (49.9)	2944 (52.1)
Arrhythmia	.2067 (6.7)	997 (6.6)	696 (6.9)	374 (6.6)
CVD	.5276 (17.1)	2394 (15.8)	1724 (17.1)	1158 (20.5)
Myocardial infarction	.602 (2.0)	306 (2.0)	192 (1.9)	104 (1.8)
Congestive heart failure	.1934 (6.3)	891 (5.9)	614 (6.1)	429 (7.6)
Peripheral arterial disease	.1086 (3.5)	473 (3.1)	376 (3.7)	237 (4.2)
Dementia	.460 (1.5)	213 (1.4)	149 (1.5)	98 (1.7)
Malignancy	.1937 (6.3)	953 (6.3)	629 (6.2)	358 (6.3)
Laboratory parameters
eGFR, mL/min/1.73 m2	84.9 ± 19.2	84.4 ± 18.6	84.9 ± 19.3	86.3 ± 20.7
Glucose, mg/dL	101.3 ± 25.2	100.9 ± 23.9	101.5 ± 26.5	101.9 ± 26.4
Total cholesterol, mg/dL	198.3 ± 37.8	197.2 ± 37.0	199.0 ± 38.2	199.8 ± 38.9
HDL-C, mg/dL	53.7 ± 22.0	53.1 ± 17.6	53.8 ± 19.1	55.3 ± 33.9
Triglyceride, mg/dL	118 [85-168]	116 [84-166]	119 [85-168]	122 [87-173]
Note: All variables are expressed as mean and standard deviation, number and percentage, or median and 25th and 75th percentiles.
Cardiovascular disease (CVD) was defined as the presence of a history of myocardial infarction, coronary artery disease, congestive heart failure, peripheral artery disease, or cerebrovascular disease.
Abbreviations: TUG, 3-m timed up and go; OLS, one-leg stand; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol.

Outcomes

During a median of 6.0 (5.3-7.3) years and 181,627 person-years, there were 905 deaths of any cause and 2142 incident CKD events. The causes of mortality are shown in Supplementary Table 2. No significant association was found between specific mortality cause and physical activity. The incidence rate of CKD per 1000 person-years was 11.8 among the overall subjects. The corresponding rate of death was 5.0. The rates of CKD development and mortality were increased in the higher TUG tertile groups (P=.001 and P=.01, respectively) (Table 2). When the subjects were divided into tertiles of OLS test scores, the rate of incident CKD was comparable among the OLS tertile groups. Although the mortality rates significantly differed among the OLS tertile groups, no clear trend of change was observed (Supplementary Table 3). Similar results were observed when cumulative incidence function plots were constructed for mortality and CKD development according to TUG (Figure 1) and OLS tertile groups (Supplementary Figure 3).

Figure 1. Cumulative incidence curves for mortality and chronic kidney disease development according to 3-m timed up and go test tertile group. Abbreviations: TUG, 3-m timed up and go test.

Table 2. Outcome event rates according to 3-m timed up and go test tertile groups.

	Overall	3-m timed up and go test
		Tertile 1	Tertile 2	Tertile 3	Pa
Number of participants	30,871	15,162	10,071	5667
Person-years	181,627	89,254	59,148	33,225
Composite renal outcome
Events, n	2196	1019	722	455
Incidence rate per 1000 person-years	12.1 (11.6-12.6)	11.4 (10.7-12.1)	12.2 (11.3-13.1)	13.7 (12.5-15.0)	.001
Incident CKD
Events, n	2142	993	711	438
Incidence rate per 1000 person-years	11.8 (11.3-12.3)	11.1 (10.5-11.8)	12.0 (11.2-12.9)	13.2 (12.0-14.5)	.002
Incident ESRD
Events, n	65	28	16	21
Incidence rate per 1000 person-years	0.36 (0.28-0.47)	0.31 (0.22-0.45)	0.27 (0.17-0.44)	0.63 (0.41-0.97)	. .03
Death
Events, n	905	434	274	197
Incidence rate per 1000 person-years	5.0 (4.7-5.3)	.4.9 (4.4-5.3)	.4.6 (4.1-5.2)	.5.9 (5.2-6.8)	. .01
Note: aLog rank test.
Abbreviations: CKD, chronic kidney disease; ESRD, end-stage renal disease

Associations between physical performance tests and CKD development

In a cause-specific model adjusting for sex and baseline eGFR, the risk of incident CKD development was elevated by 23% in the highest TUG tertile group compared with the lowest TUG tertile group (hazard ratio [HR], 1.23; 95% confidence interval [95% CI], 1.10-1.38). This risk was attenuated but still statistically significant after adjusting for additional demographic and clinical variables (HR, 1.16; 95% CI, 1.03-1.30). Similar results were observed when the log-transformed TUG score was treated as a continuous variable (Table 3). In addition, in the subdistribution hazards model with death as a competing risk for incident CKD, the relationship between TUG test scores and CKD development risk were similar to the main association of the cause-specific hazards model (Table 4). However, when the subjects were grouped into OLS tertiles, there was no association between OLS test scores and incident CKD. Significant relationships between OLS test score and CKD development risk were also not found in the analysis using subdistribution hazards model (Supplementary Table 4). Spline analysis revealed a linear relationship between the TUG test score and incident CKD risk (Figure 2). However, no significant association was found between OLS test scores and renal outcome (Supplementary Figure 4). Sensitivity analyses excluding subjects with COPD, dementia, and CVD revealed similar findings, respectively (Supplementary Table 5). In addition, sensitivity analyses excluding those who have reached outcome within 2 years of physical performance test also showed results concordant to the findings of the main analysis (Supplementary Tables 6 and 7).

Figure 2. Restricted cubic spline plot for incident chronic kidney disease according to 3-m timed up and go test. Note: Adjusted for sex, estimated glomerular filtration rate, body mass index, systolic blood pressure, chronic obstructive pulmonary disease history, dementia history, diabetes mellitus history, cardiovascular disease, smoking habit, alcohol consumption, and high-density lipoprotein cholesterol.

Table 3. Cause-specific hazard ratios according to 3-m timed up and go test for incident chronic kidney disease.

	Model 1		Model 2		Model 3
	HR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P
TUG score, per s	1.32 (1.17-1.49)	<.001	1.28 (1.13-1.44)	<.001	1.24 (1.09-1.40)	<.001
TUG score tertile
1	Ref		Ref		Ref
2	1.11 (1.01-1.22)	...04	1.11 (1.01-1.22)	.03	1.11 (1.00-1.22)	.04
3	1.23 (1.10-1.38)	<.001	1.17 (1.04-1.31)	.008	1.16 (1.03-1.30)	.01
Note: TUG score and high-density lipoprotein cholesterol were log-transformed due to skewed distribution.
Model 1: adjusted for sex and estimated glomerular filtration rate.
Model 2: adjusted for model 2 plus body mass index, systolic blood pressure, chronic obstructive pulmonary disease history, dementia history, diabetes mellitus history, and cardiovascular disease history.
Model 3: adjusted for model 3 plus smoking habit, alcohol consumption, and high-density lipoprotein cholesterol.
Abbreviations: TUG; 3-m timed up and go; HR, hazard ratio; sHR, subdistribution hazard ratio; CI, confidence interval; Ref, reference.

Table 4. Subdistribution hazard ratios according to 3-m timed up and go test for incident chronic kidney disease with death as a competing event.

	Model 1		Model 2		Model 3
	sHR (95% CI)	P	sHR (95% CI)	P	sHR (95% CI)	P
TUG score, per s	1.30 (1.13-1.48)	<.001	1.26 (1.10-1.44)	<.001	1.23 (1.07-1.41)	.004
TUG score tertile
1	Ref		Ref		Ref
2	1.11 (1.00-1.23)	.06	1.10 (0.99-1.22)	.07	1.10 (0.99-1.22)	.07
3	1.23 (1.08-1.39)	.001	1.17 (1.04-1.33)	.01	1.15 (1.01-1.29)	.03
Note: TUG score and high-density lipoprotein cholesterol were log-transformed due to skewed distribution.
aIn Fine-Gray model, mortality was considered as a competing risk.
Model 1: adjusted for sex and estimated glomerular filtration rate.
Model 2: adjusted for model 2 plus body mass index, systolic blood pressure, chronic obstructive pulmonary disease history, dementia history, diabetes mellitus history, and cardiovascular disease history.
Model 3: adjusted for model 3 plus smoking habit, alcohol consumption, and high-density lipoprotein cholesterol.
Abbreviations: TUG; 3-m timed up and go; HR, hazard ratio; sHR, subdistribution hazard ratio; CI, confidence interval; Ref, reference.

Subgroup analysis

To evaluate the modification effects of subgroups on the relationship between physical performance and incident CKD risk, subgroup analyses were performed in subgroups stratified by sex, BMI, diabetes, hypertension, and CVD (Supplementary Figures 5 and 6). The association between physical performance and incident CKD risk was similar to that in the main analysis across these subgroups.

Materials and Methods

Data source and study subjects

Detailed information regarding data source are described in the Supplementary Methods. The present study was conducted in accordance with the Declaration of Helsinki and approved by the institutional review board of Yonsei University Health System Trial Center (approval no. 4-2018-0697). The informed consent requirement was waived owing to the retrospective nature of the analysis. Data usage was also approved by the national health information data request review committee of NHIS. According to the Act on the Protection of Personal Information Maintained by Public Institutions, the NHIS provides data on health examination results and detailed medical treatments after de-identification of individual-level data. Data were retrieved from the National Health Insurance Service-National Sample Cohort Database (NHIS-NSC DB), which is a retrospective population-based sample cohort constructed on a 2.2% representative sample of the Korean population. The detailed cohort profiles with respect to the development of the NHIS-NSC DB have been previously published [43]. Data acquired during the life transition health screening examination at age 66 years were considered as baseline information. The health screening examination results obtained thereafter were used as follow-up data (Figure 3).

Figure 3. Design of the study. Note: Data from the National Health Insurance Service-National Sample Cohort Database were used. Abbreviations: ICD-10, 10^th revision of the International Statistical Classification of Diseases and Related Health Problems.

Elderly adults who underwent the life transition health screening examination between 2007 and 2008 were initially screened for enrollment (N=42,132). Subjects who met the following criteria were excluded: (1) eGFR <60 mL/min/1.73 m², presence of proteinuria in dipstick urine examination at baseline visit, or history of any renal replacement therapy including renal transplantation and dialysis (2) missing data on TUG test, OLS test, follow-up health screening examinations, or lifestyle questionnaires that include information on smoking and alcohol use. A total of 30,871 subjects were included in the final analysis (Supplementary Figure 7).

Data collection and measurements

Baseline demographic data, including demographic and physical function tests and anthropometric measurements, were collected during the life transition health screening examination. Blood samples were obtained after ≥8-h fasting. Serum creatinine level was measured using an isotope dilution mass spectrometry-calibrated method. eGFR was calculated using the creatinine-based CKD Epidemiology Collaboration equation [45]. Urine samples were collected in the morning after the first voiding. Urine tests were performed on fresh urine samples with urine reagent strips, using regularly calibrated semi-automatic urine analyzers. Urine protein amounts were determined as absent, trace, 1+, 2+, or 3+, which approximately correlate with urine protein levels of <10, 10-20, >30, >100, and >500 mg/dL, respectively. Proteinuria was considered present when the urinalysis result was higher than trace level. Comorbidities during the year before the baseline examination were defined based on International Classification of Diseases, Tenth Revision (ICD-10) codes and claim records. Hypertension was defined as follows: (1) systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg at baseline examination, (2) one or more ICD codes (I10-13 or I14) with antihypertensive claim data before baseline examination, and (3) two or more ICD codes (I10-13 or I14) before baseline examination. Diabetes mellitus was defined as the presence of one inpatient E11-E14 codes (ICD-10) or >2 outpatient E11-E14 codes in claim data or a diabetes drug code with E11-E14 codes. The detailed operational definitions of comorbidities are provided in Supplementary Table 8.

Exposure and outcome ascertainment

TUG and OLS tests were performed by trained examining physicians. For the TUG test, the time taken to get up from a standard armchair, walk a 3-m distance, turn around, and return to sitting in the chair was recorded [46]. For the OLS test, participants had to stand on one leg for as long as possible with the contralateral leg not bearing weight. The time until balance was lost or until the non-weight-bearing leg touched the floor was recorded [47].

The primary outcome was incident CKD and the secondary outcome was death of any cause. Incident CKD was defined as at least two consecutive measurements of eGFR <60 mL/min/1.73 m² during follow-up health examinations. Deaths and cause of death were ascertained from records that were linked with the Korean Statistical Information Service using unique personal identification numbers [48]. Subjects were censored at the date of the last health examination, the development of CKD, death, or the end of the study period (December 31, 2015), whichever occurred first.

Statistical analysis

Detailed information regarding statistical analysis are described in the Supplementary Method. All analyses were performed using Stata version 15.1 (Stata Corp, College Station, TX, USA), R software 3.3.3 (http://www.R-project.org), and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). The TUG and OLS test scores were analyzed as continuous variables, and tertiles of the test scores were analyzed as categorical variables. Cumulative incidence function was used to estimate the cumulative outcome curves, and the homogeneity of the each survival curve was evaluated using Gray’s test [49]. To evaluate the association between the physical performance tests and incident CKD, multistep multivariable proportional cause-specific hazards models were constructed. Death before reaching the primary outcome was considered as a competing outcome and censored at the time of death [50, 51]. Covariates hypothesized to contribute to renal function deterioration were included in the adjusted models. In model 1, sex and baseline eGFR were adjusted. Model 2 additionally adjusted for BMI, systolic blood pressure, chronic obstructive pulmonary disease (COPD) history, dementia history, diabetes, and CVD history. Finally, further adjustments were made for smoking history, alcohol consumption, and high-density lipoprotein cholesterol in model 3. Subgroup analyses were performed according to sex, BMI, diabetes, hypertension, and CVD. For sensitivity analysis, the cause-specific models were analyzed after excluding subjects with comorbid diseases that can affect physical performance tests, including CVD, COPD, and dementia. In addition, the association of physical function test results and development of CKD in Fine-Gray models was evaluated [52]. The covariates that were adjusted in the cause-specific hazards models were used in the sensitivity analysis. All tests were two sided. All P-values <.05 were considered statistically significant.

Abbreviations

TUG: 3-m timed up and go; CKD: chronic kidney disease; OLS: one-leg stand; NHIS-NSC DB: National Health Insurance Service-National Sample Cohort Database; eGFR: estimated glomerular filtration rate; ICD-10: International Classification of Diseases, Tenth Revision; NHIS: National Health Insurance Service).

Author Contributions

YSJ, and JTP made conception and design of the study; YSJ, JHJ, HWK, and JTP acquired the data; YSJ, JHJ, HWK, JTP, THY, SHH and SWK analyzed the data; YSJ and HWK made the figures; YSJ, JTP drafted paper SHH, THY, and SWK supervised the study. All authors contributed to important intellectual contents during manuscript drafting or revision and accepted accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work were appropriately investigated and resolved.

Acknowledgments

The National Health Information Database was provided by the National Health Insurance Service of Korea. We thank the National Health Insurance Service for its cooperation.

Conflicts of Interest

All authors have no relevant financial interests.

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