Stroke has become a substantial global health challenge due to its notably high incidence, ranking as the second most common cause of death and third leading cause of disability worldwide (1, 2). According to data from the Global Burden of Disease (GBD) study, between 1990 and 2019, global stroke prevalence rose by 85%, stroke-related mortality increased by 43%, and disability-adjusted life years (DALYs) attributable to stroke grew by 32% (3). Due to rapid population growth and aging, stroke incidence and related mortality have continuously risen, particularly among individuals aged 70 years and older (4). Moreover, the probability of stroke recurrence markedly increases with age (5). China, home to the world’s largest elderly population, accounts for approximately one-quarter of all new stroke cases annually and bears the highest stroke burden globally (6–8). This situation imposes substantial physical, psychological, and financial burdens on patients, while also placing considerable strain on healthcare and social services in China (9, 10).

Given the severe threat posed by stroke, early identification and management of risk factors are essential to mitigate stroke risk (11). Numerous studies have established a robust association between stroke and atherosclerosis (12, 13). Historically, evaluations of cardiovascular risk and atherosclerosis have predominantly focused on low-density lipoprotein cholesterol (LDL-C) and HDL-C measurements (14). Nonetheless, clinical observations reveal that achieving target LDL-C levels does not fully mitigate cardiovascular or cerebrovascular risks (15). Hypertriglyceridemia commonly coexists with decreased HDL-C concentrations, potentially exacerbating residual cardiovascular risks (16). The AIP, calculated as the logarithmic ratio of TG to HDL-C, precisely captures the lipid metabolism balance and presents considerable benefits compared to traditional lipid parameters (17). Research has consistently linked elevated AIP values to heightened risks of cardiovascular and cerebrovascular events, particularly major artery atherosclerotic ischemic stroke (18, 19). Nevertheless, prior studies have notable limitations: participants were often limited to specific populations such as obese individuals (20) and those with stage 0–3 cardiovascular-renal-metabolic (CKM) disease (21); data were collected from restricted communities (22) or individual medical institutions (23); and study designs were primarily cross-sectional (24), limiting their ability to establish a causal relationship between AIP and stroke risk. Currently, cohort studies exploring the association between AIP and stroke remain limited, and the possible nonlinear nature of this relationship and subgroup variations have not been thoroughly investigated. Therefore, investigating these relationships would address existing research gaps and provide empirical evidence for targeted intervention strategies and improved resource allocation in healthcare.

This study employs nationally representative data obtained from the CHARLS to examine the relationship between AIP and new-onset stroke risk. These results aim to provide novel insights into stroke etiology and assess the feasibility and effectiveness of using AIP as a predictive marker for stroke risk. Ultimately, the study aims to provide novel strategies for stroke detection and prevention.

This large-scale investigation employed a seven-year longitudinal cohort design to explore the association between AIP and the risk of incident stroke. A total of 6,808 individuals aged 45 years or older from the CHARLS database, all free of stroke at baseline, were included. After comprehensive adjustment for potential confounders, the analysis demonstrated a significant positive association between elevated AIP levels and increased stroke incidence. Notably, using RCS analysis, this study first demonstrated that the association was a nonlinear dose–response relationship. Additionally, AIP exhibited stronger predictive performance among males and older populations.

The study identified a clear inflection point in the relationship between AIP and stroke risk. This inflection point (−0.02) was significantly below the conventional “low-risk” threshold (AIP < 0.11) (41), indicating that cerebrovascular harm from dyslipidemia might begin even within the “normal range.” As AIP approached −0.02, stroke risk increased markedly, suggesting that dysregulation of lipid metabolism became the predominant risk factor during this period (42, 43). The slowing of stroke risk beyond this inflection point implied the emergence of other pathogenic mechanisms, such as inflammatory responses and oxidative stress, which reduced the relative impact of lipid abnormalities (44). This inflection point might represent a critical pathophysiological transition, challenging conventional definitions of the “normal range” for AIP and requiring a reevaluation of its safety thresholds.

Notably, the predictive capacity of AIP demonstrated significant subgroup heterogeneity. This correlation was considerably stronger among males than females, possibly due to the regulatory role of sex hormones (45). After age 40, men experience a gradual decline in testosterone (46), potentially exacerbating AIP-related pathogenic effects by influencing lipid metabolism and inflammation. On one hand, testosterone played a crucial regulatory role in lipid metabolism, with lower testosterone resulting in elevated TG and LDL-C levels, decreased HDL-C, and subsequently increased AIP (47). On the other hand, testosterone exhibited anti-inflammatory properties, and its deficiency could lead to increased inflammation, disrupted lipid metabolism, elevated oxidized LDL (oxLDL), and thus higher AIP levels (48). Furthermore, smoking and alcohol consumption could modify the relationship between testosterone and AIP by disrupting endocrine balance and increasing oxidative stress (47). These factors collectively made men more susceptible to stroke risk related to increased AIP levels, suggesting that elevated AIP among middle-aged and elderly men might serve as an early indicator of metabolic disturbances, warranting targeted clinical interventions.

Furthermore, the predictive value of AIP for stroke increased notably with age. This finding illustrated the complex interplay between aging and lipid metabolism in stroke pathogenesis. From a pathophysiological viewpoint, increasing vascular dysfunction in older adults significantly exacerbated AIP-related risk. With aging, cerebral vessels experienced changes, including reduced arterial elasticity, vascular wall thickening, endothelial dysfunction, and diminished cerebral blood flow autoregulation (49, 50). These factors collectively contributed to vascular aging. In this context, the atherogenic lipid profile indicated by elevated AIP interacted with age-associated chronic inflammation, accelerating atherosclerosis and plaque instability and reducing compensatory mechanisms against ischemic injury (51, 52). Furthermore, age-related worsening of insulin resistance and hepatic lipid metabolism impairment enhanced the sensitivity of AIP as a marker of metabolic imbalance (53). Thus, even slight AIP elevations in individuals aged ≥60 years should be considered high-risk conditions.

These findings have substantial clinical and public health implications. First, the identified inflection point (AIP = −0.02) redefines baseline stroke risk assessment. Establishing this point as a preliminary warning threshold for stroke risk evaluation is recommended, especially for high-risk groups, such as those with cerebral small-vessel disease, family stroke history, or other vascular risk factors. Clinically, patients whose AIP approaches or exceeds this threshold, even within conventional low-risk ranges, should receive intensified monitoring and targeted lifestyle modifications. Second, identified subgroup heterogeneity supports tailored preventive strategies. Clinical practice should particularly emphasize AIP assessment in males over 40, considering elevated AIP (> − 0.02) as a crucial indicator for stroke risk and possibly including testosterone evaluations. Additionally, integrating AIP into standard screening protocols for individuals aged ≥60 years is advised. AIP acts as a sensitive risk amplifier; even modest increases necessitate rigorous monitoring and proactive cardiovascular prevention. In summary, this study proposes a precise biological threshold (AIP = −0.02) suitable for clinical risk assessment, advocating dynamic, stratified, and proactive risk management. This strategy could optimize resource allocation, facilitate early identification, and enable effective intervention in high-risk groups, thereby enhancing primary stroke prevention efficacy.

Despite these significant findings, several limitations warrant consideration. First, stroke outcomes partly depended on self-reported data. Post-event verification could result in misclassification or underreporting. Second, despite adjustments for numerous established risk factors, residual confounding, especially from unmeasured or unknown variables, could not be completely ruled out due to observational study limitations. Third, the study exclusively included a Chinese population, limiting the generalizability of findings to other ethnic groups. Fourth, participants were exclusively middle-aged or elderly (≥45 years), excluding younger age groups. Thus, generalization to broader populations requires caution. Future research should employ more precise outcome measures, comprehensive strategies to manage confounding variables, and validate results across diverse cohorts. Additionally, investigating the biological mechanisms linking AIP to stroke risk is needed. Nonetheless, this study holds substantial clinical value as the first investigation identifying a nonlinear association between AIP and stroke in middle-aged and elderly Chinese populations. Its findings provide new insights into using AIP for primary stroke prevention and risk management.

Drawing on CHARLS data, this cohort study is the first to identify a nonlinear association between AIP and stroke risk among middle-aged and older Chinese adults, revealing a distinct inflection point at −0.02. This association showed stronger predictive value in males and older individuals. These results support integrating AIP into stroke risk models for high-risk groups, enabling early recognition of at-risk individuals and timely implementation of targeted interventions. Future studies should further validate the clinical utility of this threshold in diverse and multi-center populations and explore underlying molecular mechanisms. These findings offer a foundation for enhancing clinical stroke-risk management and revising consensus guidelines.