Abstract
Background
The Cardiovascular–kidney–metabolic (CKM) syndrome, a concept recently proposed by the American Heart Association (AHA), highlights the intricate connection between metabolic, renal, and cardiovascular illnesses. Furthermore, the Atherogenic Index of Plasma (AIP), a useful biomarker for evaluating the risk of Cardiovascular Diseases (CVDs), has been associated with the risk of Adverse Cardiovascular Events (ACEs). Nonetheless, its precise function in populations in CKM syndrome Stages 0–3 remains unknown.
Methods
This prospective study analyzed the data of 7,708 eligible participants (aged ≥ 45 years) from the Chinese Longitudinal Research of Ageing (CHARLS), particularly the 2011–2012 baseline survey (Wave 1). The primary exposure variable was AIP—a natural logarithm of the ratio of Triglycerides (TGs) to High-Density Lipoprotein Cholesterol (HDL-C). On the other hand, the primary endpoint was CVD incidence, which was determined based on self-reported past diagnoses. The relationship between AIP and CVD risk in the population in CKM syndrome stages 0–3 was examined using a Cox proportional risk model. Subgroup and mediation analyses were performed to further elucidate the interactions among these factors.
Results
This study involved 7,708 participants in the CKM syndrome stages 0–3 [Mean age = 58.00 years; Interquartile Range (IQR) = 52.00–65.00 years]. The risk of developing CVD increased significantly with higher AIP levels. Specifically, the risk ratio for each unit increase in AIP was 1.31 (95% CI 1.11–1.55), while the Hazard Ratio (HR) for the group with the highest AIP levels compared to the group with the lowest AIP levels was 1.22 (95% CI 1.08–1.39). Mediation analysis revealed that metabolic syndrome accounted for 12.3% of the association between AIP levels and CVD risk (p = 0.024), highlighting its significance in CVD risk assessment.
Conclusion
Herein, AIP levels correlated significantly positively with CVD risk in individuals in CKM stages 0–3, with metabolic syndrome as a key mediating factor. These findings suggest that AIP levels could be valuable not only for CVD risk assessment but also for clinical screening.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-025-02593-z.
Keywords: Atherogenic index of plasma, Cardiovascular diseases, Cardiovascular–kidney–metabolic syndrome , Prospective cohort study
Introduction
By 2021, the global prevalence of Cardiovascular Diseases (CVDs) had reached 523 million, a number that is almost double compared to the 1990 prevalence rate (271 million), making CVDs the leading cause of morbidity and mortality worldwide [1]. Due to the complex interplay between metabolic disorders, Chronic Kidney Disease (CKD), and cardiovascular dysfunction, individuals with the Cardiovascular–kidney–metabolic (CKM) syndrome could be at a higher risk for Adverse Cardiovascular Events (ACEs) [2–5]. Moreover, the American Heart Association (AHA) recently classified CKM syndrome as a systemic illness that could cause multiorgan failure, highlighting the critical need for early detection and treatment for affected patients [6]. According to research, ~ 5% of American adults suffer concurrently from heart, kidney, and metabolic disorders, a number that is constantly rising [7]. The complex interaction among these illnesses could further lead to more severe complications. For instance, diabetes might increase the risk of Hypertension (HTN) and myocardial dysfunction, heightening the risk of Heart Failure (HF) [8]. Furthermore, the CKD syndrome could exacerbate fluid and electrolyte imbalances, increasing the burden on the heart [9]. Moreover, diabetes has been established as a major risk factor for CKD, with approximately 30–40% of diabetic patients often progressing to CKD [10]. The aforementioned interconnections among diabetes, CKD, and HF highlight the need for comprehensive management of these conditions [11, 12]. The CKM syndrome, commonly associated with endothelial dysfunction and chronic inflammation, could potentially lead to impaired vascular regulation and exacerbated Atherosclerosis (AS) [13, 14]. Meanwhile, renal impairment-induced HTN could also increase the burden on the heart [15]. Additionally, metabolic abnormalities and compromised kidney function might significantly impact cardiovascular health. These interacting factors substantially elevate CVD risk [16]. Therefore, to prevent CVD progression and reduce its significant clinical impact, the AHA emphasizes the need for research focused on the preclinical stages of the CKM syndrome (Stages 0 to 3).
Atherogenic Index of Plasma (AIP), the log-transformed ratio of Triglycerides (TGs) to High-Density Lipoprotein Cholesterol (HDL-C), could be a useful biomarker for determining CVD risk [17]. As earlier implied, the cardiovascular risk factors in the CKM syndrome involve complex interactions, with the relationship between diabetes and CKD significantly increasing the incidence of CVDs. In this regard, it is noteworthy that AIP could serve as a vital biomarker for assessing CVD risk [8, 9]. Elevated AIP levels were recently associated with an increased risk of Adverse Cardiovascular Events (ACEs) and all-cause mortality [18, 19]. Furthermore, AIP was effectively utilized to predict cardiovascular outcomes in multiple studies involving patients with diabetes [20] and metabolic syndrome [21]. However, the specific role of AIP in patients with CKM syndrome remains unclear.
This study aimed to assess the relationship between AIP levels and CVD risk in individuals in CKM syndrome stages 0 to 3. A prompt diagnosis and intervention for high-risk individuals within this population could be crucial in preventing disease progression and enhancing patient outcomes.
Methods
Data source and study population
This prospective study involved the analysis of data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide cohort study of individuals aged ≥ 45 years. The survey was conducted in four phases. The baseline survey, Wave 1, was conducted from June 2011 to March 2012, and it involved 17,708 individuals recruited from 150 counties and 28 provinces in China using a multi-stage stratified probability-proportional-to-size sampling technique. The subsequent surveys were conducted biannually in 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4). This study was approved by Peking University’s Institutional Review Board and adhered to the Helsinki Declaration guidelines. Data were collected through well-trained field personnel and all participants provided informed consent [22].
The data used in this study was drawn from the baseline CHARLS survey. After excluding participants without demographic and health information, those aged < 45, and those with a history of CVD, 7,708 individuals were included in the final analysis. To ensure the integrity of the study population, rigorous selection criteria were applied, facilitating a robust analysis of the participants’ health outcomes. Figure 1 depicts the selection process.
Fig. 1.
Flowchart for study population selection
Definitions of exposure and outcome variables
Herein, the primary exposure variable was AIP, a ratio of TGs to HDL-C (both measured in mmol/L), which may be mathematically defined as log10 (TG/HDL) [19]. During CHARLS, investigators from the Chinese Center for Disease Control and Prevention (China CDC) collected fasting venous blood samples. To ensure the quality and standardization of TG and HDL-C measurements, these samples were measured at the Youanmen Clinical Trial Center, Capital Medical University, Beijing. The Enzymatic colorimetric test, a validated assay that has gained widespread acceptance in clinical experimentation, was used to determine TG and HDL-C levels.
On the other hand, the primary outcome was the onset of a new CVD during a 7-year follow-up period from 2011 to 2018. Information on CVD history was collected using standardized questionnaires. Consistent with previous research [23–25], CVD was defined as a self-reported history of heart disease and/or stroke. Regarding the diagnosis of heart disease, the participants were verbally assessed; and asked “Has your physician ever informed you that you had a heart attack, angina, Coronary Artery Disease, HF, or other heart-related problems? Furthermore, to ascertain whether a stroke had been diagnosed, the inquiry “Has your doctor ever told you that you have been diagnosed with a stroke?“, was posed. Table S10 shows the distribution of the number of participants with new CVD cases during follow-up.
Definition of CKM syndrome stages 0 to 3
As delineated in the AHA Presidential Advisory Statement, the stages of the CKM syndrome range from 0 to 4 [6]. Stage 0 is defined as the absence of risk factors, with individuals exhibiting normal weight, glucose levels, Blood Pressure (BP), lipid profiles, and renal function, as well as no indications of CVD. Notably, primordial prevention and cardiovascular health are emphasized in this stage. Stage 1 encompasses obese individuals with impaired glucose metabolism—indicators of excess or dysfunctional adiposity. Stage 2 covers individuals at moderate to high risk of CKD [estimated Glomerular Filtration Rate (eGFR): 30–60 ml/min/1.73 m2; and/or with self-reported diagnosis of CKD] and metabolic risk factors, such as hypertriglyceridemia, HTN, metabolic syndrome, and Type 2 Diabetes (T2D). Herein, eGFR was used as an indicator for assessing renal function. Stage 3 includes individuals with subclinical CVD, such as those at high 10-year CVD risk (Framingham risk score ≥ 21.5/21.6 in female/male) [26] or those at extremely high-risk CKD (eGFR < 30 ml/min/1.73 m2). The Framingham risk score estimates CVD risk based on various risk factors. Tables S4 and S5 detail the Framingham risk scoring rules. Finally, stage 4 involves individuals with clinical CVD. However, we primarily focused on new-onset CVD; hence, participants with CVD were excluded at baseline. Consequently, this study only involved individuals with CKM syndrome stages 0–3. The eGFR was determined and categorized per the KDIGO criteria using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Table S6 details the specific staging criteria for CKM stages 0–3.
Data collection
The data collected included basic information (age, gender, and education level), screening indicators [height, weight, waist circumference, and BP [both Diastolic Blood Pressure (DBP) and Systolic Blood Pressure (SBP)], laboratory parameters [Uric Acid (UA), Total Cholesterol (TC), HDL-C, TG, Low-Density Lipoprotein Cholesterol (LDL-C), serum creatinine, blood glucose, and glycosylated hemoglobin (HbA1c)], HTN status and related medication use, smoking habits, and diabetes status and related medication use. Table S3 details the diagnostic procedures for overweight, abdominal obesity, HTN, diabetes, and metabolic syndrome.
Handling of missing variables
Table S1 shows the degree of data incompleteness in this investigation. Our assessment of the missing data revealed 3 (0.04%) and 11 (0.14%) missing values for education and LDL-C, respectively. Furthermore, a normality test was conducted, revealing that LDL-C was skewed. To handle the missing data, numerical values were replaced with the median. For education, a categorical variable, a dummy variable was introduced for adherence to statistical principles. The interpolated data were further subjected to multivariate COX regression analysis to assess the impact of missing data interventions on the study results (Table S9).
Statistical analysis
First, the participants were categorized into three groups based on the tertiles of AIP levels: Low (− 1.59 to − 0.17), moderate (− 0.17 to 0.10), and high (0.10 to 1.80). The categorical variables, including sex, diabetes, HTN, and metabolic syndrome, were presented as percentages (%) and frequencies (N). All variables were non-normally distributed, as revealed by the Kolmogorov-Smirnov normality test (Table S11) and were presented as median [Interquartile Ranges (IQRs)]. Appropriate statistical tests, including the student’s t-test, Mann-Whitney U test, or χ2 test, were used to evaluate intergroup differences. The correlation between AIP levels and CVD risk in the CKM group was assessed using Cox proportional hazards models, with trend tests performed as needed. Covariates were included as potential confounders in the final models if they altered the estimates of AIP’s effect on CVD by > 10% (Table S12) or were significantly associated with CVD Table S13 [27, 28]. Covariates selected a priori based on established associations and/or plausible biological relations and then tested included gender, age, education, BMI, diabetes, smoking, SBP, DBP, glucose, LDL-C, TC, and eGFR. Model adjustment was performed to eliminate potential confounders and more accurately assess the AIP-CVD relationship. The covariates were gradually introduced for a more clear observation of their effects on the results, improving the explanatory power of the HR. Furthermore, subgroup analyses and interaction evaluations were conducted for a more thorough analysis of these relationships. A mediation analysis was also conducted to ascertain the role of metabolic syndrome as a mediator in the association between AIP and CVD risk. Statistical analyses were performed using R (version 4.2.0) and EmpowerStats (version 4.2). Results with P < 0.05 were considered statistically significant.
Results
Baseline characteristics
Herein, AIP levels were categorized into three groups: low (− 1.59 to − 0.17), moderate (− 0.17 to 0.10), and high (0.10 to 1.80). There were statistically significant age differences across these AIP classifications in the CKM population [n = 7708; mean age = 58.00 years (IQR: 52.00–65.00); males = 47.30%; females = 52.70%] (p < 0.001). The participants’ mean age was greatest [58.00 (52.00–66.00) years] and lowest [58.00 (51.00–64.00) years] in the groups with the lowest and highest AIP levels, respectively. Both BMI and waist circumference increased across the groups with increasing AIP levels (p < 0.001). Additionally, individuals with elevated AIP levels exhibited a significantly higher prevalence of comorbidities such as diabetes and HTN. These findings suggest that higher AIP levels correlate with a worse cardiovascular risk profile. In other words, the risk of developing CVDs increases with rising AIP levels (Table 1).
Table 1.
Baseline characteristics of subjects classified based on AIP levels in the CKM syndrome stages 0–3 population
| Variables | Total (− 1.59 ~ 1.80) | Low (− 1.59 ~ − 0.17) | Middle (− 0.17 ~ 0.10) | High(0.10 ~ 1.80) | P-value |
|---|---|---|---|---|---|
| N | 7708 | 2569 | 2569 | 2570 | |
| Age (year) | 58.00 (52.00–65.00) | 58.00 (52.00–66.00) | 58.00 (52.00–65.00) | 58.00 (51.00–64.00) | < 0.001 |
| Gender, % | < 0.001 | ||||
| Male | 3646 (47.30%) | 1313 (51.11%) | 1187 (46.20%) | 1146 (44.59%) | |
| Female | 4062 (52.70%) | 1256 (48.89%) | 1382 (53.80%) | 1424 (55.41%) | |
| BMI, kg/m2 | 23.04 (20.80–25.60) | 21.71 (19.86–23.79) | 22.99 (20.82–25.48) | 24.56 (22.34–27.19) | < 0.001 |
| Waist circumference, cm | 84.10 (77.50–91.20) | 80.00 (75.00–86.30) | 84.10 (78.00–91.00) | 88.90 (82.00–95.75) | < 0.001 |
| eGFR (ml/min/1.73 m2) | 95.01 (84.42–102.58) | 95.87 (86.29–103.14) | 94.76 (84.87–102.37) | 94.36 (82.08–102.41) | < 0.001 |
| TC (mg/dL) | 190.59 (167.40–215.34) | 186.34 (165.46–208.76) | 189.43 (165.08–214.56) | 196.39 (171.26–223.45) | < 0.001 |
| HDL-C (mg/dL) | 49.48 (40.59–60.31) | 62.63 (54.90–72.29) | 49.87 (44.07–56.06) | 38.66 (33.25–44.46) | <0.001 |
| LDL-C (mg/dL) | 114.05 (93.17–136.86) | 110.18 (91.62–130.77) | 119.07 (97.81–142.27) | 113.27 (89.30–138.40) | <0.001 |
| TG (mg/dL) | 104.43 (74.34–152.22) | 65.49 (54.87–77.88) | 104.43 (89.39–121.25) | 184.52 (147.79–245.15) | <0.001 |
| Uric acid (umol/L) | 4.29 (3.57–5.13) | 4.12 (3.45–4.93) | 4.20 (3.52–5.02) | 4.56 (3.78–5.44) | < 0.001 |
| Glucose (mg/dL) | 102.24 (94.32–112.86) | 99.72 (92.16–108.18) | 100.98 (93.78–110.52) | 106.92 (98.10–121.14) | <0.001 |
| SBP (mmHg) | 126.00 (113.00–141.00) | 123.00 (110.50–137.50) | 125.50 (113.00–140.00) | 128.75 (116.50–144.00) | < 0.001 |
| DBP (mmHg) | 74.00 (66.50–82.50) | 72.00 (64.50–80.50) | 74.00 (66.50–82.50) | 76.50 (69.00–84.88) | <0.001 |
| Education, % | 0.010 | ||||
| Primary school or below | 5388 (69.90%) | 1837 (71.51%) | 1812 (70.53%) | 1739 (67.67%) | |
| Middle school or above | 2317 (30.06%) | 730 (28.42%) | 757 (29.47%) | 830 (32.30%) | |
| Missing | 3(0.04%) | 2 (0.08%) | 0 (0.00%) | 1 (0.04%) | |
| Diabetes, % | < 0.001 | ||||
| Yes | 1260 (16.35%) | 260 (10.12%) | 356 (13.86%) | 644 (25.06%) | |
| No | 6448 (83.65%) | 2309 (89.88%) | 2213 (86.14%) | 1926 (74.94%) | |
| Hypertension, % | < 0.001 | ||||
| Yes | 4121 (53.46%) | 1162 (45.23%) | 1382 (53.80%) | 1577 (61.36%) | |
| No | 3587 (46.54%) | 1407 (54.77%) | 1187 (46.20%) | 993 (38.64%) | |
| Mets, % | < 0.001 | ||||
| Yes | 3954 (51.30%) | 951 (37.02%) | 1177 (45.82%) | 1826 (71.05%) | |
| No | 3754 (48.70%) | 1618 (62.98%) | 1392 (54.18%) | 744 (28.95%) | |
| Smoking, % | < 0.001 | ||||
| Yes | 3032 (39.34%) | 1087 (42.31%) | 984 (38.30%) | 961 (37.39%) | |
| No | 4676 (60.66%) | 1482 (57.69%) | 1585 (61.70%) | 1609 (62.61%) | |
| CKM stages, % | |||||
| 0 | 712 (9.24%) | 439 (17.09%) | 252 (9.81%) | 21 (0.82%) | <0.001 |
| 1 | 1149 (14.91%) | 595 (23.16%) | 466 (18.14%) | 88 (3.42%) | |
| 2 | 3530 (45.80%) | 970 (37.76%) | 1145 (44.57%) | 1415 (55.06%) | |
| 3 | 2317 (30.06%) | 565 (21.99%) | 706 (27.48%) | 1046 (40.70%) | |
| CVD, % | |||||
| Yes | 1590 (20.63%) | 447 (17.40%) | 558 (21.72%) | 585 (22.76%) | <0.001 |
| No | 6118 (79.37%) | 2122 (82.60%) | 2011 (78.28%) | 1985 (77.24%) | |
Correlation between AIP and CVD in individuals with CKM syndrome
Herein, we found a notable connection between AIP and CVD in individuals with CKM syndrome. The multicollinearity evaluation (see Supplementary Table S2) revealed no significant multicollinearity across the variables, with Variance Inflation Factors (VIF) for each covariate being < 5. In Model 1 (unadjusted), the HR per 1-unit increase in AIP was 1.39 (95% CI 1.20, 1.60), whereas the HR in the T3 group was 1.34 (95% CI 1.19, 1.52) (Table 2), both indicating that AIP correlated significantly with CVD risk. In model 2 (adjusted for sex and age), the HR increased slightly for each additional unit of AIP and the T3 group, remaining statistically significant. In the fully adjusted model, the HR per 1-unit increase in AIP decreased to 1.31 (95% CI 1.11, 1.55). Notably, although the magnitude of change in this model was relatively small, it suggested that after adjusting for multiple covariates, the significant association between AIP levels and CVD risk was still confirmed. In the T3 group, the HR decreased to 1.22 (95% CI 1.08, 1.39), further highlighting the stability of the association between elevated AIP levels and a significant increase in CVD risk. Furthermore, CVD risk increased with increasing AIP levels (p for trend = 0.003), highlighting the significance of AIP in CVD risk assessment, and further confirming that the effect of AIP on CVD remained significant after adjusting for several confounders. The Kaplan–Meier (K–M) survival curves (Figure S1) revealed an increased CVD incidence in the group with high AIP levels. The p-values of the log-rank test were all < 0.001, further supporting the elevated risk in the T2 and T3 groups compared to the T1 group.
Table 2.
Association between AIP levels and CVD occurrence in the population with CKM syndrome stages 0–3
| Events/Total | Model 1 HR (95%CI) |
p-value | Model 2 HR (95%CI) |
p-value | Model 3 HR (95%CI) |
p-value | |
|---|---|---|---|---|---|---|---|
| AIP (per1 units) | 1586/7694 | 1.39 (1.20, 1.60) | < 0.001 | 1.42 (1.22, 1.64) | < 0.001 | 1.31 (1.11, 1.55) | 0.002 |
| Categories | |||||||
| T1 | 445/2566 | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | |||
| T2 | 558/2569 | 1.27 (1.12, 1.43) | < 0.001 | 1.26 (1.12, 1.43) | < 0.001 | 1.19 (1.05, 1.35) | 0.007 |
| T3 | 583/2559 | 1.34 (1.19, 1.52) | < 0.001 | 1.36 (1.20, 1.54) | < 0.001 | 1.22 (1.08, 1.39) | 0.002 |
| P for trend | < 0.001 | < 0.001 | 0.003 | ||||
Model 1: Unadjusted.
Model 2: Adjusted for gender and age
Model 3: Adjusted for model 2 covariates and education, BMI, diabetes, smoking, SBP, DBP, glucose, LDL-C, TC, and eGFR
Subgroup analyses
Herein, subgroup and interaction analyses were conducted for different variables including age, gender, diabetes, HTN, smoking status, and CKM syndrome stage (stage 0 to stage 3) to further explore the relationship between AIP and CVD incidence (Table 3). Variables such as age, sex, diabetes, HTN, smoking status, and CKM syndrome stage showed no interaction (p for interaction > 0.05).
Table 3.
Subgroup analysis of the association between AIP levels and CVD incidence in a population with CKM syndrome stages 0–3
| Characteristics | Number of participants |
HR (95%CI) | p |
P for interaction |
|---|---|---|---|---|
| Age (years) | 0.2421 | |||
| < 65 | 5709 | 1.57 (1.29, 1.92) | < 0.0001 | |
| >=65 | 1999 | 1.24 (0.93, 1.66) | 0.1464 | |
| Gender | 0.6094 | |||
| Female | 4062 | 1.33 (1.07, 1.67) | 0.0120 | |
| Male | 3646 | 1.51 (1.19, 1.93) | 0.0009 | |
| Diabetes | 0.7094 | |||
| Yes | 1260 | 1.08 (0.75, 1.56) | 0.6839 | |
| No | 6448 | 1.36 (1.12, 1.64) | 0.0016 | |
| Hypertension | 0.8972 | |||
| Yes | 4121 | 1.33 (1.09, 1.64) | 0.0061 | |
| No | 3587 | 1.32 (1.00, 1.74) | 0.0474 | |
| Smoking | 0.8787 | |||
| Yes | 3032 | 1.47 (1.14, 1.89) | 0.0032 | |
| No | 4676 | 1.40 (1.13, 1.73) | 0.0023 | |
| CKM stage | 0.7199 | |||
| Stage 0 | 712 | 2.17 (0.62, 7.54) | 0.2231 | |
| Stage 1 | 1149 | 1.96 (0.87, 4.41) | 0.1020 | |
| Stage 2 | 3530 | 1.16 (0.90, 1.48) | 0.2500 | |
| Stage 3 | 2317 | 1.19 (0.92, 1.56) | 0.1878 |
The model was adjusted for various variables, including BMI, LDL-C, TC, and eGFR.
Mediation analysis
Mediation analysis (Table 4) revealed that the metabolic syndrome played a vital mediating role in the association between AIP and new-onset CVD. After adjusting for multiple covariates including gender, age, education, BMI, diabetes, smoking, SBP, DBP, glucose, LDL-C, TC, and eGFR, the proportion of the metabolic syndrome-mediated effect of AIP on CVD incidence was 12.3% (p = 0.024).
Table 4.
Mediation analysis of the effect of metabolic syndrome on the AIP-CVD relationship
| Independent variable | Mediator | Total effect | Indirect effect | Direct effect | Proportion mediated, % | |||
|---|---|---|---|---|---|---|---|---|
| Coefficient (95% CI) | P value | Coefficient (95% CI) | P value | Coefficient (95% CI) | P value | |||
| AIP | Metabolic syndrome | − 11.11 (− 18.05, − 4.81) | < 0.001 | − 1.36 (− 2.58, − 0.25) | 0.024 | − 9.81 (− 16.81, − 3.35) | 0.004 | 12.3% |
The model was adjusted for multiple variables, including gender, age, education, BMI, diabetes, smoking, SBP, DBP, glucose, LDL-C, TC, and eGFR.
Discussion
Herein, the clinical data of 7,708 individuals [Average age = 58.00 years (IQR: 52.00–65.00)] was analyzed prospectively to examine the relationship between AIP levels and CVD risk. According to the multivariate Cox regression analysis results, in the unadjusted model, the HR was 1.34 (95% CI 1.19, 1.52) in the T3 group compared to the T1 group, indicating a 34% increase in CVD risk in the group with the highest AIP levels compared to that with the lowest AIP levels. In the fully adjusted model, the HR for the T3 group decreased to 1.22 (95% CI 1.08, 1.39), further confirming that the relationship between elevated AIP levels and a significantly higher CVD risk remained stable even after adjusting for multiple confounders. Moreover, subgroup and interaction analyses revealed no significant interactions in the five subgroups (p for interaction > 0.05), implying that in the study population, the relationship between high AIP levels and elevated CVD risk was stable across the subgroups. This phenomenon could also be attributed to the fact that the sample sizes of some subgroups were not large enough. Moreover, mediation analysis revealed that metabolic syndrome influenced 12.3% of AIP’s total effect on CVD risk (coefficient − 1.36, p = 0.024), highlighting its crucial role in cardiovascular health among older adults. To the best of our knowledge, this is the first study to examine the relationship between AIP and CVD risk in relation to the CKM syndrome.
After adjusting for confounders, compared to the group with the lowest levels, we found that the group with the highest AIP levels exhibited a higher CVD risk (by 22%), highlighting the potential significance of AIP as a stable predictor of CVD risk, as well as a useful biomarker for clinical assessments. In other words, CVD risk increased significantly with increasing AIP levels. This finding is consistent with multiple previous studies. For instance, Duyimuhan et al. [29] conducted a retrospective cohort analysis utilizing the NHANES database, reporting a significant correlation between AIP levels and both all-cause- and CVD-related mortality in hypertensive patients, further highlighting the robust capability of AIP levels as a CVD risk predictor. Zhang et al. [30] also reported a link between AIP levels and the risk of Myocardial Infarction (MI) within the general population, further establishing the relevance of AIP in CVD risk assessment. Additionally, Alifu et al. [31] found a link between AIP levels and poor long-term outcomes in individuals with chronic coronary syndrome, further supporting the predictive usefulness of AIP in CVD assessment. Moreover, a previous Chinese cohort study found that high AIP levels correlated significantly with an increased incidence of cardiovascular events in patients with Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA) [32]. Yin et al. [33] also showed that T2D, Insulin Resistance (IR), and AIP exhibited a non-linear relationship, highlighting the significance of AIP levels in predicting CVD risk, particularly within metabolic syndrome-affected populations.
It is also noteworthy that we identified several similarities and differences in our study compared to other previous studies regarding the AIP-CVD relationship. For instance, consistent with the findings of Duyimuhan and Maimaiti [29] and Zhang et al. [30], we found that increased AIP levels correlated significantly with increased CVD incidence, reinforcing the notion that AIP is a strong predictor of CVD risk. Additionally, consistent with Alifu et al.’s [31] study, our subgroup analyses aimed at elucidating the impact of demographic factors revealed that age and gender did not significantly influence the AIP-CVD relationship. Besides these similarities, our study also differed from other studies in several key aspects. For instance, our cohort comprised 7,708 participants aged ≥ 45 years, focusing primarily on older adults, whereas other previous studies covered varied populations, such as hypertensive patients or the general population [29, 34]. Furthermore, this prospective analysis involved data collected over multiple waves from 2011 to 2018, allowing for the assessment of causality between AIP levels and CVD incidents, a phenomenon that differs from the retrospective data collection methods used in earlier studies [33, 35]. Additionally, we uniquely assessed the mediating role of metabolic syndrome and found that it accounted for 12.3% of the total effect of AIP on CVD risk, a focus area that was less emphasized in prior studies [36, 37]. Metabolic syndrome, a well-known pivotal link between CVD and metabolic abnormalities, is highly prevalent and robustly associated with cardiovascular events, making it a focal point of research. Examining the mediating role of metabolic syndrome in the AIP-CVD relationship could result in a more effective identification of high-risk populations and the subsequent enhancement of clinical interventions. This study focused on older adults and its findings may crucially explain the significance of metabolic syndrome as a mediator, especially given the notable prevalence of AIP elevations and metabolic conditions in this population. Overall, our findings adds to the increasing research evidence on the role of AIP in cardiovascular health, especially in older adults and in relation to metabolic syndrome.
In a CKM syndrome population, AIP levels could crucially influence CVD onset, primarily via mechanisms involving lipid metabolism, Oxidative Stress (OS), and inflammation. Furthermore, lipid metabolism disturbances are fundamental to the pathogenesis of Atherosclerosis (AS) and metabolic syndrome. According to research, elevated TG levels could activate the Cholesteryl Ester Transfer Protein (CETP), leading to decreased HDL-C levels and increased LDL-C and Very Low-Density Lipoprotein Cholesterol (VLDL-C) levels [38], Furthermore, high TG levels could contribute to the production of small, dense low-density lipoproteins (sdLDL-C), which might more readily penetrate the vascular endothelium, provoking endothelial dysfunction and inflammatory responses [39]. In metabolic syndrome, lipid abnormalities are characterized by elevated Total Cholesterol (TC), TG, and LDL-C levels, along with reduced HDL-C levels, changes that not only heighten AS risk but also exacerbate CVD via thrombosis promotion [40, 41]. Notably, OS is the other critical factor in AS and metabolic syndrome. According to research, obesity and IR could increase Reactive Oxygen Species (ROS) production, directly damaging Vascular Endothelial Cells (VECs) and promoting lipid peroxidation, leading to oxidized Low-Density Lipoprotein (ox-LDL) formation, a key driver of atherosclerotic plaque development [42]. Inflammation also plays a significant role in these conditions. Due to factors secreted by macrophages in adipose tissue, including Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-alpha), patients with metabolic syndrome often experience chronic low-grade inflammation [43, 44]. In addition to facilitating atherosclerotic plaque formation, these inflammatory mediators also heighten the risk of thrombosis by activating Endothelial Cells (ECs) and platelets, a phenomenon that correlates closely with the extent of AS and the incidence of cardiovascular events [45, 46]. Therefore, strengthening the regular detection of AIP and metabolic syndrome screening could be significant in reducing AS and CVD incidences.
Despite its valuable insights, this study has certain limitations. First, instead of the more recent PREVENT equations, we used the Framingham 10-year CVD risk score to define subclinical CVD. Despite being validated in Asian cohorts [47], the Framingham score may still not capture all risk factors. Second, CVD diagnoses were solely based on self-reports by participants in the CHARLS, potentially resulting in discrepancies relative to the actual incidence rates. However, Xie et al. [48]. reported that 77.5% of self-reported cardiac conditions were consistent with medical records in the English Longitudinal Study of Ageing (ELSA). Third, our sample only included middle-aged and older Chinese individuals, limiting the generalizability of our results. Fourth, we used observational data, highlighting potential biases resulting from confounding factors. To mitigate the potential bias, we meticulously considered as many relevant factors as possible in the analysis. However, we could still not rule out the influence of confounding factors, such as the use of specific lipid- and glucose-lowering medications, a phenomenon that might have slightly biased the results. Finally, this was a single-center study and potential residual confounding factors might have affected the results.
Conclusion
Herein, CVD risk correlated significantly positively with AIP levels in individuals with CKM syndrome. Furthermore, metabolic syndrome accounted for 12.3% of AIP’s overall impact on CVD risk, highlighting its significance in CVD risk assessment. Therefore, within CKM syndrome phases 0–3, assessing AIP levels may be a practical and efficient screening technique for identifying those at a high risk of CVD.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Abbreviations
- AIP
Atherogenic index of plasma
- ACEs
Adverse cardiovascular events
- AHA
American Heart Association
- CKD
Chronic kidney disease
- CKD-EPI
Chronic kidney disease epidemiology collaboration
- CKM
Cardiovascular–kidney–metabolic
- CVDs
Cardiovascular diseases
- eGFR
estimated glomerular filtration rate
- HDL-C
High-density lipoprotein cholesterol
- HTN
Hypertension
- IR
Insulin resistance
- LDL-C
Low-density lipoprotein cholesterol
- T2D
Type 2 diabetes
- TGs
Triglycerides
- TC
Total cholesterol
Author contributions
All the authors solely contributed to this paper; YHH wrote this manuscript; YL analyzed the data, JL, XYL, and MYY acquisition of data; WPC designed this study and reviewed this manuscript.
Funding
This study received support from the Science and Technology Department of Jilin Province (Project No. YDZJ202201ZYTS110).
Availability of data and materials
No datasets were generated or analysed during the current study.
Declarations
Ethical approval
The research was approved by the Peking University Institutional Review Board and carried out in compliance with the ethical standards outlined in the Declaration of Helsinki. Field personnel performed standardized, verified interviews in compliance with the Declaration of Helsinki’s ethical norms and with approval from the Peking University Institutional Review Board, and all participants gave their informed permission.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
No datasets were generated or analysed during the current study.