Abstract
Objective
This study explored whether lipid disorders or an elevated atherogenic index of plasma (AIP, a risk factor for cardiovascular diseases) could predict major kidney function decline.
Methods
We conducted a retrospective 7-year cohort study of 3712 Chinese adults followed up between 2010 and 2017. Major kidney function decline was defined as a ≥ 30% reduction in the estimated glomerular filtration rate (eGFR) from baseline. Multivariable logistic regression models were used to evaluate the relationship between lipid profiles and major kidney function decline. Smoking habits, waist circumference, and physical activity were not assessed.
Results
During the 7-year follow-up, 1.70% (n = 63) of the participants developed major kidney function decline. After adjustment for potential confounders, the odds ratios (ORs) for developing eGFR decline with per standard deviation increase were 1.23 [95% confidence interval (CI): 1.06–1.43] for triglyceride and 2.55 (95% CI: 1.01–6.42) for AIP in all participants. Furthermore, in the stratified analysis, we found sex-related differences; triglyceride and AIP were only independently associated with the risk of eGFR decline in men (OR, 1.27, 95% CI: 1.08–1.48; OR, 3.98, 95% CI: 1.22–12.99, respectively). When the participants were divided into groups according to the baseline lipid status, association was observed only between abnormal AIP and eGFR decline (all p values < 0.05).
Conclusion
Our findings suggest that a higher serum triglyceride level or an elevated AIP increases the risk of major kidney function decline in Chinese men with normal kidney function. Thus, assessment of AIP may help identify the risk of eGFR decline.
Keywords: Atherogenic index of plasma, triglyceride, glomerular filtration rate, risk factors
Introduction
Chronic kidney disease (CKD) is now a major public health concern worldwide and is associated with end-stage renal disease as well as cardiovascular morbidity and mortality [1–4]. The prevalence of CKD is estimated to be 8%–16% in the general adult population [5] and nearly 10.8% in China [2]. A decline in the estimated glomerular filtration rate (eGFR) might be associated with adverse outcomes and CKD development, even among those people with normal kidney function. Thus, management of modifiable eGFR decline risk factors is crucial for preventing disease incidence.
Lipid disorders (such as dyslipidaemia) have been identified as critical contributing factors for atherosclerosis and cardiovascular disease (CVD) [6]. Previous epidemiologic observations have suggested an independent association between serum lipids and CKD development [7–10] and usefulness of lipid-lowering therapy in slowing the rate of CKD progression [11–13]. Interestingly, conclusions regarding the association between serum lipids and CKD development are divergent and conflicting [14–17], which might be caused by the different ethnicities of the groups studied. Meanwhile, dyslipidaemia might have a greater influence on CKD development in men than in women [18,19]. Atherogenic index of plasma (AIP), calculated as the logarithmically transformed ratio of triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) (lg[TG/HDL-C]), is a novel marker of atherosclerosis and CVD. In addition, related studies have shown AIP to be a more accurate predictor of CVD than traditional lipid parameters [20–23]. However, only a few studies have investigated the association between AIP and eGFR decline or have revealed the potential clinical usefulness of AIP [24]. Most studies investigating the association of serum lipids with kidney function have examined only patients with impaired kidney function (eGFR < 60 mL/min/1.73 m2). A better understanding on the relationship between potential risk factors and eGFR decline in the general population with a completely normal kidney function is of great clinical interest.
Therefore, this study investigated the independent association of serum lipid profiles as well as AIP with the development of major kidney function decline and presented potential sex-related differences in a cohort study in China. The definition of major kidney function decline and follow-up period was set based on previous studies [25,26].
Materials and methods
Participants
In the present study, we enrolled participants aged 18–85 years who had undergone a comprehensive medical examination at baseline (2010) and whose results were reevaluated 7 years later (2017), with both examinations conducted at the health manage center of Tongji Hospital (Wuhan, China). All participants were of Chinese ethinicity. Initially, 5393 individuals were identified as potential participants. The exclusion criteria were as follows: (1) baseline eGFR < 90 mL/min/1.73 m2 (n = 820); (2) past history of CVD (n = 64) or past history of diabetes (n = 245) at baseline; and (3) missing data for any of the study variables (n = 748). Finally, a total of 3712 eligible participants (2008 men and 1704 women) were included for our analysis. The study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (The Institutional Review Board Approval Number: TJ-C20160115). The study conforms to the principles outlined in the Declaration of Helsinki, and written informed consent was obtained from all participants (Figure 1).
Figure 1.
Flowchart of the study participants.
Anthropometric and biochemical measurements
Routine anthropometric measurements were performed by trained examiners. Height and weight were measured to the nearest 0.1 cm and 0.1 kg, respectively, while the participants were asked to wear light clothing and stand barefoot. Body mass index (BMI) was calculated as weight (kg)/height (m)2. After the participants rested quietly for a minimum of 5 min, systolic and diastolic blood pressures (SBP and DBP, respectively) were measured using a standard electronic sphygmomanometer (HBP-9020; OMRON, Dalian, China). Data on sex, age, ethnicity, and individual history of disease were collected using a questionnaire. Fasting (over 12 h) blood samples were collected from each participant and sent to the clinical laboratory of Tongji Hospital for standard biochemical measurement within 2 h of collection. Total cholesterol (TC), TG, low-density lipoprotein cholesterol (LDL-C), HDL-C, fasting plasma glucose (FPG), uric acid, and creatinine were determined using standard laboratory procedures.
Criteria and definitions
As a study focused on GFR decline, the outcome major kidney function decline was defined as a ≥ 30% reduction in the eGFR from baseline to a value of <90 mL/min/1.73 m2. This criterion was set based on the previous study conclusion that a 30% reduction in the eGFR could be recommended as an alternative end point for CKD progression [25]. The 7 year period was set based on Young’s study which used a similar length of follow-up to define new-onset CKD [26]. The eGFR was calculated using the CKD-EPI equation [27]. For men with Scr (in mg/dL) ≤ 0.9, eGFR = 141 × (Scr/0.9)−0.411 × 0.993Age, and for men with Scr > 0.9, eGFR = 141 × (Scr/0.9)−1.209 × 0.993Age. For women with Scr ≤ 0.7, eGFR = 144 × (Scr/0.7)−0.329 × 0.993Age, and for women with Scr > 0.7, eGFR = 144 × (Scr/0.7) −1.209 × 0.993Age. In our study, the cutoff values for higher TC, higher TG, higher LDL-C, and lower HDL-C were 6.22, 2.26, 4.14, and 1.04 mmol/L, respectively [28]. AIP is believed to be a useful independent predictor of CVD [29]. In the current study, we also used AIP to predict the risk of major kidney function decline. AIP > 0.15 was regarded as abnormal [29]. We also divided the 2008 male participants and 1704 female participants into tertiles. Tertile 1 (T1) had the lowest mean AIP, and Tertile 3 (T3) had the highest AIP.
Statistical analysis
Data are expressed as means ± standard deviation (SD) for normally distributed variables, and as median with IQR for non-normally distributed data. Categorical variables are expressed as frequencies. Comparisons between two groups were examined using the independent t-test, Mann–Whitney U test, or chi-square test where appropriate. Multivariable logistic regression models were used to determine the relationship between lipid profiles and the prevalence of major kidney function decline, with data presented as odds ratios (ORs) and 95% confidence intervals (CIs). In this study, we used two models to adjust potential confounders for major kidney function decline. Model 1 was adjusted for age and sex. Model 2 included the variables of Model 1 and baseline BMI, FPG, uric acid, eGFR, SBP, and DBP. All statistical analyses were performed using SPSS software version 17.0 (SPSS Inc, Chicago, IL, USA). Two-tailed p values of <0.05 were considered statistically significant.
Results
General characteristics of the participants
Table 1 presents the general characteristics of the participants. A total of 3712 subjects were included in the study, and 54.1% of the participants were male. The mean age of the study population was 40.55 years. After a 7-year follow-up, 63 participants (1.70%) developed major kidney function decline. The mean annual eGFR decline was 1.8 mL/min/1.73 m2. The male participants had a higher BMI, a lower level of HDL-C, higher levels of blood pressure, TG, TC, LDL-C, AIP, FPG, creatinine, and uric acid, and a higher prevalence of hypertension (all p values < 0.05). No significant difference was observed in age. The data of participants in 2017 was presented as supplemental table 1.
Table 1.
Baseline characteristics of participants relative to development of major renal function decline during the 7-year follow-up period.
| Overall | Men | Women | p Value | |
|---|---|---|---|---|
| n | 3712 | 2008 | 1704 | |
| Age (years) | 40.55 ± 10.74 | 40.82 ± 9.95 | 40.23 ± 11.58 | 0.102 |
| BMI (kg/m2) | 23.60 ± 3.61 | 24.72 ± 3.07 | 22.28 ± 3.74 | <0.001 |
| SBP (mmHg) | 121.07 ± 17.05 | 124.88 ± 16.64 | 116.59 ± 16.44 | <0.001 |
| DBP (mmHg) | 77.31 ± 11.88 | 80.80 ± 11.60 | 73.20 ± 10.86 | <0.001 |
| TC (mmol/L) | 4.62 ± 0.88 | 4.68 ± 0.87 | 4.55 ± 0.89 | <0.001 |
| TG (mmol/L) | 1.05 (0.72–1.61) | 1.31 (0.92–1.90) | 0.81 (0.61–1.16) | <0.001 |
| HDL-C (mmol/L) | 1.34 ± 0.32 | 1.21 ± 0.26 | 1.50 ± 0.31 | <0.001 |
| LDL-C (mmol/L) | 2.77 ± 0.75 | 2.89 ± 0.73 | 2.63 ± 0.74 | <0.001 |
| AIP | −0.07 ± 0.32 | 0.06 ± 0.29 | −0.23 ± 0.27 | <0.001 |
| FPG (mmol/L) | 5.10 ± 0.52 | 5.15 ± 0.53 | 5.05 ± 0.50 | <0.001 |
| UA (mg/dL) | 310.88 ± 84.32 | 362.25 ± 70.54 | 250.34 ± 53.40 | <0.001 |
| SCr (μmol/L) | 65.62 ± 12.68 | 74.88 ± 8.10 | 54.71 ± 7.28 | <0.001 |
| eGFR (mL/min/1.73 m2) | 110.20 ± 10.79 | 107.81 ± 9.53 | 113.01 ± 11.48 | <0.001 |
| Hypertension, n (%) | 785 | 563 (28.0) | 222 (13.0) | <0.001 |
Data are mean ± SD, median (IQR) or percentage.
BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; AIP: atherogenic index of plasma; FBG: fasting plasma glucose; UA: uric acid; SCr: serum creatinine; eGFR: estimated glomerular filtration rate.
Logistic regression analyses for associations of serum lipids and AIP with major kidney function decline risk
Univariate and multivariable logistic regression models were used to determine the relationship between lipid profiles and the major kidney function decline risk (Table 2). In unadjusted models, TG and AIP were significantly associated with major kidney function decline in all participants. After adjustment for age and sex (Model 1), each 1 − SD increase in TG and AIP was associated with 1.28-fold (95% CI: 1.11–1.46) and 2.93-fold (95% CI: 1.27–6.76) increases in the risk of major kidney function decline, respectively. In the fully adjusted model (Model 2), the associations between TG and AIP and the prevalence of major kidney function decline were not materially changed (OR, 1.23, 95% CI: 1.06–1.43; OR, 2.55, 95% CI: 1.01–6.42, respectively) (all p values < 0.05).
Table 2.
Adjusted ORs and 95% CIs for the presence of major renal function decline according to baseline serum lipids levels and AIP.
| Unadjusted |
Model 1 |
Model 2 |
||||
|---|---|---|---|---|---|---|
| OR (95% CI) | p Value | OR (95% CI) | p Value | OR (95% CI) | p Value | |
| Total | ||||||
| TG | 1.26 (1.10–1.43) | 0.001 | 1.28 (1.11–1.46) | <0.001 | 1.23 (1.06–1.43) | 0.006 |
| TC | 1.03 (0.78–1.36) | 0.833 | 1.03 (0.77–1.39) | 0.844 | 1.02 (0.75–1.38) | 0.907 |
| HDL-C | 0.79 (0.36–1.75) | 0.559 | 0.78 (0.32–1.92) | 0.591 | 0.85 (0.33–2.19) | 0.738 |
| LDL-C | 0.79 (0.56–1.12) | 0.186 | 0.76 (0.53–1.10) | 0.151 | 0.78 (0.54–1.14) | 0.196 |
| AIP | 2.39 (1.14–5.00) | 0.021 | 2.93 (1.27–6.76) | 0.012 | 2.55 (1.01–6.42) | 0.047 |
| Men | ||||||
| TG | 1.27 (1.10–1.47) | 0.001 | 1.28 (1.10–1.48) | 0.001 | 1.27 (1.08–1.48) | 0.003 |
| TC | 1.02 (0.70–1.50) | 0.922 | 1.05 (0.71–1.55) | 0.804 | 1.08 (0.72–1.60) | 0.714 |
| HDL-C | 0.93 (0.25–3.42) | 0.914 | 0.94 (0.26–3.49) | 0.930 | 0.94 (0.23–3.85) | 0.933 |
| LDL-C | 0.70 (0.43–1.12) | 0.137 | 0.71 (0.44–1.15) | 0.161 | 0.76 (0.46–1.25) | 0.283 |
| AIP | 3.47 (1.20–10.09) | 0.022 | 3.73 (1.28–10.92) | 0.016 | 3.98 (1.22–12.99) | 0.022 |
| Women | ||||||
| TG | 1.26 (0.84–1.91) | 0.267 | 1.00 (0.98–1.04) | 0.627 | 1.07 (0.64–1.81) | 0.790 |
| TC | 1.04 (0.69–1.57) | 0.858 | 0.95 (0.59–1.52) | 0.825 | 1.91 (0.56–1.48) | 0.694 |
| HDL-C | 0.67 (0.19–2.30) | 0.526 | 0.69 (0.20–2.35) | 0.554 | 0.80 (0.22–2.89) | 0.737 |
| LDL-C | 0.89 (0.53–1.51) | 0.674 | 0.79 (0.44–1.40) | 0.416 | 0.75 (0.42–1.35) | 0.339 |
| AIP | 2.04 (0.57–7.29) | 0.271 | 1.83 (0.46–7.29) | 0.393 | 1.29 (0.28–5.95) | 0.746 |
Model 1: adjusted for baseline age and gender.
Model 2: adjusted for covariates in model 1 plus baseline BMI, FPG, uric acid, eGFR, SBP and DBP.
Each variable is expressed per SD and was analyzed in a separate regression model.
SD: standard deviation; CIs: confidence intervals; TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; AIP: atherogenic index of plasma; BMI: body mass index; FBG: fasting blood glucose; eGFR: estimated glomerular filtration rate.
To investigate whether the associations among serum lipids, AIP, and the occurrence of major kidney function decline vary by sex, we further conducted subgroup analyses. After multiple adjustments, the associations among elevated TG and AIP and increased risk of major kidney function decline were only observed in the male participants (OR, 1.27, 95% CI: 1.08–1.48, p = 0.003; OR, 3.98, 95% CI: 1.22–12.99, p = 0.022).
Furthermore, we observed similar results when the participants were divided into groups according to the baseline lipid status (Table 3). In the fully adjusted model, an elevated risk of major kidney function decline was observed in all participants with an abnormal AIP (OR, 2.18, 95% CI: 1.18–4.05, p = 0.013) and in the male participants with an abnormal AIP (OR, 2.16, 95% CI: 1.02–4.55, p = 0.044). However, no association was observed between higher TG and increased risk of major kidney function decline regardless of sex. In addition, in the female participants, dyslipidaemia was not associated with the prevalence of major kidney function decline.
Table 3.
Adjusted ORs and 95% CIs for the presence of major renal function decline according to baseline dyslipidaemia and abnormal AIP.
| Unadjusted |
Model 1 |
Model 2 |
||||
|---|---|---|---|---|---|---|
| OR (95% CI) | p Value | OR (95% CI) | p Value | OR (95% CI) | p Value | |
| Total | ||||||
| Elevated TG | 2.17 (1.17–4.04) | 0.014 | 2.26 (1.18–4.34) | 0.014 | 1.74 (0.86–3.53) | 0.123 |
| Elevated TC | 2.23 (0.95–5.23) | 0.067 | 2.26 (0.95–5.39) | 0.066 | 1.98 (0.81–4.83) | 0.132 |
| Reduced HDL-C | 1.61 (0.91–2.85) | 0.105 | 1.65 (0.90–3.04) | 0.107 | 1.55 (0.82–2.94) | 0.177 |
| Elevated LDL-C | 1.04 (0.32–3.34) | 0.953 | 1.03 (0.32–3.34) | 0.963 | 0.95 (0.29–3.14) | 0.933 |
| Abnormal AIP | 2.17 (1.30–3.62) | 0.003 | 2.43 (1.38–4.29) | 0.002 | 2.18 (1.18–4.05) | 0.013 |
| Men | ||||||
| Elevated TG | 2.04 (0.97–4.28) | 0.061 | 2.16 (1.02–4.59) | 0.045 | 1.78 (0.77–4.11) | 0.173 |
| Elevated TC | 2.70 (0.93–7.81) | 0.067 | 2.81 (0.97–8.18) | 0.058 | 2.49 (0.82–7.61) | 0.109 |
| Reduced HDL-C | 1.35 (0.67–2.74) | 0.400 | 1.35 (0.67–2.74) | 0.400 | 1.34 (0.63–2.87) | 0.451 |
| Elevated LDL-C | 1.05 (0.25–4.42) | 0.951 | 1.07 (0.25–4.52) | 0.927 | 1.07 (0.25–4.69) | 0.925 |
| Abnormal AIP | 2.09 (1.07–4.11) | 0.030 | 2.15 (1.10–4.22) | 0.026 | 2.16 (1.02–4.55) | 0.044 |
| Women | ||||||
| Elevated TG | 2.93 (0.86–9.94) | 0.085 | 2.70 (0.77–9.52) | 0.121 | 2.02 (0.52–7.88) | 0.310 |
| Elevated TC | 1.64 (0.38–7.05) | 0.505 | 1.44 (0.32–6.49) | 0.635 | 1.31 (0.29–6.00) | 0.726 |
| Reduced HDL-C | 2.97 (1.00–8.75) | 0.048 | 2.87 (0.97–8.49) | 0.057 | 2.57 (0.83–7.96) | 0.101 |
| Elevated LDL-C | 0.99 (0.13–7.46) | 0.998 | 0.86 (0.11–6.62) | 0.881 | 0.75 (0.10–5.92) | 0.787 |
| Abnormal AIP | 3.09 (1.23–7.75) | 0.016 | 2.98 (1.13–7.85) | 0.028 | 2.41 (0.82–7.04) | 0.107 |
Model 1: adjusted for baseline age and gender.
Model 2: adjusted for covariates in model 1 plus baseline BMI, FPG, uric acid, eGFR, SBP and DBP.
CIs: confidence intervals; TC: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; AIP: atherogenic index of plasma; BMI: body mass index; FBG: fasting blood glucose; eGFR: estimated glomerular filtration rate.
Prevalence of major kidney function decline according to AIP tertiles at baseline
After adjustment for age, the prevalence of major kidney function decline according to AIP tertiles was 1.2%, 1.6%, and 2.4% in the male participants and 1.7%, 1.3%, and 1.7% in the female participants (Figure 2). While the prevalence approximately doubled in the highest tertile than in the lowest tertile in the male participants, no significant linear trend was observed through the increasing tertiles in both sexes (p for trend = 0.09 in men, p for trend = in women). In addition, to verify the prediction efficiency of AIP, ROC curves analysis was generated for men with different outcomes (supplemental figure 1).
Figure 2.
Adjusted prevalence of major kidney function decline according to the tertiles (T) of Atherogenic index of plasma (AIP).
Discussion
In this study focused on eGFR decline, after adjustment for potential confounders, higher serum TG and AIP were significantly associated with the development of major kidney function decline in men. In women, the serum lipids or AIP was not found to be a risk factor for major kidney function decline.
Mounting evidence has indicated that people with increased TG have a higher risk of CKD after adjustment for other risk factors [8–10,15]. In the present study, our results showed that baseline serum TG levels, but not other lipid parameters, had a strong positive association with major kidney function decline. In fact, the findings on the relationship between serum lipids and CKD development are inconclusive. Our results are similar to the conclusions of previous studies that TG levels are related to kidney function, which were also confirmed in the elderly population in Cao’s research [30]. Interestingly, some reports have presented a contradictory conclusion that no association exists between TG and kidney function [17]. Even in studies conducted in the Chinese population, the results are inconsistent [31,32]. The contrasting findings of these studies could be explained by the different characteristics of the study participants or differences in the study design. There is not much evidence to show the association between serum lipids and the development of major kidney function decline. Additionally, most previous studies on the association between serum lipids and kidney function have focused on patients with impaired kidney function (eGFR < 60 mL/min/1.73 m2) [14–16]. However, even a mildly decreased eGFR (60–74 mL/min/1.73 m2) has been reported to be associated with higher risk of CKD [33]. Our study showed that the associations between serum lipids and kidney function are also present in the participants with eGFR ≥ 90 mL/min/1.73 m2. In summary, the novelty of our study lies in the endpoint setting (major kidney function decline) and in extending the generality of the relationship to the general population with a completely normal kidney function.
In the stratified analysis, the association between higher TG levels and development of major kidney function decline was only observed in men. This sex-related difference was in line with the results of a previous study [18]. Zhang et al. found that serum TG was the only suitable predictor of CKD in men. However, in women, none of the serum lipids and the lipid ratio can be used as a predictor of CKD [18]. In fact, men have been reported to exhibit worse major kidney function decline progression than women [34,35]. However, the mechanisms underlying the sex-related difference in this association have not yet been defined. The difference in sex hormones and the sensitivity of their hormone receptors may affect the development of renal dysfunction through different pathophysiological pathways [34]. Female hormones might be involved in protecting women from CKD development.
Though smoking habits were not assessed in our study, it should be emphasized that smoking is closely associated with dyslipidaemia, especially higher TG and lower HDL-C [36]. In addition, research has shown the ability of smoking to modulate the postprandial hypertriglyceridemia, which represents the non-fasting TG levels and predicts the incidence of CVD [37]. Interestingly, evidence showed that metabolic effects of smoking on serum lipids might be altered by gender [38]. Therefore, the smoking data need to be gathered and quantified carefully in further studies.
AIP was first described by Dobiásová et al. [29] as a biomarker of plasma atherosclerosis. In their later study, AIP was found to be inversely correlated with the diameter of LDL-C particles and to be a surrogate for the small-dense LDL level [39], which is one of the major causative factors of arteriosclerosis and CVD [40]. In fact, a growing number of studies have suggested that AIP is a strong marker for predicting CVD risk [20,22,23,41]. A recent study showed that AIP is a predictor of subclinical renal damage, defined as an eGFR between 30 and 60 mL/min/1.73 m2 [42]. In our study, we found that high AIP was associated with an increased risk of major kidney function decline even after adjustment for multiple covariates. Our study further demonstrated that the association between AIP and renal dysfunction might exist in the general population with eGFR ≥ 90 mL/min/1.73 m2, and showed that AIP can not only predict CVD morbidity but also predict the development of renal dysfunction, which added to evidence indicating that CVD and CKD may have a similar pathophysiology. In fact, both the processes share certain pathophysiological mechanisms, such as endothelial dysfunction, increased oxidative stress, vascular ossification, and inflammation [43–45].
However, the mechanisms through which dyslipidaemia can potentially accelerate renal disease progression remain unclear. One of the possible mechanisms is that an increase in reabsorption of phospholipids and cholesterol by tubular epithelial cells is associated with dyslipidaemia. This reabsorption could then stimulate tubulointerstitial inflammation, foam cell formation, and tissue injury [46,47]. Moreover, increased levels of lipoproteins could increase the formation of proinflammatory cytokines [48], thus inducing glomerulosclerosis [49]. In addition, impaired renal function may accelerate lipid permeability and excretion in the glomerulus, further exacerbating dyslipidaemia in a vicious cycle [15].
Limitations and strengths
Our study also has several limitations that merit consideration. First, this was a retrospective single-center study, which was unable to show whether the serum lipid profile is an independent risk factor for eGFR decline. Second, although albuminuria was also a crucial marker for kidney damage, we were unable to perform the analysis on albuminuria, as we did not have urinary albumin data at baseline and during the period thereafter. Evidence showed that the central obesity predicted the development of kidney injury [30], but waist circumference was not detected in this study. Other confounding factors, such as smoking and drinking habits and physical activity, were not assessed. Lastly, one important member in the lipid profiles, apolipoprotein, were not included. Given the previous evidence that increased apolipoprotein B might be associated with progression of CKD in diabetes patients [50], apolipoprotein should be considered in future studies.
Despite these limitations, the present study has several strengths. We excluded participants with known CVD and diabetes at baseline to eliminate the possibility of confounding due to concomitant diseases in relatively healthy individuals. In addition, we adjusted for many potential covariates, including age, BMI, FBG, uric acid, eGFR, SBP, DBP, which thus made our results more reliable.
Conclusion
In summary, our findings showed that higher serum TG and AIP were significantly associated with the development of major kidney function decline in men. In women, none of the serum lipids or AIP was a risk factor for major kidney function decline. Further prospective studies are required to support our results and to compare the predictive values of individual lipid parameters with that of AIP in kidney function decline development.
Supplementary Material
Acknowledgments
The authors thank all the participants and the staff of the Physical Examination Center of Tongji Hospital for their contributions to this study.
Funding Statement
This work was supported by grants from National Natural Science Foundation of P.R. China [No. 81800609].
Disclosure statement
The authors declare that there is no conflict of interest regarding the publication of this paper.
References
- 1.Lopez AD, Mathers CD, Ezzati M, et al. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–1757. [DOI] [PubMed] [Google Scholar]
- 2.Zhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet. 2012;379:815–822. [DOI] [PubMed] [Google Scholar]
- 3.Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–1305. [DOI] [PubMed] [Google Scholar]
- 4.Foley RN, Murray AM, Li S, et al. Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States medicare population, 1998 to 1999. JASN. 2005;16:489–495. [DOI] [PubMed] [Google Scholar]
- 5.Jha V, Garcia-Garcia G, Iseki K, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382:260–272. [DOI] [PubMed] [Google Scholar]
- 6.Gu D, Gupta A, Muntner P, et al. Prevalence of cardiovascular disease risk factor clustering among the adult population of China: results from the International Collaborative Study of Cardiovascular Disease in Asia (InterAsia). Circulation. 2005;112:658–665. [DOI] [PubMed] [Google Scholar]
- 7.Schaeffner ES, Kurth T, Curhan GC, et al. Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol. 2003;14:2084–2091. [DOI] [PubMed] [Google Scholar]
- 8.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] [PubMed] [Google Scholar]
- 9.Kurella M, Lo JC, Chertow GM.. Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. JASN. 2005;16:2134–2140. [DOI] [PubMed] [Google Scholar]
- 10.Yamagata K, Ishida K, Sairenchi T, et al. Risk factors for chronic kidney disease in a community-based population: a 10-year follow-up study. Kidney Int. 2007;71:159–166. [DOI] [PubMed] [Google Scholar]
- 11.Strippoli GFM, Navaneethan SD, Johnson DW, et al. Effects of statins in patients with chronic kidney disease: meta-analysis and meta-regression of randomised controlled trials. BMJ. 2008;336:645–651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhang X, Xiang C, Zhou YH, et al. Effect of statins on cardiovascular events in patients with mild to moderate chronic kidney disease: a systematic review and meta-analysis of randomized clinical trials. BMC Cardiovasc Disord. 2014;14:19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Baigent C, Landray MJ, Reith C, et al. ; SHARP Investigators . The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377:2181–2192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Zoppini G, Targher G, Chonchol M, et al. Higher HDL cholesterol levels are associated with a lower incidence of chronic kidney disease in patients with type 2 diabetes. Nutr Metab Cardiovasc Dis. 2009;19:580–586. [DOI] [PubMed] [Google Scholar]
- 15.Kang HT, Shim JY, Lee YJ, et al. Association between the ratio of triglycerides to high-density lipoprotein cholesterol and chronic kidney disease in Korean adults: the 2005 Korean National Health and Nutrition Examination Survey. Kidney Blood Press Res. 2011;34:173–179. [DOI] [PubMed] [Google Scholar]
- 16.Ji B, Zhang S, Gong L, et al. The risk factors of mild decline in estimated glomerular filtration rate in a community-based population. Clin Biochem. 2013;46:750–754. [DOI] [PubMed] [Google Scholar]
- 17.Rahman M, Yang W, Akkina S, et al. Relation of serum lipids and lipoproteins with progression of CKD: the CRIC study. CJASN. 2014;9:1190–1198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhang L, Yuan Z, Chen W, et al. Serum lipid profiles, lipid ratios and chronic kidney disease in a Chinese population. IJERPH. 2014;11:7622–7635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Qin X, Wang Y, Li Y, et al. Risk factors for renal function decline in adults with normal kidney function: a 7-year cohort study. J Epidemiol Community Health. 2015;69:782–788. [DOI] [PubMed] [Google Scholar]
- 20.Onat A, Can G, Kaya H, et al. "Atherogenic index of plasma" (log10 triglyceride/high-density lipoprotein-cholesterol) predicts high blood pressure, diabetes, and vascular events. J Clin Lipidol. 2010;4:89–98. [DOI] [PubMed] [Google Scholar]
- 21.Shen S, Lu Y, Qi H, et al. Association between ideal cardiovascular health and the atherogenic index of plasma. Medicine. 2016;95:e3866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Edwards MK, Blaha MJ, Loprinzi PD.. Atherogenic index of plasma and triglyceride/high-density lipoprotein cholesterol ratio predict mortality risk better than individual cholesterol risk factors, among an older adult population. Mayo Clin Proc. 2017;92:680–681. [DOI] [PubMed] [Google Scholar]
- 23.Essiarab F, Taki H, Lebrazi H, et al. Usefulness of lipid ratios and atherogenic index of plasma in obese moroccan women with or without metabolic syndrome. Ethn Dis. 2014;24:207–212. [PubMed] [Google Scholar]
- 24.Smajic J, Hasic S, Rasic S.. High-density lipoprotein cholesterol, apolipoprotein E and atherogenic index of plasma are associated with risk of chronic kidney disease. Med Glas (Zenica). 2018;15:115–121. [DOI] [PubMed] [Google Scholar]
- 25.Coresh J, Turin TC, Matsushita K, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA. 2014;311:2518–2531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Young BA, Katz R, Boulware LE, et al. Risk factors for rapid kidney function decline among African Americans: the Jackson Heart Study (JHS). Am J Kidney Dis. 2016;68:229–239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Levey AS, Stevens LA, Schmid CH, et al. ; for the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) . A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Jellinger PS, et al. American Association of Clinical Endocrinologists’ Guidelines for Management of Dyslipidemia and Prevention of Atherosclerosis. Endocr Pract. 2012;18:1–78. [DOI] [PubMed] [Google Scholar]
- 29.Dobiasova M. AIP–atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice. Vnitrni Lekarstvi. 2006;52:64–71. [PubMed] [Google Scholar]
- 30.Cao Y, Sun G, Liu R, et al. Plasma triglyceride levels and central obesity predict the development of kidney injury in Chinese community older adults. Renal Fail. 2019;41:946–953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Wen J, Chen Y, Huang Y, et al. Association of the TG/HDL-C and non-HDL-C/HDL-C ratios with chronic kidney disease in an adult Chinese population. Kidney Blood Press Res. 2017;42:1141–1154. [DOI] [PubMed] [Google Scholar]
- 32.Zuo PY, Chen XL, Liu YW, et al. Non-HDL-cholesterol to HDL-cholesterol ratio as an independent risk factor for the development of chronic kidney disease. Nutr Metab Cardiovasc Dis. 2015;25:582–587. [DOI] [PubMed] [Google Scholar]
- 33.Tohidi M, Hasheminia M, Mohebi R, et al. Incidence of chronic kidney disease and its risk factors, results of over 10 year follow up in an Iranian cohort. Plos One. 2012;7:e45304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Neugarten J, Golestaneh L.. Influence of sex on the progression of chronic kidney disease. Mayo Clin Proc. 2019;94:1339–1356. [DOI] [PubMed] [Google Scholar]
- 35.Sakurai M, Kobayashi J, Takeda Y, et al. Sex differences in associations among obesity, metabolic abnormalities, and chronic kidney disease in Japanese men and women. J Epidemiol. 2016;26:440–446. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hata Y, Nakajima K.. Life-style and serum lipids and lipoproteins. J Atheroscler Thromb. 2000;7:177–197. [DOI] [PubMed] [Google Scholar]
- 37.Leon-Acuña A, Torres-Peña JD, Alcala-Diaz JF, et al. Lifestyle factors modulate postprandial hypertriglyceridemia: From the CORDIOPREV study. Atherosclerosis. 2019;290:118–124. [DOI] [PubMed] [Google Scholar]
- 38.Lee MH, Ahn SV, Hur NW, et al. Gender differences in the association between smoking and dyslipidemia: 2005 Korean National Health and Nutrition Examination Survey. Clin Chim Acta. 2011;412:1600–1605. [DOI] [PubMed] [Google Scholar]
- 39.Dobiás˘Ová M, Frohlich J.. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FERHDL). Clin Biochem. 2001;34:583–588. [DOI] [PubMed] [Google Scholar]
- 40.Grundy SM. 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) Final Report. Circulation. 2002;106:3143–3421. [PubMed] [Google Scholar]
- 41.Cai G, Shi G, Xue S, et al. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine. 2017;96:e8058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yuan Y, Hu JW, Wang Y, et al. Association between atherogenic index of plasma and subclinical renal damage over a 12-year follow-up: Hanzhong adolescent hypertension study. Eur J Clin Nutr. 2020;74:278–284. [DOI] [PubMed] [Google Scholar]
- 43.Cachofeiro V, Goicochea M, de Vinuesa SG, et al. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int. 2008;74:S4–S9. [DOI] [PubMed] [Google Scholar]
- 44.Mann J, Hilgers KF, Veelken R, et al. [Chronic kidney disease and the cardiovascular system]. Internist (Berl). 2008;49:413. [DOI] [PubMed] [Google Scholar]
- 45.Rajendran P, Rengarajan T, Thangavel J, et al. The vascular endothelium and human diseases. Int J Biol Sci. 2013;9:1057–1069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Abrass CK. Cellular lipid metabolism and the role of lipids in progressive renal disease. Am J Nephrol. 2004;24:46–53. [DOI] [PubMed] [Google Scholar]
- 47.Magil AB. Interstitial foam cells and oxidized lipoprotein in human glomerular disease. Mod Pathol. 1999;12:33–40. [PubMed] [Google Scholar]
- 48.Keane WF, O'Donnell MP, Kasiske BL, et al. Oxidative modification of low-density lipoproteins by mesangial cells. J Am Soc Nephrol. 1993;4:187–194. [DOI] [PubMed] [Google Scholar]
- 49.Klahr S, Schreiner G, Ichikawa I.. The progression of renal disease. N Engl J Med. 1988;318:1657–1666. [DOI] [PubMed] [Google Scholar]
- 50.Zhao WB, Zhu L, Rahman T.. Increased serum concentration of apolipoprotein B is associated with an increased risk of reaching renal replacement therapy in patients with diabetic kidney disease. Renal Fail. 2020;42:323–328. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.