Preamble

Association Between Atherogenic Index of Plasma and New-Onset Chronic Kidney Disease

Abstract

Objective: To investigate the association between the Atherogenic Index of Plasma (AIP) and the risk of new-onset chronic kidney disease (CKD) in a community-based population.

Methods: This study utilized data from a longitudinal cohort. Participants were categorized into quartiles based on their baseline AIP levels. The primary outcome was new-onset CKD, defined as an estimated glomerular filtration rate (eGFR) < 60 $mL/min/1.73 m^2$ or the presence of proteinuria during follow-up. Cox proportional hazards regression models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between AIP and CKD.

Results: A total of 85,253 participants were included in the final analysis. During a median follow-up period of 13.97 years, 18,175 cases of new-onset CKD were identified. After adjusting for potential confounders, including age, sex, body mass index (BMI), blood pressure, and glucose levels, higher AIP levels were significantly associated with an increased risk of CKD. Compared to the lowest quartile (Q1), the adjusted HR for the highest quartile (Q4) was 1.51 (95% CI: 1.43–1.59). Sensitivity analyses and subgroup analyses further confirmed the robustness of these findings.

Conclusion: A higher AIP is independently associated with an increased risk of new-onset CKD. AIP may serve as a valuable and simple clinical marker for identifying individuals at high risk of developing chronic kidney disease in primary care settings.

Introduction

Chronic kidney disease (CKD) has become a major global public health challenge due to its high prevalence and its significant contribution to the burden of cardiovascular disease and end-stage renal disease. Early identification of modifiable risk factors is crucial for the prevention and management of CKD. While traditional lipid parameters such as total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) are established risk factors for cardiovascular disease, their relationship with CKD remains complex and sometimes inconsistent.

The Atherogenic Index of Plasma (AIP), calculated as the logarithm of the ratio of molar concentrations of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ($\log_{10}(TG/HDL-C)$), has emerged as a comprehensive marker of lipoprotein particle size and lipid metabolism.

Affiliations

Department of Cardiology, Tangshan Workers' Hospital, Tangshan, Hebei Province; Department of Interventional Radiology, Tangshan Workers' Hospital, Tangshan, Hebei Province; Department of Cardiology, Kailuan General Hospital, Tangshan, Hebei Province; School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei Province.

Background

Chronic kidney disease (CKD) represents an increasingly severe global public health challenge, characterized by a rising annual incidence and a strong correlation with the development of atherosclerosis. The Atherogenic Index of Plasma (AIP), a straightforward metric derived from lipid profiles, has been demonstrated as an effective predictor of cardiovascular event risk. However, research regarding the specific relationship between AIP and the risk of developing CKD remains insufficient, necessitating further investigation.

Objective: To investigate the correlation between AIP and the incidence of new-onset CKD.

Methods

This prospective cohort study selected employees of the Kailuan Group in Tangshan City, Hebei Province, who underwent health examinations between June 2006 and October 2007 as the initial study population ($n = 101,510$; including 81,110 males and 20,400 females, aged 18–98 years). After screening based on specific inclusion and exclusion criteria, a total of 85,253 participants were included in the final analysis.

The study subjects were divided into four groups based on baseline Atherogenic Index of Plasma (AIP) quartiles: the Q1 group (AIP < -0.58), the Q2 group (-0.58 ≤ AIP < -0.17), the Q3 group (-0.17 ≤ AIP < 0.29), and the Q4 group (AIP ≥ 0.29). Participants were followed until December 31, 2021, with the primary endpoint defined as the development of new-onset chronic kidney disease (CKD).

Cumulative incidence curves were constructed using the Kaplan-Meier method, and differences between groups were compared using the Log-rank test. The association between AIP and the risk of CKD was analyzed using Cox proportional hazards regression models.

Results

During a median follow-up period of 13.97 (13.53, 14.17) years, a total of 18,175 patients developed chronic kidney disease (CKD). As the Atherogenic Index of Plasma (AIP) increased across the quartiles (Q1 to Q4), the cumulative incidence rates of CKD were 16.87%, 21.49%, 22.31%, and 24.47%, respectively. The corresponding incidence densities were 13.48, 17.83, 18.56, and 20.77 per 1,000 person-years.

After adjusting for relevant confounding factors, Cox proportional hazards regression analysis demonstrated that, compared with the Q1 group, the hazard ratios (HRs) for developing CKD in the Q2–Q4 groups were 1.24 (1.18–1.29), 1.26 (1.21–1.33), and 1.51 (1.43–1.59), respectively ($P < 0.001$). Further sensitivity analyses were conducted by separately excluding patients who developed CKD within the first two years of follow-up, patients who experienced all-cause mortality during the follow-up period, patients taking antihypertensive, hypoglycemic, or lipid-lowering medications at baseline, and patients who suffered myocardial infarction or stroke during the follow-up period. These analyses revealed that the risk in the Q4 group remained consistent with the primary analysis, indicating the robustness of the results.

Subgroup analyses indicated significant interactions between AIP and several variables, including age, sex, BMI, history of hypertension, and smoking status ($P < 0.001$). Specifically, the association between AIP and CKD risk was more pronounced in subgroups consisting of individuals aged <60 years, males, those with obesity, and those with a history of smoking ($P < 0.05$).

Conclusion

High Atherogenic Index of Plasma (AIP) is an independent risk factor for new-onset chronic kidney disease (CKD) and can predict the risk of developing CKD earlier than a Body Mass Index (BMI) $\ge 28 \text{ kg/m}^2$.

Keywords: Renal Insufficiency, Chronic; Chronic Kidney Disease; Atherogenic Index of Plasma; Risk Factors; Cox Model

Introduction

Chronic Kidney Disease (CKD) has become a major global public health challenge due to its increasing prevalence and the significant burden it places on healthcare systems. Identifying modifiable risk factors and early predictors is crucial for the primary prevention of renal decline. While obesity, typically measured by Body Mass Index (BMI), is a well-established risk factor for metabolic and renal diseases, it may not fully capture the metabolic complexities associated with lipid-driven renal damage.

The Atherogenic Index of Plasma (AIP), calculated as the logarithm of the ratio of molar concentrations of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), has emerged as a sensitive marker for lipoprotein particle size and a robust indicator of cardiovascular risk. Recent evidence suggests that dyslipidemia plays a critical role in the pathogenesis of CKD through mechanisms such as oxidative stress, inflammation, and lipid nephrotoxicity. However, the longitudinal relationship between AIP and the development of new-onset CKD, particularly in comparison to traditional markers like BMI, requires further investigation.

This study aims to evaluate whether a high AIP serves as an independent risk factor for new-onset CKD and to determine its predictive value relative to clinical obesity (BMI $\ge 28 \text{ kg/m}^2$) using a Cox proportional hazards model.

[TABLE:1]

Methods and Materials

The study utilized longitudinal data to track the incidence of CKD among participants. Chronic kidney disease was defined based on established clinical guidelines, focusing on the sustained reduction of the estimated glomerular filtration rate (eGFR) or the presence of albuminuria. The Atherogenic Index of Plasma was calculated using the formula: $\text{AIP} = \log_{10}(\text{TG}/\text{HDL-C})$.

Statistical analysis was performed using the Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for new-onset CKD. Adjustments were made for confounding variables.

Background

Chronic kidney disease (CKD) is a major global public health problem, and its increasing prevalence is closely linked to atherosclerosis. Zhang X C, Qi Q, Wu X Y, et al. The association between the atherogenic index of plasma and new-onset chronic kidney disease [J]. Chinese General Practice, 2025. [Epub ahead of print].

Editorial Office of Chinese General Practice. This is an open access article under the CC BY-NC-ND 4.0 license.

The atherogenic index of plasma (AIP), a simple lipid-based indicator, has demonstrated predictive value for cardiovascular diseases. However, studies examining the association between AIP and the risk of developing CKD remain limited.

Objective: To investigate the association between the AIP and new-onset CKD.

Methods

This prospective cohort study utilized data from 101,510 employees of the Kailuan Group in Tangshan, Hebei Province, who underwent health examinations between June 2006 and October 2007. After applying inclusion and exclusion criteria, 85,253 participants were enrolled. Participants were stratified into four groups (Q1 to Q4) based on baseline AIP quartiles: Q1 (AIP < -0.58), Q2 (-0.58 ≤ AIP < -0.17), Q3 (-0.17 ≤ AIP < 0.29), and Q4 (AIP ≥ 0.29). Follow-up continued until December 31, 2021, with new-onset CKD as the endpoint. The cumulative incidence of CKD was plotted using the Kaplan-Meier method, and intergroup differences were assessed with the log-rank test. The association between AIP and CKD was evaluated using Cox proportional hazards regression models.

Results

A total of 18,175 patients developed CKD during 13.97 (13.53, 14.17) years of follow-up. With increasing AIP, the cumulative incidence of CKD in the Q1-Q4 groups was 16.87%, 21.49%, 22.31%, and 24.47%, with incidence densities of 13.48/1,000 person-years, 17.83/1,000 person-years, 18.56/1,000 person-years, and 20.77/1,000 person-years, respectively. After correcting for relevant confounders, the hazard ratios for incident CKD were 1.24 (1.18–1.29), 1.26 (1.21–1.33), and 1.51 (1.43–1.59) in the Q2-Q4 groups compared with the Q1 group ($P < 0.001$). Further analysis showed that when patients with CKD events in the first 2 years of follow-up, patients with all-cause mortality events during follow-up, patients taking antihypertensive, hypoglycaemic, and lipid-lowering medications at baseline, and patients with myocardial infarction or stroke during follow-up were excluded, the risk of the Q4 group was similar to the results of the main analysis, indicating robustness. Subgroup analyses showed significant interactions for age, sex, BMI, history of hypertension, and history of smoking ($P_{interaction} < 0.001$).

Introduction

Global epidemiological statistics indicate that chronic kidney disease (CKD) has become one of the most significant threats to human health. As of 2017, approximately 698 million people worldwide were suffering from CKD, with China accounting for nearly 20% of the global prevalence. CKD significantly increases the risk of end-stage renal disease, renal anemia, atherosclerotic cardiovascular disease (ASCVD), and mortality \cite{1-3}. Consequently, the early identification of risk factors associated with the onset of CKD has become an urgent priority to reduce its incidence.

Research has demonstrated that increased arteriosclerosis (AS), as reflected by elevated brachial-ankle pulse wave velocity (BaPWV), increases the risk of developing new-onset CKD. Furthermore, the atherogenic index of plasma (AIP) is a known risk factor for the occurrence and progression of AS \cite{5-6}. Elevated AIP levels are positively correlated with an increased risk of ASCVD and serve as an independent risk factor for new-onset ischemic stroke. Notably, this pathogenic effect may begin to manifest in the early stages (within 2 to 4 years).

Given that AIP acts as an upstream risk factor for AS, we hypothesize that AIP may predict the risk of CKD onset even earlier than BaPWV. If this holds true, assessing and intervening in AIP levels before the formation of AS could significantly and effectively reduce the risk of CKD. This study aims to determine whether the application of AIP can achieve an effective assessment of CKD risk prior to the clinical manifestation of AS.

1.1 Study Population

The study population consisted of active and retired employees of the Kailuan Group who underwent health examinations at Kailuan General Hospital and its 11 affiliated hospitals in Tangshan, Hebei Province, between June 2006 and October 2007. A total of 101,510 individuals were initially screened (81,110 males and 20,400 females, aged 18–98 years). The inclusion criteria were: (1) participation in the 2006–2007 (first) health examination; (2) normal cognitive ability with the capacity to independently complete the questionnaires; and (3) agreement to participate in the study and signing of informed consent. The exclusion criteria were: (1) missing or extreme values for triglyceride (TG) or high-density lipoprotein cholesterol (HDL-C); (2) a prior diagnosis of chronic kidney disease (CKD); and (3) a history of malignant tumors. After excluding 1,472 cases with missing or extreme data, 14,484 cases with a history of CKD, and 301 cases with a history of cancer, a final total of 85,253 participants were included in the study. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Kailuan General Hospital (Approval No. [2006] Yi Lun Zi No. 5). All participants provided informed consent.

Detailed information regarding the collection of demographic characteristics, general clinical data, laboratory indicators, and the overall study design has been described in previously published literature by our research group. For the collection of laboratory data, 5 mL of fasting venous blood was collected from the antecubital vein of each participant in the morning and placed in ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes. After collection, the blood samples were allowed to stand at room temperature for 30 minutes and then centrifuged at 3,000 rpm for 10 minutes. The supernatant serum was collected for analysis. Subgroup analyses indicated that the association between the Atherogenic Index of Plasma (AIP) and the risk of the target outcome was more significant in subgroups defined by age < 60 years, sex, and BMI $\ge 28$ kg/m$^2$ ($P < 0.05$).

Conclusion

An elevated AIP is an independent risk factor for new-onset CKD and serves as an early predictor of its development.

Key words: Renal insufficiency, chronic; Chronic kidney disease; Atherogenic index of plasma; Risk factors;

Serological indicators were measured within 4 hours using a Beckman automated biochemical analyzer. The relevant definitions and diagnostic criteria are as follows:

1.3.1 CKD Diagnostic Criteria

According to the Clinical Practice Guidelines \cite{10}, an estimated glomerular filtration rate (eGFR) < 60 mL·min$^{-1}$·(1.73 m$^2$)$^{-1}$ for more than 3 months, regardless of whether there is evidence of kidney damage, is sufficient to diagnose Chronic Kidney Disease (CKD).

Assessment of eGFR: Serum creatinine (Scr) is measured using the sarcosine oxidase method. The eGFR is then calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, as shown in Table 1 [TABLE:1].

• Female $\leq 62$: $144 \times (S_{cr}/62)^{-0.329} \times (0.993)^{age}$
• Female $> 62$: $144 \times (S_{cr}/62)^{-1.209} \times (0.993)^{age}$
• Male $\le 80$: $141 \times (Scr/80)^{-0.411} \times (0.993)^{age}$
• Male $> 80$: $141 \times (Scr/80)^{-1.209} \times (0.993)^{age}$

Note: Scr = serum creatinine; eGFR = estimated glomerular filtration rate.

1.3.2 AIP Calculation and Grouping

The logarithmic transformation of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) was used to calculate the Atherogenic Index of Plasma (AIP), according to the formula: $AIP = \log_{10}(TG/HDL-C)$.

Based on the baseline AIP quartiles, the study subjects were divided into four groups: the Q1 group ($AIP < -0.58$), the Q2 group ($-0.58 \leq AIP < -0.17$), the Q3 group ($-0.17 \leq AIP < 0.29$), and the Q4 group ($AIP \geq 0.29$) \cite{13-14}.

1.3.3 Diagnosis of Hypertension, Diabetes, and Dyslipidemia

Diagnosis was based on Chinese national guidelines \cite{15-17}. Income levels were categorized into two groups based on monthly per capita income: $\ge 800$ RMB and $< 800$ RMB. Educational attainment was divided into two categories: high school or above, and junior high school or below. Physical exercise was defined as exercising $\ge 3$ times per week, with each session lasting $\ge 30$ minutes. A history of smoking was defined as smoking an average of $\ge 1$ cigarette per day for a duration of $\ge 1$ year. Body Mass Index (BMI) was calculated as weight (kg) divided by the square of height ($m^2$).

The baseline for this study was established using the health examination data from 2006–2007, marking the start of the follow-up period. The follow-up endpoints were defined as the new onset of Chronic Kidney Disease (CKD), death, or the conclusion of the follow-up period on December 31, 2021.

If the study subject did not experience the endpoint event, the follow-up termination date was set as December 31, 2021. If a subject died during the follow-up period without experiencing the endpoint event, the follow-up termination date was recorded as the date of death.

Statistical Methods

Statistical analyses were performed using SAS software (version 9.4). Quantitative data following a normal distribution are expressed as mean ± standard deviation ($\bar{x} \pm s$), and comparisons between groups were conducted using one-way analysis of variance (ANOVA). Quantitative data with a non-normal distribution are presented as medians and interquartile ranges [M (Q1, Q3)], with intergroup comparisons performed using non-parametric rank-sum tests. Categorical data are expressed as frequencies or percentages, and comparisons between groups were analyzed using the $\chi^2$ test.

Cumulative incidence curves were constructed using the Kaplan-Meier method, and differences between groups were compared using the log-rank test. The correlation between the Atherogenic Index of Plasma (AIP) and Chronic Kidney Disease (CKD) was analyzed using Cox proportional hazards regression models. A two-tailed $P < 0.05$ was considered to indicate a statistically significant difference.

2 Results

2.1 Comparison of General Characteristics Among Study Groups

The total study population consisted of 85,253 participants, including 68,563 males (80.42%) and 16,690 females (19.58%), with a mean age of $50.75 \pm 12.15$ years. Statistically significant differences ($P < 0.05$) were observed among the four groups across all baseline characteristics, including AIP, sex, age, BMI, uric acid (UA), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), educational level, average monthly income, physical exercise, smoking history, alcohol consumption history, history of hypertension, history of diabetes, dyslipidemia, and the use of antihypertensive, hypoglycemic, or lipid-lowering medications. These results are detailed in [TABLE:2].

2.2 Comparison of CKD Incidence Among Study Groups

During a median follow-up period of 13.97 (13.53, 14.17) years, a total of 18,175 participants developed chronic kidney disease (CKD), resulting in an incidence rate of 26.51%. As AIP levels increased across the four groups, there was a corresponding progressive increase in the proportion of new-onset CKD cases, incidence density, and cumulative incidence. These trends were statistically significant ($P < 0.05$), as shown in [TABLE:3] and [FIGURE:1].

2.3 Risk Analysis

The assessment of disease risk constitutes a fundamental component of modern epidemiological research and clinical preventive medicine. By leveraging machine learning and deep learning architectures, researchers can now integrate multi-dimensional data—including genetic markers, environmental exposures, and lifestyle factors—to construct highly accurate predictive models. These models are designed to identify high-risk individuals at an early stage, thereby facilitating timely clinical interventions and personalized healthcare strategies.

After adjusting for confounding factors—including age, gender, smoking history, physical activity, income level, educational attainment, history of diabetes, dyslipidemia, and hypertension, use of antihypertensive, hypoglycemic, and lipid-lowering medications, as well as UA, BMI, SBP, DBP, TG, LDL-C, HDL-C, and Hs-CRP—the results indicated that compared to the Q1 group, the hazard ratios (HRs) for the incidence of CKD in groups Q2 through Q4 were 1.24 (1.18–1.29), 1.26 (1.21–1.33), and 1.51 (1.43–1.59), respectively ($P < 0.001$). These findings are detailed in [TABLE:4].

Restricted cubic spline analysis revealed a non-linear association between the Atherogenic Index of Plasma (AIP) and the risk of CKD ($P_{non-linearity} < 0.001$, $P_{overall} < 0.001$). Specifically, the risk of CKD increased significantly when the AIP exceeded -0.12, as shown in [FIGURE:2].

To assess the robustness of the findings, sensitivity analyses were conducted by sequentially excluding patients who developed CKD during the first two years of follow-up ($n = 4,542$), patients who experienced all-cause mortality during the follow-up period ($n = 12,362$), patients taking antihypertensive, hypoglycemic, or lipid-lowering medications at baseline ($n = 9,944$), and patients who suffered myocardial infarction or stroke during the follow-up period.

Incidence of CKD [n (%)]: Q1: 3,602 (16.90), density 13.48; Q2: 4,595 (21.56), density 17.83; Q3: 4,756 (22.31), density 18.56; Q4: 5,222 (24.50), density 20.77. $P$-trend values: <0.001.

After excluding specific populations, the risk associated with the Q4 group remained consistent with the primary analysis, demonstrating the robustness of the results (see [TABLE:5]). Subgroup analyses were further performed based on age, gender, BMI, hypertension, diabetes, smoking history, and alcohol consumption. In the analysis of CKD as the primary outcome, significant interactions were observed between AIP and the subgroups of age, gender, BMI, history of hypertension, and smoking history ($P < 0.001$).

3 Discussion

The Atherogenic Index of Plasma (AIP) is calculated based on the values of triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C). It serves as a significant indicator for assessing lipid metabolism and cardiovascular risk.

[TABLE:1]

Note: AIP = Atherogenic Index of Plasma; UA = Uric Acid; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; FBG = Fasting Blood Glucose; TG = Triglycerides; TC = Total Cholesterol; HDL-C = High-Density Lipoprotein Cholesterol; LDL-C = Low-Density Lipoprotein Cholesterol; Hs-CRP = High-sensitivity C-reactive protein. Data are presented as mean ± standard deviation, median (interquartile range), or n (%).

While lipid indicators reflect the association between pro-atherogenic and protective lipoproteins, they are also related to the particle size of pre-atherogenic and anti-atherogenic lipoproteins. As the primary components of blood lipids, triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) exert a significant influence on the occurrence and progression of atherosclerosis (AS).

A previous study involving 64,574 participants analyzed the impact of TG on acute myocardial infarction (AMI). Following a follow-up period of 10.92 years, the results demonstrated that elevated TG levels are an independent risk factor for AMI in the population. Even when TG levels were within the normal reference range ($\le 1.66$ mmol/L), prolonged cumulative exposure to TG still increased the risk of AMI. This confirms the pro-atherogenic role of TG in real-world settings. The atherogenic effect of TG stems from remnant lipoprotein cholesterol, which is composed of intermediate-density lipoprotein cholesterol (IDL-C), very-low-density lipoprotein (VLDL) remnants, and chylomicron remnants.

Lipid metabolism is a continuous and interactive process. Smaller lipoprotein cholesterol particles are rich in TG; lipoprotein lipase hydrolyzes these remnant lipoprotein cholesterols into denser lipoprotein cholesterol, which then exerts its pro-atherogenic effects. Recent research has also confirmed that the high incidence risk of atherosclerotic cardiovascular disease (ASCVD) is associated with an increase in cholesterol within TG-rich lipoproteins.

Another study involving 92,297 participants investigated the correlation between HDL-C and ASCVD. After an average follow-up of 7.9 years, the results indicated that low levels of HDL-C ($< 1.23$ mmol/L) increase the risk of ASCVD.

The aforementioned research findings indicate that both increased TG levels and decreased HDL-C levels are positively correlated with the occurrence and development of ASCVD. Their combined application, the Atherogenic Index of Plasma (AIP), can better reflect the impact of lipid levels on the risk of AS development and can predict ischemic cardiovascular and cerebrovascular events earlier than the onset of AS \cite{7-8, 21}. AS increases the risk of new-onset CKD, which is consistent with the results of this study. As AIP increases, the risk of new-onset CKD also rises. Specifically, compared with the Q1 group, the Hazard Ratios (HR) for CKD in the Q2–Q4 groups were 1.24, 1.26, and 1.51, respectively ($P < 0.001$).

In organs characterized by low resistance and high flow systems, high pulsatility can lead to glomerular damage, hypoxia, and fibrosis, ultimately resulting in a decreased glomerular filtration rate and accelerated progression of chronic kidney disease (CKD). The repeated minor physical mechanical pressure of elevated blood pressure on the inner walls of arterial vessels is a primary risk factor for arterial stiffness (AS). Research has demonstrated that evaluating AS via Brachial-Ankle Pulse Wave Velocity (BaPWV) is significantly superior to blood pressure indicators for predicting the risk of atherosclerotic cardiovascular disease (ASCVD).

Simultaneously, elevated TG levels, decreased HDL-C levels, and increased sdLDL are characteristic of dyslipidemia in diabetic patients. This lipid profile reduces insulin sensitivity and induces insulin resistance (IR), thereby increasing the residual risk of ASCVD in diabetic populations. A cross-sectional study on the relationship between AIP and diabetes found that diabetic individuals have higher AIP values than non-diabetic individuals. The correlation between AIP and IR can also explain why a high body mass index (BMI) increases the risk of CKD.

Clinical value and limitations of this study: This study utilizes a large cohort with a long follow-up period, which enhances the scientific rigor and reliability of the results regarding disease progression. Furthermore, this study is among the first to evaluate the risk of CKD onset using a numerical value derived from lipid components that reflects the degree of AS. Lipid testing is simple and accessible, and the AIP index offers excellent feasibility and reproducibility. However, some limitations exist: first, there is a gender imbalance in the study cohort. Second, the AIP value was derived from a single measurement.

In conclusion, as a calculated index derived from lipid profiles, AIP is an effective predictor of the risk of new-onset CKD. It significantly improves the methods for risk assessment in the primary prevention of CKD in clinical practice and substantially advances the window for intervention in high-risk populations.

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