Abstract
Objective
The atherogenic index of plasma (AIP) is a marker of atherosclerosis, while frailty reflects cumulative physiological decline. However, the combined impact of AIP-frailty index (AIP-FI) has not been adequately explored. This study aimed to investigate the association between AIP-FI and the risk of cardiovascular disease (CVD), stroke, and heart disease.
Methods
This prospective cohort study included 6896 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS) without CVD, stroke, or heart disease at baseline. The Cox proportional hazard models and restricted cubic spline (RCS) analysis were applied to explore the association between AIP-FI with the risk of CVD, stroke, and heart disease.
Results
During a median follow-up period of 9 years, 1648 (23.9%) of CVD events, 548 (7.9%) of stroke events, and 1280 (18.6%) of heart disease events were recorded. Cox regression analysis revealed that each 1-unit increment in the AIP-FI was significantly associated with higher risk of CVD (HR: 2.95, 95% CI 2.15, 4.05), stroke (HR: 3.14, 95% CI 1.88, 5.26), and heart disease (HR: 2.72, 95% CI 1.06, 1.89, 3.92). The RCS revealed a significant positive nonlinear relationship between AIP-FI with the risk of CVD, stroke, and heart disease (all P-overall < 0.05, and all P for non-linear < 0.05).
Conclusions
Our study demonstrated that higher AIP-FI was significantly associated with increased risk of CVD, stroke, and heart disease. By integrating metabolic and frailty information, AIP-FI offers an effective and accessible tool for cardiovascular risk assessment, supporting earlier prevention and intervention strategies in the middle-aged and elderly Chinese populations.
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-025-03051-6.
Keywords: Atherogenic index of plasma, Frailty index, Cardiovascular disease, Stroke, Heart disease, CHARLS
Research Insights
What is currently known about this topic?
AIP is a marker of atherosclerosis, while the FI reflects cumulative physiological decline.
What is the key research question?
What is the association between AIP-FI and the risk of CVD, stroke, and heart disease?
What is new?
This is the first large-scale study to demonstrate this association between the AIP-FI and the risk of CVD, stroke, and heart disease.
How might this study influence clinical practice?
Our research findings demonstrate a significant positive nonlinear association between AIP-FI and the risk of CVD, stroke, and heart disease. AIP-FI represents a simple and practical composite indicator for cardiovascular risk stratification, with potential utility in the early identification of high-risk individuals and the development of targeted preventive strategies.
Introduction
Cardiovascular diseases (CVD), including stroke and heart disease, are the leading causes of death and disability worldwide [1, 2]. Between 1990 and 2019, the prevalence of CVD increased from 271 to 523 million, and the number of deaths rose from 12.1 million to 18.6 million [3]. Data from the Global Burden of Disease (GBD) indicate that the incidence, prevalence, and mortality rates of stroke increased by 5.3%, 19.3%, and 5.3%, respectively, from 2010 to 2017 [4]. Likewise, heart disease cases have continued to rise globally [5]. Although conventional risk factors explain part of the cardiovascular disease burden, they fall short of capturing the intricate metabolic disturbances and multisystem physiological dysregulation that drive cardiovascular vulnerability. This gap has led to a growing interest in multidimensional biomarkers that can reflect broader aspects of cardiometabolic health.
The atherogenic index of plasma (AIP), defined as the logarithmic ratio of triglycerides to high-density lipoprotein cholesterol, is a sensitive indicator of lipid metabolic imbalance and has been tightly linked to insulin resistance and atherosclerotic processes [6–9]. The Frailty Index (FI), a rigorously validated marker of biological aging, characterizes an individual’s overall health status by quantifying accumulated deficits across physical, medical, cognitive, and psychological domains [10]. FI captures the decline in physiological reserve and the heightened vulnerability to internal and external stressors—an integrative state strongly related to cardiometabolic deterioration [11]. Although AIP and FI are both associated with cardiovascular outcomes, they represent different pathological dimensions and are independently evaluated [12–14]. Previous evidence suggests that metabolic dysregulation and frailty frequently coexist and may jointly accelerate vascular injury [15–17]. However, no prior study has explored their combined influence on cardiovascular outcomes. Integrating AIP and FI into a single composite metric (AIP-FI) may therefore provide a more comprehensive representation of biological aging and metabolic burden, offering a novel framework for refined cardiovascular risk assessment.
We hypothesized that higher AIP-FI levels would be associated with increased risks of CVD, stroke, and heart disease in middle-aged and older adults. To explore this, we using data from the China Health and Retirement Longitudinal Study (CHARLS). This population-based investigation highlights the potential of AIP-FI as a practical tool for improving cardiovascular risk assessment and prevention strategies in aging communities.
Methods
Study design
The data for this cohort study were obtained from the CHARLS, a nationally representative prospective survey targeting middle-aged and older adults aged ≥ 45 years in China. The baseline survey was conducted in 2011, enrolling approximately 17,700 participants from 10,000 households across 150 counties or districts and 450 villages or resident committees. To date, five survey waves have been completed (2011, 2013, 2015, 2018, and 2020) [18]. The CHARLS study was approved by the Ethics Committee of Peking University, and all participants provided written informed consent before participation.
Study population
The research flow for participant selection is illustrated in Fig. 1. Of these, 6056 participants were excluded due to missing baseline data on the AIP, and 3171 were excluded owing to unavailable FI data. In addition, 254 participants were excluded for having incomplete age information or being younger than 45 years. Furthermore, 1331 individuals were excluded due to a history of CVD, stroke, or heart disease at baseline, missing outcome information, loss to follow-up, or incomplete survival data. After applying these exclusion criteria, 10,812 participants were removed, and a total of 6896 participants were included in the final analysis.
Fig. 1.
Flow diagram of participant selection from the CHARLS cohort
Data collection
Baseline information was collected by trained interviewers using standardized questionnaires. The collected data included: (1) demographic and lifestyle factors: sex, age, residence, education level, marital status, smoking, and drinking habits; (2) anthropometric measurements-height, weight, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP); (3) disease and medication history: hypertension, diabetes, dyslipidemia, and the use of corresponding medications; and (4) laboratory parameters: fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and glycated hemoglobin (HbA1c).
Calculation of AIP-FI
The FI was developed using a method previously reported in literature and is applicable to the CHARLS dataset [19–22]. In this study, 28 health-related variables were included (excluding stroke and heart disease), covering chronic diseases, physical function, depressive symptoms, disability, and cognitive performance (Table S1). Each item was scored as 0 (deficit absent) or 1 (deficit present), except for the cognitive variable, which ranged from 0 to 1, with higher scores indicating poorer cognitive function. The FI calculation for each participant is the sum of current health deficits divided by the 28. The calculation formula of the AIP index was as follows [9]: AIP = log10 (TG/HDL-C). The combined AIP and FI metric was derived using a multiplicative model: AIP-FI = AIP × FI, consistent with prior epidemiological frameworks in which product terms are applied to capture potential interaction effects between metabolic dysregulation and functional deficits [23–26].
Definitions
Hypertension was defined based on any of the following criteria: (1) self-reported hypertension diagnosed by a physician, (2) use of antihypertensive medications, (3) SBP ≥ 140 mmHg, (4) DBP ≥ 90 mmHg [27]. Diabetes was identified by meeting at least one of these criteria: (1) FPG ≥ 126 mg/dL, (2) use of antidiabetic medications, (3) self-reported diabetes diagnosed by a doctor [28]. Dyslipidemia was identified by TG ≥ 150 mg/dL, TC ≥ 240 mg/dL, HDL-C < 40 mg/dL, LDL-C ≥ 160 mg/dL, self-reported dyslipidemia diagnosed by a physician, or current use of lipid-lowering drugs [29].
Assessment of incident CVD, stroke, and heart disease
The primary outcome of this study was to evaluate the risk of CVD, stroke, and heart disease, which were evaluated through two key questions: (1) “Have you ever been diagnosed by a physician with heart attack, coronary artery disease, angina, heart failure, or any other cardiac conditions?” (2) “Have you ever been diagnosed with stroke by a physician?”. All participants were followed up through five-wave interviews conducted from 2011 to either the occurrence of CVD, stroke, and heart disease, or 2020, whichever came first [30].
Statistical analysis
The extent of missing data in this study is presented in Table S2. To mitigate potential bias, we utilized multiple imputations to address the missing values. Normally distributed continuous data were presented as mean ± standard deviation and analyzed for statistical significance using a one-way ANOVA. Non-normally distributed continuous data were expressed as median and interquartile range and analyzed by the Kruskal–Wallis test. Categorical data were described with counts and percentages and assessed using the chi-square test.
The exposure variable (AIP-FI) was analyzed both as a continuous measure and as a categorical variable. For categorical analyses, AIP-FI was divided into quartiles (Q1-Q4) based on its distribution in the study population, with Q1 serving as the reference category. The Cox proportional hazards model was used to estimate hazard ratios (HRs) and 95% confidence levels (CIs) for the association between AIP-FI with the risk of CVD, stroke, and heart disease. To account for potential confounders, we conducted the analysis in three models. Model 1 was unadjusted; Model 2 included adjustments for sex, age, residence, marital status, education level, smoking status, and drinking status; and Model 3 contained additional adjustments for hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c. Additionally, a fully adjusted restricted cubic splines (RCS) analysis was performed to explore the dose–response relationship between the AIP-FI and the risk of CVD, stroke, and heart disease.
To further explore the relationship between AIP-FI and the risk of CVD, stroke, and heart disease, subgroup and interaction analysis was carried out. These analyses were stratified by several factors, including sex, age, residence, smoking status, drinking status, hypertension, diabetes, dyslipidemia, and BMI. To enhance the robustness of our findings, we carried out three robust sensitivity analyses. Firstly, we reanalyzed the dataset after excluding all participants of missing values. Secondly, non-fasting participants were removed from the dataset prior to conducting further analyses. Thirdly, we removed all deceased participants and conducted a reanalysis. Lastly, E-values were calculated for Model 3 to estimate the minimum strength of association between unmeasured confounders and AIP-FI that could potentially explain the observed relationship with the risk of CVD, stroke, and heart disease [31].
Results
Population characteristics
The study included 6896 participants from the CHARLS. The average age of participants was 58.0 ± 8.8 years, and 3445 (50.0%) were men. Additionally, individuals with higher quartiles of the AIP-FI were more likely older, women, former smokers, never drinkers, and tended to have hypertension, diabetes, and dyslipidemia than those with lower quartiles of the AIP-FI. For body measurements and laboratory test, higher AIP-FI quartiles were associated with increased BMI, SBP, SDP, FBG, TC, TG, and HbA1c. In contrast, higher quartiles of the AIP-FI were associated with lower levels of HDL-C. The demographic and clinical characteristics of all patients are presented in Table 1.
Table 1.
Patient demographics and baseline characteristics
| Characteristic | AIP-FI quartiles | P-value | ||||
|---|---|---|---|---|---|---|
| Overall | Q1 | Q2 | Q3 | Q4 | ||
| No. of subjects | 6896 | 1724 | 1724 | 1724 | 1724 | |
| Sex | < 0.001 | |||||
| Women | 3451 (50.0%) | 748 (43.4%) | 787 (45.6%) | 896 (52.0%) | 1020 (59.2%) | |
| Men | 3445 (50.0%) | 976 (56.6%) | 937 (54.4%) | 828 (48.0%) | 704 (40.8%) | |
| Age, year | 58.0 ± 8.8 | 57.3 ± 8.9 | 57.2 ± 8.8 | 58.3 ± 8.6 | 59.4 ± 8.6 | < 0.001 |
| Residence | < 0.001 | |||||
| Rural | 4311 (62.5%) | 1136 (65.9%) | 1023 (59.3%) | 1032 (59.9%) | 1120 (65.0%) | |
| Urban | 2585 (37.5%) | 588 (34.1%) | 701 (40.7%) | 692 (40.1%) | 604 (35.0%) | |
| Marital status | < 0.001 | |||||
| Married | 6215 (90.1%) | 1563 (90.7%) | 1589 (92.2%) | 1545 (89.6%) | 1518 (88.1%) | |
| Other | 681 (9.9%) | 161 (9.3%) | 135 (7.8%) | 179 (10.4%) | 206 (11.9%) | |
| Education level | < 0.001 | |||||
| No formal education | 2920 (42.3%) | 692 (40.1%) | 612 (35.5%) | 725 (42.1%) | 891 (51.7%) | |
| Primary school | 1560 (22.6%) | 373 (21.6%) | 429 (24.9%) | 379 (22.0%) | 379 (22.0%) | |
| Middle school | 1549 (22.5%) | 434 (25.2%) | 419 (24.3%) | 396 (23.0%) | 300 (17.4%) | |
| High school or above | 867 (12.6%) | 225 (13.1%) | 264 (15.3%) | 224 (13.0%) | 154 (8.9%) | |
| Smoking status | < 0.001 | |||||
| Never | 4093 (59.4%) | 966 (56.0%) | 958 (55.6%) | 1054 (61.1%) | 1115 (64.7%) | |
| Former | 566 (8.2%) | 127 (7.4%) | 144 (8.4%) | 147 (8.5%) | 148 (8.6%) | |
| Current | 2237 (32.4%) | 631 (36.6%) | 622 (36.1%) | 523 (30.3%) | 461 (26.7%) | |
| Drinking status | < 0.001 | |||||
| Never | 4033 (58.5%) | 888 (51.5%) | 1019 (59.1%) | 1046 (60.7%) | 1080 (62.6%) | |
| Former | 536 (7.8%) | 115 (6.7%) | 104 (6.0%) | 150 (8.7%) | 167 (9.7%) | |
| Current | 2327 (33.7%) | 721 (41.8%) | 601 (34.9%) | 528 (30.6%) | 477 (27.7%) | |
| Hypertension | 2523 (36.6%) | 434 (25.2%) | 480 (27.8%) | 690 (40.0%) | 919 (53.3%) | < 0.001 |
| Diabetes | 1070 (15.5%) | 135 (7.8%) | 178 (10.3%) | 270 (15.7%) | 487 (28.2%) | < 0.001 |
| Dyslipidemia | 3257 (47.2%) | 260 (15.1%) | 718 (41.6%) | 962 (55.8%) | 1317 (76.4%) | < 0.001 |
| Hypertension medications | 1089 (15.8%) | 115 (6.7%) | 124 (7.2%) | 333 (19.3%) | 517 (30.0%) | < 0.001 |
| Diabetes medications | 225 (3.3%) | 25 (1.5%) | 16 (0.9%) | 49 (2.8%) | 135 (7.8%) | < 0.001 |
| Dyslipidemia medications | 250 (3.6%) | 26 (1.5%) | 33 (1.9%) | 64 (3.7%) | 127 (7.4%) | < 0.001 |
| Height, m | 1.6 ± 0.1 | 1.6 ± 0.1 | 1.6 ± 0.1 | 1.6 ± 0.1 | 1.6 ± 0.1 | < 0.001 |
| Weight, kg | 59.4 ± 10.9 | 56.4 ± 9.8 | 59.4 ± 10.6 | 60.3 ± 11.1 | 61.4 ± 11.4 | < 0.001 |
| BMI, kg/m2 | 23.4 (21.2, 25.7) | 22.2 (20.2, 24.1) | 23.3 (21.1, 25.3) | 23.9 (21.6, 26.1) | 24.6 (22.2, 26.9) | < 0.001 |
| SBP, mmhg | 129.4 ± 19.6 | 126.1 ± 18.5 | 127.5 ± 18.4 | 131.0 ± 20.1 | 133.1 ± 20.5 | < 0.001 |
| DBP, mmhg | 75.7 ± 11.3 | 74.1 ± 11.0 | 75.2 ± 11.0 | 76.3 ± 11.4 | 77.1 ± 11.6 | < 0.001 |
| FBG, mg/dl | 102.1 (94.3, 112.5) | 99.4 (92.2, 107.6) | 100.8 (93.9, 109.6) | 103.0 (94.5, 113.0) | 106.2 (97.2, 122.6) | < 0.001 |
| TC, mg/dl | 193.1 ± 38.2 | 187.0 ± 34.2 | 190.4 ± 36.6 | 195.0 ± 38.0 | 200.0 ± 42.1 | < 0.001 |
| TG, mg/dl | 105.3 (74.3, 154.0) | 62.0 (52.2, 76.1) | 98.2 (77.9, 131.9) | 121.2 (94.7, 159.3) | 162.0 (123.9, 233.6) | < 0.001 |
| HDL-C, mg/dl | 51.2 ± 15.2 | 65.2 ± 14.9 | 50.5 ± 12.2 | 47.5 ± 11.3 | 41.5 ± 10.9 | < 0.001 |
| LDL-C, mg/dl | 115.9 ± 35.3 | 111.0 ± 30.3 | 117.7 ± 32.9 | 120.1 ± 35.4 | 114.8 ± 41.1 | < 0.001 |
| HbA1c, % | 5.1 (4.9, 5.4) | 5.1 (4.8, 5.3) | 5.1 (4.9, 5.4) | 5.1 (4.9, 5.4) | 5.2 (4.9, 5.5) | < 0.001 |
BMI, Body mass index; DBP, Diastolic blood pressure; FBG, Fasting blood glucose; HbA1c, Hemoglobin A1c; HDL-C, High density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; SBP, Systolic blood pressure; TC, Total cholesterol; TG, Triglycerides; AIP-FI: Atherogenic index of plasma-frailty index
Association between the AIP-FI and the risk of CVD
During a median follow-up period of 9 years, a total of 1648 (23.9%) participants experienced CVD, 548 (7.9%) participants experienced stroke, and 1280 (18.6%) participants experienced heart disease. HRs for continuous AIP-FI represent per one-unit increase from the observed baseline distribution. HRs for quartile analyses are relative to Q1 (lowest quartile). After adjust the potential confounding variables (Model 3), each 1-unit increase in AIP-FI was linked to a 195% rise in CVD risk (HR: 2.95 1.08, 95% CI 2.15, 4.05). Additionally, compared with Q1, participants in Q3 (HR: 1.25, 95% CI 1.07, 1.46) and Q4 (HR: 1.66, 95% CI 1.41, 1.95) had significantly increased CVD risk, while participants in Q2 did not show a significant increase in CVD risk (Table 2). Figure 2 illustrates RCS analyses demonstrating a nonlinear and dose-dependent association between AIP-FI and CVD risk across all progressively adjusted models. Notably, the risk increases at higher AIP-FI values, and this pattern remains consistent after full adjustment (all P for overall < 0.05 and P for non-linear < 0.05). This association suggests that higher AIP-FI may represent an integrated metabolic and physiological burden that predisposes individuals to cardiovascular events.
Table 2.
Association between AIP-FI and CVD incidence
| Characteristic | Event, n | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P | HR | 95%CI | P | HR | 95%CI | P | ||
| AIP-FI (per 1 unit) | 1648 | 4.17 | 3.31, 5.25 | < 0.001 | 3.62 | 2.85, 4.60 | < 0.001 | 2.95 | 2.15, 4.05 | < 0.001 |
| Quartile | ||||||||||
| Q1 | 310 | Ref | Ref | Ref | ||||||
| Q2 | 332 | 1.09 | 0.93, 1.27 | 0.268 | 1.07 | 0.92, 1.25 | 0.371 | 1.04 | 0.88, 1.21 | 0.658 |
| Q3 | 427 | 1.46 | 1.26, 1.69 | < 0.001 | 1.40 | 1.21, 1.62 | < 0.001 | 1.25 | 1.07, 1.46 | 0.005 |
| Q4 | 579 | 2.08 | 1.81, 2.38 | < 0.001 | 1.95 | 1.70, 2.25 | < 0.001 | 1.66 | 1.41, 1.95 | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||||||
Model 1: unadjusted for any covariates
Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status
Model 3: adjusted for sex, age, residence, marital status, education level, smoking status, drinking status, hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
HR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; CVD, Cardiovascular disease; FBG, Fasting blood glucose; HbA1c, Hemoglobin A1c; LDL-C, Low-density lipoprotein cholesterol; TC, Total cholesterol; AIP-FI: Atherogenic index of plasma-frailty index
Fig. 2.
Restricted cubic spline of relationship between AIP-FI and the risk of CVD. Higher AIPFI is associated with increasing CVD risk, as shown by nonlinear dose–response curves across all adjusted models. A Model 1: unadjusted for any covariates; B Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status; C Model 3: further adjusted for hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
Association between the AIP-FI and the risk of stroke
The associations between AIP-FI and the risk of stroke were detailed in Table 3. After full adjustment, each one-unit increase in AIP-FI was associated with a substantially higher stroke risk (HR: 3.14, 95% CI 1.88, 5.26). Additionally, quartile analyses showed a similar upward gradient, with the highest quartile demonstrating the strongest association relative to Q1 (Table 3). As shown in Fig. 3, RCS curves revealed a nonlinear association between AIP-FI and stroke risk. The risk of stroke rose steadily with higher AIP-FI values, and this pattern persisted across all levels of model adjustment (all P-overall < 0.05 and P for non-linear < 0.05). These results imply that elevated AIP-FI may reflect underlying multidimensional physiological deterioration that predisposes individuals to stroke.
Table 3.
Association between AIP-FI and stroke incidence
| Characteristic | Event, n | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P | HR | 95%CI | P | HR | 95%CI | P | ||
| AIP-FI (per 1 unit) | 548 | 5.34 | 3.77, 7.58 | < 0.001 | 4.79 | 3.32, 6.89 | < 0.001 | 3.14 | 1.88, 5.26 | < 0.001 |
| Quartile | ||||||||||
| Q1 | 72 | Ref | Ref | Ref | ||||||
| Q2 | 105 | 1.48 | 1.10, 2.00 | 0.010 | 1.50 | 1.11, 2.03 | 0.008 | 1.43 | 1.05, 1.94 | 0.022 |
| Q3 | 161 | 2.31 | 1.75, 3.05 | < 0.001 | 2.31 | 1.74, 3.05 | < 0.001 | 1.93 | 1.44, 2.59 | < 0.001 |
| Q4 | 210 | 3.04 | 2.32, 3.97 | < 0.001 | 2.98 | 2.27, 3.90 | < 0.001 | 2.22 | 1.64, 3.01 | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||||||
Model 1: unadjusted for any covariates
Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status
Model 3: adjusted for sex, age, residence, marital status, education level, smoking status, drinking status, hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
HR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; FBG; Fasting blood glucose; HbA1c, Hemoglobin A1c; LDL-C, Low-density lipoprotein cholesterol; TC, Total cholesterol; AIP-FI: Atherogenic index of plasma-frailty index
Fig. 3.
Restricted cubic spline of relationship between AIP-FI and the risk of stroke. Stroke risk rises progressively with higher AIP-FI, with consistent nonlinear patterns across all adjusted models. A Model 1: unadjusted for any covariates; B Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status; C Model 3: further adjusted for hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
Association between the AIP-FI and the risk of heart disease
During the follow-up period, 1280 participants developed heart disease. In fully adjusted models (Model 3), each one-unit increase in AIP-FI was associated with a 172% higher risk of heart disease (HR: 2.72, 95% CI 1.89, 3.92). When AIP-FI was categorized into quartiles, only participants in Q4 (HR: 1.59, 95% CI 1.33, 1.90) exhibited a significantly elevated risk of heart disease compared with Q1, whereas Q2 and Q3 did not show statistically significant associations (Table 4). Figure 4 illustrates a nonlinear relationship between AIP-FI and heart disease risk, with progressively higher AIP-FI values associated with increasingly higher risk. This pattern persisted across all covariate-adjusted models, underscoring the robustness of the association (all P for overall < 0.05 and P for non-linear < 0.05). These findings indicate that higher AIP-FI reflects a composite burden of metabolic and frailty-related deficits, contributing to heightened vulnerability to heart disease.
Table 4.
Association between AIP-FI and heart disease incidence
| Characteristic | Event, n | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P | HR | 95%CI | P | HR | 95%CI | P | ||
| AIP-FI (per 1 unit) | 1280 | 3.54 | 2.70, 4.64 | < 0.001 | 3.08 | 2.32, 4.08 | < 0.001 | 2.72 | 1.89, 3.92 | < 0.001 |
| Quartile | ||||||||||
| Q1 | 254 | Ref | Ref | Ref | ||||||
| Q2 | 257 | 1.02 | 0.86, 1.21 | 0.832 | 1.00 | 0.84, 1.18 | 0.956 | 0.97 | 0.81, 1.16 | 0.713 |
| Q3 | 321 | 1.32 | 1.12, 1.55 | 0.001 | 1.24 | 1.05, 1.47 | 0.010 | 1.14 | 0.96, 1.36 | 0.137 |
| Q4 | 448 | 1.91 | 1.64, 2.23 | < 0.001 | 1.77 | 1.51, 2.07 | < 0.001 | 1.59 | 1.33, 1.90 | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||||||
Model 1: unadjusted for any covariates
Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status
Model 3: adjusted for sex, age, residence, marital status, education level, smoking status, drinking status, hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
HR, Hazard ratio; CI, Confidence interval; BMI, Body mass index; CVD: Cardiovascular disease; FBG, Fasting blood glucose; HbA1c, Hemoglobin A1c; LDL-C, Low-density lipoprotein cholesterol; TC, Total cholesterol; AIP-FI: Atherogenic index of plasma-frailty index
Fig. 4.
Restricted cubic spline of relationship between AIP-FI and the risk of heart disease. Heart disease risk increases with rising AIP-FI, similar trends were observed across all models. A Model 1: unadjusted for any covariates; B Model 2: adjusted for sex, age, residence, marital status, education level, smoking status, and drinking status; C Model 3: further adjusted for hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
Subgroup analysis
Figure 5 summarizes the subgroup and interaction analyses for CVD, stroke, and heart disease, revealing broadly consistent associations between higher AIP-FI and increased cardiovascular risk across demographic and clinical strata after multivariable adjustment. The subgroup analysis revealed that AIP-FI had a significant effect on CVD and heart disease risk across all various populations, with statistical differences observed. The results also showed that higher AIP-FI was closely associated with increased risk of stroke within subgroups of age 45–60, never and current smokers, never drinkers, BMI 24–28 and ≥ 28, regardless of sex, residence, hypertension, diabetes, and dyslipidemia. For CVD, interaction analysis showed that AIP-FI interacted with sex (P for interaction = 0.002). For stroke, interaction analysis showed that AIP-FI interacted with age (P for interaction = 0.03), hypertension (P for interaction = 0.015), and dyslipidemia (P for interaction = 0.002). For heart disease, interaction analysis showed that AIP-FI interacted with sex (P for interaction = 0.046). In addition, no significant interaction was observed between AIP-FI and any other variables.
Fig. 5.
Subgroup and interaction analyses. Significant positive associations between AIP-FI and the risks of CVD (A), stroke (B), and heart disease (C) were observed across the majority of demographic groups. Adjusted for sex, age, residence, marital status, education level, smoking status, drinking status, hypertension, diabetes, dyslipidemia, hypertension medications, diabetes medications, dyslipidemia medications, BMI, FBG, TC, LDL-C, and HbA1c
Sensitivity analysis
To assess the robustness of the findings, we conducted three sensitivity analyses. The results did not materially change after excluding all missing data (Tables S3, S4, and S5). Additionally, when we removed all non-fasting participants and reanalyzed the results, our conclusions did not alter (Tables S6, S7, and S8). Thirdly, when we removed all deceased participants and reanalyzed the results, our conclusions did not alter (Tables S9, S10, and S11). Finely, the E-values for the associations of AIP with CVD, stroke, and heart disease were 2.82, 5.73, and 2.68, respectively. These findings suggest that only a relatively strong and substantial unmeasured confounder could fully explain the observed associations, supporting the stability of our results.
Discussion
This study provides new large-scale evidence on the associations between AIP-FI and the risks of CVD, stroke, and heart disease in middle-aged and older Chinese adults. The AIP-FI levels were strongly associated with an increased risk of CVD, stroke, and heart disease. This relationship remained significant even after fully adjusting for covariates. Additionally, the RCS regression model revealed a significant positive nonlinear relationship between AIP-FI and the risk of CVD, stroke, and heart disease. Furthermore, subgroup analysis and interaction analysis proved the consistency of the relationship between AIP-FI and the risk of CVD, stroke, and heart disease. The results of all three sensitivity analyses—including those excluding participants with missing data, non-fasting individuals, and deceased participants—were consistent with the primary analysis, confirming the robustness and reliability of the observed associations. Overall, this study suggests that AIP-FI may serve as a reliable biomarker for stratifying the risk of CVD, stroke, and heart disease.
The AIP has emerged as a robust biomarker reflecting insulin resistance, atherosclerosis, and cardiovascular risk. Min et al. demonstrated that individuals with elevated AIP had a higher risk of CVD, particularly among those with abnormal glucose metabolism [25]. Hu et al. further confirmed the predictive role of AIP for CVD across cardiovascular-kidney-metabolic (CKM) syndrome stages 0–3 [32]. Consistent with these findings, You et al. and Zheng et al. reported that AIP was significantly associated with both cardiovascular and all-cause mortality [33, 34]. In stroke research, Qu et al. identified a strong association between AIP and new-onset stroke, especially in participants with pre-diabetes or diabetes [30]. Liu et al. and Zheng et al. also found that the higher baseline and cumulative AIP levels were independently associated with stroke risk in patients with CKM syndrome stages 0–3 [35, 36]. In the context of heart disease, Wu et al. revealed that AIP was linked to incident heart disease among individuals with normal glucose regulation [37], while Zheng et al. demonstrated that persistently high AIP levels increase the risk of heart failure in hypertensive patients [38]. On the other hand, the frailty index is another important risk factor for cardiovascular events. He et al. found that worsening frailty increases CVD risk, whereas recovery lowers it [39]. Song et al. showed that both baseline FI and frailty progression independently predict stroke in older adults [40]. Li et al. further demonstrated that frailty increases all-cause mortality and heart failure risk [41]. These findings suggest a complex interplay between AIP, FI, and cardiovascular outcomes that merits further exploration.
In this study, we proposed a novel composite indicator, the atherogenic index of plasm–frailty index (AIP-FI), integrating metabolic and biological aging dimensions to evaluate cardiovascular risk. Using data from 6896 participants aged 45 years and older in the CHARLS, we observed that elevated AIP-FI was consistently associated with higher risks of CVD, stroke, and heart disease. These findings support the concept that combined metabolic dysfunction and multisystem physiological decline jointly contribute to cardiovascular vulnerability. The associations remained robust across multiple sensitivity analyses and population subgroups, underscoring the potential relevance of AIP-FI as an integrative indicator of cardiometabolic and aging-related risk. Collectively, these findings indicate that higher AIP-FI reflects a state of combined metabolic and physiological vulnerability that is strongly associated with increased risks of CVD, stroke, and heart disease.
The mechanisms linking AIP-FI with the development of CVD, stroke, and heart disease remain uncertain; however, several potential pathways have been proposed. Firstly, AIP-FI, defined as the product of AIP and FI, integrates two interrelated dimensions: AIP primarily reflects insulin resistance, while FI represents cumulative physiological decline. Growing evidence suggests a synergistic interaction between these two conditions that accelerates vascular injury [42–45]. Both insulin resistance and frailty are well-established contributors to the pathogenesis of CVD, stroke, and heart disease [46, 47]. Secondly, insulin resistance induces oxidative stress, reduces nitric oxide bioavailability, promotes endothelial dysfunction, and upregulates pro-inflammatory cytokines [17, 48–51]. Meanwhile, frailty is characterized by systemic inflammation, impaired immune function, and mitochondrial dysfunction, all of which may contribute to endothelial damage and vascular aging, ultimately promoting atherosclerosis and elevating cardiovascular risk [52, 53]. Thirdly, higher triglyceride levels promote the formation of small, dense LDL particles that are more susceptible to oxidation, a feature strongly linked with atherogenesis. Additionally, variations in HDL-C esterification efficiency and lipoprotein particle size can alter AIP values, thereby influencing lipid-related metabolic pathways implicated in stroke development [54–57]. Finally, individuals exhibiting both insulin resistance and frailty often present with comorbidities such as hypertension, diabetes, and obesity—well-recognized high-risk factors for CVD, stroke, and heart disease [58–63]. It is important to note that the mechanistic pathways described are speculative, as our observational data do not allow direct testing of these biological processes. These pathways should therefore be interpreted cautiously and considered potential explanations requiring further empirical investigation. In clinical practice, these findings suggest that controlling AIP-FI within a target range may be crucial in preventing CVD, stroke, and heart disease. Additionally, recognizing the link between AIP-FI and the risk of CVD, stroke, and heart disease can enhance overall risk assessment, enabling clinicians to make more informed decisions and develop personalized treatment and management strategies.
This research presents several notable advantages. To our knowledge, it is the first to propose AIP-FI, a composite risk indicator integrating lipid-related insulin resistance (AIP) and physiological decline (FI), to evaluate the risk of CVD, stroke, and heart disease. This integrative approach allows for a more nuanced assessment of cardiovascular vulnerability. The use of a nationally representative longitudinal dataset enhances the validity and generalizability of our results. Moreover, subgroup and interaction analyses provided valuable insight into population-specific associations, offering potential for refined risk stratification. Together, these strengths underscore the novelty and translational potential of AIP-FI as a multidimensional marker of vascular risk.
It is essential to acknowledge several limitations associated with this study. Firstly, the cross-sectional assessment of baseline AIP and FI precludes conclusions regarding temporal changes and cumulative exposure. Secondly, stroke diagnoses were self-reported by participants based on evaluations from their healthcare providers, which may not accurately reflect the actual incidence of stroke and introduce misclassification bias. Thirdly, despite rigorous covariate adjustments, residual confounding from unmeasured factors—such as diet quality and dietary patterns, physical activity and sedentary time, sleep-disordered breathing, medication adherence and treatment intensity, and socioeconomic—cannot be excluded, these variables may affect AIP-FI and cardiovascular outcomes. Although E-value analyses suggested that an unmeasured confounder would need to be strongly associated with both exposure and outcome to fully explain the results, some degree of residual confounding remains possible. Future studies incorporating these domains with more comprehensive measurements are needed to further validate our findings. Fourthly, because the sample size was fixed by the CHARLS cohort design, we were unable to perform an a priori power calculation. Lastly, the study’s focus on middle-aged and elderly Chinese participants may limit extrapolation to other ethnic and age groups.
Future research should seek to validate these findings in independent cohorts and across diverse populations to enhance generalizability. Longitudinal studies with repeated assessments of AIP-FI and related biological markers would help clarify temporal relationships and potential dynamic changes in cardiometabolic. Mechanistic investigations incorporating inflammatory, metabolic, and vascular biomarkers, as well as imaging or functional assessments, are needed to better understand the pathways linking AIP-FI to cardiovascular outcomes. Additionally, advanced modeling approaches—such as causal inference methods or mediation analyses—may help disentangle the relative contributions of metabolic dysfunction and physiological decline to cardiovascular risk.
Conclusion
In this nationally representative cohort, higher AIP-FI was consistently associated with increased risks of CVD, stroke, and heart disease, with these patterns remaining stable across major demographic and clinical subgroups. Although the observational design and single baseline measurement may allow residual confounding and unmeasured metabolic or physiological mechanisms, the findings warrant confirmation in external cohorts. Future studies using repeated AIP-FI assessments and mechanistic biomarkers are needed. These findings position AIP-FI as a potential integrative biomarker for refining cardiovascular risk stratification and guiding targeted preventive strategies in clinical practice.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
This study utilized data from the CHARLS database. The authors express their gratitude to the CHARLS research team and all individuals who participated in the study.
Author contributions
GH conceived and designed the study and wrote the main manuscript text. GH analyzed the data. YC conducted the literature search and prepared figures. DZ and YT performed the manuscript review. All authors reviewed and approved the final manuscript.
Funding
This work was supported by the “Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2023ZD0503900, 2023ZD0503902)” and the “Jiangsu Medical Association Interventional Medicine Phase III Special Fund Project (SYH-3201140-0089)”.
Data availability
The data supporting the findings of this study are available on the CHARLS website (http://charls.pku.edu.cn/).
Declarations
Ethics approval and consent to participate
CHARLS was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015 for the household survey and IRB00001052-11014 for blood samples), and all participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Guijun Huo and Yan Chen contributed equally to this work.
Contributor Information
Yao Tang, Email: tyty4803@163.com.
Dayong Zhou, Email: zhoudy@njmu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting the findings of this study are available on the CHARLS website (http://charls.pku.edu.cn/).