Abstract
Aims: Hypertriglyceridemia is a risk factor for chronic kidney disease (CKD). However, whether or not it predicts the risk of CKD progression is unknown. This study evaluated the association between serum triglyceride (TG) levels and kidney disease progression in patients with non-dialysis-dependent CKD.
Methods: The Fukuoka Kidney disease Registry (FKR) study was a multicenter, prospective longitudinal cohort study. In total, 4,100 patients with CKD were followed up for 5 years. The primary outcome was the incidence of CKD progression, defined as a ≥ 1.5-fold increase in serum creatinine level or the development of end-stage kidney disease. The patients were divided into quartiles according to baseline serum TG levels under non-fasting conditions: Q1 ＜87 mg/dL; Q2, 87–120 mg/dL; Q3, 121–170 mg/dL, and Q4 ＞170 mg/dL.
Results: During the 5-year observation period, 1,410 patients met the criteria for CKD progression. The multivariable-adjusted Cox proportional hazards model showed a significant association between high serum TG level and the risk of CKD progression in the model without macroalbuminuria as a covariate (multivariable hazard ratio[HR] for Q4 versus Q1, 1.20; 95% CI, 1.03–1.41; P=0.022), but the significance disappeared after adjusting for macroalbuminuria (HR for Q4 versus Q1, 1.06; 95% CI, 0.90–1.24; P=0.507).
Conclusions: The present findings suggest that individuals with high serum TG levels are more likely to develop CKD progression than those without; however, whether or not higher serum TG levels reflect elevated macroalbuminuria or lead to CKD progression via elevated macroalbuminuria is unclear.
Introduction/Aim
The prevalence of chronic kidney disease (CKD) has been increasing worldwide. CKD progression can lead to end-stage kidney disease (ESKD), which imposes a health burden on individuals and an economic burden on healthcare providers1-4). Therefore, prevention of CKD progression is important from both medical and economic perspectives. Currently, CKD progression is controlled by managing known risk factors, such as hypertension, diabetes mellitus, hyperuricemia, obesity, smoking, and anemia5-7). However, CKD progression and development of ESKD cannot be decreased1-4), suggesting that there may be untreated residual risk factors for CKD progression that remain to be identified.
Dyslipidemia, characterized by high triglyceride (TG) and low high-density lipoprotein (HDL)-cholesterol levels, which are common in CKD patients8-10), may be a residual risk factor. In the general population, metabolic syndrome and hypertriglyceridemia, a component of metabolic syndrome, have been identified as risk factors for CKD development11-14). Furthermore, high serum TG levels were recently reported to be a residual risk factor for atherosclerotic cardiovascular disease (CVD) in patients with CKD15, 16). However, despite these associations and the fact that most risk factors for atherosclerotic CVD are common for CKD progression17-20), the findings from studies on the relationship between hypertriglyceridemia and CKD progression have been inconclusive.
One reason for the conflicting results in these studies may be the CKD populations that were examined. Although several studies using data from healthcare checkup programs or medical records21-25) have included patients with CKD, the patients were either in the early stages of CKD or represented only a small number of participants. A large cohort study on US Veterans, which included approximately 40,000 CKD patients, had the limitation of notable sex bias, with 95% of the participants being male26). Given the need for residual risk factor management in patients with CKD, it is important to determine causality in the relationship between serum TG levels and kidney prognosis in a relevant population. However, to achieve this, prospective community-based studies with appropriate sample sizes and follow-up durations are required in patients with advanced CKD.
Prospective cohort studies have been conducted in patients with CKD27, 28), particularly the Chronic Renal Insufficiency Cohort (CRIC) study, which is a large prospective cohort study of CKD patients. However, the CRIC study has the limitation of being restricted to patients with an estimated glomerular filtration rate (eGFR) of 20–70 mL/min/1.73 m2, age ＜75 years old, and specific causes of kidney disease. The Fukuoka Kidney disease Registry (FKR) Study is a Japanese prospective cohort study involving almost 4,500 non-dialysis-dependent CKD patients, reflecting a more real-world population than previous studies because it is not limited by the age, kidney function, or cause of kidney disease.
In addition, non-fasting TG levels have recently attracted attention29-31). Conventionally, serum lipids have been measured under fasting conditions, and serum TG levels have been found to be greatly affected by fasting time. However, it was recently shown that non-fasting TG levels are equally30, 31), and sometimes more strongly29), associated with the risk of CVD development and mortality. Although several global guidelines have set targets for non-fasting TG levels32-34), there are few reports on the relationship between non-fasting TG levels and the kidney prognosis.
The present study evaluated the relationship between non-fasting serum TG levels and the kidney prognosis using data from the FKR Study.
Methods
Study Population
The FKR Study is a multicenter, longitudinal, observational study of non-dialysis-dependent CKD outpatients under the care of nephrologists. Details of the FKR Study design have been described previously35), A total of 4,476 outpatients ≥ 16 years old with CKD were enrolled from January 2013 to March 2017 at 12 outpatient sites in Fukuoka Prefecture in the northern region of Kyushu Island. CKD was defined as abnormalities of the kidney structure or function that persisted for over three months, in accordance with the Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guidelines36). Patients were followed until death or withdrawal of consent, initiation of kidney replacement therapy, or five years from the start of observation, whichever came first. Of the 4,476 patients, excluding those who withdrew consent (n=2) and those who never visited after giving consent (n=22), 4,452 were followed up. Among them, 4,100 patients were included in this study after excluding 352 patients with missing baseline data. None of the patients had missing outcomes.
The study protocol was approved by the Institutional Review Board of the Clinical Research Ethics Committee of Kyushu University (Approval Number. 469-09), and registered in the clinical trial registry (University Hospital Medical Information Network, UMIN000007988). Written informed consent was obtained from all patients before participation in the study. This study was conducted in accordance with the Declaration of Helsinki and the ethical guidelines for clinical research.
Outcomes and Covariates
The primary outcome was CKD progression, defined as a ≥ 1.5-fold increase in the serum creatinine level from baseline or the development of ESKD. ESKD is defined as the need for kidney replacement therapy (maintenance hemodialysis, peritoneal dialysis, or kidney transplantation) or death due to kidney disease.
The main exposure was baseline non-fasting serum TG levels. Patients were divided into quartiles (Q1–Q4) based on baseline serum TG levels as follows: Q1 (n=1,009), ＜87 mg/dL; Q2 (n=1,030), 87–120 mg/dL; Q3 (n=1,019), 121–170 mg/dL; and Q4 (n=1,042), ＞170 mg/dL. Data on age, sex, cause of CKD, height, and weight were collected from the patients’ clinical records at baseline. The history of cardiovascular events and information on medications were collected from clinical records and questionnaires at baseline. Serum and urinary biochemical parameters, such as creatinine, albumin, HDL cholesterol, TG, C-reactive protein (CRP), urinary albumin, and urinary creatinine, were measured in the central laboratory. Blood and urine samples were collected at baseline under non-fasting conditions; the eGFR was calculated in patients ＜18 years old using the Schwartz formula and in patients ≥ 18 years old using the formula appropriate for Japanese patients: eGFR (mL/min/1.73 m2)=194×creatinine−1.094×age−0.287 (×0.739 in females)37-39). The body mass index (BMI) was calculated as follows: BMI (kg/m2)= weight (kg)÷(height (m))2. Non-HDL cholesterol was calculated by subtracting HDL cholesterol from total cholesterol. Low-density lipoprotein (LDL) cholesterol was determined using the Griedewald formula or the direct measurement method for patients with TG concentrations ＜400 and ≥ 400 mg/dL, respectively. The cause of kidney disease was classified as primary chronic glomerulonephritis (CGN) proven by a kidney biopsy, clinically diagnosed CGN without a kidney biopsy, hypertensive nephrosclerosis, diabetic nephropathy, or other causes. A history of CVD events included a history of ischemic heart disease, congestive heart failure, stroke (brain infarction or hemorrhaging), peripheral artery disease, thoracic aortic aneurysm, or abdominal aortic aneurysm. Macroalbuminuria was defined as a urine albumin-to-creatinine ratio of ≥ 300 mg/g. Obesity was defined as a BMI ≥ 25.0 kg/m2, according to the “Guidelines for the Management of Obesity Disease” published by the Japanese Society for the Study of Obesity (2020)40).
Statistical Analysis
All continuous variables were described as medians and interquartile ranges, and categorical variables were described as the number of patients and percentages. The distribution of baseline characteristics across baseline serum TG levels was compared using the following trend analyses: the Jonckheere–Terpstra test for continuous variables and the Cochran–Armitage test for categorical variables.
The event-free survival rate for CKD progression based on the quartile of baseline serum TG levels was evaluated using Kaplan–Meier curves and log-rank tests. Unadjusted, sex- and age-adjusted, and multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the outcomes based on baseline serum TG levels were estimated using a Cox proportional hazards risk model. The assumption of proportional hazards was evaluated using the Schoenfeld residuals. Multivariable-adjusted analyses were performed using the following plausible risk factors based on previous studies of kidney disease progression: age; sex; BMI; systolic blood pressure; use of TG-lowering drugs, polyunsaturated fatty acids, or fibrates; use of renin-angiotensin-aldosterone system (RAAS) blockers; cause of CKD; history of CVD; blood hemoglobin level; serum albumin level; log-transformed CRP level; eGFR (model 2); and presence of macroalbuminuria (added in model 3). As both increases and decreases in the BMI and SBP were considered to be associated with a poor kidney prognosis, these two factors were categorized into quartiles.
Subgroup analyses were performed to estimate the heterogeneity according to the subgroups of risk factors. For the sensitivity analysis, we performed three types of analyses. First, we evaluated hazard ratios using a Fine-Gray regression model by setting all-cause death as a competing risk. Second, we performed the analysis after excluding individuals followed for ≤ 12 months to account for the influence of rapidly progressing kidney disease or rapidly worsening complications. In addition, we performed several analyses to evaluate the relationship between serum TG levels and CKD progression by modifying and adding covariates.
In all analyses, a two-tailed P-value of ＜0.05 was considered statistically significant. All statistical analyses were performed using R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria) and SAS software (version 9.4; SAS Institute, Cary, NC, USA).
Results
Baseline Characteristics
The baseline characteristics according to quartiles of baseline serum TG levels were shown in Table 1. Serum TG and CRP levels were not normally distributed and were, therefore, log-transformed when treated as continuous variables. Patients with higher TG levels were more likely to be male (P＜0.001) and more likely to have diabetic nephropathy (P＜0.001) as the cause of CKD than those with lower levels. Higher TG levels were associated with a higher prevalence of diabetes mellitus and macroalbuminuria (both P＜0.001) and a higher frequency of current smoking (P＜0.001). In addition, the BMI, systolic blood pressure, blood hemoglobin levels, serum creatinine, eGFR, serum total cholesterol, serum non-HDL cholesterol, LDL- cholesterol, and CRP levels were significantly higher (all P＜0.001) in patients with higher TG levels than in those with lower levels. Serum HDL-cholesterol levels were significantly lower in patients with higher TG levels than in those with lower levels (P＜0.001). Finally, the use of TG-lowering drugs and RAAS blockers was more frequent in patients with higher TG levels than in those with lower levels (both P＜0.001).
Table 1.Baseline characteristics according to baseline serum triglyceride quartiles (
n = 4,100)
|
Baseline serum triglyceride levels, mg/dL |
| Q1 (＜87) n=1,009
|
Q2 (87–120) n=1,030
|
Q3 (121–170) n=1,019
|
Q4 (＞170) n=1,042
|
P for trend
|
| Basic information
|
| Age, years |
66.0 (47.0–76.0) |
68.0 (57.0–77.0) |
68.0 (59.5–76.0) |
65.0 (52.3–73.0) |
0.586 |
| Sex, male, n (%)
|
494 (49.0) |
570 (55.3) |
563 (55.3) |
657 (63.1) |
＜0.001 |
| Cause of CKD, n (%)
|
| CGN without biopsy |
150 (14.9) |
147 (14.3) |
151 (14.8) |
137 (13.1) |
0.342 |
| Biopsy proven primary CGN |
405 (40.1) |
337 (32.7) |
345 (33.9) |
369 (35.5) |
0.056 |
| Diabetic nephropathy |
75 ( 7.4) |
121 (11.7) |
107 (10.5) |
151 (14.5) |
＜0.001 |
| Hypertensive nephrosclerosis |
195 (19.3) |
233 (22.6) |
213 (20.9) |
221 (21.2) |
0.498 |
| Others |
184 (18.2) |
192 (18.6) |
203 (19.9) |
164 (15.7) |
0.239 |
| Presence of diabetes, n (%)
|
202 (20.0) |
283 (27.5) |
289 (28.4) |
347 (33.3) |
＜0.001 |
| History of CVD, n (%)
|
197 (19.5) |
256 (24.9) |
238 (23.4) |
245 (23.5) |
0.078 |
| Current drinking, n (%)
|
506 (63.6) |
516 (61.5) |
471 (56.1) |
547 (64.5) |
0.753 |
| Current smoker, n (%)
|
69 ( 8.0) |
91 ( 9.9) |
101 (10.9) |
151 (16.0) |
＜0.001 |
| Past smoker, n (%)
|
331 (56.2) |
366 (55.0) |
415 (60.7) |
427 (57.7) |
0.243 |
| Never smoker, n (%)
|
469 (54.5) |
469 (51.2) |
422 (45.5) |
381 (40.3) |
＜0.001 |
| BMI, kg/m2
|
21.3 (19.5–23.5) |
22.7 (20.5–25.0) |
23.1 (21.0–25.6) |
24.7 (22.3–27.3) |
＜0.001 |
| Systolic blood pressure, mmHg |
128.0 (116.0–140.0) |
130.0 (119.0–142.0) |
130.0 (120.0–142.0) |
130.8 (120.0–142.0) |
＜0.001 |
| Presence of macro albuminuria, n (%)
|
356 (35.4) |
430 (41.7) |
439 (43.1) |
544 (52.3) |
＜0.001 |
| Laboratory tests
|
| Hemoglobin, g/dL |
12.6 (11.2–13.8) |
12.6 (11.2–14.0) |
12.7 (11.4–14.1) |
13.2 (11.8–14.8) |
＜0.001 |
| Serum albumin, g/dL |
4.1 (3.8–4.3) |
4.0 (3.8–4.3) |
4.1 (3.8–4.3) |
4.1 (3.8–4.3) |
0.798 |
| Serum creatinine, mg/dL |
1.1 (0.8–1.9) |
1.3 (0.9–2.1) |
1.4 (0.9–2.2) |
1.4 (0.9–2.1) |
＜0.001 |
| eGFR, mL/min/1.73m2
|
46.5 (26.3–69.1) |
40.2 (22.9–58.9) |
37.2 (21.9–55.0) |
38.3 (24.2–55.6) |
＜0.001 |
| Serum total cholesterol, mg/dL |
186.0 (160.0–212.0) |
187.0 (165.0–210.0) |
193.0 (170.0–218.0) |
202.0 (179.0–231.0) |
＜0.001 |
| Serum HDL-cholesterol, mg/dL |
68.0 (56.0–82.0) |
60.0 (49.0–71.0) |
54.0 (45.0–64.8) |
46.0 (39.0–56.0) |
＜0.001 |
| Serum LDL-cholesterol, mg/dL |
100.0 (81.8–119.8) |
105.4 (85.9–125.6) |
108.2 (88.8–128.8) |
105.8 (84.0–130.3) |
＜0.001 |
| Serum non-HDL-cholesterol, mg/dL |
113.0 (95.0–133.0) |
126.0 (107.0–146.0) |
136.0 (117.0–158.0) |
153.0 (132.0–180.0) |
＜0.001 |
| Serum C-reactive protein, mg/dL |
0.04 (0.01–0.09) |
0.04 (0.02–0.12) |
0.05 (0.02–0.13) |
0.07 (0.03–0.16) |
＜0.001 |
| Medications
|
| Use of triglyceride-lowering drugs, n (%)
|
16 ( 1.6) |
31 ( 3.0) |
53 ( 5.2) |
102 ( 9.8) |
＜0.001 |
| Use of statin, n (%)
|
328 (32.5) |
391 (38.0) |
436 (42.8) |
513 (49.2) |
＜0.001 |
| Use of ezetimibe, n (%)
|
16 (1.6) |
38 (3.7) |
37 (3.6) |
74 (7.1) |
＜0.001 |
| Use of RAAS blockers, n (%)
|
654 (64.8) |
739 (71.7) |
719 (70.6) |
790 (75.8) |
＜0.001 |
Baseline data are expressed as the median (interquartile range) or number (%). The Cochran–Armitage test was used to determine P for a trend of categorical variables. The Jonckheere–Terpstra test was used to determine P for a trend of continuous variables. A two-tailed P-value ＜0.05 was considered statistically significant.
Abbreviations: BMI, body mass index; CGN, chronic glomerulonephritis; CKD, chronic kidney disease; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Q, quartiles based on baseline serum triglyceride values, RAAS, renin-angiotensin-aldosterone system.
During a median observational period of 5 years (interquartile range: 2.5–5.0 years), 1,410 patients met the criteria for CKD progression, 793 of whom developed ESKD. Kaplan–Meier curves showed higher probabilities of CKD progression in the higher quartiles of serum TG levels than in the lower quartiles (Fig.1). In the multivariable-adjusted Cox proportional hazards model, the risk of CKD progression was 1.20-fold (95% CI, 1.03–1.41; P=0.022) higher in the highest quartile (Q4) than in the lowest quartile (Q1) (Table 2, model 2). The HR for a 1-standard deviation (SD) increase in log-transformed serum TG level was 1.08 (1.02–1.15; P=0.005). However, when macroalbuminuria was added to the relevant Cox model, the association between serum TG levels and CKD progression was not significant (Table 2, Model 3). The assumption of proportional hazard was assessed by Schoenfeld residuals but was not maintained during the observational period (P=0.02). Therefore, we considered the results from the Cox proportional hazards model as an average hazard risk over the entire observation period rather than a constant hazard risk throughout the observation period.
Table 2.Hazard ratios for CKD progression according to quartiles and 1-SD increment of serum triglyceride levels
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
293/1,009 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.020 |
1.00 (reference) |
|
0.435 |
| Q2 (87–120 mg/dL) |
353/1,030 |
1.10 (0.94–1.28) |
0.249 |
|
0.98 (0.84–1.15) |
0.836 |
|
0.92 (0.79–1.08) |
0.291 |
|
| Q3 (121–170 mg/dL) |
356/1,019 |
1.09 (0.94–1.28) |
0.255 |
|
0.99 (0.85–1.16) |
0.917 |
|
0.91 (0.77–1.07) |
0.235 |
|
| Q4 (＞170 mg/dL) |
408/1,042 |
1.13 (1.13–1.52) |
＜0.001 |
|
1.20 (1.03–1.41) |
0.022 |
|
1.06 (0.90–1.24) |
0.507 |
|
| Per 1SD higher in lnTG levels |
|
1.12 (1.06–1.18) |
＜0.001 |
|
1.08 (1.02–1.15) |
0.005 |
|
1.03 (0.97–1.09) |
0.292 |
|
a Model 1: adjusted for age; sex
b Model 2: adjusted for age; sex; BMI(categorized in quartiles); systolic blood pressure(categorized in quartiles); cause of CKD; history of CVD; presence of diabetes mellitus; blood hemoglobin; albumin; log-transformed serum C-reactive protein; estimated glomerular filtration rate; use of TG-lowering drugs; use of RAAS blockers.
c Model 3: adjusted for Model 2 factors and presence of macroalbuminuria.
A two-tailed P-value ＜0.05 was considered statistically significant.
Abbreviations: BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HR, hazard ratio; lnTG, log-transformed TG; Q, quartile of serum triglyceride level; RAAS, renin-angiotensin-aldosterone system; SD,standard deviation; TG, triglyceride.
The results of the subgroup analysis are shown in Fig.2. There was no significant difference in the multivariable-adjusted HR of serum TG for CKD progression among the risk factor subgroups (P for interaction ＞0.06). However, the association between serum TG and CKD progression tended to be stronger in obese patients (BMI ≥ 25 kg/m2) than in non-obese patients (BMI ＜25 kg/m2) (P for interaction =0.066). Patients were divided into 2 groups of BMI ≥ 25 kg/m2 and ＜25 kg/m2, and HRs for CKD progression were determined for each quartile of baseline TG, with the lowest quartile (Q1) as the reference (Fig.3). In the BMI ≥ 25 kg/m2 group, HRs increased with increasing baseline TG (P for trend ＜0.001), with an HR of 1.96 (1.35–2.84) for Q4. This relationship was not significant in the BMI ＜25 kg/m2 group (P for trend=0.51). The covariates in this multivariable analysis were the same as those in Model 2 of the main analysis, which did not include the presence of macroalbuminuria but excluded the BMI. The results were similar when the presence of macroalbuminuria was added as a covariate, and the results are shown in Supplementary Fig.1.
For sensitivity analyses, we performed three types of analyses. First, we evaluated the association between serum TG levels and CKD progression using a Fine-Gray regression model, with all-cause death as a competing risk. These results were not substantially altered in this analysis (Supplementary Table 1).
Supplementary Table 1.Hazard ratios for CKD progression according to quartiles and 1-SD increment of serum triglyceride levels by Fine-Gray regression model with all cause of death as a competing risk
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
290/1,006 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.017 |
1.00 (reference) |
|
0.20 |
| Q2 (87–120 mg/dL) |
353/1,030 |
1.11 (0.95–1.30) |
0.19 |
|
0.96 (0.81–1.14) |
0.63 |
|
0.90 (0.75–1.08) |
0.27 |
|
| Q3 (121–170 mg/dL) |
356/1,019 |
1.12 (0.96–1.31) |
0.17 |
|
1.00 (0.85–1.19) |
0.97 |
|
0.94 (0.79–1.12) |
0.49 |
|
| Q4 (＞170 mg/dL) |
408/1,042 |
1.34 (1.15–1.56) |
＜0.001 |
|
1.21 (1.02–1.44) |
0.027 |
|
1.09 (0.92–1.31) |
0.33 |
|
| Per 1SD higher in lnTG levels |
|
1.12 (1.07–1.18) |
＜0.001 |
|
1.09 (1.02–1.15) |
0.006 |
|
1.05 (0.98–1.11) |
0.15 |
|
a Model 1: adjusted for age; sex
b Model 2: adjusted for age; sex; BMI(categorized in quartiles); systolic blood pressure(categorized in quartiles); cause of CKD; history of CVD; presence of diabetes mellitus; blood hemoglobin; albumin; log-transformed serum C-reactive protein; estimated glomerular filtration rate; use of TG-lowering drugs; and use of ACE-I or ARB.
c Model 3: adjusted for Model 2 factors and the presence of macroalbuminuria.
Multivariable-adjusted HRs were analyzed by the Fine-Gray regression model with all cause of death as a competing risk. A two-tailed P-value ＜ 0.05 was considered statistically significant.
Abbreviations: ACE-I, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HR, hazard ratio; lnTG, log-transformed TG; Q, quartile of serum triglyceride level; SD, standard deviation; TG, triglyceride.
In the second sensitivity analysis, cases followed up for ≤ 12 months were excluded to account for patients with rapidly progressing kidney disease or rapidly worsening fatal complications. The association between serum TG levels and CKD risk progression did not substantially change (Supplementary Fig.2 and Supplementary Table 2). The assumption of proportional hazards was assessed by Schoenfeld residuals, and the hazard ratio remained constant throughout the observational period from one year to five years.
Supplementary Table 2.Hazard ratios for CKD progression according to quartiles and 1-SD increment of serum triglyceride levels by Cox proportional hazard model except for the patients followed for ≤ 12months
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
205/909 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.002 |
1.00 (reference) |
|
0.082 |
| Q2 (87–120 mg/dL) |
271/935 |
1.21 (1.00–1.45) |
0.044 |
|
1.07 (0.89–1.29) |
0.462 |
|
1.00 (0.83–1.21) |
0.985 |
|
| Q3 (121–170 mg/dL) |
277/926 |
1.21 (1.01–1.46) |
0.036 |
|
1.07 (0.89–1.29) |
0.452 |
|
0.98 (0.82–1.19) |
0.866 |
|
| Q4 (>170 mg/dL) |
324/949 |
1.50 (1.26–1.79) |
＜0.001 |
|
1.34 (1.12–1.62) |
0.002 |
|
1.18 (0.98–1.42) |
0.084 |
|
| Per 1SD higher in lnTG levels |
|
1.18 (1.11–1.25) |
＜0.001 |
|
1.13 (1.06–1.20) |
0.002 |
|
1.07 (1.00–1.15) |
0.034 |
|
a Model 1: adjusted for age; sex
b Model 2: adjusted for age; sex; BMI(categorized in quartiles); systolic blood pressure(categorized in quartiles); cause of CKD; history of CVD; presence of diabetes mellitus; blood hemoglobin; albumin; log-transformed serum C-reactive protein; estimated glomerular filtration rate; use of TG-lowering drugs; and use of ACE-I or ARB.
c Model 3: adjusted for Model 2 factors and the presence of macroalbuminuria.
Multivariable-adjusted HRs were analyzed by the Cox proportional hazard risk model. A two-tailed P-value ＜0.05 was considered statistically significant.
Abbreviations: ACE-I, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HR, hazard ratio; lnTG, log-transformed TG; Q, quartile of serum triglyceride level; SD, standard deviation; TG, triglyceride.
We also performed several analyses, adjusting for covariates based on prior reports and previous FKR Study Collaboration Group reports41-46). First, in the population with available smoking data (n=3,648), we conducted an analysis with smoking status (past and current smoking) added as a covariate (Supplementary Table 3). We further conducted analyses with the addition of HDL-C quartiles as covariates and individual factors in the history of CVD events (a history of ischemic heart disease, congestive heart failure, brain infarction, brain hemorrhaging, peripheral artery disease, thoracic aortic aneurysm, or abdominal aortic aneurysm) as independent covariates (Supplementary Table 3). Finally, we performed analyses with factors that might be expected to influence CKD progression based on previous FKR Study Collaboration Group reports41-46), such as hematuria, constipation, and the Charlson comorbidity index, as covariates (Supplementary Table 3). In all of these analyses, there were no significant changes in the association between serum TG levels and CKD progression.
Supplementary Table 3.Hazard ratios for CKD progression according to quartiles and 1-SD increment of serum triglyceride levels
| A. Analysis in patients with available data of smoking status (n = 3,648)
|
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
252/860 |
1.00 (reference) |
|
0.006 |
1.00 (reference) |
|
0.061 |
1.00 (reference) |
|
0.954 |
| Q2 (87–120 mg/dL) |
321/916 |
1.14 (0.96–1.34) |
0.132 |
|
1.04 (0.88–1.23) |
0.656 |
|
0.97 (0.83–1.15) |
0.765 |
|
| Q3 (121–170 mg/dL) |
318/928 |
1.08 (0.91–1.27) |
0.378 |
|
1.00 (0.85–1.19) |
0.979 |
|
0.89 (0.75–1.06) |
0.201 |
|
| Q4 (＞170 mg/dL) |
364/944 |
1.29 (1.10–1.51) |
0.00202 |
|
1.19 (1.01–1.41) |
0.043 |
|
1.02 (0.86–1.21) |
0.797 |
|
| Per 1SD higher in lnTG levels |
|
1.11 (1.05–1.17) |
＜0.001 |
|
1.07 (1.01–1.14) |
0.025 |
|
1.01 (0.95–1.08) |
0.733 |
|
| B. Analysis with each factor of the history of CVD events treated as an independent covariate (n = 4,099)
|
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
293/1,009 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.028 |
1.00 (reference) |
|
0.529 |
| Q2 (87–120 mg/dL) |
353/1,030 |
1.10 (0.94–1.28) |
0.249 |
|
0.98 (0.84–1.15) |
0.849 |
|
0.92 (0.79–1.08) |
0.300 |
|
| Q3 (121–170 mg/dL) |
356/1,019 |
1.09 (0.94–1.28) |
0.255 |
|
0.99 (0.85–1.17) |
0.938 |
|
0.90 (0.77–1.06) |
0.213 |
|
| Q4 (＞170 mg/dL) |
408/1,041 |
1.13 (1.13–1.52) |
＜0.001 |
|
1.19 (1.01–1.39) |
0.032 |
|
1.05 (0.89–1.23) |
0.593 |
|
| Per 1SD higher in lnTG levels |
|
1.12 (1.06–1.18) |
＜0.001 |
|
1.08 (1.02–1.14) |
0.006 |
|
1.03 (0.97–1.09) |
0.333 |
|
| C. Analysis with HDL-cholesterol quartiles as covariate (n = 4,094)
|
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
290/1,006 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.009 |
1.00 (reference) |
|
0.536 |
| Q2 (87–120 mg/dL) |
353/1,030 |
1.10 (0.94–1.29) |
0.214 |
|
1.00 (0.85–1.17) |
0.984 |
|
0.92 (0.78–1.07) |
0.273 |
|
| Q3 (121–170 mg/dL) |
356/1,018 |
1.11 (0.95–1.29) |
0.210 |
|
1.02 (0.87–1.20) |
0.836 |
|
0.90 (0.77–1.06) |
0.224 |
|
| Q4 (＞170 mg/dL) |
407/1,040 |
1.32 (1.14–1.54) |
＜0.001 |
|
1.24 (1.05–1.47) |
0.010 |
|
1.04 (0.88–1.23) |
0.624 |
|
| Per 1SD higher in lnTG levels |
|
1.12 (1.06–1.18) |
＜0.001 |
|
1.10 (1.04–1.17) |
0.001 |
|
1.03(0.97–1.09) |
0.349 |
|
| D. Analysis added following factors as covaritates, hematuria, constipation, and the Charlson comorbidity index (n = 3,051)
|
| Serum TG level quartiles |
No. of events/ No. of patients |
Model 1a
|
Model 2b
|
Model 3c
|
| HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
HR (95% CI) |
P-value
|
P for trend
|
| Q1 (＜87 mg/dL) |
225/768 |
1.00 (reference) |
|
＜0.001 |
1.00 (reference) |
|
0.041 |
1.00 (reference) |
|
0.501 |
| Q2 (87–120 mg/dL) |
261/756 |
1.11 (0.93–1.33) |
0.247 |
|
1.04 (0.87–1.25) |
0.661 |
|
0.98 (0.82–1.18) |
0.832 |
|
| Q3 (121–170 mg/dL) |
276/748 |
1.17 (0.98–1.39) |
0.089 |
|
1.03 (0.86–1.24) |
0.736 |
|
0.94 (0.78–1.12) |
0.487 |
|
| Q4 (＞170 mg/dL) |
311/779 |
1.33 (1.12–1.58) |
0.001 |
|
1.22 (1.02–1.46) |
0.033 |
|
1.08 (0.90–1.29) |
0.434 |
|
| Per 1SD higher in lnTG levels |
|
1.13 (1.06–1.20) |
＜0.001 |
|
1.08 (1.02–1.15) |
0.014 |
|
1.03 (0.96–1.10) |
0.437 |
|
a Model 1: adjusted for age; sex
b Model 2: adjusted for age; sex; BMI(categorized in quartiles); systolic blood pressure(categorized in quartiles); cause of CKD; history of CVD; presence of diabetes mellitus; blood hemoglobin; albumin; log-transformed serum C-reactive protein; estimated glomerular filtration rate; use of TG-lowering drugs; use of RAAS blockers.
In addition, the following factors were added in each analysis.
Analysis A; Smoking Status
Analysis B; each component of history of CVD (history of ischemic heart disease, congestive heart failure, brain infarction, brain hemorrhage, peripheral artery disease, thoracic aortic aneurysm, or abdominal aortic aneurysm), instead of history of CVD
Analysis C; HDL-cholesterol quartile
Analysis D; presence of hematuria, constipation, Charlson comorbidity index
c Model 3: adjusted for Model 2 factors and the presence of macroalbuminuria.
A two-tailed P-value ＜0.05 was considered statistically significant.
Abbreviations: BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HDL, high-density lipoprotein; HR, hazard ratio; lnTG, log-transformed TG; Q, quartile of serum triglyceride level; RAAS, renin-angiotensin-aldosterone system; SD,standard deviation; TG, triglyceride.
Discussion
In this multicenter, prospective, longitudinal cohort study, we evaluated the association between baseline non-fasting serum TG levels and CKD progression based on a multivariable-adjusted Cox proportional hazards model. In the multivariable-adjusted model, increased baseline serum TG levels were significantly associated with CKD progression; however, this significant association disappeared when macroalbuminuria was included in the relevant model. In the subgroup analyses, the association between high baseline TG levels and the risk of CKD progression tended to be stronger in the higher BMI group than in the lower BMI group. The present findings suggest that individuals with high serum TG levels are more likely to develop CKD progression than those without such high levels, possibly due to elevated macroalbuminuria. These findings also imply the importance of paying particular attention to patients with obesity.
The relationship between hypertriglyceridemia (elevated serum TG levels) and CKD progression has been the subject of conflicting research. Several retrospective observational studies have shown a significant association between this factor and CKD progression22, 26). For example, data from a US veteran cohort revealed a significant association between hypertriglyceridemia and risk of CKD progression, particularly in non-CKD and CKD stage G3 patients26). However, the study had limited data on albuminuria, and the main analysis was not adjusted for albuminuria. Similarly, the Japanese Annual Health Examination Program found a significant association with the rate of eGFR decline in patients with CKD22). However, these studies primarily focused on early CKD and were not adjusted for proteinuria or albuminuria.
In contrast, the CRIC study28), a prospective cohort study involving patients with a wider range of eGFRs (20–70 mL/min/1.73 m2), found a significant association between serum TG levels and CKD progression when unadjusted, but the significance disappeared after adjusting for several variables, including 24-h excretion proteinuria, similar to the findings in the present study. The present study is unique in that it is a prospective cohort study involving a wide spectrum of patients with CKD from the early to advanced stages. The results of the study revealed that the significance of the association between hypertriglyceridemia and CKD progression changed depending on whether or not macroalbuminuria was included as a covariate in the multivariate-adjusted model, which is consistent with previous studies. Although the results of previous studies may have been affected by differences in the sample size and subject characteristics, it is also possible that the results may have been significantly affected by whether or not the analyses were adjusted for albuminuria or proteinuria.
In the multivariable-adjusted Cox proportional hazards model, a significant association between higher baseline serum TG levels and the risk of CKD progression disappeared after macroalbuminuria was included in the relevant model. This finding suggests that macroalbuminuria is a confounding factor leading to CKD progression through elevated TG levels. This is supported by previous findings that increased albuminuria can trigger mechanisms such as accelerated lipoprotein synthesis in the liver and decreased lipoprotein metabolism47-49). Although most of these reports were related to nephrotic syndrome, these mechanisms have been reported to be caused by an increase in albuminuria rather than a decrease in serum albumin49), and similar mechanisms might exist in patients with a low degree of albuminuria. However, since albuminuria has been acknowledged to be a risk factor for CKD progression, it is possible that high serum TG levels or insulin resistance related to elevated serum TG levels may be involved in CKD progression through the deterioration of albuminuria.
Animal studies have demonstrated associations between high TG levels and intracellular lipid accumulation in the kidney, leading to mesangial cell expansion, glomerulosclerosis, and injury of podocytes leading to the elevation of proteinuria50, 51). In addition, dyslipidemia, including hypertriglyceridemia, induces systemic vascular atherosclerosis and may promote proteinuria through the following factors: oxidative stress, endoplasmic reticulum stress, and activation of the renin-angiotensin-aldosterone system52). Insulin resistance may also play an important role. Individuals with high serum TG levels often have a background of insulin resistance53), which is a known risk factor for albuminuria54). Therefore, individuals with high serum TG levels in a background of insulin resistance may have contributed to CKD progression via elevated albuminuria. The present study could not determine whether higher serum TG levels simply reflected higher albuminuria levels or were associated with the exacerbation of albuminuria; however, we may be able to conclude that individuals with high serum TG levels were more likely to develop CKD progression than those without. Further studies are required to determine the role of macroalbuminuria in the association between high serum TG levels and CKD progression.
In our subgroup analyses, there was a stronger association between hypertriglyceridemia and CKD progression in obese patients (BMI ≥ 25 kg/m2) than in nonobese patients, although the statistical power of this tendency was insufficient to show a significant difference in the effects. In the non-obese group, CKD progression risk was the same, regardless of serum TG level; however, in the obese group, the risk of CKD progression was almost twice as high in the highest serum TG group compared with the lowest group. The possible effects of obesity on CKD progression include glomerular hyperfiltration, increased inflammatory cytokine levels, oxidative stress, insulin resistance, and activation of the renin-angiotensin-aldosterone system55, 56). Although these mechanisms are based on studies examining the effect of obesity on the CKD onset, the same mechanisms are likely to exist in CKD progression. In addition, increased inflammatory cytokine levels and oxidative stress, as well as activation of the renin-angiotensin-aldosterone system, in obese patients55) may synergistically amplify the effect of hypertriglyceridemia-induced kidney injury. The present findings suggest that improving both obesity and the conditions that cause hypertriglyceridemia may have a synergistic effect in preventing CKD progression. However, further investigations are needed to determine whether or not controlling the conditions that cause hypertriglyceridemia and obesity can prevent CKD progression.
In a sensitivity analysis that excluded patients followed for ≤ 12 months, the difference in the event-free survival among the TG quartile groups was more apparent, and the assumption of proportional hazards was maintained in the sensitivity analysis. This may have arisen because patients followed up for ≤ 12 months included those close to having ESKD or with rapidly worsening CKD, so their exclusion may have attenuated the association between hypertriglyceridemia and CKD progression. This finding may indicate that hypertriglyceridemia is a residual risk factor for CKD progression, similar to recent reports that hypertriglyceridemia is a residual risk factor for atherosclerotic cardiovascular events in patients with CKD15, 57).
Our study has several strengths. First, the follow-up rate was very high (97.5%), and the determination of outcomes was based on primary records and was thus highly reliable. In addition, the study population included a wide range of patients with different ages, CKD stages, and CKD causes; therefore, the selection bias was likely to be low. However, our study also had several limitations. First, serum TG levels and covariates were only measured at baseline, which may have led to misclassification if TG levels, and covariates changed during the follow-up period. Second, unknown or unmeasured confounders may have existed. Third, blood samples were collected only in a non-fasting state, and some parameters may have differed if measured in a fasting state, meaning that we may have missed some associations. Furthermore, we did not evaluate insulin resistance. Finally, the study was limited to Japanese patients under the care of nephrologists, and the results may not be generalizable to other patient groups.
Conclusion
In conclusion, the present study found that individuals with high serum TG levels are more likely to develop CKD progression than those without high levels among Japanese patients with non-dialysis-dependent CKD. However, whether higher TG levels simply reflect albuminuria levels or they are associated with CKD progression via exacerbation of albuminuria is unclear. Furthermore, the risk tended to be particularly pronounced in obese patients. Further studies are needed to assess whether or not intensive intervention can prevent CKD progression in these patients.
Acknowledgments and Notice of Grant Support
We would like to express our sincere thanks to the participants of the FKR Study, the members of the FKR Study Group, and all personnel at the participating institutions that were involved in the study. The following personnel (institutions) participated in the study: Satoru Fujimi (Fukuoka Renal Clinic), Hideki Hirakata (Fukuoka Renal Clinic), Tadashi Hirano (Hakujyuji Hospital), Tetsuhiko Yoshida (Hamanomachi Hospital), Takashi Deguchi (Hamanomachi Hospital), Hideki Yotsueda (Harasanshin Hospital), Kiichiro Fujisaki (Iizuka Hospital), Keita Takae (Japanese Red Cross Fukuoka Hospital), Koji Mitsuiki (Hara Sanshin Hospital), Akinori Nagashima (Japanese Red Cross Karatsu Hospital), Ritsuko Katafuchi (Kano Hospital), Hidetoshi Kanai (Kokura Memorial Hospital), Kenji Harada (Kokura Memorial Hospital), Tohru Mizumasa (Kyushu Central Hospital), Takanari Kitazono (Kyushu University), Toshiaki Nakano (Kyushu University), Toshiharu Ninomiya (Kyushu University), Kumiko Torisu (Kyushu University), Akihiro Tsuchimoto (Kyushu University), Shunsuke Yamada (Kyushu University), Hiroto Hiyamuta (Kyushu University), Shigeru Tanaka (Kyushu University), Dai Matsuo (Munakata Medical Association Hospital), Yusuke Kuroki (National Fukuoka-Higashi Medical Center), Hiroshi Nagae (National Fukuoka-Higashi Medical Center), Masaru Nakayama (National Kyushu Medical Center), Kazuhiko Tsuruya (Nara Medical University), Masaharu Nagata (Shin-eikai Hospital), Taihei Yanagida (Steel Memorial Yawata Hospital), and Shotaro Ohnaka (Tagawa Municipal Hospital). We also thank Philippa Gunn from Edanz (https://jp.edanz.com/ac) for editing this manuscript. This FKR Study did not receive any specific funding.
Authors’ Contributions
Mai Seki contributed to the study design, statistical analysis, data interpretation, and manuscript drafting. Toshiaki Nakano contributed to the study design, data acquisition, data interpretation, and drafting of the manuscript, and supervised the study. Shigeru Tanaka contributed to the study design, data acquisition, statistical analysis, data interpretation, and manuscript drafting. Hiromasa Kitamura contributed to study design, data acquisition, statistical analysis, data interpretation, and drafting of the manuscript. Hiroto Hiyamuta contributed to data acquisition and manuscript drafting. Toshiharu Ninomiya contributed to critical revision of the manuscript and supervised the study. Kazuhiko Tsuruya contributed to critical revision of the manuscript and supervised the study. Takanari Kitazono critically revised and supervised the study. All authors critically reviewed the draft and approved the final version for submission.
Conflict of Interest:
K Tsuruya received honoraria from Kowa Company, Ltd. (Tokyo, Japan). The authors declare that they have no relevant financial interests.
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