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Association of atherogenic index of plasma trajectory with the incidence of cardiovascular disease over a 12-year follow-up: findings from the ELSA cohort study

Cardiovascular Diabetology volume 24, Article number: 124 (2025) Cite this article

Abstract

Background

Atherogenic index of plasma (AIP) at baseline has been associated with increased morbidity and mortality from cardiovascular disease (CVD). However, the relationship between long-term AIP trajectories and CVD remains unclear. Therefore, this study aimed to investigate the associations between AIP trajectories and the incidence of CVD in the English population.

Method

The study data analysis was based on the English Longitudinal Study of Aging (ELSA) from 2004 to 2017. The study population consisted of individuals aged 50 years and older in England. AIP was calculated as log10 (triglycerides/high-density lipoprotein cholesterol). Group-based trajectory model (GBTM) was applied to identify the trajectory of the AIP index from Wave 2 to 8 over a 12-year follow-up. Cox proportional hazard models were then used to analyze the associations between different AIP index trajectory groups and the incidence of CVD.

Results

A total of 3976 participants with completed AIP data in Wave 2 and more than two AIP measurements between Wave 2 and Wave 8 were enrolled in the ELSA cohort. The participants were divided into three groups [low-stable group (n = 1146), moderate-stable group (n = 2110), high-stable group (n = 720)] using a GBTM model. After adjusting for potential confounders, participants in the high-stable group indicated an increased risk of developing incident of CVD compared to those in the low-stable AIP group [Hazard Ratio (HR) 1.33; 95% Confidence Interval (CI) 1.02–1.74, P = 0.033]. However, no differences in the incidence of CVD (HR 1.20, 95%CI 0.98–1.48, P = 0.082) were observed in the moderate-stable group. Subgroup analysis indicated similar results for participants under 63 years old and those with high alcohol consumption.

Conclusions

A high and sustainable level of the AIP index may contribute to the incidence of CVD. The trajectories of the AIP index can help identify older English individuals at increased risk of CVD who deserve primitive preventive and therapeutic approaches.

Introduction

As global life expectancy continues to rise, age-related health issues, particularly cardiovascular disease (CVD), are becoming increasingly prevalent. CVD remains the leading cause of death and disability worldwide, surpassing cancer in its impact on disability-adjusted life years [1]. This burden is expected to grow further as populations age, underscoring the urgent need for effective prevention and intervention strategies. Predictive models suggest that by 2050, concerted efforts to reduce exposure to established risk factors and improve access to interventions could significantly enhance global health outcomes [2, 3]. Among these risk factors, dyslipidemia plays a critical role in the development of CVD. In 2019, dyslipidemia emerged as the 8th leading cause of death globally, imposing a substantial societal burden in terms of both disease prevalence and economic costs [4, 5].

Defined by elevated levels of low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG), as well as reduced high-density lipoprotein cholesterol (HDL-C), dyslipidemia has long been a focal point of clinical guidelines aimed at lowering LDL-C to reduce CVD risk [6, 7]. However, emerging evidence indicates that targeting a single lipid marker is insufficient to fully address residual cardiovascular risk [8]. Given the escalating health challenges associated with aging populations, there is a pressing need to identify and validate novel predictors of CVD risk. This study seeks to address this gap by exploring innovative approaches to CVD risk assessment, with the ultimate goal of improving early detection and intervention strategies in an era of increasing life expectancy.

The atherogenic index of plasma (AIP), calculated as log10 (TG/HDL-C), is an emerging biomarker that reflects the balance between atherogenic and protective lipoproteins in the blood [9]. Research has demonstrated that AIP surpasses traditional lipid markers (TG, TC, LDL-C, and HDL-C) and serves as a more comprehensive and effective indicator for assessing cardiometabolic risk [10]. Increasing evidence suggests that elevated AIP can predict coronary artery disease, heart failure, stroke, and major adverse cardiovascular events (MACE) [10,11,12,13]. A systematic review and meta-analysis showed that elevated AIP was associated with increased risk of cardiovascular death, myocardial infarction, revascularization, no-reflow phenomenon, and stent thrombosis [14]. A higher AIP was significantly associated with an increased risk of diabetes mortality [15]. AIP is an independent predictor for vulnerable plaques beyond traditional factors [16]. Interventions targeting AIP can improve the balance of an individual’s lipoprotein profile, thereby reducing future cardiovascular risk [17]. Compared to traditional cardiovascular risk assessment tools, AIP demonstrates a more robust and comprehensive ability to evaluate CVD risk [18]. Despite its significant predictive value for CVD, the dynamic nature of lipid metabolism and individual lifestyle differences may render single lipid measurements insufficient to reflect an individual’s long-term lipid status [19]. To address this limitation, trajectory modeling has emerged as a powerful tool for identifying long-term trends in lipid profiles. Group-based trajectory modeling (GBTM) is a methodological approach designed to identify subpopulations of individuals who exhibit similar developmental trajectories within a broader population, classifying them into distinct groups or categories through unsupervised learning [20]. This approach enables more precise and personalized prevention and intervention strategies for CVD. The dynamic changes in the AIP have shown significantly greater predictive value than static metrics in forecasting outcomes for populations with abnormal glucose metabolism and in predicting the risk of heart failure [11, 21]. Consequently, using long-term AIP trajectories to predict cardiovascular risk holds greater significance. However, few studies have explored the association between long-term AIP fluctuations and CVD.

Hence, we prospectively used data from the English Longitudinal Study of Aging (ELSA) and employed GBTM to identify the association between longitudinal AIP trajectories and CVD in the UK population. Additionally, the risk differences across subgroups of traditional risk factors were examined.

Methods

Study participants and design

The data analyzed in this prospective cohort study were sourced from the ELSA database. The ELSA is a prospective cohort study in the UK that includes participants aged 50 or older to document the experiences of aging in the 21st century. The study collects high-quality data on economic, social, health, psychological, cognitive, biological, and genetic factors. In brief, the study began in 2002 with questionnaires surveyed every two years. Additional nurse visits were performed to assess anthropometric and biochemical indicators every four years [14]. As the first nurse visit occurred during Wave 2 (2004–2005), this study utilized data from Waves 2 to 8 (2004–2005 to 2016–2017). The exclusion criteria met one of as follows: (1) incomplete initial nurse visits for AIP measurement; (2) fewer than two visits for the AIP index. All participants provided informed consent, and ELSA was approved by the London Multicenter Research Ethics Committee (MREC/01/2/91). This study was exempted from review by the Institutional Review Board of the 920th Hospital of Joint Logistics Support Force, Chinese People’s Liberation Army (PLA), as it involved the use of de-identified, open-access data, which does not constitute human subject research.

Figure 1 displays the flowchart of the study process. Among the 19,808 participants enrolled at Wave 2, we first excluded 12,142 patients without a nurse visit. An additional 3690 participants also were excluded due to missing AIP index data in Wave 2 or more than two visits in follow-up. Finally, 3976 participants were included in the analyses of longitudinal AIP trajectories.

Fig. 1
figure 1

The flow chart of inclusion and exclusion. AIP Atherogenic index of plasma

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Definition of the AIP index

Blood samples were collected after participants were instructed to fast according to their scheduled appointment times. Trained and experienced nurses confirmed that participants were in a fasting state before collection; if not fasting, the samples were additionally labeled. Using a butterfly needle, the nurses sequentially collected blood into five small tubes [22]. Blood tests were conducted at the Royal Victoria Infirmary (Newcastle-upon-Tyne, UK) [23]. TG and HDL-C were quantified using standardized laboratory methods on the Roche c702 analyser. The AIP index was calculated using the formula: AIP = log10 (TG/HDL-C), where TG and HDL-C levels were measured in mmol/L [24]. Additional details of the instruments used for other blood index tests are provided in Table S1. We collected the AIP index during 12-year follow-up: Wave 2 (2004–2005), Wave 4 (2008–2009), Wave 6 (2012–2013), Wave 8 (2016–2017) (Fig. 2).

Fig. 2
figure 2

Diagram showing study design and time frame for the analyses of AIP index trajectories findings

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Assessment of outcomes

Notably, CVD was determined based on participants’ self-reports of being diagnosed with heart disease (angina, heart attack, congestive heart failure, and other heart problems) or stroke, as reported during the most recent follow-up. In subsequent waves, participants were required to confirm or dispute their previous self-reported CVD events. If participants disputed their earlier reports, the data were retrospectively corrected. This method of CVD ascertainment is consistent with previous studies using the ELSA cohort [25, 26]. In each wave of the ELSA study, participants were asked, “Has a doctor ever told you that you have heart disease, including angina, heart attack, congestive heart failure, or other heart problems?” and “Has a doctor ever told you that you have had a stroke?” Participants who reported being diagnosed with heart disease or stroke were classified as having CVD. The time-to-event data for new CVD cases were calculated based on the intervals between the baseline assessment and the first reported CVD event in the follow-up questionnaires. Since CVD events could only be identified during the periodic follow-up waves, the exact timing of event occurrence was determined according to the specific survey wave in which the event was first reported. In general, new CVD events could only be confirmed to have occurred within the interval of a particular follow-up wave, rather than on an exact date. The calculation of time of CVD event was consistent with the previous study [27, 28].

Covariates at baseline

Age, gender (male and female), education (high school or lower, some college, college and above), smoking status (never, former and current), drinking status (never and ever), and the maximum alcohol consumption per day (0, 1, 2, and > 2 units/day) were obtained through participant questionnaires and interviews [29]. Physical activity levels were categorized into three groups: vigorous (does heavy manual work or engages in vigorous leisure activity more than once a week), moderate (engages in physical work or moderate leisure-time activity more than once a week or participates in vigorous leisure activity once a week to 1–3 times a month), and low/inactive (not working or having a sedentary or standing occupation or engages in moderate leisure-time activity once a week or mild leisure-time activity at least 1–3 times a month) [26]. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg or a diastolic blood pressure ≥ 90 mmHg [30], or when participants answered “yes” to the question, “Do you take medicines for high blood pressure?“. Diabetes was defined when participants answered “yes” to the question, “Has a doctor ever told you that you have diabetes?” or fasting glucose ≥ 7.0 mmol/L or glycated hemoglobin ≥ 6.5% [31]. The weight-adjusted waist index (WWI) was calculated as waist circumference (cm) divided by the square root of body weight (kg) [32], with weight and waist circumference measured by experienced nurses. Research has shown that WWI is a superior indicator of body composition, particularly central obesity, which is strongly associated with cardiometabolic morbidity and mortality. In contrast, Body Mass Index (BMI) exhibits a reverse J-shaped relationship with these outcomes, which may limit its utility in certain populations [32]. Furthermore, central obesity, as reflected by WWI, has been shown to be significantly associated with elevated AIP levels [33].Blood glucose (mmol/L), TG (mmol/L), HDL-C (mmol/L), LDL-C (mmol/L), TC (mmol/L) and hemoglobin (mmol/L) levels, were collected after participants were instructed to fast according to their scheduled appointment times. Trained and experienced nurses confirmed that participants were in a fasting state (> 8 h) before collection; if not fasting, the samples were additionally labeled. Using a butterfly needle, the nurses sequentially collected blood into five small tubes. The blood samples were then sent to an external laboratory, where a series of analyses were performed to measure the levels of specific compounds in the blood [22]. We referred to studies from the ELSA cohort and other large-scale cohorts that have investigated the relationship between CVD and lipid metabolism [10, 29, 34, 35]. These covariates are recognized as clinically relevant confounding factors that exhibit strong associations with both cardiovascular and metabolic outcomes.

Statistical analysis

We first performed GBTM to identify the longitudinal trajectories of AIP levels from 2004 to 2017 using “traj” package [36]. Sored normal model was used for continuous outcomes. The optimal number (range 2–5) and shape of AIP index trajectories (linear, quadratic, or cubic) were determined based on the following criteria: (1) the lowest Bayesian information criterion (BIC) and Akaike information criterion (AIC); (2) no less than 5% of the participants within each trajectory group; (3) higher average posterior probabilities for each trajectory group (AvePP, > 0.70) [37]. Finally, three distinct AIP index trajectories were determined as the best-fitting model. The clinical significance of the locus group was also considered in this model selection process as revealed in Table S2. Despite the lower AIC and BIC values for the two-group trajectory model, we chose the three-group model based on its superior ability to capture population heterogeneity and clinical relevance. The three-group model identifies distinct subgroups (low-risk, moderate-risk, and high-risk), enabling more precise risk stratification and aligning with the natural progression of many diseases.

Furthermore, continuous variables with a normal distribution were presented as means and standard errors, whereas those without a normal distribution were described as medians (interquartile range). Categorical variables were presented as frequencies and percentages. The differences among the different AIP index trajectories were analyzed using analysis of variance (ANOVA) with continuous variables and chi-square tests with categorical variables. To evaluate the associations of different AIP trajectories with CVD, we constructed multivariable Cox proportional hazards regression models to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for each trajectory group, assuming the proportional hazards assumption was met. The models were as follows: Model 1 (no adjustment); Model 2 (adjusted for age, gender, WWI, education, smoking status, and drinking status); Model 3 (adjusted for age, gender, WWI, education, smoking status, drinking status, diabetes, hypertension, CVD in baseline, TC, and LDL-C). Additionally, Kaplan-Meier analysis was used to calculate the cumulative incidence of CVD for each trajectory model and assess the incidence of CVD during the follow-up period.

Finally, the subgroup analysis in our study was conducted as a post hoc analysis based on data from the ELSA. The subgroup analysis was designed to explore potential heterogeneity in the relationship between AIP trajectory and CVD risk across different population subgroups stratified by age (median, < 63 or ≥ 63 years), gender (female or male), WWI (median, < 10.9 or ≥ 10.9 cm/√kg), drinking status (never or ever), the maximum alcohol consumption per day (0, 1, 2, or > 2 units/day), physical activity (low, moderate, or high) and baseline CVD (yes or no) to evaluate potential interactions in these associations. Then several sensitivity analyses were conducted to assess the robustness of the results. To avoid redundancy with the subgroup analysis results, we conducted sensitivity analyses separately for subgroups excluding individuals with diabetes, hypertension, smoking history, and statin therapy, in order to validate the association between AIP trajectories and CVD within these specific populations. All statistical analyses were conducted using R software (version 4.3.0) and Stata 18 software (StataCorp, College Station, TX). All P-values were two-sided, and P < 0.05 was considered statistically significant.

Results

Baseline characteristics

Table 1 displays the characteristics of participants based on the AIP index trajectories. The study enrolled 3,976 participants. Based on the trend changes in AIP from 2004 to 2017, three distinct trajectory models were identified through GBTM (Fig. 3): the low-stable group (n = 1146), which maintained a continuous low AIP level; the moderate-stable group (n = 2110), which maintained a continuous moderate AIP level; the high-stable group (n = 720), which maintained a continuous high AIP level. The AIP values ranged from − 04 to -0.2 in the stable low AIP trajectory group, from − 0.1 to 0.1 in the moderate-stable AIP trajectory group, and from 0.2 to 0.4 in the high-stable AIP trajectory group. Compared to the low-stable group, the high-stable group demonstrated a higher proportion of males, higher WWI, lower educational levels, more former-current smokers, fewer alcohol consumption, lower physical activity levels, a higher proportion of hypertension, diabetes, and statins treatments, lower LDL-C levels, lower HDL-C levels, higher TG levels, higher blood glucose, higher TC levels, and higher hemoglobin levels (P < 0.05) (Table 1).

Table 1 Characteristics of subjects among different atherogenic index of plasma trajectory
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Fig. 3
figure 3

The trajectory of AIP index trajectories over a 12-year follow-up (wave 2-wave 8). High-stable group (n = 720); Moderate-stable group (n = 2110); Low-stable group (n = 1146)

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CVD risk and AIP trajectories

During the 12-year follow-up period, 849 participants developed new CVD events. Kaplan-Meier curves revealed that participants in the high-stable group, with elevated AIP levels, displayed a higher cumulative incidence of CVD than participants in the other trajectory groups (P < 0.001) (Fig. 4). The unadjusted Cox proportional hazard models (Model 1) indicated that HR and 95% CI for CVD risk in the moderate-stable group and high-stable group compared to the low-stable group were [1.30, (1.09, 1.55)] and [1.57, (1.27, 1.95)], respectively. After adjusting for age, gender, WWI, education, smoking status, and drinking (Model 2), the HR and 95% CI for CVD risk in the moderate-stable group and high-stable group compared to the low-stable group were [1.20, (0.98, 1.47)] and [1.37, (1.07, 1.76)], respectively. After further adjusting for diabetes, hypertension, baseline CVD, TC, LDL-C (Model 3), the HR and 95% CI for CVD risk in the moderate-stable group and high-stable group compared to the low-stable group were [1.20, (0.98, 1.48)] and [1.33, (1.02, 1.74)], respectively (Table 2).

Fig. 4
figure 4

Association of AIP index trajectories with incident of CVD in ELSA cohort

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Table 2 Association of AIP index trajectories with incident of CVD in ELSA cohort
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Subgroup and sensitivity analysis

Table 3 displays the subgroup analysis results. After adjusting for age, gender, WWI, education, smoking status, drinking status, diabetes, hypertension, CVD in baseline, TC and LDL-C (model 3). We found that AIP trajectories interacted with age, alcohol consumption. Among participants aged < 63 years, those with high-stable AIP levels indicated a higher CVD risk than those with low-stable levels [HR, (95% CI): 2.04, (1.30, 3.19)]. Among participants with a daily alcohol consumption of more than 2 units/day, high-stable AIP levels predicted a higher CVD risk than low-stable levels [HR, (95% CI): 2.83, (1.37, 5.86)]. No significant interaction effects were observed for gender, WWI, drinking status, physical activity, or baseline CVD (P for interaction > 0.05). Sensitivity analyses were conducted in participants excluding diabetes, hypertension, smoking history, and those receiving statins treatment. Results were similar to the primary analyses after exclusion of participants treated with statins treatment (Table S6), but no associations after exclusion of participants with diabetes, hypertension, and smoking history (Table S3–S5).

Table 3 Subgroup analysis of the association between AIP index trajectories and incident of CVD
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Discussion

In this prospective cohort study, we identified three distinct AIP trajectory patterns and reported an association between long-term AIP trajectories and future CVD risk in the UK population. Compared to individuals with low-stable AIP levels over time, those with high-stable AIP levels were at a higher risk for future CVD, particularly among individuals aged < 63 years and those with a daily alcohol consumption of more than 2 units/day. These results suggested that AIP control levels were a strong predictor of CVD events, which could be crucial in addressing the health challenges posed by the aging population.

In the ELSA cohort, the AIP trajectory exhibited a stable and slightly declining trend over time, which aligns with the findings reported by Zhang et al. [38]. We both utilized the GBTM method to model the trajectories of AIP. GBTM is particularly well-suited for longitudinal data analysis due to its unique ability to capture dynamic changes effectively, while also providing results that are both intuitive and easily interpretable [20]. In our study, the AIP trajectories for all three groups (low-stable, moderate-stable, and high-stable) showed a parallel decreasing trend over the follow-up period. Chun et al. [39] findings showed that identified two AIP trajectories (decreasing and increasing) in a Korean cohort, finding the increasing group had a higher CVD risk (HR 1.31, 95% CI 1.02–1.69). Zheng et al. [40] classified five AIP control levels in patients with Cardiovascular-kidney-metabolic (CKM) syndrome stages 0–3, and found that poor control levels of AIP were associated with an increased risk of CVD events in this population. This observation of different AIP trajectories may be attributed to several factors: (1) Population characteristics: The ELSA cohort is a population-based study with a relatively healthy aging population, whereas the studies by Chun et al. and Zheng et al. focused on specific populations (Korean Genome and Epidemiology Study participants and CKM syndrome patients, respectively). These differences in study populations may lead to variations in AIP dynamics. (2) Follow-up duration and frequency: The follow-up duration and measurement frequency in our study may differ from those in previous studies. For example, Chun et al. had 3 visits over 6 years, while our study had a different follow-up schedule. This could influence the observed trends in AIP trajectories. (3) Statistical modeling approach: The GBTM used in our study identified three distinct groups based on their AIP levels, while Chun et al. used latent variable mixture modeling and Zheng et al. used k-means cluster analysis. Different modeling approaches may lead to different observed trends.

Our baseline findings paralleled those of previous studies in middle-aged and older populations [21]. Participants with high-stable AIP levels were more likely to exhibit traditional cardiovascular risk factors, such as male, WWI ≥ 10.9 cm/√kg, low educational level, smoking, alcohol consumption, low physical activity, diabetes, and hypertension, than those with stable low AIP levels [41]. However, in our baseline analysis, participants with high-stable AIP levels exhibited lower baseline LDL-C levels than those with low-stable AIP levels, highlighting that monitoring a single lipid parameter alone cannot address residual cardiovascular risk [42]. We found that the high-stable AIP group had a higher prevalence of statins use, which likely contributed to their lower LDL-C levels. To further address the influence of statins use, we conducted sensitivity analyses by excluding individuals on statins therapy. The results consistently showed that the high AIP trajectory was associated with a higher risk of CVD. These findings remain robust and align with our initial conclusions.

Several potential mechanisms have been proposed to explain the relationship between long-term changes in the AIP and CVD risk. Notably, AIP was a composite biomarker reflecting the balance between TG and HDL-C. Impaired TG metabolism was considered a residual lipid risk factor beyond LDL-C, closely linked to CVD development and adverse outcomes [43]. The HDL-C was crucial in the protective reverse cholesterol transport process, aiding in the recycling and disposal of excess cholesterol [44]. Elevated AIP levels over time may indicate an accumulation of excess cholesterol crystals in the arterial intima, and prolonged accumulation can lead to severe cardiovascular pathology. Besides, AIP has been demonstrated to be associated with the molecular particle size of LDL-C. Higher AIP levels correlate with smaller LDL-C particles, specifically an increased proportion of small dense LDL-C (sdLDL-C), which are more susceptible to penetrating the vascular wall and depositing in the endothelium, leading to hemodynamic disturbances [45]. Furthermore, the esterification of HDL-C and sdLDL-C are predictors of insulin resistance, which can induce endothelial dysfunction, vascular inflammation, and dyslipidemia, collectively contributing to the onset and progression of CVD [46]. Accordingly, chronically elevated AIP levels may reflect severe systemic metabolic dysregulation.

After adjusting for potential confounders, our study found that participants with consistently high AIP levels demonstrated a significantly higher risk of CVD than those with consistently low AIP levels. After categorizing age into two groups using the median of 63 years, the subgroup analysis consistently showed a stronger association between AIP and CVD risk in participants aged < 63 years compared to those ≥ 63 years. This may be attributed to age-related lipid profile changes, as older adults tend to have lower TG and LDL-C levels and higher HDL-C levels, potentially reducing AIP [47, 48]. Younger individuals (< 63 years) generally have more active metabolic states, making dyslipidemia a more direct CVD risk factor [49]. Additionally, the younger group may have more homogeneous health conditions, reducing confounding factors, while older individuals are more likely to have competing risks (e.g., frailty, chronic diseases) that dilute the AIP-CVD association. This finding highlights the importance of early intervention in younger individuals with elevated AIP levels to reduce CVD risk. For those with alcohol consumption > 2 units/day, consistently high AIP levels were associated with a significantly higher CVD risk. Previous evidence has firmly established the relationship between male gender, smoking status, alcohol consumption, and increased CVD risk [50]. Sensitivity analyses were conducted in participants excluding diabetes, hypertension, smoking history, and those receiving statins treatment. Results were similar to the primary analyses after exclusion of participants treated with statins treatment, but no associations after exclusion of participants treated with diabetes, hypertension, and smoking history. The sensitivity analysis showed that after excluding participants with diabetes, hypertension, or smoking history, the association between AIP trajectories and CVD risk disappeared. This may reflect the critical role of these factors in the relationship between AIP and CVD risk. Diabetes, hypertension, and smoking history are major CVD risk factors that may indirectly modulate the AIP-CVD relationship through mechanisms such as lipid metabolism, inflammation, or endothelial dysfunction. Additionally, the predictive value of AIP may be more significant in specific populations, while in healthier populations, its predictive ability may be overshadowed by other dominant risk factors. The reduced sample size after exclusions may also have lowered statistical power, making it difficult to detect significant associations. Therefore, future studies should further explore the interactions between AIP and other risk factors to better understand its role in CVD risk prediction.

Our study has several strengths. Our results suggest that AIP, as a low-cost, efficient, and convenient indicator, may have clinical significance in treating new CVD cases. Traditional CVD risk assessment tools, including the Framingham Risk Score (FRS) [51] and QRISK [52], have been instrumental in early identification and stratification of at-risk individuals. Despite their utility, these tools face significant challenges, particularly in evaluating the intermediate-risk category. This group comprises individuals who may benefit from intensive medical interventions as well as those for whom lifestyle changes alone are adequate. Additionally, the emergence of SMuRF-less patients -those lacking standard modifiable cardiovascular risk factors (SMuRFs) - highlights the limitations of conventional scoring systems, as these individuals often exhibit lower risk scores yet remain vulnerable to CVD [53]. Incorporating novel biomarkers, such as AIP trajectories, could offer enhanced predictive capabilities, addressing gaps in current methodologies and improving risk stratification for intermediate-risk and SMuRF-less groups. Further studies should focus on combining traditional risk scores with innovative approaches to refine CVD risk prediction and support tailored clinical interventions [54]. Based on existing evidence and the temporal patterns observed in our study, personalized prevention strategies based on AIP levels should include targeted dietary modifications (reducing saturated fats, increasing fiber and omega-3 fatty acids), regular physical activity (150 min of aerobic exercise weekly), and weight management (5–10% body weight reduction for overweight individuals). For those with persistently elevated AIP and additional risk factors, pharmacological interventions such as statins or GLP-1 receptor agonists may be considered. Integrating these approaches with routine AIP monitoring can optimize cardiovascular risk reduction and support individualized care.

Our study has some limitations. Firstly, as with any observational research, residual or unknown confounding factors may still influence the causal relationship between AIP trajectories and CVD risk despite adjustments for potential covariates. Moreover, the adjusted covariates were collected only at the baseline, and their values may change over time. Secondly, as our participants were exclusively from the middle-aged and elderly population in the UK, the generalizability of our findings may be limited. Further investigation into diverse ethnic populations is needed to validate these results. Thirdly, our study included angina as a cardiovascular outcome based on patient self-reports. However, angina is a subjective symptom and may not always be associated with objective evidence of ischemia. For instance, some cases of angina could be attributed to non-ischemic causes, such as microvascular angina, which might lead to an overestimation of cardiovascular events. This limitation is particularly relevant in the context of the ELSA study, where medical documentation or biomarkers (troponin, imaging) were not available to corroborate angina diagnoses. Therefore, relying solely on self-reported angina as an indicator of cardiovascular disease may introduce bias. To address this, future studies should incorporate objective diagnostic tests to confirm ischemic events, thereby improving the accuracy of cardiovascular outcome assessments [55]. Fourthly, atrial fibrillation is a significant risk factor for ischemic stroke, and stroke is included in the composite outcomes of CVD [56]. However, the absence of records related to atrial fibrillation in the ELSA study may also introduce potential bias. Finally, the detailed mechanisms by which AIP contributes to atherosclerosis remain underexplored and warrant further experimental and clinical studies for a more comprehensive understanding.

Conclusions

In conclusion, individuals with persistently elevated AIP levels over time are at a higher risk of developing CVD, a relationship that may be age-dependent and more pronounced in younger age groups. Consequently, our findings underscore the importance of continuously monitoring AIP-level changes in clinical practice to prevent the occurrence of CVD. We now suggest that AIP should be measured at intervals of every 2 years for individuals at higher cardiovascular risk (those with metabolic syndrome, diabetes, or a history of elevated AIP levels). For individuals at lower risk, monitoring every 3–4 years may be sufficient. This tailored approach aims to optimize early detection of AIP changes and facilitate timely interventions to mitigate cardiovascular risk. We have also emphasized the importance of integrating AIP monitoring into routine clinical practice as part of a broader personalized prevention strategy. This includes combining AIP assessment with other risk factors (lipid profile, blood pressure, and glycemic status) to guide individualized treatment plans.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AIP:

Atherogenic index of plasma

CVD:

Cardiovascular disease

ELSA:

The English Longitudinal Study of Aging

LDL-C:

Low-density lipoprotein cholesterol

TC:

Total cholesterol

TG:

Triglycerides

HDL-C:

High-density lipoprotein cholesterol

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Acknowledgements

We sincerely thank all the research participants and members of the ELSA Research Group for their contributions. We thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.

Funding

This work has been supported by the Science and Technology Department of Yunnan Province (202101AY070001-030).

Author information

Author notes

  1. Xicong Li, Lifei Lu, Yubiao Chen and Baiyun Liu contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China

    Lifei Lu, Yubiao Chen, Baiyun Liu & Huajing Yang

  2. Kunming Medical University, Kunming, 650032, Yunnan, China

    Xicong Li

  3. 920th Hospital of Joint Logistics Support Force, PLA, Kunming, 650032, Yunnan, China

    Xicong Li, Bei Liu & Ruiwei Guo

  4. Guangzhou National Laboratory, Guangzhou, China

    Yubiao Chen & Baiyun Liu

  5. Department of Cardiology, 922th Hospital of Joint Logistics Support Force, PLA, Hengyang, 421000, Hunan, China

    Ruiwei Guo

  6. Department of Respiratory and Critical Care Medicine Key Laboratory of Respiratory Disease of Zhejiang Province, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China

    Heshen Tian

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  1. Xicong Li
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Contributions

GRW, LLF, LXC, CYB, and LBY conceived and designed the study. GRW, LLF, LXC, CYB, LB, THS, YHJ conducted the research. CYB, LLF and LXC analyzed the data. LXC and LLF wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ruiwei Guo.

Ethics declarations

Ethics approval and consent to participate

This study adhered to the ethical guidelines outlined in the Declaration of Helsinki. All participants provided informed consent, and ELSA was approved by the London Multicenter Research Ethics Committee (MREC/01/2/91). This study was exempted from review by the Institutional Review Board of the 920th Hospital of Joint Logistics Support Force, Chinese People’s Liberation Army (PLA), as it involved the use of de-identified, open-access data, which does not constitute human subject research.

Competing interests

The authors declare no competing interests.

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Li, X., Lu, L., Chen, Y. et al. Association of atherogenic index of plasma trajectory with the incidence of cardiovascular disease over a 12-year follow-up: findings from the ELSA cohort study. Cardiovasc Diabetol 24, 124 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02677-w

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Keywords

Cardiovascular Diabetology

ISSN: 1475-2840