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. 2026 Apr 28;17:1836134. doi: 10.3389/fneur.2026.1836134

Long-term LDL cholesterol burden and stroke: mechanistic insights and emerging strategies for personalized lipid management

Zhuanfang Li 1, Lan Ye 1, Jie Li 1, Yinping Wang 2,*, Jun Yang 1,*
PMCID: PMC13160751  PMID: 42131837

Abstract

Low-density lipoprotein cholesterol (LDL-C) is a causal and modifiable driver of atherosclerotic cardiovascular disease and an important contributor to atherosclerotic ischemic stroke. Increasing evidence suggests that cumulative LDL-C exposure—the combined effect of LDL-C magnitude and duration over time—captures vascular injury more accurately than a single measurement obtained at one clinical encounter. This review uses cumulative LDL-C exposure as an organizing framework for stroke prevention. We summarize current approaches to quantifying long-term LDL-C burden, review epidemiological evidence linking cumulative exposure to incident and recurrent ischemic stroke, and discuss biological mechanisms that may explain these associations. We then examine how lipid-lowering therapies, including statins, PCSK9 inhibitors, and small-interfering RNA-based agents, may reduce cumulative vascular injury. Particular attention is paid to the clinical challenge of balancing ischemic benefit against potential hemorrhagic vulnerability in selected high-risk phenotypes, such as patients with probable cerebral amyloid angiopathy or multiple lobar cerebral microbleeds. We propose a precision-management framework that integrates longitudinal lipid exposure, stroke subtype, genetics, neuroimaging, and dynamic risk assessment. By shifting attention from static lipid values to lifelong exposure, cumulative LDL-C burden may offer a more coherent basis for individualized cerebrovascular prevention.

Keywords: atherosclerosis, cumulative exposure, ischemic stroke, low-density lipoprotein cholesterol, PCSK9 inhibitors, precision medicine

1. Introduction

Stroke remains a major global cause of death and long-term disability, and ischemic stroke accounts for the large majority of cases (1, 2). Recent Global Burden of Disease 2021-based analyses further show that the burden specifically attributable to high LDL-C has continued to increase worldwide. From 1990 to 2021, disability-adjusted life years (DALYs) due to ischemic stroke attributable to high LDL-C increased by 44.55%, underscoring that LDL-related cerebrovascular risk remains a substantial and growing global public health problem (2). Among modifiable vascular risk factors, elevated low-density lipoprotein cholesterol (LDL-C) is central to the pathogenesis of atherosclerotic cardiovascular disease and is especially relevant to large-artery atherosclerotic stroke (3, 4). However, conventional lipid assessment still relies heavily on single LDL-C measurements, which provide only a snapshot of a biological process that unfolds over decades.

This limitation is increasingly important. Vascular injury related to LDL-C is cumulative: both the intensity of exposure and the duration of exposure matter. Recent work has therefore emphasized the concept of cumulative LDL-C burden, an integrated measure of LDL-C exposure across the life course, as a potentially more informative predictor of atherosclerotic disease progression than a single contemporary LDL-C value (5, 6). In cerebrovascular disease, this framework may help explain why patients with apparently acceptable LDL-C levels at one time point can still carry substantial residual risk if their lifetime exposure has been high.

This perspective has direct clinical implications. Even moderately elevated LDL-C may become harmful when sustained over many years, whereas early and persistent LDL-C lowering may shift the entire trajectory of vascular injury. At the same time, increasingly intensive lipid lowering has renewed concern about possible hemorrhagic complications in selected patients, especially those with prior lobar intracerebral hemorrhage, probable cerebral amyloid angiopathy, or extensive cerebral microbleed burden. Against this background, the present review examines how cumulative LDL-C exposure can be measured, how it relates to ischemic and hemorrhagic stroke phenotypes, which mechanisms may underlie these associations, and how this framework may inform safer and more individualized lipid management.

2. Quantifying cumulative LDL-C exposure: methods and challenges

A meaningful discussion of cumulative LDL-C burden begins with measurement. LDL-C changes over time in response to aging, diet, treatment intensity, adherence, and genetic background. Accordingly, different analytic approaches have been developed to estimate long- term exposure.

2.1. Classical estimation models

The simplest historical approach treats cumulative exposure as a rough product of age and LDL-C level. Although intuitive, this approximation ignores time-varying treatment effects and does not reflect the true shape of LDL-C trajectories. More informative approaches use repeated measurements to estimate a time-weighted average (TWA), cumulative burden above a predefined threshold, or area-under-the-curve-like metrics derived from serial LDL-C values. These methods better capture the longitudinal nature of exposure and have been used in major cohort analyses evaluating cardiovascular and cerebrovascular outcomes (5, 6).

2.2. Emerging techniques and remaining limitations

More recent approaches extend beyond repeated averages and attempt to reconstruct life-course exposure more explicitly. These include trajectory-based models, threshold-based burden models, and genetics-informed estimates that infer lifelong LDL-C exposure from inherited variation. Such approaches are conceptually appealing because they align more closely with the biological hypothesis that arterial injury accumulates gradually over time. However, they also have important limitations. Most require dense longitudinal data, assumptions about missing values and treatment adherence, and careful handling of regression dilution bias and survivor bias. In addition, no universal threshold defines “high cumulative LDL-C burden,” and different studies operationalize this concept differently.

For stroke research, this lack of standardization matters. The apparent strength of the association between cumulative LDL-C burden and stroke risk will depend partly on how well long-term exposure is measured. Future studies should therefore move toward integrated models that combine serial LDL-C values with treatment history, adherence, polygenic susceptibility, and imaging markers of vascular injury. At present, a repeated-measure time-weighted average or area-under-the-curve-like metric may be the most practical choice for stroke-oriented research because it can be derived from routinely collected serial LDL-C values while still capturing both intensity and duration of exposure (5, 6). Until such methods are standardized, cumulative exposure should be viewed as a promising but still methodologically evolving construct.

3. Epidemiological evidence linking cumulative LDL-C exposure and ischemic stroke

3.1. Large-artery atherosclerosis and first stroke risk

The biological and epidemiological rationale for cumulative LDL-C burden is strongest in atherosclerotic disease. Genetic, mechanistic, and clinical evidence consistently supports LDL as a causal factor in atherosclerosis (3, 7). In the cerebrovascular field, prolonged LDL-C exposure is increasingly linked to extracranial and intracranial atherosclerosis, plaque progression, and incident ischemic stroke. Cohort data suggest that cumulative LDL-C burden is associated with asymptomatic intracranial atherosclerotic stenosis and future ischemic stroke even after adjustment for conventional vascular risk factors (8, 9).

A key implication is that single LDL-C measurements may underestimate true vascular risk. Two individuals with the same LDL-C value at one clinic visit may have very different life- course exposure histories, and therefore very different cumulative arterial injury. This distinction is especially relevant to large-artery atherosclerotic stroke, where plaque burden, remodeling, and instability reflect prolonged exposure rather than a single recent laboratory value.

3.2. Recurrence risk and secondary prevention

The relevance of cumulative LDL-C exposure persists after stroke has occurred. In secondary prevention, longitudinal LDL-C patterns may be more informative than one-off measurements: lower on-treatment LDL-C has been associated with reduced recurrence risk, and in Chinese patients, lower follow-up LDL-C was linked to fewer recurrent vascular events (10, 11). Together, these findings suggest that post-stroke risk is shaped by lipid exposure over time, particularly in atherosclerotic or other plaque-driven phenotypes, whereas interpretation should be more cautious in cardioembolic and small-vessel-predominant stroke, where the benefit of intensive LDL-C lowering is less certain (8, 9, 12). They also suggest potential population heterogeneity, such that the cumulative LDL-C burden framework may require cautious interpretation in East Asian populations rather than direct extrapolation from Western data (11–13).

4. Potential pathophysiological mechanisms

The association between cumulative LDL-C burden and stroke risk is biologically plausible because prolonged lipid exposure affects multiple components of the vascular and neurovascular system.

First, sustained LDL-C elevation promotes plaque initiation, growth, and destabilization. Endothelial dysfunction, oxidative stress, monocyte recruitment, macrophage activation, and foam-cell formation together enlarge the lipid-rich necrotic core and thin the fibrous cap, increasing the likelihood of rupture or erosion (14, 15). In cerebrovascular beds, these processes favor thromboembolism and artery-to-artery embolic events.

Second, oxidized LDL may impair blood–brain barrier (BBB) integrity. Experimental work shows that oxidized LDL can disrupt tight junction proteins, increase endothelial permeability, and amplify oxidative stress, all of which may weaken the neurovascular unit (16, 17). In aging brains, such injury may interact with amyloid-beta and tau-related vascular vulnerability, further aggravating neurovascular dysfunction (18).

Third, prolonged LDL-related oxidative signaling may promote a prothrombotic state. Oxidized LDL can activate platelet scavenger receptors such as CD36 and LOX-1, thereby enhancing platelet activation, aggregation, and thrombo-inflammatory signaling (19, 20). This mechanism may be particularly relevant to recurrent ischemic stroke, where persistent vascular inflammation and thrombogenicity can reinforce one another over time.

Taken together, these pathways suggest that cumulative LDL-C burden is not merely a statistical marker. Rather, it reflects a biologically meaningful process involving chronic endothelial injury, plaque vulnerability, BBB dysfunction, and thrombo-inflammatory amplification.

5. Clinical implications and therapeutic strategies

5.1. Lipid-lowering therapy and the reduction of future LDL-C burden

If long-term LDL-C exposure drives vascular injury, therapy should aim not only to lower LDL-C now but also to reduce future cumulative burden. This framing helps explain why early, sustained, and sufficiently intensive lipid lowering can yield durable cerebrovascular benefit.

5.1.1. Statins: foundational therapy in secondary prevention

Statins remain the foundation of lipid-lowering therapy because they reduce LDL-C through HMG-CoA reductase inhibition and also improve endothelial function, reduce inflammation, and stabilize plaques. In secondary stroke prevention, the SPARCL trial showed that high-dose atorvastatin reduced recurrent stroke despite a small increase in hemorrhagic stroke, and the Treat Stroke to Target (TST) trial showed that targeting LDL-C below 70 mg/dL after ischemic stroke or transient ischemic attack with atherosclerosis reduced subsequent cardiovascular events compared with a higher target range (21).

Viewed through the cumulative-exposure lens, the value of statins lies not simply in producing a lower LDL-C number at one visit, but in changing the patient’s exposure trajectory over years. This is particularly relevant for patients with large-artery atherosclerosis or other evidence of substantial historical lipid burden, in whom sustained lowering is likely to generate greater absolute benefit.

5.1.2. PCSK9 inhibitors and long-acting RNA-based agents

PCSK9 inhibition offers an especially strong rationale in patients with persistent residual risk despite statins. In the FOURIER trial, evolocumab added to statin therapy lowered LDL-C to very low levels and reduced cardiovascular events, including ischemic stroke, without a clear signal for major safety excess during trial follow-up (22, 23). Extended follow-up in FOURIER-OLE further suggested that achieved LDL-C levels below 20 mg/dL were associated with lower long-term cardiovascular risk without an obvious increase in major safety outcomes, although these analyses were not designed specifically around stroke subtypes (24). Importantly, evidence is no longer limited to individual trials. A 2024 systematic review and meta-analysis showed that PCSK9 inhibitors reduced stroke risk overall (RR 0.75, 95% CI 0.66–0.86) without increasing mortality, providing stronger support for their role in cerebrovascular prevention (25). Small-interfering RNA-based therapy has further expanded therapeutic options. Inclisiran reduces hepatic PCSK9 synthesis and achieves durable LDL-C lowering with infrequent dosing, which may improve long-term adherence in clinical practice (26). However, this point needs to be stated carefully: while LDL-C lowering within clisiran is well established, its benefit on hard cardiovascular outcomes has not yet been confirmed in a completed large- scale randomized outcomes trial. Ongoing studies such as ORION-4 are expected to clarify whether this durable lipid-lowering effect translates into fewer major cardiovascular events, including stroke. For patients with high cumulative LDL-C burden, statin intolerance, or difficulty sustaining control over time, such long-acting strategies may be especially relevant because cumulative exposure depends not only on biological efficacy but also on treatment persistence.

5.1.3. Lp(a) as a residual atherothrombotic risk factor and emerging RNA-targeted therapies

Beyond LDL-C, lipoprotein(a) [Lp(a)] should also be incorporated into stroke risk assessment and precision lipid management frameworks. Current evidence suggests that elevated Lp(a) is associated with an increased risk of ischemic stroke, with this association appearing more pronounced in younger patients and in large-artery atherosclerotic stroke (27, 28). Emerging evidence also suggests a possible role for remnant cholesterol in residual cerebrovascular risk beyond LDL-C, although current evidence remains less robust than that for Lp(a). Among patients with prior stroke or transient ischemic attack (TIA), higher Lp(a) levels have also been linked to stroke recurrence and worse functional outcomes (29). Because Lp(a) is largely genetically determined and remains relatively stable throughout life, at least one lifetime measurement is recommended in clinical practice to identify additional residual risk.

RNA-targeted therapies for Lp(a), including pelacarsen, olpasiran, zerlasiran, and lepodisiran, have shown marked and sustained Lp(a)-lowering effects in early-phase studies, suggesting potential clinical value (30–33). However, current evidence remains limited to surrogate endpoint improvement, and no completed randomized outcomes trial has yet demonstrated a reduction in stroke incidence or recurrence. Ongoing phase 3 trials, such as Lp(a)HORIZON and OCEAN(a)-Outcomes, include stroke-related endpoints and are expected to clarify whether substantial Lp(a) lowering translates into clinical benefit. Until such results are available, Lp(a) should be regarded as an important stroke risk-enhancing factor and a promising therapeutic target, rather than an intervention target already validated by stroke outcomes trials (34).

5.2. Balancing ischemic benefit and hemorrhagic risk

As LDL-C lowering becomes more intensive, a central clinical question emerges: how far should LDL-C be lowered in patients who may also carry hemorrhagic vulnerability? The answer is unlikely to be uniform across all stroke populations.

5.2.1. Identifying hemorrhagic-risk phenotypes

Current evidence does not support a simple conclusion that intensive LDL-C lowering uniformly increases intracerebral hemorrhage (ICH) risk. Instead, concern appears greatest in selected phenotypes, particularly patients with prior lobar ICH, probable cerebral amyloid angiopathy (CAA), multiple strictly lobar cerebral microbleeds, or cortical superficial siderosis. APOE epsilon2 and epsilon4 are established susceptibility alleles for CAA-related lobar ICH and may help identify patients with greater hemorrhagic vulnerability (35, 36). Likewise, MRI markers such as microbleed burden and distribution provide clinically meaningful clues about underlying small-vessel pathology and future ICH risk (37, 38).

However, available quantitative data suggest that caution is warranted once LDL-C is reduced to very low levels in selected patients. A 2021 meta-analysis reported a significantly increased risk of hemorrhagic stroke when LDL-C levels fell below 55 mg/dL, and a 2025 Mendelian randomization study further suggested that lower LDL-C itself, rather than statin exposure per se, may contribute to intracerebral hemorrhage susceptibility (39, 40). These findings do not justify abandoning intensive LDL-C lowering in all patients, but they do support a more individualized risk–benefit assessment in those with prior lobar ICH, probable cerebral amyloid angiopathy, or a substantial burden of strictly lobar cerebral microbleeds (39).

5.2.2. Dynamic risk stratification

A fixed LDL-C target is unlikely to capture the complexity of balancing recurrent ischemic risk against hemorrhagic vulnerability. A more realistic approach would incorporate cumulative LDL-C burden, stroke mechanism, plaque phenotype, prior hemorrhage history, MRI markers of small-vessel disease, blood pressure control, age, and concurrent antithrombotic therapy. Such a framework could move beyond a uniform “lower is always better” paradigm and instead estimate the net clinical benefit of different treatment intensities in different phenotypes.

Future multimodal prediction tools, potentially based on machine learning or Bayesian modeling, may improve this process. Still, these tools will require transparent validation, robust handling of missing data, and clinically interpretable outputs before they can guide routine treatment decisions.

5.2.3. Implementing stratified treatment pathways

In practice, patients with high ischemic risk and low hemorrhagic concern may reasonably be treated according to current intensive LDL-C-lowering paradigms, including combination therapy where needed. By contrast, inpatients with prior lobar ICH, probable CAA, or marked lobar microbleed burden,a more stepwise strategy may be appropriate. Such an approach might begin with careful control of blood pressure and other vascular risks, followed by moderate- intensity statin therapy or ezetimibe-based treatment (41, 42), with periodic clinical and imaging reassessment. If ischemic risk remains substantial, escalation to combination therapy can be considered through individualized shared decision-making rather than by automatic adherence to a single numerical target.

5.3. Toward clinically implementable precision lipid management

Precision lipid management in stroke should now move from conceptual framing toward clinically implementable pathways. In current practice, the most pragmatic approach may be to combine repeated LDL-C measurements or a time-weighted exposure metric with stroke mechanism, prior vascular events, hemorrhagic-risk markers, treatment tolerance, and expected adherence, thereby identifying patients most likely to benefit from earlier intensification or longer-acting therapy.

Implementation will also depend on care pathways that support serial lipid measurement, structured follow-up, adherence assessment, and periodic treatment reassessment rather than one-time target setting alone. In this context, agents such as bempedoic acid (30) and evinacumab (43), and other emerging options may expand treatment choices for selected patients, but their place in stroke care will need to be defined within subtype-specific and risk-stratified algorithms.

6. Future perspectives

Beyond near-term clinical implementation, the following section highlights research-oriented directions that may refine, validate, or operationalize the cumulative LDL-C burden framework overtime.

6.1. Mendelian genetics and causal inference

Mendelian randomization has strengthened the concept that lifelong LDL-C lowering reduces atherosclerotic risk, but stroke-specific inference remains more nuanced. For example, PCSK9- related variants show clear associations with coronary risk reduction, whereas associations with ischemic stroke are weaker and less consistent, likely reflecting limited statistical power, etiologic heterogeneity of ischemic stroke, and differences between lifelong genetic exposure and later pharmacologic intervention (44, 45). These nuances should not be overinterpreted as contradictions; rather, they underscore the complexity of translating genetic exposure models into stroke medicine.

6.2. Imaging correlates of cumulative LDL-C exposure

Imaging can serve as a phenotypic bridge between cumulative LDL-C burden and clinical stroke risk. High-resolution carotid and intracranial vessel-wall MRI can identify lipid-rich necrotic core, intraplaque hemorrhage, fibrous cap status, remodeling, and enhancement - features that may reflect prolonged atherogenic injury (46, 47). PET-based imaging may add information about inflammatory activity, whereas radiomic analysis may improve discrimination of high-risk intracranial plaques in specialized research settings (48). Integrating such imaging markers with longitudinal LDL-C exposure may eventually allow a more individualized assessment of vascular vulnerability.

6.3. Predictive modeling and artificial intelligence

Artificial intelligence is increasingly used in stroke prediction and outcome modeling, but translation into practice remains limited by data quality, missingness, bias, and explainability (49, 50). In the context of cumulative LDL-C exposure, the most promising future models are likely to be multimodal - combining longitudinal lipid trajectories, clinical covariates, stroke subtype, and imaging markers. Such models should be seen as decision-support tools rather than replacements for clinical judgment.

7. Conclusion

Cumulative LDL-C exposure offers a more biologically and clinically informative framework for cerebrovascular risk assessment than single time-point lipid measurement. Available evidence supports its association with ischemic stroke, particularly atherosclerotic stroke, and with recurrent vascular events after stroke. This relationship is mechanistically plausible through chronic effects on plaque vulnerability, endothelial and blood–brain barrier integrity, inflammation, and thrombosis. Clinically, the concept reinforces the importance of early and sustained LDL-C lowering while also highlighting the need for individualized treatment intensity in patients with competing hemorrhagic vulnerability. Future progress will depend on better measurement of cumulative exposure and on integrating lipid burden with genetics, imaging, and dynamic risk prediction.

Funding Statement

The author(s) declared that financial support was not received for this work and/or its publication.

Edited by: Fan Wang, Aerospace Clinical Medical College of Peking University, China

Reviewed by: Tao Xu, Shanghai Changzheng Hospital, China

Dongying Zhang, Chongqing Emergency Medical Center, China

Abbreviations: ASCVD, atherosclerotic cardiovascular disease; BBB, blood–brain barrier; CAA, cerebral amyloid angiopathy; CMBs, cerebral microbleeds; ICH, intracerebral hemorrhage; LDL-C, low-density lipoprotein cholesterol; MRI, magnetic resonance imaging; oxLDL, oxidized low-density lipoprotein; PCSK9, proprotein convertase subtilisin/kexin type 9; TWA, time-weighted average.

Author contributions

ZL: Writing – original draft, Writing – review & editing. LY: Writing – original draft. JL: Writing – original draft. YW: Writing – review & editing. JY: Supervision, Writing – review & editing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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