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Association between atherogenic index of plasma and new-onset stroke in a population with cardiovascular-kidney-metabolic syndrome stages 0–3: insights from CHARLS

Cardiovascular Diabetology volume 24, Article number: 168 (2025) Cite this article

Abstract

Background

The associations between atherogenic index of plasma (AIP) and cardiovascular disease (CVD) have been widely reported; However, such association to the incidence of stroke in the population with Cardiovascular-Kidney-Metabolic (CKM) syndrome remains ambiguous.

Methods

A total of 7754 participants with CKM syndrome stages 0–3 from the China Health and Retirement Longitudinal Study were enrolled in this study. The incidence of new-onset stroke events was the primary outcome of this study. We used Kaplan-Meier survival curves and Cox proportional hazards models to explore the association between baseline AIP levels and the risk of stroke in the population with CKM syndrome stages 0–3. Additionally, we utilized restricted cubic spline plots to analyze the form of this association.

Results

During a median follow-up of 6.8 years, 455 participants (5.9%) with CKM syndrome experienced their first stroke events. AIP was positively associated with the risk of stroke in the population with CKM syndrome stages 0–3. Kaplan–Meier curves analysis demonstrated a significant difference in stroke incidence across the AIP groups among the entire cohort. In the fully adjusted Model 3, the results revealed a significantly elevated risk of stroke for participants in the Q2, Q3, and Q4 groups compared to those in the Q1 group, with respective HR (95% CI) value of 1.352 (1.009–1.811), 1.421 (1.064–1.897), and 1.414 (1.052–1.900). Restricted cubic spline plots revealed the association of AIP and stroke risk was nonlinear (P-overall < 0.05, P-non-linear < 0.05), with inflection points of 0.32.

Conclusion

This study provides evidence that baseline AIP levels were significantly positively associated with the risk of stroke in individuals with CKM syndrome stages 0–3, and AIP may serve as an effective risk marker for early identification of high-risk individuals prone to stroke within the CKM stages syndrome 0–3 population.

Graphical abstract

Introduction

Stroke ranks among the top causes of mortality globally and is also a significant factor leading to disability [1]. In 2021, it has led to 7.3 million deaths and 160.5 million disability-adjusted life-years (DALYs) worldwide [2]. Furthermore, it is estimated that the annual economic burden of stroke amounts to $73.7 billion [3]. Given the growing recognition of the intricate interplay among obesity, metabolic disorders, chronic kidney disease (CKD), and cardiovascular dysfunction, CKM syndrome population may be at an enhanced risk for cardiovascular disease (CVD) [4,5,6]. The CKM syndrome, as defined by the American Heart Association (AHA), is a systemic disorder, which results in dysfunction of multiple organs and an increased risk of CVD [7]. Additionally, studies have shown that the most significant clinical burden of CKM syndrome is disproportionately associated with CVD, with stroke, as one of the common manifestations of CVD, accounting for a considerable proportion [8]. Based on this, to prevent the progression of CVD in CKM syndrome and to reduce the significant clinical burden it imposes, the AHA emphasizes that research should be focused on the preclinical stages (Stages 0–3) of this population. Therefore, it is urgent to explore cost-effective and reproducible indicators to improve identify patients at high risk of stroke in the early stages [9].

The AIP, calculated by taking the logarithm of the ratio of triglycerides to high-density lipoprotein cholesterol, functions as a highly sensitive biomarker of lipoprotein profiles [10]. Research has shown that AIP plays a significant role in assessing the CVD risk [11, 12]. Moreover, AIP has considerable predictive value for intracranial arterial stenosis, ischemic stroke risk, and adverse outcomes following stroke [13,14,15,16]. However, given the complexity of the interactions within CKM syndrome, the potential of AIP to predict stroke in patients with CKM syndrome has not been fully explored.

Therefore, considering the significant impact of CKM syndrome on the progression of CVD, it is crucial to explore the association between AIP and the incidence of stroke among the population with CKM syndrome stages 0–3 [17, 18]. Revealing this association can help refine stroke intervention strategies and improve the effectiveness of stroke prevention efforts.

Methods

Study population

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study that focuses on Chinese adults aged 45 and above. CHARLS adopted a multi-stage stratified probability-proportional-to-size sampling approach, recruiting participants from urban and rural regions across 28 provinces and 150 counties in China, ensuring a diverse and comprehensive sample [19].Data collection was carried out by well-trained fieldwork personnel. They conducted face-to-face interviews with the participants, utilizing standardized questionnaires to confirm the consistency and reliability of the data. All participants provided written informed consent. CHARLS was conducted in compliance with the Declaration of Helsinki and has been granted ethical clearance by the Institutional Review Board at Peking University (IRB00001052-11015). For this study, participants who were interviewed between 2011 and 2012 were designated as the baseline of the cohort and were subsequently followed up in the years 2013, 2015, and 2018.

Figure 1 shows the strict inclusion and exclusion criteria of this study. Firstly, 2597 participants with pre-existing CVD, 247 with incomplete CVD data at baseline, and 3317 with lost stroke follow-up data were excluded. Furthermore, 20 participants lacking age data and 266 under the age of 45 were eliminated. Additionally, 3433 participants without AIP data were removed, along with 74 whose AIP values exceeded three standard deviations (SD) above the mean. Finally, this study enrolled 7754 participants.

Fig. 1
figure 1

Flowchart of the study participants

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Data collection

The data collected around the objectives of this study cover the following aspects: (1) demographic information: age, gender, marital status and education levels; (2) health status and functioning: smoking status, drinking status, hypertension, hypertension medication, dyslipidemia, dyslipidemia medication, diabetes, diabetes medication; (3) body measurements: height, weight, waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP); (4) laboratory test data: cholesterol (TC), total high-density lipoprotein cholesterol (HDL-c), Glycated Hemoglobin A1c (HbA1C), serum creatinine (Scr), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c), fasting blood glucose(FBG).

Definitions

The AIP was calculated by using the formula log (TG/HDL-C) [10].The staging of CKM Syndrome is classified according to the AHA Presidential Advisory Statement. The staging of CKM syndrome is as follows: Stage 0 implies the absence of CKM syndrome risk factors; Stage 1 is prominently marked by excessive fat accumulation or disordered fat metabolism; Stage 2 encompasses metabolic risk factors such as metabolic syndrome, hypertension, type 2 diabetes mellitus, high triglycerides, or the presence of CKD; Stage 3 is typically characterized by subclinical cardiovascular disease or very high-risk CKD (stage G4 or G5) [7]. For the purpose of staging, very high-risk CKD (stage G4 or G5) and a High 10-year CVD risk predicted by the Framingham Risk Score, were regarded as equivalent factors for the risk of subclinical CVD [7, 20]. The specific staging criteria are detailed in Table S6.

Hypertension was diagnosed when the average SBP/DBP reached 140/90 mmHg or higher, or when there was a self-reported diagnosis by a physician, or when any antihypertensive medications were being used [21]. Diabetes was defined as an FBG level of 126 mg/dl or above, or a HbA1c level of 6.5% or higher, or a self-reported diagnosis by a physician, or the use of hypoglycemic medications [22].

Outcome ascertainment

The incidence of first stroke events is the primary outcome of this study within the follow-up duration. The following questions were used to evaluate self-reported stroke events: “Have you been diagnosed with stroke by a doctor”; Determine the specific time of stroke events based on the participants’ responses to the relevant questions: “When was the stroke first diagnosed or known by yourself?”, “When was your most recent stroke?”. The study followed up all the participants starting from the baseline and conducted four follow-up sessions. The follow-up continued until either a stroke occurred or 2018 arrived, whichever came first.

Statistical analysis

The participants were categorized into four groups based on baseline AIP quartile: Quartile 1 (Q1) AIP ≤ 0.12; Quartile 2 (Q2) AIP ≤ 0.32; Quartile 3 (Q3) ≤ 0.54; and Quartile 4 (Q4) > 0.54. To enhance the reliability of the study, AIP was analyzed not only as a categorical variable but also as a continuous variable. We described the characteristics of the participants included in the study: continuous variables are presented as mean ± standard deviation, and categorical variables are presented by frequency and proportion.

The Kaplan-Meier curve was used to estimate the cumulative incidence of stroke based on AIP groups, with differences assessed using the log-rank test. Additionally, Cox proportional hazards regression was applied to explore the association between baseline AIP levels and the new-onset stroke risk, calculating the hazard ratio (HR) and 95% confidence interval (CI). Before analysis, the proportional hazards assumption was visually examined by assessing the Schoenfeld residuals. The multicollinearity test revealed that all covariates had a variance inflation factor (VIF) below 5 (Table S5), indicating the absence of substantial multicollinearity among the covariates [23]. Additionally, restricted cubic splines (RCS) analysis, incorporating multivariable-adjusted Cox regression, was performed to illustrate the linear or nonlinear relationship between baseline AIP levels and stroke risk. The missing data for the participants in this study were shown in Table S1, and multiple imputation was used to estimate the missing values, assuming the data were missing at random. We conducted all analyses with R statistical software version 4.4.1 (R Foundation) and a two-sided P < 0.05 was regarded as statistically significant.

Results

General characteristics of participants

Table 1 presents the baseline clinical and demographic characteristics of participants grouped by AIP quartiles. The study enrolled 7754 participants, with 53.37% of them being female and a median age of 58.0 (IQR: 52.0–65.0) years. At baseline, participants in the higher AIP groups were generally younger, more likely to be married, and had lower rates of smoking and drinking compared to those in the lowest group. The prevalence of hypertension, metabolic syndrome, diabetes, and dyslipidemia was higher among participants in the upper AIP groups. Additionally, these participants exhibited elevated levels of SBP, DBP, BMI, FBG, TC, WC, HbA1cand TG, while their HDL-C levels were lower. Table S2 compared the general characteristics of included and excluded participants.

Table 1 Baseline characteristics of individuals classified by quartiles of AIP
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The association between AIP and the incidence of new-onset stroke in a population with CKM syndrome stages 0–3.

During an average follow-up period of 6.8 years, 455 participants (5.9%) experienced their first stroke event. The Kaplan-Meier cumulative incidence curve analysis showed a consistent increase in stroke incidence from the Q1 to Q4 groups, with a statistically significant difference (Fig. 2;log-rank test, P = 0.00024). Cox proportional hazards models indicated that baseline AIP was associated with a higher risk of new-onset stroke. Baseline AIP was assessed as both a continuous variable and a categorical variable (quartiles) in three Cox models (Table 2). In the unadjusted Model 1, a significant association between AIP and stroke risk was observed, with each 1-unit increase in AIP corresponding to a 98% higher risk of stroke (HR = 1.984, 95% CI: 1.499–2.626). After adjusting for potential confounders, Model 3 revealed that a 1-unit increase in baseline AIP was associated with a 48% higher risk of stroke (HR = 1.480, 95% CI: 1.091–2.009). In the fully adjusted Model 3, compared to the lowest quartile (Q1), the HR for stroke risk steadily increased across higher AIP quartiles: Q2 (HR = 1.352, 95% CI: 1.009–1.811), Q3 (HR = 1.421, 95% CI: 1.064–1.897), and Q4 (HR = 1.414, 95% CI: 1.052–1.900), aligning with the previous findings. Additionally, a significant trend of increasing stroke risk across the AIP quartiles was observed in Model 3 (p-trend < 0.05).

Table 2 Association between the AIP and stroke incidence in a population with CKM syndrome stages 0–3
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Fig. 2
figure 2

Kaplan–Meier analysis for stroke was based on AIP quartiles in a population with CKM syndrome stages 0–3

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To further investigate the potential linear or nonlinear relationship between AIP and stroke risk, we employed RCS analysis. Based on the Bayesian minimum value, 4 was determined to be the optimal number of knots. The RCS analysis demonstrated a nonlinear association between AIP and stroke risk in the population with CKM syndrome stages 0–3 (P-overall = 0.004; P-non-linear = 0.011), with an inflection point at 0.32. Similarly, a nonlinear pattern was observed in participants with stages 1–3 (P-overall < 0.001; P-non-linear = 0.002), showing an inflection point at 0.36 (Fig. 3).

Fig. 3
figure 3

RCS analysis between AIP and stroke incidence in a population with CKM syndrome stages 0-3 (A) or stages 1-3 (B). The model was adjusted for age, gender, marital status, drinking statues, smoking statues, BMI, SBP, DBP, hypertension, diabetes, TC, FBG, HbA1c

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Subgroup analysis

To explore the association between AIP and first stroke events across various demographic factors, we conducted subgroup and interaction analyses based on age, gender, drinking status, smoking status, and CKM syndrome stages. The results of subgroup analysis and interaction analysis clearly showed that within all subgroups divided by different factors, an increase in AIP levels was consistently associated with an increase in the incidence of stroke, with no significant interaction effects detected (p for interaction > 0.05) (Fig. 4).

Fig. 4
figure 4

Subgroup and interaction analyses of the association between AIP and stroke incidence in a population with CKM syndrome stages 0–3

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Sensitivity analyses

We conducted several sensitivity analyses to assess the reliability of our study. First, after excluding participants with a relatively large amount of missing data on SBP, DBP, WC, and BMI, further analysis indicated that the association between baseline AIP levels and stroke risk remained consistent with the primary findings (Table S3). In addition, recognizing that Stage 0 participants may have minimal metabolic or cardiovascular risk factors or may not meet clinically significant thresholds [24], we conducted an additional analysis excluding these participants, which also supported the primary results (Table S4). Lastly, we included stage 4 patients in the analysis but still excluded those with stroke (Table S7). The results showed consistency with the primary analysis.

Discussion

Based on baseline AIP assessments, we enrolled participants with CKM syndrome in stages 0–3 and employed Cox models to evaluate the association between AIP and new-onset stroke. This study revealed a significant association between elevated baseline AIP levels and increased risk of new-onset stroke across stages 0–3 of CKM syndrome, consistent with previous findings regarding AIP’s association with stroke risk and prognosis. Each 1-unit increase in AIP was associated with a 48% increased stroke risk, while participants in the highest AIP quartile demonstrated approximately 1.4-fold higher stroke risk compared to those in the lowest quartile. Importantly, our analysis identified a nonlinear relationship between AIP and stroke risk, with an inflection point at 0.32. These findings not only validate the clinical utility of AIP assessment in middle-aged and elderly CKM syndrome patients but also provide valuable insights for precise risk stratification in this population.

Previous studies have indicated that both cumulative AIP and elevated baseline levels are significantly linked to an increased risk of ischemic stroke in the general population [13, 15]. Nevertheless, considering the complex interplay among metabolic disorders, CKD, and CVD, it is crucial to examine the association between AIP and the first stroke incidence specifically within the context of CKM syndrome. The association between AIP and stroke observed in our study is consistent with other studies using CHARLS, but the magnitude and form of the association differ. For example, in terms of incident stroke outcomes, we estimated an HR of 1.480(95% CI:1.091–2.009) per 1-unit increase and 1.414(95% CI: 1.052–1.900) in Q4, whereas Zhai et al. reported an HR of 1.24 (95% CI:1.14–1.35) per 1 SD increase and 1.69 (95% CI: 1.32–2.16) in Q4 [25], which was higher than the estimates in our study. Furthermore, while the RCS analysis in Qu et al. demonstrated a linear relationship between baseline AIP levels and stroke risk across different glucose metabolism statuses [26], our findings revealed a nonlinear association, which is consistent with the reports of Zhang et al. [13]. The difference may stem from strict criteria and varied study populations. We adopted more stringent exclusion criteria, which comprehensively considered the history of CVD, and the use of antihypertensive and hypoglycemic drugs. Another reason could be that the extent of the association of AIP for stroke may vary among different populations, which further highlights the crucial importance of exploring stroke risk markers within the CKM syndrome population. In subgroup and interaction analyses, no statistically significant interactions were observed across demographic subgroups, indicating the robustness of our findings. Although statistical analyses showed no statistical significance (p > 0.05) in the female subgroup, the HR value was still greater than 1 and the p-value showed marginal significance. This suggests that the association may exist in the female subgroup, but due to data limitations, the association was not found to be statistically significant in this study. The differences observed in this study may be due to inherent gender differences between men and women as well as differences in their lifestyles. Firstly, inherent gender differences lead to an inconsistent risk of developing CVD. At any given age, the risk of CVD in women is one-third to one-half that of men [27, 28]. In addition, there are significant sex differences in lipid and lipoprotein metabolism, which to some extent may also explain the results of this study [29]. On the other hand, the prevalence of smoking and alcohol consumption was much higher in males than in females. Unhealthy lifestyles may amplify the atherogenic effect of AIP [30, 31]. Additionally, our findings showed that stroke rates were significantly higher in those under 65 years of age and in males compared with other population groups, highlighting the potential clinical value of using the AIP assessment as a valid risk stratification tool for these subgroups.

RCS analysis indicated that this association was non-linear, which suggested that different levels of the AIP in the CKM syndrome population might have had dynamic effects: before the inflection point, that is, when AIP < 0.32, it meant that mild lipid metabolism disorders (such as hypertriglyceridemia) might have significantly activated endothelial dysfunction, thereby promoting the formation of subclinical atherosclerotic plaques [32, 33]. At this stage, intervening in AIP (such as through lifestyle adjustments or lipid-lowering drug therapy) might have achieved the best results in stroke prevention. After the inflection point, namely when AIP ≥ 0.32, lipid toxicity might have caused irreversible vascular damage (such as thinning or calcification of the plaque fibrous cap). Meanwhile, the superimposed effects of mechanisms such as insulin resistance and oxidative stress might also have occurred, which would have weakened the effect of AIP [34, 35]. The HR for the risk of stroke was still greater than 1 at this point compared with the pre-inflection point, but the HR might no longer have increased significantly with elevated AIP.

Numerous studies have shown that metabolic disorders (including dyslipidemia and hyperglycemia) and insulin resistance (IR) are significant risk factors for stroke, while the CKM syndrome is closely associated with these factors [36,37,38]. AIP, as a reliable indicator of dyslipidemia, has been extensively utilized in CVD research, especially in the coronary artery disease [12, 39, 40]. From epidemiological perspective, AIP is strongly associated with IR and plays a significant role in predicting obesity [41], diabetes [42], and metabolic syndrome [43, 44], making it a crucial predictive indicator. Recent studies have further elucidated that an elevated AIP level is clearly associated with IR severity and progression to type 2 diabetes [45, 46]. Moreover, one study suggests that the impact of AIP on the risk of stroke may be particularly significant in populations with disorders of glucose metabolism [26]. Given these findings, investigating the association between AIP and stroke in CKM syndrome population is urgently needed. To our knowledge, this study holds significant importance in the context of CKM syndrome research. It is the first to focus specifically on the CKM syndrome population, evaluating the predictive value of baseline AIP levels for stroke risk and providing new insights and valuable references for stroke prevention and management.

Although the exact mechanism between AIP and stroke is not yet clear, several potential theoretical hypotheses have been proposed. First, AIP reflects atherogenic lipoprotein profiles, particularly small dense LDL (sdLDL) and small HDL particles, which promote atherosclerotic plaque formation [47,48,49]. As a substitute marker for sdLDL, elevated AIP indicates smaller LDL particle size and increased atherogenicity. Furthermore, atherosclerosis contributes to many metabolic disorders like hypertension and diabetes, which not only contribute to the advancement of CKM syndrome but also serve as important risk factors for ischemic stroke [50, 51]. Second, AIP inversely correlates with HDL’s protective functions, including reverse cholesterol transport and vasculoprotective effects [35, 52]. Thus, elevated AIP represents both increased triglyceride-mediated vascular damage and reduced HDL-associated protection. Furthermore, AIP may interact with other risk factors for cerebrovascular diseases, potentially exacerbating the development of stroke. Nevertheless, further research is still needed to fully elucidate these mechanisms.

Strengths

This study has several strengths. First, as a large-scale prospective cohort study, it provides novel insights into the association between baseline AIP levels and new-onset stroke risk specifically among the CKM syndrome population, including the identification of a nonlinear relationship. Second, we rigorously controlled for potential confounders and conducted comprehensive sensitivity analyses, ensuring the robustness and reliability of our findings. Third, our analysis revealed that the AIP-stroke association persists across CKM stages 1–3, further validating the clinical relevance of this biomarker. Therefore, focusing on AIP as an intervention target offers critical clinical guidance to aid in the prevention and delaying of CKM progression.

Limitations

However, there are some limitations in this study that cannot be ignored. Firstly, disease diagnosis depends on self-reports, likely leading to underestimated prevalence and inability to distinguish stroke types, thus affecting result accuracy. Secondly, the subjects are only middle-aged and elderly Chinese, restricting result generalizability. Third, the study only measured baseline AIP levels, ignoring dynamic changes during follow-up. Fourthly, due to the lack of stroke specific types in CHARLS, this study was unable to respectively assess the effect of AIP on ischemic or hemorrhagic stroke. Fifthly, due to strict exclusion criteria, a certain number of participants were excluded from this study, resulting in a limited number of participants being included, which may have led to attrition bias. Sixthly, as the survival times obtained by CHARLS are in years rather than days, the calculation of cumulative incidence curves may be less statistically valid due to the possible discrepancy between the actual time of stroke onset and the time of diagnosis. Finally, for CKM syndrome staging, rather than using the latest PREVENT equation, the Framingham 10-year CVD risk score was employed, potentially affecting staging accuracy. Therefore, it is necessary to conduct further in-depth research in other large-scale cohort studies to verify these findings of this study and provide more persuasive evidence for research in related fields.

Conclusion

In middle-aged and elderly populations with CKM syndrome stages 0–3, elevated baseline AIP levels were significantly associated with an increased risk of new-onset stroke, and a nonlinear relationship was observed between baseline AIP and the risk of stroke. These findings highlight the critical importance of incorporating AIP assessment into comprehensive stroke prevention and management strategies for a population with CKM syndrome.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AIP:

Atherogenic index of plasma

CVD:

Cardiovascular disease

CKM:

Cardiovascular-kidney-metabolic

CKD:

Chronic kidney disease

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

BMI:

Body mass index

FBG:

Fasting blood glucose

HbA1c:

Hemoglobin A1c

TC:

Total cholesterol

TG:

Triglyceride

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

RCS:

Restricted cubic spline

HR:

Hazard ratio

CI:

Confidence interval

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Acknowledgements

We would like to thank all the members of the CHALRS for their contributions and the participants who contributed their data.

Funding

This study was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202200480 ).

Author information

Author notes

  1. Yingcheng Liu, Wenlong Li and Hanwen Zhou have contributed equally to this article.

Authors and Affiliations

  1. Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China

    Yingcheng Liu, Wenlong Li, Hanwen Zhou, Haijiao Zeng, Jiaming Jiang, Qian Wang, Tian Liu, Xinjing Liu, Pinyi Chen & Xiaoni Zhong

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  1. Yingcheng Liu
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  2. Wenlong Li
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Contributions

LYC and ZHW were involved in the design of this study; JJM and LT were responsible for data management; LYC, LWL and ZHJ were involved in the writing of this paper; LYC, WQ and LXJ participated in the statistical analysis; LYC, CPY, ZHW and ZXN participated in the data review and manuscript revision. All authors have reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Xiaoni Zhong.

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Ethics approval and consent to participate

This study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). The participants provided their written informed consent to participate in this study.

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The authors declare no competing interests.

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Liu, Y., Li, W., Zhou, H. et al. Association between atherogenic index of plasma and new-onset stroke in a population with cardiovascular-kidney-metabolic syndrome stages 0–3: insights from CHARLS. Cardiovasc Diabetol 24, 168 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02732-6

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Keywords

Cardiovascular Diabetology

ISSN: 1475-2840