Abstract
Atherogenic index of plasma (AIP), as a low-cost and easily accessible biomarker of inflammation, has attracted much attention in various disease studies in recent years. Specifically, existing research has suggested AIP index is an important marker of insulin resistance and a significant risk factor for cardiovascular disease, indicating its potential relevance to diabetic complications. However, few studies have investigated association between AIP and chronic kidney disease (CKD) in the diabetic population. This study aimed to investigate the relationship between AIP and the risk of CKD in patients with diabetes mellitus. A cross-sectional analysis of 2005–2016 National Health and Nutrition Examination Survey data was conducted. AIP was categorized into quartiles, and weighted multivariate logistic regression and restricted cubic splines were used to assess associations. Subgroup analyses and interaction tests were performed. Among 2726 diabetic patients, 38.51% had CKD. After adjusting for confounders, AIP was positively associated with CKD (OR = 1.31, 95% CI 1.04 to 1.65, p = 0.025). The highest AIP quartile showed a 2.12-fold increased CKD risk (95% CI 1.26 to 3.55, p = 0.005). A linear relationship was confirmed, and subgroup analyses showed consistent associations. This study has shown that increased AIP index increases the risk of developing CKD in diabetic patients. AIP may be a potential marker to predict CKD, emphasizing early intervention to reduce the likelihood of developing CKD.
Keywords: Chronic kidney disease, atherogenic index of plasma, eGFR, NHANES, cross-sectional study, interaction
1. Introduction
According to the International Diabetes Federation, the global prevalence of diabetes was 463 million in 2019 and is expected to rise to 700 million by 2045 [1]. The enormous economic losses caused by diabetes continue to burden society through direct medical and indirect costs. In 2022, the total estimated cost of diagnosed diabetes in the United States was $412.9 billion, including $306.6 billion in direct medical costs and $106.3 billion in indirect costs attributable to diabetes. The average medical expenditure for patients diagnosed with diabetes is 2.6 times higher than that of people without diabetes [2]. Chronic kidney disease (CKD) is the most common microvascular complications of diabetes, which is represented by albuminuria and a decreased estimated glomerular filtration rate (eGFR) [3]. It affects approximately 40% of populations with diabetes [4]. CKD is also the leading cause of end-stage kidney disease (ESKD) requiring dialysis or transplantation in the United States and worldwide [5]. In addition, CKD can significantly increase cardiovascular morbidity and mortality and reduce the health-related quality of life of diabetes [6]. The study found that the prevalence of CKD remains high (64 - 81.6/1000) among United States patients with diabetes in 2015–2020, and will increase further as the prevalence of diabetes increases significantly [7]. The cost burden of CKD and ESKD are enormous. To reduce the social and economic burden of CKD in patients with diabetes mellitus, early diagnosis and aggressive intervention are necessary [8]. Therefore, it is important to identify modifiable risk factors associated with CKD for the prevention of ESKD.
Hyperglycemia, insulin resistance, and dyslipidemia commonly coexist, and they may induce a variety of metabolic disorders closely related to oxidative stress and inflammatory processes, ultimately resulting in a vicious cycle of alternating cause and effect and exacerbating injury [4]. Dyslipidemia is highly prevalent in patients with diabetic kidney disease, primarily characterized by elevated triglyceride (TG) levels, decreased high-density lipoprotein cholesterol (HDL-C), and normal to mildly elevated low-density lipoprotein cholesterol (LDL-C) [9]. In 2021, the Chinese Cardiometabolic Diseases and Cancer Cohort study revealed that a high TG level (TG ≥ 1.70 mmol/L) served as an independent risk factor for the development of diabetic kidney disease in individuals with new-onset type 2 diabetes mellitus [10]. A large-scale global case-control study has demonstrated that each 0.5 mmol/L increment in TG is associated with a 23% increased risk of developing diabetic kidney disease, while each 0.2 mmol/L elevation in HDL-C is linked to a 14% reduced risk of diabetic kidney disease [11]. However, most previous studies have focused on broader metabolic markers or individual lipid components, without considering the complex markers [12].
There is growing evidence that some lipid ratios may provide additional information on lipid metabolism compared to traditional lipid parameters. Atherogenic index of plasma (AIP) was introduced by Dobiasova in 2001 and calculated as the logarithm of the ratio of TG to HDL-C [13]. AIP was a reliable marker for assessing atherosclerosis and serious cardiovascular events. In addition, it has been found that AIP can be a useful indicator for identifying individuals with diabetes, and the higher the AIP, the higher the risk of diabetes [14–16].
To address this research gap, we conducted a cross-sectional analysis based on the 2005–2016 National Health and Nutrition Examination Survey (NHANES) to explore the association between AIP and CKD in a nationally representative sample of U.S. adults.
2. Materials and methods
2.1. Study population
Data from the NHANES, was published by the Centers for Disease Control and Prevention, which was a nationally representative survey conducted by the National Center for Health Statistics [17]. The survey applied a cross-sectional, stratified, multistage probability method to a sample of the United States populations and provided nationally representative health and nutrition statistics on the civilian, noninstitutionalized population of the United States. NHANES study protocol was approved by the National Center for Health Statistics Ethics Review Committee and all participants provided written informed consent. More details about the study design and data of the NHANES are publicly available on website (http://www.cdc.gov/nchs/nhanes.htm).
The data for this study came from the NHANES database across six cycles, from 2005 to 2016, encompassing a total of 60936 participants. The criteria for participant inclusion in our investigation were as follows: (1) diabetic patients; (2) complete AIP data; (3) availability of albumin creatinine ratio (ACR) and eGFR data; (4) aged 20 years or older; and (5) Sampling weights for fasting subsamples (WTSAF2YR) > 0. We excluded 26756 participants under the age of 20 years, 19411 participants with missing AIP, 197 with missing urine ACR, 50 participants with missing eGFR, 11577 participants without diabetes, and 219 participants with missing data of WTSAF2YR. Eventually, 2726 diabetic patients were enrolled in this study (Figure 1).
Figure 1.
Flowchart of participant selection. Abbreviations: AIP, atherogenic index of plasma; ACR, albumin creatinine ratio; eGFR, estimated glomerular filtration rate.
2.2. Definition of atherogenic index of plasma
AIP was a quantitative index for assessing lipid profiles. AIP was calculated by the ratio of fasting TG to HDL-C followed by logarithmic transformation. The formula was as follows: AIP = Log [TG (mmol/L)/HDL-C (mmol/L)].
2.3. Definition of diabetes mellitus and CKD
According to the American Diabetes Association guidelines, diabetes mellitus was diagnosed based on one of the following: (a) medical diagnosis by a healthcare professional; (b) glycosylated hemoglobin levels of 6.5% or higher; (c) fasting blood glucose levels of 7. 0 mmol/L or higher; (d) a random or 2 h oral glucose tolerance test with blood glucose 11.1 mmol/L or higher; (e) current treatment for diabetes. According to the Kidney Disease Improving Global Outcomes 2021 Clinical Practice Guideline [18], diabetic kidney disease was defined as the diabetes patients with ACR ≥ 30 mg/g and/or eGFR < 60 mL/min/1.73 m2. The eGFR was calculated by using the Chronic Kidney Disease Epidemiology Collaboration equation [19]: eGFR (mL/min/1.73 m2) = 141 × min (Scr/κ, 1)α × max (Scr/κ, 1)−1.209 × 0.993^Age × 1.018[if women] × 1.159 [if black]). κ was 0.7(women) or 0.9(men),α was −0.329(women) or −0.411(men), and min/max indicate the minimum/maximum of Scr/κ or 1. The ACR was calculated by using the urinary albumin-to-creatinine ratio.
2.4. Covariates
Based on previous research, we included a number of covariates that might influence the results of the study. Age, sex, race, education level, marital status, poverty-to-income ratio (PIR), body mass index (BMI), presence of hypertension, presence of coronary heart disease, presence of congestive heart failure, smoking status, drinking status, total cholesterol (TC), LDL-C, alanine aminotransferase (ALT) and aspartate aminotransferase (AST).
Among these covariates, race was categorized into the following four groups: Mexican American, non-Hispanic white, non-Hispanic black and Other. The PIR classification method divides household income by the federal poverty line for the survey year and adjusts household size to be < 1.0, 1.0–3.0 and ≥ 3.0 [20]. Education level was categorized as below high school, high school graduate or equivalent, and college or above. Marital status was categorized as married/living with a partner or living alone (including unmarried, widowed, divorced, and separated). BMI was calculated by dividing weight (kg) by height (m2), and according to the World Health Organization criteria, BMI was classified into four categories: underweight (<18.5 kg/m2), normal weight (18.5–25.0 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (≥30.0 kg/m2). Drinking status was categorized into nondrinker, 1–5 drinks/month, 5–10 drinks/month, 10+ drinks/month. Smoking status was recorded as never smoker (lifetime smokers <100 cigarettes, current nonsmokers), former smoker (lifetime smokers ≥100 cigarettes, current nonsmokers), or current smoker. Hypertension was defined as one of the following: taking blood pressure-lowering medication, diagnosed by a physician as having a high systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg.
2.5. Statistical analysis
Continuous variables were summarized as mean ± standard deviation, and categorical variables as counts (n) and percentages (%). Weighted t-tests and chi-square tests were used to compare continuous and categorical variables, respectively. The AIP was categorized into quartiles for analysis.
Weighted multivariate logistic regression models were used to estimate the odds ratios for the risk of CKD associated with AIP. Model 1 was adjusted for no confounding factors. Model 2 was adjusted for age, sex and race. Model 3 was adjusted for age, sex, race, education levels, marital status, PIR, BMI, ALT, AST, TC, LDL-C, smoking status, alcohol consumption status, hypertension, coronary heart disease, and congestive heart failure. While restricted cubic spline analysis was used to evaluate potential nonlinear dose-response relationships between AIP and CKD. Finally, stratified analyses and interaction tests were used to explore potential differences between different diabetes. Statistical analysis was performed using R software version 4.3.2. Statistical significance was defined as a two-tailed P-value < 0.05.
3. Results
3.1. Baseline characteristics
The baseline characteristics of the diabetic patients in this study are shown in Table 1. A total of 2726 diabetic patients were involved in this study, with average age of 59.42 years, and 50.51% were males, 49.48% were females. In our study, 1050 (38.51%) diabetic patients were categorized as CKD. The mean of AIP for the non-CKD and CKD diabetic patients were 0.190 and 0.276, respectively (p < 0.05). Furthermore, there were significant difference in age, educational level, marital status, PIR, drinking status, hypertension, coronary heart disease, congestive heart failure, LDL-C, and ALT levels between the CKD and non-CKD groups (P-values all < 0.05).
Table 1.
Basic characteristics of participants with diabetes (n = 2726) in the NHANES 2005–2016.
| Characteristic | Total (n = 2726) |
Non-CKD (n = 1676) |
CKD (n = 1050) |
P-value |
|---|---|---|---|---|
| Age, mean ± SD (years) | 59.421 ± 14.073 | 56.716 ± 13.408 | 64.457 ± 13.900 | <0.001 |
| Sex (%) | 0.352 | |||
| Male | 1415 (50.512%) | 872 (51.434%) | 543 (48.795%) | |
| Female | 1311 (49.488%) | 804 (48.566%) | 507 (51.205%) | |
| Poverty to income ratio, % | <0.001 | |||
| ≥ 1.0 | 587 (16.327%) | 343 (15.067%) | 244 (18.694%) | |
| 1.0–3.0 | 1157 (42.492%) | 690 (39.391%) | 467 (48.317%) | |
| < 3.0 | 740 (41.181%) | 504 (45.542%) | 236 (32.988%) | |
| Race/ethnicity (%) | 0.715 | |||
| Mexican American | 511 (9.802%) | 321 (9.718%) | 190 (9.956%) | |
| Non-hispanic white | 1068 (63.992%) | 619 (63.879%) | 449 (64.202%) | |
| Non-hispanic black | 611 (13.518%) | 374 (13.264%) | 237 (13.991%) | |
| Other | 536 (12.689%) | 362 (13.139%) | 174 (11.850%) | |
| Education level (%) | 0.003 | |||
| Below high school | 937 (24.998%) | 560 (22.610%) | 413 (29.446%) | |
| High school | 659 (25.901%) | 401 (24.650%) | 258 (28.231%) | |
| More than high school | 1091 (49.102%) | 714 (52.740%) | 377 (42.323%) | |
| Marital status (%) | <0.001 | |||
| Married/living with a partner | 1551 (83.615%) | 1002 (88.757%) | 549 (74.554%) | |
| Living alone | 379 (16.385%) | 166 (11.243%) | 213 (25.446%) | |
| BMI (kg/m2) (%) | 0.109 | |||
| Underweight | 16 (0.538%) | 9 (0.474%) | 7 (0.657%) | |
| Normal | 392 (13.039%) | 225 (11.683%) | 167 (15.601%) | |
| Overweight | 812 (28.457%) | 523 (29.817%) | 289 (25.887%) | |
| Obese | 1461 (57.966%) | 899 (58.025%) | 562 (57.854%) | |
| Smoker (%) | 0.187 | |||
| Current smoker | 451 (16.506%) | 276 (16.076%) | 175 (17.309%) | |
| Former smoker | 882 (32.892%) | 509 (31.466%) | 373 (35.549%) | |
| Never smoker | 1390 (50.602%) | 890 (52.458%) | 500 (47.142%) | |
| Alcohol user (%) | 0.008 | |||
| Nondrinker | 927 (32.823%) | 532 (29.810%) | 395 (38.339%) | |
| 1–5 drinks/month | 1256 (50.757%) | 782 (52.465%) | 474 (47.629%) | |
| 5–10 drinks/month | 122 (4.861%) | 82 (5.551%) | 40 (3.598%) | |
| 10 + drinks/month | 257 (11.559%) | 170 (12.174%) | 87 (10.434%) | |
| Hypertension history (%) | <0.001 | |||
| Yes | 1923 (69.500%) | 1,061 (62.422%) | 862 (82.681%) | |
| No | 803 (30.500%) | 615 (37.578%) | 188 (17.319%) | |
| Coronary heart disease (%) | <0.001 | |||
| Yes | 249 (9.492%) | 106 (6.635%) | 143 (14.842%) | |
| No | 2453(90.508%) | 1561 (93.365%) | 892 (85.158%) | |
| Congestive heart failure (%) | <0.001 | |||
| Yes | 163 (6.223%) | 75 (4.144%) | 88 (10.127%) | |
| No | 2545 (93.777%) | 1594 (95.856%) | 951 (89.873%) | |
| TC (mmol/L), mean ± SD | 4.901 ± 1.187 | 4.907 ± 1.142 | 4.888 ± 1.266 | 0.320 |
| LDL-C (mmol/L), mean ± SD | 2.785 ± 0.962 | 2.830 ± 0.935 | 2.697 ± 1.007 | 0.005 |
| TG (mmol/L), mean ± SD | 1.884 ± 1.833 | 1.814 ± 1.886 | 2.015 ± 1.724 | 0.008 |
| HDL-C (mmol/L), mean ± SD | 1.289 ± 0.423 | 1.279 ± 0.368 | 1.308 ± 0.510 | 0.861 |
| BUN (mmol/L), mean ± SD | 5.559 ± 2.700 | 4.871 ± 1.573 | 6.841 ± 3.707 | <0.001 |
| Scr (μmol/L), mean ± SD | 83.867 ± 45.020 | 73.337 ± 15.669 | 103.478 ± 68.961 | <0.001 |
| eGFR (mL/min/1.73 m²), mean ± SD | 85.343 ± 24.918 | 93.085 ± 18.022 | 70.927 ± 29.212 | <0.001 |
| FBG (mmol/L), mean ± SD | 8.252 ± 3.175 | 8.039 ± 2.842 | 8.649 ± 3.686 | 0.005 |
| HbA1c (%), mean ± SD | 6.986 ± 1.689 | 6.852 ± 1.564 | 7.236 ± 1.876 | <0.001 |
| ALT(U/L), mean ± SD | 28.130 ± 18.686 | 29.456 ± 19.588 | 25.662 ± 16.610 | <0.001 |
| AST(U/L), mean ± SD | 27.267 ± 19.084 | 27.591 ± 21.009 | 26.664 ± 14.845 | 0.287 |
| AIP, mean ± SD | 0.220 ± 0.777 | 0.190 ± 0.752 | 0.276 ± 0.820 | 0.029 |
Mean ± SD was for continuous variables; The percentage (95% confidence interval) was for categorical variables.
Abbreviations: AIP, atherosclerotic index of plasma; CKD, chronic kidney disease; SD, standard deviation; NHANES, national health and nutrition examination survey; BMI, body mass index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbA1c, glycosylated hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase.
3.2. AIP is associated with an increased incidence of CKD with diabetes mellitus
In our study, we analyzed the effect of AIP on CKD by using weighted logistic regression models with stepwise adjustment for confounders. Model 1 did not adjust for any confounding factors. Model 2 was adjusted for age, sex and race. Model 3 was adjusted for age, sex, race, education levels, marital status, PIR, BMI, ALT, AST, TC, LDL-C, smoking status, alcohol consumption status, hypertension, coronary heart disease, and congestive heart failure.
Table 2 shows the relationship between AIP as a continuous variable and categorical variables with CKD. When AIP was included in the model as a continuous variable, the results showed a significant association between AIP and the risk of CKD. Model 1 was unadjusted (OR [95% CI = 1.15 [1.02 to 1.31], P-value= 0.027), model 2 was partially adjusted model (OR [95% CI] = 1.34 [1.16 to 1.54], P-value < 0.001), and model 3 was fully adjusted model (OR [95% CI = 1.31 [1.04 to 1.65], P-value= 0.025). Based on the results of those analysis, when AIP was included in the model as a categorical variable, the highest AIP index quartile indicated a statistically significant increased risk of CKD compared to the lowest quartile. Model 1 was unadjusted (Q4: OR [95% CI = 1.41 [1.06 to 1.88], P-value = 0.018), model 2 was partially adjusted model (OR [95% CI] = 1.87 [1.34 to 2.62], P-value < 0.001), and model 3 was fully adjusted model (OR [95% CI = 2.12 [1.26 to 3.55], P-value= 0.005).
Table 2.
Odds ratios for chronic kidney disease across quartiles of the atherogenic index of plasma in patients with diabetes.
| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| variables | OR (95% CI), P value | OR (95% CI), P value | OR (95% CI), P value |
| AIP | 1.15 (1.02 to 1.31) 0.027 | 1.34 (1.16 to 1.54) <0.001 | 1.31 (1.04 to 1.65) 0.025 |
| Q1 | Reference | Reference | Reference |
| Q2 | 1.11 (0.84 to 1.46) 0.477 | 1.12 (0.83 to 1.51) 0.452 | 1.30 (0.84 to 2.02) 0.236 |
| Q3 | 1.04 (0.80 to 1.35) 0.757 | 1.16 (0.89 to 1.50) 0.278 | 1.16 (0.78 to 1.73) 0.457 |
| Q4 | 1.41 (1.06 to 1.88) 0.018 | 1.87 (1.34 to 2.62) <0.001 | 2.12 (1.26 to 3.55) 0.005 |
| P for trend | 0.020 | <0.001 | 0.005 |
Model 1 did not adjust for any cConfounding factors.
Model 2 adjusted for age, sex and race.
Model 3 adjusted for age, sex, race, education levels, marital status, poverty to income ratio, BMI, ALT, AST, TC, LDL-C, smoking status, alcohol consumption status, hypertension, coronary heart disease, and congestive heart failure.
Abbreviations: BMI, body Mass Index; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; CI, confidence interval; AIP, atherogenic index of plasma; Q1-Q4, quartiles 1 through 4.
Patients were stratified by quartiles of AIP: Q1 (<-0.29), Q2 (−0.29 to 0.21), Q3 (0.21 to 0.67), and Q4 (≥0.67).
In addition, we explored the nonlinear relationship between the AIP index and CKD with restricted cubic splines based on model 3. As seen in Figure 2, the dose-response analysis with restricted cubic splines showed a nearly linear relationship between the AIP index and the risk of CKD (P for nonlinear = 0.56).
Figure 2.
Restricted cubic splines for the association between atherogenic index of plasma and chronic kidney disease. Abbreviation: AIP, atherogenic index of plasma.
3.3. Subgroup analyses
Stratified analyses and interaction tests were used to further explore the association of AIP with the risk of CKD (Figure 3). After stratification by sex, age, race, and BMI, no significant interactions were observed in any of the strata (all interactions p > 0.05).
Figure 3.
Forest Plot of subgroup analysis and interaction effects for odds ratios and 95% confidence intervals of associations between AIP and CKD. Abbreviations: AIP, atherogenic index of plasma; CKD, chronic kidney disease; BMI, body mass index; OR, odds ratio; CI, confidence interval.
4. Discussion
This was a large cross-sectional study based on data from the NHANES from 2005 to 2016, including 2726 diabetic patients. We found that CKD patients had a higher AIP index than non-CKD patients. Foremost, our study emphasized that AIP was a risk factor for the development of CKD. With full adjustment for covariates in the continuous model, AIP was found to be positively associated with CKD. In the categorical model, the highest group was positively associated with the risk of developing CKD. This positive association persisted in further sensitivity analyses. In addition, restricted cubic splines showed a linear relationship between AIP and the risk of CKD.
Persistent hyperglycemia in diabetic patients was a major driver of CKD development, and disorders of glucose metabolism and lipid metabolism interacted with each other in a mutually reinforcing manner. Lipids were involved in cell signaling, immunity, substance transport, and maintenance of cellular metabolism in the body [3]. Lipid metabolism disorders are a common comorbidity of diabetes mellitus and characterized by elevated levels of TG, free fatty acids, small and dense low-density lipoprotein cholesterol (sdLDL-C) particles, and decreased levels of HDL-C [3,21]. Mutiples studies suggested that dyslipidemia plays an important role in the progression of the kidney disease in patients with diabetic patients [21–23]. Hyperlipidemia might induce the proliferation of glomerular endothelial cells and mesangial cells, cause decrease in glomerular filtration rate and affect renal function [24]. Lipids might also affect podocyte function, thereby impairing the integrity of the glomerular relaxation barrier and the synthesis of several components, including collagen, laminin, and fibronectin, leading to the development of proteinuria [9]. Kidney tubular epithelial cells had a very high energy requirement, fatty acid oxidation defects and increased lipid uptake, particularly in the context of hyperlipidemia and proteinuria, exacerbated lipid overaccumulation and contributed to the development of kidney disease [25]. Under the hyperglycemic and hyperlipidemic environment, regardless of the mechanisms involved, once lipids accumulated in the kidney, they might lead to several deleterious consequences through increased oxidative stress and the release of several inflammatory cytokines and growth factors, which translated into glomerulosclerosis and tubulointerstitial injury [3,9,26].
AIP is a simple and accessible indicator that combines HDL-C with TG levels to provide a more thorough profile of dyslipidemia. AIP was found to be inversely proportional to the diameter of oxidized LDL particles and can be used as a quantitative measure of small dense low-density lipoprotein (sdLDL-C) particles [27]. On the one hand, sdLDL-C has a long half-life in plasma and was more readily absorbed into arterial tissue due to its reduced LDL receptor affinity and smaller particle size, and then be oxidized to oxidized LDL. Oxidized LDL was phagocytosed by macrophages and transformed into foam cells, then the foam cells fused and ruptured, releasing a large amount of cholesterol, and constituted the core part of atherosclerotic plaque [28,29]. on the other hand, sdLDL-C was more vulnerable to various atherogenic modifications, including dehydrogenation, glycosylation, and oxidation, which have potential inflammation-inducing effects [30]. Oxidized LDL, a marker of endothelial dysfunction and oxidative stress, could be over-absorbed by many types of renal cells, thereby promoting glomerulosclerosis and renal fibrosis, and ultimately accelerating the development of CKD [31–33]. Patients with higher sdLDL -C levels are usually at significant risk of developing microvascular complications. Methods such as nuclear magnetic resonance and ultrafast centrifugation can be used to evaluate sdLDL-C, but the high cost and lengthy process of these methods make them unsuitable for clinical practice. The new lipid indicator AIP has emerged as a potentially more effective marker for assessing vascular risk.
In a study that included diabetes, TG level >1.7 mmol/L, as well as the level of HDL-C < 1.03 mmol/L in men and <1.29 mmol/L in women were found to be independent risk factors for developing CKD over 4 years [34]. A cross-sectional study has revealed a non-linear association between AIP and CKD, characterized by an inflection point of −0.55 [35]. For instance, several retrospective studies reported that increased AIP was associated with the development of microalbuminuria in Chinese diabetes [36,37]. Another study has demonstrated that, in comparison with other lipid markers, AIP exhibits superior precision and discriminatory ability in predicting albuminuria among individuals with diabetes [38]. However, in a study that involved 2523 diabetes with ESKD there were no significant differences in the prevalence of CKD as measured by urinary microalbumin among AIP tertiles [39]. In a study of patients with biopsy-confirmed diabetic kidney disease, the ratio of TG to HDL-C levels were associated with the prevalence of cardiovascular disease, but not with ESKD [40]. Our studies in the US diabetes have highlighted the negative effects of AIP on declining kidney function.
The mechanism of the association between AIP and CKD is not clear. Studies have shown that CKD was associated with ectopic lipid deposition. Ectopic lipid deposition is the excessive accumulation of lipids, especially TG, accumulation in non-adipose tissues, leading to lipotoxic injury of non-adipocytes, resulting in a series of pathophysiological changes [41]. Insulin resistance resulted in diminished insulin-dependent inhibition of lipolysis and in the inability of adipocytes to accept TG from very low density lipoprotein or to hydrolyze intracellular TG, leading to the release of more free fatty acids into the circulation, leading to excess production of reactive oxygen species, mitochondrial damage, and decreased function, all of which were associated with the development and progression of adverse health events [42]. HDL exerted a variety of cardiovascular protective effects, including anti-atherosclerosis, by mediating reverse cholesterol transport, anti-inflammatory and antioxidant mechanisms [43,44]. In diabetic patients with early or terminal CKD, elevated serum advanced glycation end products concentrations were associated with impaired HDL antioxidant capacity [45]. Specific HDL subpopulations were also associated with high levels of inflammatory markers [9]. In addition, HDL has the potential to regulate glucose metabolism in diabetes [46].
These findings indicate that AIP could potentially act as an effective tool for assessing CKD risk and formulating targeted intervention strategies based on AIP levels. However, further longitudinal studies are necessary to validate these findings. Additional research is also needed to uncover the mechanisms through which AIP influences CKD and to identify potential targets for therapy.
5. Strengths and limitation
Our data come from the NHANES database, known for its strictly regimented data collection protocols and extensive sample sizes, which provide considerable credibility and reliability to our data definitions. Using stratified and subgroup analyses, we delve into the association between AIP and CKD with diabetes mellitus. The AIP index measure can be applied more extensively in clinical and screening settings. However, the limitations of our study should be noted. First, this was a cross-sectional study design prevented us from establishing causality. Second, although a range of covariates were considered, there may still be unmeasured confounders that influence the relationship between AIP and CKD, such as genetic factors and dietary patterns. In addition, some of our data were derived from patient self-reports and may be subject to recall bias.
6. Conclusion
In conclusion, our study demonstrate that elevated levels of AIP were significantly associated with CKD risk in a representative cohort of adult diabetic patients in the US. AIP may be a potential predictor of CKD development. Meanwhile, it is an important guideline for preventing the occurrence and development of CKD in diabetic patients and even for the treatment of CKD in clinical practice.
Acknowledgements
We thank the participants in this study and the National Health and Nutrition Examination Survey for providing data.
Funding Statement
This work was supported by the National Natural Science Foundation of China (82272993 to Xiaokun Gang), the Jilin Provincial Natural Science Foundation Freedom to Explore Key Project (YDZJ202401410ZYTS to Xiaokun Gang), the Jilin Provincial Science and Technology Innovation Center Project (YDZJ202402042CXJD to Xiaokun Gang), the Department of Science and Technology of Jilin Province (YDZJ202202CXJD042 to Guixia Wang) and the Jilin Province Health Talent Special Project (JLSCZD2019-016 to Guixia Wang).
Author contributions
Mingyue Cui completed the final data organization and drafted the manuscript. Statistical analysis was done by Mingyue Cui and Xianchao Xiao. Zhao Xue and Yingxuan Wang interpreted the results. The manuscript was reviewed by Xiaokun Gang and Guixia Wang. All authors reviewed and approved the final version of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
This study utilized publicly available datasets for analysis. The data is accessible through the following link: https://www.cdc.gov/nchs/nhanes.
References
- 1.Sun H, Saeedi P, Karuranga S, et al. IDF diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. doi: 10.1016/j.diabres.2021.109119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Parker ED, Lin J, Mahoney T, et al. Economic costs of diabetes in the U.S. in 2022. Diabetes Care. 2024;47(1):26–43. doi: 10.2337/dci23-0085. [DOI] [PubMed] [Google Scholar]
- 3.Li Y, Liu Y, Liu S, et al. Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther. 2023;8(1):152. doi: 10.1038/s41392-023-01400-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Naaman SC, Bakris GL.. Diabetic nephropathy: update on pillars of therapy slowing progression. Diabetes Care. 2023;46(9):1574–1586. doi: 10.2337/dci23-0030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hoffman A, Maciejewski ML, Wang V.. Payer mix among patients receiving dialysis-reply. JAMA. 2020;324(9):901. doi: 10.1001/jama.2020.10774. [DOI] [PubMed] [Google Scholar]
- 6.Umanath K, Lewis JB.. Update on diabetic nephropathy: core curriculum 2018. Am J Kidney Dis. 2018;71(6):884–895. doi: 10.1053/j.ajkd.2017.10.026. [DOI] [PubMed] [Google Scholar]
- 7.Tuttle KR, Jones CR, Daratha KB, et al. Incidence of chronic kidney disease among adults with diabetes, 2015-2020. N Engl J Med. 2022;387(15):1430–1431. doi: 10.1056/NEJMc2207018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kim MK, Kim DM.. Current status of diabetic kidney disease and latest trends in management. J Diabetes Investig. 2022;13(12):1961–1962. doi: 10.1111/jdi.13895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Russo G, Piscitelli P, Giandalia A, et al. Atherogenic dyslipidemia and diabetic nephropathy. J Nephrol. 2020;33(5):1001–1008. doi: 10.1007/s40620-020-00739-8. [DOI] [PubMed] [Google Scholar]
- 10.Gong L, Wang C, Ning G, et al. High concentrations of triglycerides are associated with diabetic kidney disease in new-onset type 2 diabetes in China: findings from the China cardiometabolic disease and cancer cohort (4C) study. Diabetes Obes. Metab. 2021;23(11):2551–2560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sacks FM, Hermans MP, Fioretto P, et al. Association between plasma triglycerides and high-density lipoprotein cholesterol and microvascular kidney disease and retinopathy in type 2 diabetes mellitus: a global case-control study in 13 countries. Circulation. 2014;129(9):999–1008. doi: 10.1161/CIRCULATIONAHA.113.002529. [DOI] [PubMed] [Google Scholar]
- 12.Opazo-Ríos L, Mas S, Marín-Royo G, et al. Lipotoxicity and diabetic nephropathy: novel mechanistic insights and therapeutic opportunities. Int J Mol Sci. 2020;21(7):2632. doi: 10.3390/ijms21072632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Dobiásová 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 (FER(HDL)). Clin Biochem. 2001;34(7):583–588. doi: 10.1016/s0009-9120(01)00263-6. [DOI] [PubMed] [Google Scholar]
- 14.Sun Y, Li F, Zhou Y, et al. Nonlinear association between atherogenic index of plasma and type 2 diabetes mellitus in overweight and obesity patients: evidence from Chinese medical examination data. Cardiovasc Diabetol. 2024;23(1):226. doi: 10.1186/s12933-024-02330-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Yi Q, Ren Z, Bai G, et al. The longitudinal effect of the atherogenic index of plasma on type 2 diabetes in middle-aged and older Chinese. Acta Diabetol. 2022;59(2):269–279. doi: 10.1007/s00592-021-01801-y. [DOI] [PubMed] [Google Scholar]
- 16.Shi Y, Wen M.. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011-2018 population. Cardiovasc Diabetol. 2023;22(1):19. doi: 10.1186/s12933-023-01740-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Johnson CL, Paulose-Ram R, Ogden CL, et al. National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat 2. 2013;161(161):1–24. [PubMed] [Google Scholar]
- 18.Rovin BH, Adler SG, Barratt J, et al. Executive summary of the KDIGO 2021 guideline for the management of glomerular diseases. Kidney Int. 2021;100(4):753–779. doi: 10.1016/j.kint.2021.05.015. [DOI] [PubMed] [Google Scholar]
- 19.Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi: 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.O’Hearn M, Lauren BN, Wong JB, et al. Trends and disparities in cardiometabolic health among U.S. adults, 1999-2018. J Am Coll Cardiol. 2022;80(2):138–151. doi: 10.1016/j.jacc.2022.04.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wang Y, Liu T, Wu Y, et al. Lipid homeostasis in diabetic kidney disease. Int J Biol Sci. 2024;20(10):3710–3724. doi: 10.7150/ijbs.95216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Rutledge JC, Ng KF, Aung HH, et al. Role of triglyceride-rich lipoproteins in diabetic nephropathy. Nat Rev Nephrol. 2010;6(6):361–370. doi: 10.1038/nrneph.2010.59. [DOI] [PubMed] [Google Scholar]
- 23.Nakamichi R, Hayashi K, Itoh H.. Effects of High Glucose and Lipotoxicity on Diabetic Podocytes. Nutrients. 2021;13(1):241. doi: 10.3390/nu13010241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Mitrofanova A, Merscher S, Fornoni A.. Kidney lipid dysmetabolism and lipid droplet accumulation in chronic kidney disease. Nat Rev Nephrol. 2023;19(10):629–645. doi: 10.1038/s41581-023-00741-w. [DOI] [PubMed] [Google Scholar]
- 25.Lee LE, Doke T, Mukhi D, et al. The key role of altered tubule cell lipid metabolism in kidney disease development. Kidney Int. 2024;106(1):24–34. doi: 10.1016/j.kint.2024.02.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yang W, Luo Y, Yang S, et al. Ectopic lipid accumulation: potential role in tubular injury and inflammation in diabetic kidney disease. Clin Sci (Lond). 2018;132(22):2407–2422. doi: 10.1042/CS20180702. [DOI] [PubMed] [Google Scholar]
- 27.Qin Z, Zhou K, Li Y, et al. The atherogenic index of plasma plays an important role in predicting the prognosis of type 2 diabetic subjects undergoing percutaneous coronary intervention: results from an observational cohort study in China. Cardiovasc Diabetol. 2020;19(1):23. doi: 10.1186/s12933-020-0989-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lu CF, Liu WS, Chen ZH, et al. Comparisons of the relationships between multiple lipid indices and diabetic kidney disease in patients with Type 2 diabetes: a cross-sectional study. Front Endocrinol (Lausanne). 2022;13:888599. doi: 10.3389/fendo.2022.888599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Berneis KK, Krauss RM.. Metabolic origins and clinical significance of LDL heterogeneity. J Lipid Res. 2002;43(9):1363–1379. doi: 10.1194/jlr.r200004-jlr200. [DOI] [PubMed] [Google Scholar]
- 30.Yang Q, Zou Y, Lang Y, et al. Estimated small dense low-density lipoprotein-cholesterol and the risk of kidney and cardiovascular outcomes in diabetic kidney disease. Ren Fail. 2024;46(2):2369701. doi: 10.1080/0886022X.2024.2369701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Roumeliotis S, Georgianos PI, Roumeliotis A, et al. Oxidized LDL modifies the association between proteinuria and deterioration of kidney function in proteinuric diabetic kidney disease. Life (Basel, Switzerland). 2021;11(6):504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Yamamoto N, Toyoda M, Abe M, et al. Lectin-like oxidized LDL receptor-1 (LOX-1) expression in the tubulointerstitial area likely plays an important role in human diabetic nephropathy. Intern Med. 2009;48(4):189–194. doi: 10.2169/internalmedicine.48.1251. [DOI] [PubMed] [Google Scholar]
- 33.Gutwein P, Abdel-Bakky MS, Doberstein K, et al. CXCL16 and oxLDL are induced in the onset of diabetic nephropathy. J Cell Mol Med. 2009;13(9b):3809–3825. doi: 10.1111/j.1582-4934.2009.00761.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Russo GT, De Cosmo S, Viazzi F, et al. Plasma triglycerides and HDL-C levels predict the development of diabetic kidney disease in subjects with Type 2 diabetes: the AMD annals initiative. Diabetes Care. 2016;39(12):2278–2287. doi: 10.2337/dc16-1246. [DOI] [PubMed] [Google Scholar]
- 35.Wang B, Jiang C, Qu Y, et al. Nonlinear association between atherogenic index of plasma and chronic kidney disease: a nationwide cross-sectional study. Lipids Health Dis. 2024;23(1):312. doi: 10.1186/s12944-024-02288-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Xu J, Zhou H, Xiang G.. Association of atherogenic index of plasma with retinopathy and nephropathy in adult patients with Type 2 diabetes mellitus aged > 18 years. Can J Diabetes. 2022;46(7):708–714. doi: 10.1016/j.jcjd.2022.04.008. [DOI] [PubMed] [Google Scholar]
- 37.Zhang J, Liu C, Peng Y, et al. Impact of baseline and trajectory of the atherogenic index of plasma on incident diabetic kidney disease and retinopathy in participants with type 2 diabetes: a longitudinal cohort study. Lipids Health Dis. 2024;23(1):11. doi: 10.1186/s12944-024-02003-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Zhu L, Lv T, Song S, et al. Association of atherogenic index of plasma with kidney dysfunction in diabetic individuals: findings from two national population-based studies. BMC Endocr Disord. 2025;25(1):105. doi: 10.1186/s12902-025-01925-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Li Z, Huang Q, Sun L, et al. Atherogenic index in Type 2 diabetes and its relationship with chronic microvascular complications. Int J Endocrinol. 2018;2018:1765835–1765839. doi: 10.1155/2018/1765835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Che B, Zhong C, Zhang R, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34. doi: 10.1186/s12933-023-01762-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Su K, Yi B, Yao BQ, et al. Liraglutide attenuates renal tubular ectopic lipid deposition in rats with diabetic nephropathy by inhibiting lipid synthesis and promoting lipolysis. Pharmacol Res. 2020;156:104778. doi: 10.1016/j.phrs.2020.104778. [DOI] [PubMed] [Google Scholar]
- 42.Tsutsui H, Kinugawa S, Matsushima S.. Oxidative stress and heart failure. Am J Physiol Heart Circ Physiol. 2011;301(6):H2181–90. doi: 10.1152/ajpheart.00554.2011. [DOI] [PubMed] [Google Scholar]
- 43.Navab M, Anantharamaiah GM, Reddy ST, et al. HDL as a biomarker, potential therapeutic target, and therapy. Diabetes. 2009;58(12):2711–2717. doi: 10.2337/db09-0538. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Tabet F, Rye KA.. High-density lipoproteins, inflammation and oxidative stress. Clin Sci (Lond). 2009;116(2):87–98. doi: 10.1042/CS20080106. [DOI] [PubMed] [Google Scholar]
- 45.Zhou H, Tan KC, Shiu SW, et al. Increased serum advanced glycation end products are associated with impairment in HDL antioxidative capacity in diabetic nephropathy. Nephrol Dial Transplant. 2008;23(3):927–933. doi: 10.1093/ndt/gfm631. [DOI] [PubMed] [Google Scholar]
- 46.Drew BG, Duffy SJ, Formosa MF, et al. High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus. Circulation. 2009;119(15):2103–2111. doi: 10.1161/CIRCULATIONAHA.108.843219. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study utilized publicly available datasets for analysis. The data is accessible through the following link: https://www.cdc.gov/nchs/nhanes.