-
The characteristics of the participants with and without CKD are listed in Table 1. Overall, the mean age of the study population was 57.9 ± 8.9 years, and 38.7% (n = 817) participants were men. A total of 9.5% (n = 200) participants had CKD. Compared with participants without CKD, those with CKD were more likely to be older and have higher levels of BMI, waist circumference, FPG, BP, UACR, and lower levels of eGFR (all P < 0.05). The proportions of hypertension, diabetes, antidiabetic medications, and antihypertensive medications were significantly higher in those with CKD (both P < 0.001). In addition, CVs of FPG, SBP, DBP, TC, and LDL-c significantly increased in participants with CKD compared with those without (all P < 0.05). No significant differences in sex distribution, proportions of current smoking, current drinking, being physically active, dyslipidemia, lipid-lowering medications, and the levels of lipids and CVs of TG and HDL-c were found between the two groups (all P > 0.05).
Table 1. Characteristics of participants with and without CKD
Characteristics Total Without CKD With CKD P value Participants, n 2,109 1,909 200 Age (years) 57.9 ± 8.9 57.5 ± 8.7 61.9 ± 9.9 < 0.001* Men, n (%) 817 (38.7) 751 (39.3) 66 (33.0) 0.080 BMI (kg/m2) 25.1 ± 3.5 24.9 ± 3.5 26.2 ± 3.8 < 0.001* Waist circumference (cm) 84.2 ± 9.6 83.9 ± 9.6 87.1 ± 9.9 < 0.001* Lifestyle factors, n (%) Current smoking 503 (23.9) 465 (24.4) 38 (19.0) 0.091 Current drinking 368 (17.4) 334 (17.5) 34 (17.0) 0.860 Regular exercise 106 (5.0) 101 (5.3) 5 (2.5) 0.086 FPG (mg/dL) 101.2 ± 32.8 99.6 ± 31.2 115.8 ± 42.8 < 0.001* Blood pressure (mmHg) SBP 131 ± 21 130 ± 20 142 ± 24 < 0.001* DBP 79 ± 10 79 ± 10 83 ± 11 < 0.001* Lipid profiles (mg/dL) TG 105.3 (75.2–154.1) 104.5 (74.4–151.9) 118.8 (77.1–168.4) 0.073 TC 197.5 ± 36.0 197.3 ± 35.7 199.1 ± 38.7 0.513 HDL-c 53.8 ± 11.5 53.9 ± 11.5 53.4 ± 11.6 0.588 LDL-c 95.0 ± 25.8 95.1 ± 25.6 94.5 ± 27.3 0.784 eGFR at the second visit (mL/min per 1.73 m2) 91.0 ± 12.5 91.4 ± 12.3 87.6 ± 14.0 < 0.001* eGFR at the third visit (mL/min per 1.73 m2) 91.7 ± 11.9 92.6 ± 10.7 83.5 ± 18.6 < 0.001* UACR at the second visit (mg/g) 4.4 (2.0–10.6) 4.1 (2.0–10.0) 8.5 (2.9–16.7) < 0.001* UACR at the third visit (mg/g) 7.8 (5.2–12.9) 7.3 (5.0–10.8) 51.5 (37.4–84.1) < 0.001* Diabetes, n (%) 447 (21.2) 362 (19.0) 85 (42.5) < 0.001* Hypertension, n (%) 838 (39.7) 711 (37.2) 127 (63.5) < 0.001* Dyslipidemia, n (%) 597 (28.3) 532 (27.9) 65 (32.5) 0.167 Medications Antidiabetic medications at three visits, n (%) 398 (18.9) 319 (16.7) 79 (39.5) < 0.001* Antihypertensive medications at three visits, n (%) 504 (23.9) 427 (22.4) 77 (38.5) < 0.001* Lipid-lowering medications at three visits, n (%) 22 (1.0) 17 (0.9) 5 (2.5) 0.078 Variability, coefficient of variation FPG, % 12.4 ± 10.2 11.9 ± 9.5 17.7 ± 13.9 < 0.001* SBP, % 7.3 ± 4.2 7.2 ± 4.1 8.3 ± 4.5 < 0.001* DBP, % 6.6 ± 3.8 6.5 ± 3.8 7.4 ± 3.9 0.002* TG, % 26.7 ± 17.4 26.5 ± 17.3 28.5 ± 17.5 0.124 TC, % 9.5 ± 6.3 9.3 ± 6.1 11.0 ± 7.8 0.004* HDL-c, % 10.7 ± 7.5 10.6 ± 7.4 11.5 ± 8.4 0.141 LDL-c, % 20.7 ± 11.1 20.5 ± 10.9 22.6 ± 12.7 0.028* Note. Data are means ± SDs and medians (IQRs) for continuous variables, or numbers (percentages) for categorical variables. *Statistically significant. BMI, Body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; IQR, interquartile range; LDL-c, low-density lipoprotein cholesterol; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; UACR, urinary albumin-to-creatinine ratio. -
Table 2 shows the associations of VVV of FPG with the risk of CKD. After adjustment for confounding factors including mean FPG, the risk of CKD increased with increasing CV quartiles of FPG (P value for trend = 0.017). Participants in the highest quartile of CV-FPG had a 70% increased risk for CKD than those in the lowest quartile (OR = 1.70, 95% CI 1.06–2.72). In addition, the risk increased by 2% for each 1 increment in CV-FPG (OR = 1.02, 95% CI 1.00–1.03).
Table 2. Odds ratios and 95% confidence intervals of CKD by the VVV of FPG
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-FPG (%) Q1 (0–6.22) 33/529 6.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (6.22–9.65) 34/526 6.5 0.99 (0.60–1.63) 1.00 (0.60–1.66) 0.97 (0.58–1.61) Q3 (9.65–14.96) 43/527 8.2 1.30 (0.81–2.10) 1.19 (0.73–1.93) 1.10 (0.68–1.79) Q4 (14.96–78.23) 90/527 17.1 2.78 (1.81–4.27) 2.06 (1.30–3.26) 1.70 (1.06–2.72) P for trend < 0.001* < 0.001* 0.017* Each 1 increment 1.04 (1.03–1.05) 1.03 (1.01–1.04) 1.02 (1.00–1.03) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus antidiabetic medications, baseline SBP, LDL-c, log10TG, and eGFR at the second visit. Model 3: adjusted for model 2 plus mean FPG. *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VVV, visit-to-visit variability; OR, odds ratios. -
Table 3 shows the associations of VVV of BP with the risk of CKD. After adjustment for confounding factors including mean SBP, participants in the highest quartile of CV-SBP had a 62% increased risk for CKD than those in the lowest quartile (OR = 1.62, 95% CI 1.04–2.50). In addition, the risk increased by 4% for each 1 increment in CV-SBP (OR = 1.04, 95% CI 1.01–1.08). However, no significant association with the risk of CKD was found for higher quartiles of CV-DBP than for the lowest quartile.
Table 3. Odds ratios and 95% confidence intervals of CKD by the VVV of BP
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-SBP (%) Q1 (0–4.29) 38/528 7.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (4.29–6.65) 52/526 9.9 1.38 (0.88–2.15) 1.31 (0.84–2.07) 1.31 (0.83–2.06) Q3 (6.65–9.83) 43/528 8.1 1.07 (0.67–1.70) 1.07 (0.67–1.71) 1.05 (0.66–1.69) Q4 (9.83–31.87) 67/527 12.7 1.76 (1.15–2.70) 1.69 (1.10–2.62) 1.62 (1.04–2.50) P for trend 0.027* 0.041* 0.072 Each 1 increment 1.05 (1.02–1.09) 1.05 (1.01–1.08) 1.04 (1.01–1.08) Quartiles of CV-DBP (%) Q1 (0–3.91) 38/528 7.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (3.91–6.06) 40/526 7.6 1.01 (0.63–1.61) 1.02 (0.64–1.64) 1.02 (0.64–1.65) Q3 (6.06–8.70) 54/528 10.2 1.34 (0.86–2.08) 1.32 (0.84–2.06) 1.34 (0.85–2.10) Q4 (8.70–32.39) 68/527 12.9 1.61 (1.05–2.47) 1.41 (0.91–2.18) 1.47 (0.95–2.28) P for trend 0.011* 0.069 0.044* Each 1 increment 1.04 (1.00–1.08) 1.02 (0.98–1.06) 1.02 (0.99–1.06) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus antihypertensive medications, baseline DBP (for the CV of SBP), SBP (for the CV of DBP), FPG, LDL-c, log10TG, and eGFR at the second visit. Model 3: adjusted for model 2 plus mean SBP (for the CV of SBP) or DBP (for the CV of DBP). *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; VVV, visit-to-visit variability; OR, odds ratios; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride. -
Table 4 shows the associations of VVV of lipids with the risk of CKD. After adjustment for confounding factors including mean LDL-c, participants in the highest quartile of CV-LDL had an 85% increased risk for CKD compared with the lowest quartile (OR = 1.85, 95% CI 1.23–2.80). In addition, the risk increased by 2% for each 1 increment in CV-LDL (OR = 1.02, 95% CI 1.00–1.03). However, no significant associations with the risk of CKD were found for higher quartiles of CV-TG, CV-TC, and CV-HDL compared with their lowest quartiles.
Table 4. Odds ratios and 95% confidence intervals of CKD by the VVV of lipids
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-TG (%) Q1 (0–14.62) 34/527 6.5 1 (ref.) 1 (ref.) 1 (ref.) Q2 (14.62–23.21) 59/528 11.2 1.83 (1.17–2.86) 1.76 (1.11–2.77) 1.75 (1.11–2.77) Q3 (23.21–34.29) 50/527 9.5 1.63 (1.03–2.58) 1.63 (1.02–2.60) 1.62 (1.02–2.59) Q4 (34.29–137.16) 57/527 10.8 1.78 (1.14–2.80) 1.56 (0.98–2.47) 1.54 (0.96–2.45) P for trend 0.035* 0.130 0.148 Each 1 increment 1.01 (1.00–1.02) 1.01 (1.00–1.01) 1.01 (1.00–1.01) Quartiles of CV-TC (%) Q1 (0–5.20) 51/526 9.7 1 (ref.) 1 (ref.) 1 (ref.) Q2 (5.20–8.11) 31/529 5.9 0.58 (0.37–0.94) 0.60 (0.37–0.96) 0.60 (0.37–0.96) Q3 (8.11–12.08) 52/527 9.9 1.04 (0.69–1.57) 0.98 (0.64–1.49) 0.98 (0.64–1.49) Q4 (12.08–59.43) 66/527 12.5 1.35 (0.91–2.01) 1.26 (0.84–1.89) 1.26 (0.84–1.90) P for trend 0.026* 0.088 0.090 Each 1 increment 1.04 (1.01–1.06) 1.03 (1.01–1.05) 1.03 (1.01–1.05) Quartiles of CV-HDL (%) Q1 (0–5.74) 51/528 9.7 1 (ref.) 1 (ref.) 1 (ref.) Q2 (5.74–9.13) 42/526 8.0 0.83 (0.54–1.28) 0.79 (0.51–1.24) 0.79 (0.51–1.23) Q3 (9.13–13.85) 46/527 8.7 0.87 (0.57–1.33) 0.88 (0.57–1.36) 0.87 (0.56–1.35) Q4 (13.85–68.96) 61/528 11.6 1.18 (0.79–1.77) 1.14 (0.75–1.71) 1.11 (0.74–1.68) P for trend 0.375 0.445 0.505 Each 1 increment 1.01 (0.99–1.03) 1.01 (0.99–1.03) 1.01 (0.99–1.03) Quartiles of CV-LDL (%) Q1 (0–13.05) 48/528 9.1 1 (ref.) 1 (ref.) 1 (ref.) Q2 (13.05–19.08) 44/527 8.3 0.91 (0.59–1.41) 0.87 (0.56–1.36) 0.87 (0.56–1.36) Q3 (19.08–26.73) 33/526 6.3 0.75 (0.47–1.19) 0.70 (0.43–1.13) 0.70 (0.43–1.13) Q4 (26.73–79.55) 75/528 14.2 1.91 (1.28–2.84) 1.86 (1.24–2.79) 1.85 (1.23–2.80) P for trend 0.003* 0.005* 0.007* Each 1 increment 1.02 (1.01–1.03) 1.02 (1.00–1.03) 1.02 (1.00–1.03) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus lipid-lowering medications, baseline SBP, FPG, LDL-c (for the CV of TG), log10TG (for the CV of TC, HDL-c, and LDL-c), and eGFR at the second visit. Model 3: adjusted for model 2 plus mean TG (for the CV of TG), mean TC (for the CV of TC), mean HDL-c (for the CV of HDL-c), or mean LDL-c (for the CV of LDL-c). *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; OR, odds ratios; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VVV, visit-to-visit variability. -
The CV-FPG, CV-SBP, and CV-LDL significantly associated with CKD were used to calculate the variability score. Therefore, the variability score had a range of 0–3. The cumulative incidence of CKD was 5.5%, 10.0%, 16.6%, and 32.0% in participants with a variability score of 0, 1, 2, and 3, respectively (Figure 2). Using participants with a variability score of 0 as the reference, the risk of CKD increased by 58% (OR = 1.58, 95% CI 1.08–2.32), 121% (OR = 2.21, 95% CI 1.40–3.49), and 548% (OR = 6.48, 95% CI 3.18–13.21) for participants with a variability score of 1, 2, and 3, respectively, after adjusting for traditional risk factors including mean FPG, mean SBP, and mean LDL-c (P value for trend < 0.001). The graded relationship between the variability score and the risk of CKD was consistent among subgroups of men and women, age < 65 and ≥ 65 years, BMI < 25 and ≥ 25 kg/m2, with and without current smoking, with and without current drinking, and with and without diabetes (all P values for interaction > 0.05; Figure 3).
Figure 2. Cumulative incidence of CKD and the association between variability score and CKD risk. The hollow columns indicate a cumulative incidence of CKD. The black circles and bars indicate ORs and 95% CIs of CKD risk using participants with a score = 0 as the reference. The regression analysis was adjusted for age, sex, waist circumference, current drinking, current smoking, regular exercise, antidiabetic medications, antihypertensive medications, lipid-lowering medications, baseline log10TG, eGFR at the second visit, mean FPG, mean SBP, and mean LDL-c. OR: odds ratio; CI: confidence interval; FPG: fasting plasma glucose; SBP: systolic blood pressure; LDL-c: low-density lipoprotein cholesterol.
Figure 3. Associations of variability score with the risks of CKD stratified by sex (A), age (B), BMI (C), current smoking (D), current drinking (E), and diabetes status (F). The regression analysis was adjusted for age, sex, waist circumference, current drinking, current smoking, regular exercise, antidiabetic medications, antihypertensive medications, lipid-lowering medications, baseline log10TG, eGFR at the second visit, mean FPG, mean SBP, and mean LDL-c. OR: odds ratio; CI: confidence interval; BMI: body mass index; FPG: fasting plasma glucose; SBP: systolic blood pressure; LDL-c: low-density lipoprotein cholesterol.
doi: 10.3967/bes2021.106
Association of Visit-to-Visit Variabilities in Metabolic Factors with Chronic Kidney Disease in Chinese Adults Living in Shanghai
-
Abstract:
Objective This study aimed to examine the association of visit-to-visit variabilities in metabolic factors with chronic kidney disease (CKD) in Shanghai community residents. Methods We used data from a cohort study of community residents who participated in three examinations in 2008, 2009, and 2013, respectively. Fasting plasma glucose (FPG) level, blood pressure (BP), and lipid levels were determined in 2,109 participants at all three visits, and CKD was evaluated between the second and the third visits. Visit-to-visit variabilities in metabolic factors were described by coefficients of variation (CV) at three visits. A variability score was calculated by adding the numbers of metabolic factors with a high variability defined as the highest quartile of CV. CKD was defined as the estimated glomerular filtration rate < 60 mL/min per 1.73 m2 or urinary albumin-to-creatinine ratio ≥ 30 mg/g. Results A total of 200 (9.5%) participants had CKD at the third visit. Compared with the lowest quartile of CV, the highest quartile was associated with a 70% increased risk of CKD for FPG [odds ratio, OR = 1.70; 95% confidence interval (CI) 1.06–2.72], 62% for systolic BP (OR = 1.62, 95% CI 1.04–2.50), and 85% for low-density lipoprotein cholesterol (OR = 1.85, 95% CI 1.23–2.80). Furthermore, the risk of CKD increased significantly with an increasing variability score. Compared with participants with score 0, participants with scores of 1, 2, and 3 were associated with 58% (OR = 1.58, 95% CI 1.08–2.32), 121% (OR = 2.21, 95% CI 1.40–3.49), and 548% (OR = 6.48, 95% CI 3.18–13.21) higher risks of CKD, respectively. Conclusion The visit-to-visit variabilities in metabolic factors were significantly associated with the risks of CKD in Shanghai community residents. -
Key words:
- Chronic kidney disease /
- Cohort /
- Metabolic factor /
- Visit-to-visit variability
注释: -
Figure 1. Flow chart of the study population. eGFR: Estimated glomerular filtration rate; UACR: urinary albumin-to-creatinine ratio; ACEIs: angiotensin-converting enzyme inhibitors; ARBs: angiotensin receptor blockers; FPG: fasting plasma glucose; BP: blood pressure; TG: triglyceride; TC: total cholesterol; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol.
Figure 2. Cumulative incidence of CKD and the association between variability score and CKD risk. The hollow columns indicate a cumulative incidence of CKD. The black circles and bars indicate ORs and 95% CIs of CKD risk using participants with a score = 0 as the reference. The regression analysis was adjusted for age, sex, waist circumference, current drinking, current smoking, regular exercise, antidiabetic medications, antihypertensive medications, lipid-lowering medications, baseline log10TG, eGFR at the second visit, mean FPG, mean SBP, and mean LDL-c. OR: odds ratio; CI: confidence interval; FPG: fasting plasma glucose; SBP: systolic blood pressure; LDL-c: low-density lipoprotein cholesterol.
Figure 3. Associations of variability score with the risks of CKD stratified by sex (A), age (B), BMI (C), current smoking (D), current drinking (E), and diabetes status (F). The regression analysis was adjusted for age, sex, waist circumference, current drinking, current smoking, regular exercise, antidiabetic medications, antihypertensive medications, lipid-lowering medications, baseline log10TG, eGFR at the second visit, mean FPG, mean SBP, and mean LDL-c. OR: odds ratio; CI: confidence interval; BMI: body mass index; FPG: fasting plasma glucose; SBP: systolic blood pressure; LDL-c: low-density lipoprotein cholesterol.
Table 1. Characteristics of participants with and without CKD
Characteristics Total Without CKD With CKD P value Participants, n 2,109 1,909 200 Age (years) 57.9 ± 8.9 57.5 ± 8.7 61.9 ± 9.9 < 0.001* Men, n (%) 817 (38.7) 751 (39.3) 66 (33.0) 0.080 BMI (kg/m2) 25.1 ± 3.5 24.9 ± 3.5 26.2 ± 3.8 < 0.001* Waist circumference (cm) 84.2 ± 9.6 83.9 ± 9.6 87.1 ± 9.9 < 0.001* Lifestyle factors, n (%) Current smoking 503 (23.9) 465 (24.4) 38 (19.0) 0.091 Current drinking 368 (17.4) 334 (17.5) 34 (17.0) 0.860 Regular exercise 106 (5.0) 101 (5.3) 5 (2.5) 0.086 FPG (mg/dL) 101.2 ± 32.8 99.6 ± 31.2 115.8 ± 42.8 < 0.001* Blood pressure (mmHg) SBP 131 ± 21 130 ± 20 142 ± 24 < 0.001* DBP 79 ± 10 79 ± 10 83 ± 11 < 0.001* Lipid profiles (mg/dL) TG 105.3 (75.2–154.1) 104.5 (74.4–151.9) 118.8 (77.1–168.4) 0.073 TC 197.5 ± 36.0 197.3 ± 35.7 199.1 ± 38.7 0.513 HDL-c 53.8 ± 11.5 53.9 ± 11.5 53.4 ± 11.6 0.588 LDL-c 95.0 ± 25.8 95.1 ± 25.6 94.5 ± 27.3 0.784 eGFR at the second visit (mL/min per 1.73 m2) 91.0 ± 12.5 91.4 ± 12.3 87.6 ± 14.0 < 0.001* eGFR at the third visit (mL/min per 1.73 m2) 91.7 ± 11.9 92.6 ± 10.7 83.5 ± 18.6 < 0.001* UACR at the second visit (mg/g) 4.4 (2.0–10.6) 4.1 (2.0–10.0) 8.5 (2.9–16.7) < 0.001* UACR at the third visit (mg/g) 7.8 (5.2–12.9) 7.3 (5.0–10.8) 51.5 (37.4–84.1) < 0.001* Diabetes, n (%) 447 (21.2) 362 (19.0) 85 (42.5) < 0.001* Hypertension, n (%) 838 (39.7) 711 (37.2) 127 (63.5) < 0.001* Dyslipidemia, n (%) 597 (28.3) 532 (27.9) 65 (32.5) 0.167 Medications Antidiabetic medications at three visits, n (%) 398 (18.9) 319 (16.7) 79 (39.5) < 0.001* Antihypertensive medications at three visits, n (%) 504 (23.9) 427 (22.4) 77 (38.5) < 0.001* Lipid-lowering medications at three visits, n (%) 22 (1.0) 17 (0.9) 5 (2.5) 0.078 Variability, coefficient of variation FPG, % 12.4 ± 10.2 11.9 ± 9.5 17.7 ± 13.9 < 0.001* SBP, % 7.3 ± 4.2 7.2 ± 4.1 8.3 ± 4.5 < 0.001* DBP, % 6.6 ± 3.8 6.5 ± 3.8 7.4 ± 3.9 0.002* TG, % 26.7 ± 17.4 26.5 ± 17.3 28.5 ± 17.5 0.124 TC, % 9.5 ± 6.3 9.3 ± 6.1 11.0 ± 7.8 0.004* HDL-c, % 10.7 ± 7.5 10.6 ± 7.4 11.5 ± 8.4 0.141 LDL-c, % 20.7 ± 11.1 20.5 ± 10.9 22.6 ± 12.7 0.028* Note. Data are means ± SDs and medians (IQRs) for continuous variables, or numbers (percentages) for categorical variables. *Statistically significant. BMI, Body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; IQR, interquartile range; LDL-c, low-density lipoprotein cholesterol; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; UACR, urinary albumin-to-creatinine ratio. Table 2. Odds ratios and 95% confidence intervals of CKD by the VVV of FPG
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-FPG (%) Q1 (0–6.22) 33/529 6.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (6.22–9.65) 34/526 6.5 0.99 (0.60–1.63) 1.00 (0.60–1.66) 0.97 (0.58–1.61) Q3 (9.65–14.96) 43/527 8.2 1.30 (0.81–2.10) 1.19 (0.73–1.93) 1.10 (0.68–1.79) Q4 (14.96–78.23) 90/527 17.1 2.78 (1.81–4.27) 2.06 (1.30–3.26) 1.70 (1.06–2.72) P for trend < 0.001* < 0.001* 0.017* Each 1 increment 1.04 (1.03–1.05) 1.03 (1.01–1.04) 1.02 (1.00–1.03) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus antidiabetic medications, baseline SBP, LDL-c, log10TG, and eGFR at the second visit. Model 3: adjusted for model 2 plus mean FPG. *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VVV, visit-to-visit variability; OR, odds ratios. Table 3. Odds ratios and 95% confidence intervals of CKD by the VVV of BP
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-SBP (%) Q1 (0–4.29) 38/528 7.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (4.29–6.65) 52/526 9.9 1.38 (0.88–2.15) 1.31 (0.84–2.07) 1.31 (0.83–2.06) Q3 (6.65–9.83) 43/528 8.1 1.07 (0.67–1.70) 1.07 (0.67–1.71) 1.05 (0.66–1.69) Q4 (9.83–31.87) 67/527 12.7 1.76 (1.15–2.70) 1.69 (1.10–2.62) 1.62 (1.04–2.50) P for trend 0.027* 0.041* 0.072 Each 1 increment 1.05 (1.02–1.09) 1.05 (1.01–1.08) 1.04 (1.01–1.08) Quartiles of CV-DBP (%) Q1 (0–3.91) 38/528 7.2 1 (ref.) 1 (ref.) 1 (ref.) Q2 (3.91–6.06) 40/526 7.6 1.01 (0.63–1.61) 1.02 (0.64–1.64) 1.02 (0.64–1.65) Q3 (6.06–8.70) 54/528 10.2 1.34 (0.86–2.08) 1.32 (0.84–2.06) 1.34 (0.85–2.10) Q4 (8.70–32.39) 68/527 12.9 1.61 (1.05–2.47) 1.41 (0.91–2.18) 1.47 (0.95–2.28) P for trend 0.011* 0.069 0.044* Each 1 increment 1.04 (1.00–1.08) 1.02 (0.98–1.06) 1.02 (0.99–1.06) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus antihypertensive medications, baseline DBP (for the CV of SBP), SBP (for the CV of DBP), FPG, LDL-c, log10TG, and eGFR at the second visit. Model 3: adjusted for model 2 plus mean SBP (for the CV of SBP) or DBP (for the CV of DBP). *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; VVV, visit-to-visit variability; OR, odds ratios; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride. Table 4. Odds ratios and 95% confidence intervals of CKD by the VVV of lipids
Measures of variability Cases/
No. of participantsCumulative incidence (%) OR (95% CI) Model 1 Model 2 Model 3 Quartiles of CV-TG (%) Q1 (0–14.62) 34/527 6.5 1 (ref.) 1 (ref.) 1 (ref.) Q2 (14.62–23.21) 59/528 11.2 1.83 (1.17–2.86) 1.76 (1.11–2.77) 1.75 (1.11–2.77) Q3 (23.21–34.29) 50/527 9.5 1.63 (1.03–2.58) 1.63 (1.02–2.60) 1.62 (1.02–2.59) Q4 (34.29–137.16) 57/527 10.8 1.78 (1.14–2.80) 1.56 (0.98–2.47) 1.54 (0.96–2.45) P for trend 0.035* 0.130 0.148 Each 1 increment 1.01 (1.00–1.02) 1.01 (1.00–1.01) 1.01 (1.00–1.01) Quartiles of CV-TC (%) Q1 (0–5.20) 51/526 9.7 1 (ref.) 1 (ref.) 1 (ref.) Q2 (5.20–8.11) 31/529 5.9 0.58 (0.37–0.94) 0.60 (0.37–0.96) 0.60 (0.37–0.96) Q3 (8.11–12.08) 52/527 9.9 1.04 (0.69–1.57) 0.98 (0.64–1.49) 0.98 (0.64–1.49) Q4 (12.08–59.43) 66/527 12.5 1.35 (0.91–2.01) 1.26 (0.84–1.89) 1.26 (0.84–1.90) P for trend 0.026* 0.088 0.090 Each 1 increment 1.04 (1.01–1.06) 1.03 (1.01–1.05) 1.03 (1.01–1.05) Quartiles of CV-HDL (%) Q1 (0–5.74) 51/528 9.7 1 (ref.) 1 (ref.) 1 (ref.) Q2 (5.74–9.13) 42/526 8.0 0.83 (0.54–1.28) 0.79 (0.51–1.24) 0.79 (0.51–1.23) Q3 (9.13–13.85) 46/527 8.7 0.87 (0.57–1.33) 0.88 (0.57–1.36) 0.87 (0.56–1.35) Q4 (13.85–68.96) 61/528 11.6 1.18 (0.79–1.77) 1.14 (0.75–1.71) 1.11 (0.74–1.68) P for trend 0.375 0.445 0.505 Each 1 increment 1.01 (0.99–1.03) 1.01 (0.99–1.03) 1.01 (0.99–1.03) Quartiles of CV-LDL (%) Q1 (0–13.05) 48/528 9.1 1 (ref.) 1 (ref.) 1 (ref.) Q2 (13.05–19.08) 44/527 8.3 0.91 (0.59–1.41) 0.87 (0.56–1.36) 0.87 (0.56–1.36) Q3 (19.08–26.73) 33/526 6.3 0.75 (0.47–1.19) 0.70 (0.43–1.13) 0.70 (0.43–1.13) Q4 (26.73–79.55) 75/528 14.2 1.91 (1.28–2.84) 1.86 (1.24–2.79) 1.85 (1.23–2.80) P for trend 0.003* 0.005* 0.007* Each 1 increment 1.02 (1.01–1.03) 1.02 (1.00–1.03) 1.02 (1.00–1.03) Note. Model 1: adjusted for age, sex, waist circumference, current drinking, current smoking, and regular exercise. Model 2: adjusted for model 1 plus lipid-lowering medications, baseline SBP, FPG, LDL-c (for the CV of TG), log10TG (for the CV of TC, HDL-c, and LDL-c), and eGFR at the second visit. Model 3: adjusted for model 2 plus mean TG (for the CV of TG), mean TC (for the CV of TC), mean HDL-c (for the CV of HDL-c), or mean LDL-c (for the CV of LDL-c). *Statistically significant. CI, Confidence interval; CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; OR, odds ratios; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VVV, visit-to-visit variability. -
[1] Marshall T. When measurements are misleading: modelling the effects of blood pressure misclassification in the English population. BMJ, 2004; 328, 933. doi: 10.1136/bmj.328.7445.933 [2] Ceriello A, Monnier L, Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol, 2019; 7, 221−30. doi: 10.1016/S2213-8587(18)30136-0 [3] Bangalore S, Breazna A, DeMicco DA, et al. Visit-to-visit low-density lipoprotein cholesterol variability and risk of cardiovascular outcomes: insights from the TNT trial. J Am Coll Cardiol, 2015; 65, 1539−48. doi: 10.1016/j.jacc.2015.02.017 [4] Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet, 2010; 375, 895−905. doi: 10.1016/S0140-6736(10)60308-X [5] Hata J, Arima H, Rothwell PM, et al. Effects of visit-to-visit variability in systolic blood pressure on macrovascular and microvascular complications in patients with type 2 diabetes mellitus: the ADVANCE trial. Circulation, 2013; 128, 1325−34. doi: 10.1161/CIRCULATIONAHA.113.002717 [6] Mehlum MH, Liestøl K, Kjeldsen SE, et al. Blood pressure variability and risk of cardiovascular events and death in patients with hypertension and different baseline risks. Eur Heart J, 2018; 39, 2243−51. doi: 10.1093/eurheartj/ehx760 [7] Kim MK, Han K, Kim HS, et al. Cholesterol variability and the risk of mortality, myocardial infarction, and stroke: a nationwide population-based study. Eur Heart J, 2017; 38, 3560−66. doi: 10.1093/eurheartj/ehx585 [8] Boey E, Gay GMW, Poh KK, et al. Visit-to-visit variability in LDL- and HDL-cholesterol is associated with adverse events after ST-segment elevation myocardial infarction: a 5-year follow-up study. Atherosclerosis, 2016; 244, 86−92. doi: 10.1016/j.atherosclerosis.2015.10.110 [9] Bancks MP, Carson AP, Lewis CE, et al. Fasting glucose variability in young adulthood and incident diabetes, cardiovascular disease and all-cause mortality. Diabetologia, 2019; 62, 1366−74. doi: 10.1007/s00125-019-4901-6 [10] Echouffo-Tcheugui JB, Zhao SZ, Brock G, et al. Visit-to-visit glycemic variability and risks of cardiovascular events and all-cause mortality: the ALLHAT study. Diabetes Care, 2019; 42, 486−93. doi: 10.2337/dc18-1430 [11] Lin CC, Yang CP, Li CI, et al. Visit-to-visit variability of fasting plasma glucose as predictor of ischemic stroke: competing risk analysis in a national cohort of Taiwan Diabetes Study. BMC Med, 2014; 12, 165. doi: 10.1186/s12916-014-0165-7 [12] Bae EH, Lim SY, Han KD, et al. Association between systolic and diastolic blood pressure variability and the risk of end-stage renal disease. Hypertension, 2019; 74, 880−7. doi: 10.1161/HYPERTENSIONAHA.119.13422 [13] Kim MK, Han K, Koh ES, et al. Variability in total cholesterol is associated with the risk of end-stage renal disease: a nationwide population-based study. Arterioscl Thromb Vasc Biol, 2017; 37, 1963−70. doi: 10.1161/ATVBAHA.117.309803 [14] Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA, 2007; 298, 2038−47. doi: 10.1001/jama.298.17.2038 [15] Thomas G, Sehgal AR, Kashyap SR, et al. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol, 2011; 6, 2364−73. doi: 10.2215/CJN.02180311 [16] Nashar K, Egan BM. Relationship between chronic kidney disease and metabolic syndrome: current perspectives. Diabetes, Metab Syndr Obes, 2014; 7, 421−35. [17] Parati G, Ochoa JE, Lombardi C, et al. Assessment and management of blood-pressure variability. Nat Rev Cardiol, 2013; 10, 143−55. doi: 10.1038/nrcardio.2013.1 [18] Horváth EM, Benkő R, Kiss L, et al. Rapid 'glycaemic swings' induce nitrosative stress, activate poly (ADP-ribose) polymerase and impair endothelial function in a rat model of diabetes mellitus. Diabetologia, 2009; 52, 952−61. doi: 10.1007/s00125-009-1304-0 [19] Quagliaro L, Piconi L, Assaloni R, et al. Intermittent high glucose enhances apoptosis related to oxidative stress in human umbilical vein endothelial cells: the role of protein kinase C and NAD(P)H-oxidase activation. Diabetes, 2003; 52, 2795−804. doi: 10.2337/diabetes.52.11.2795 [20] Eto M, Toba K, Akishita M, et al. Reduced endothelial vasomotor function and enhanced neointimal formation after vascular injury in a rat model of blood pressure lability. Hypertens Res, 2003; 26, 991−8. doi: 10.1291/hypres.26.991 [21] Kawai T, Ohishi M, Kamide K, et al. The impact of visit-to-visit variability in blood pressure on renal function. Hypertens Res, 2012; 35, 239−43. doi: 10.1038/hr.2011.170 [22] Ning G, Bi YF, Wang TG, et al. Relationship of urinary bisphenol A concentration to risk for prevalent type 2 diabetes in Chinese adults: a cross-sectional analysis. Ann Intern Med, 2011; 155, 368−74. doi: 10.7326/0003-4819-155-6-201109200-00005 [23] Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc, 2003; 35, 1381−95. doi: 10.1249/01.MSS.0000078924.61453.FB [24] Lloyd-Jones DM, Hong YL, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation, 2010; 121, 586−613. doi: 10.1161/CIRCULATIONAHA.109.192703 [25] Expert Panel on Detection, Evaluation, Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA, 2001; 285, 2486−97. doi: 10.1001/jama.285.19.2486 [26] Kim MK, Han K, Park YM, et al. Associations of variability in blood pressure, glucose and cholesterol concentrations, and body mass index with mortality and cardiovascular outcomes in the general population. Circulation, 2018; 138, 2627−37. doi: 10.1161/CIRCULATIONAHA.118.034978 [27] Kim MK, Han K, Kim HS, et al. Effects of variability in blood pressure, glucose, and cholesterol concentrations, and body mass index on end-stage renal disease in the general population of Korea. J Clin Med, 2019; 8, 755. doi: 10.3390/jcm8050755 [28] Kidney Disease Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl, 2013; 3, 1−150. doi: 10.1038/kisup.2012.73 [29] Plantinga LC, Crews DC, Coresh J, et al. Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or prediabetes. Clin J Am Soc Nephrol, 2010; 5, 673−82. doi: 10.2215/CJN.07891109 [30] Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med, 2009; 150, 604−12. doi: 10.7326/0003-4819-150-9-200905050-00006 [31] Ceriello A, Esposito K, Piconi L, et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes, 2008; 57, 1349−54. doi: 10.2337/db08-0063 [32] Mendez CE, Mok KT, Ata A, et al. Increased glycemic variability is independently associated with length of stay and mortality in noncritically ill hospitalized patients. Diabetes Care, 2013; 36, 4091−7. doi: 10.2337/dc12-2430 [33] Johnson-Rabbett B, Seaquist ER. Hypoglycemia in diabetes: the dark side of diabetes treatment. A patient-centered review. J Diabetes, 2019; 11, 711−8. doi: 10.1111/1753-0407.12933 [34] Cardoso CRL, Leite NC, Moram CBM, et al. Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: the Rio de Janeiro Type 2 Diabetes Cohort Study. Cardiovasc Diabetol, 2018; 17, 33. doi: 10.1186/s12933-018-0677-0 [35] Li SY, Nemeth I, Donnelly L, et al. Visit-to-Visit HbA1c variability is associated with cardiovascular disease and microvascular complications in patients with newly diagnosed type 2 diabetes. Diabetes Care, 2020; 43, 426−32. doi: 10.2337/dc19-0823 [36] Takao T, Suka M, Yanagisawa H, et al. Predictive ability of visit-to-visit variability in HbA1c and systolic blood pressure for the development of microalbuminuria and retinopathy in people with type 2 diabetes. Diabetes Res Clin Pract, 2017; 128, 15−23. doi: 10.1016/j.diabres.2017.03.027 [37] Ceriello A, De Cosmo S, Rossi MC, et al. Variability in HbA1c, blood pressure, lipid parameters and serum uric acid, and risk of development of chronic kidney disease in type 2 diabetes. Diabetes Obes Metab, 2017; 19, 1570−8. doi: 10.1111/dom.12976 [38] Yano Y, Fujimoto S, Kramer H, et al. Long-term blood pressure variability, new-onset diabetes mellitus, and new-onset chronic kidney disease in the Japanese general population. Hypertension, 2015; 66, 30−6. doi: 10.1161/HYPERTENSIONAHA.115.05472 [39] Whittle J, Lynch AI, Tanner RM, et al. Visit-to-visit variability of BP and CKD outcomes: results from the ALLHAT. Clin J Am Soc Nephrol, 2016; 11, 471−80. doi: 10.2215/CJN.04660415 [40] Chia YC, Lim HM, Ching SM. Long-term visit-to-visit blood pressure variability and renal function decline in patients with hypertension over 15 years. J Am Heart Assoc, 2016; 5, e003825. [41] Epstein M, Vaziri ND. Statins in the management of dyslipidemia associated with chronic kidney disease. Nat Rev Nephrol, 2012; 8, 214−23. [42] Diamond JR, Karnovsky MJ. Focal and segmental glomerulosclerosis: analogies to atherosclerosis. Kidney Int, 1988; 33, 917−24. doi: 10.1038/ki.1988.87 [43] Muntner P, Coresh J, Smith JC, et al. Plasma lipids and risk of developing renal dysfunction: the atherosclerosis risk in communities study. Kidney Int, 2000; 58, 293−301. doi: 10.1046/j.1523-1755.2000.00165.x [44] Yan YQ, Huang YQ, Zhou D, et al. Visit-to-visit variability in total cholesterol correlates with the progression of renal function decline in a Chinese community-based hypertensive population. Kidney Blood Press Res, 2019; 44, 727−42. doi: 10.1159/000501367