Zhiwei Hu
Hua Dong1- 1Department of Endocrinology, The First People’s Hospital of Jiashan, Jiashan Hospital Affiliated of Jiaxing University, Jiaxing, Zhejiang, China
- 2The First People’s Hospital of Jiashan, Jiashan Hospital Affiliated of Jiaxing University, Jiaxing, Zhejiang, China
By Sun J, Zhang X, Yang M, Yang S and Zeng H (2025) Front. Endocrinol. 16:1732824. doi: 10.3389/fendo.2025.1732824
1 Introduction
Sun et al. (1) conducted a large-scale cross-sectional study with 9,503 Chinese health screening participants. They investigated the associations between composite inflammatory indices—systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)—and carotid plaque formation. They identified AISI as the most robust inflammatory biomarker for carotid plaque prediction and developed a four-variable model—including age, fasting glucose, AISI, and diabetes mellitus—that showed favorable discriminative performance (AUC = 0.744 in the validation cohort). Notably, TC and LDL-C, both well-known causal risk factors for atherosclerosis, showed no significant association with carotid plaque in univariate analysis (P = 0.118 and P = 0.345, respectively). This finding contradicts both the well-validated pathophysiology of atherosclerotic cardiovascular disease (ASCVD) and recent high-impact evidence confirming LDL-C’s role in plaque development. Additionally, the study relied on single-timepoint measurements of inflammatory indices, which limits its ability to capture the dynamic nature of systemic inflammation. This dynamic process is critical in carotid plaque initiation and progression, as supported by longitudinal studies (3).
2 Discrepancy between TC/LDL-C and carotid plaque: challenging established ASCVD pathophysiology
The guidelines of ESC and ACC/AHA both agree that LDL-C is a major risk factor for atherosclerotic plaques during the entire process of plaque formation and development (2, 3). Separate meta-analyses of over 200 prospective cohort studies, Mendelian randomization studies, and randomized trials demonstrate a remarkably consistent dose-dependent log-linear association between the absolute magnitude of exposure of the vasculature to LDL-C and the risk of ASCVD (4). This effect appears to increase with longer duration of LDL-C exposure. Sun et al.’s failure to find an association between TC/LDL-C and carotid plaque suggests possible methodological or contextual limitations obscuring this relationship.
Three key factors may explain this discrepancy: (1) Unmeasured lipid subfractions: The study assessed only TC and LDL-C, omitting sdLDL-C and apolipoprotein B (apoB). Studies have shown that higher levels of sdLDL are significantly associated with an increased risk of ASCVD because sdLDL has low affinity for LDL receptors and a long residence time in the plasma (5). (2) Lack of adjustment for lipid-modifying interventions: The study did not report on statin use or adherence to lipid-lowering diets. These factors may weaken the observed lipid-plaque association. (3) Insensitive plaque detection criteria: Using the Mannheim Consensus criteria (focal thickening ≥1.5mm) may miss early subclinical plaques, where LDL-C-mediated lipid infiltration is most responsive to lipid levels. A review found that early plaque burden correlates strongly with lipid markers when detected by high-resolution imaging, highlighting the need for more sensitive detection methods (6).
To address this limitation, three improvements are recommended. First, incorporate lipid subfraction analysis by measuring sdLDL-C and apoB. Second, stratify by lipid-modifying interventions by adjusting analyses for statin use and the dietary inflammatory index. Third, enhance plaque characterization by combining structural ultrasound with functional assessments such as plaque attenuation values.
3 Single-timepoint inflammatory index measurement: limitations in capturing dynamic inflammatory burden
Systemic inflammation is a dynamic process. Transient elevations caused by acute, self-limited conditions differ fundamentally from chronic low-grade inflammation in their clinical relevance to atherosclerosis. A study (7) analyzed data from 3,927 individuals without carotid atherosclerosis who underwent yearly health check-ups at the Department of Health Management of Nanfang Hospital between 2011 and 2018. The median follow-up was 4.42 years. The study demonstrated that the continuous SIRI, a composite of neutrophils, monocytes, and lymphocytes, significantly predicted carotid plaque incidence. This predictive effect was more pronounced in subgroups without hypertension, diabetes, or hyperlipidemia. This supports the notion that persistent inflammatory burden—not isolated elevations—drives plaque progression and vulnerability.
The single-measure approach in Sun et al.’s study has two critical limitations. First, it may misclassify inflammatory status. A single elevated AISI or SIRI value could reflect acute inflammation, such as a recent upper respiratory tract infection not excluded by the 3-month criterion, rather than chronic pathology. Chronic inflammation of the vascular wall is a central pathogenesis of atherosclerosis (8). This may lead to an overestimation of the association between inflammation and prevalent plaque. Second, static baseline measurements lack predictive validity. They fail to capture inflammatory trajectories, such as progressive increases in AISI over time, which better predict incident plaque.
To address these limitations, we propose three improvements: (1) use a prospective longitudinal design with annual measurements of inflammatory indices to track changes and their links to new carotid plaques; (2) create composite scores that incorporate long-term inflammatory exposure, such as the average AISI over 2 years, to better capture chronic burden; (3) exclude participants with acute inflammation identified by questionnaires or biomarker panels (C-reactive protein>10 mg/L), isolating the effects of chronic inflammation.
4 Discussion
Sun et al.’s study provides valuable insights into AISI’s role in carotid plaque prediction. However, the LDL-C-carotid plaque discrepancy and the limitation of single-timepoint inflammatory measurements reduce its clinical translational value. Resolving the LDL-C paradox requires integrating lipid subfraction analysis (sdLDL-C, apoB) and adjusting for lipid-modifying treatments such as statins and PCSK9 inhibitors—steps that align findings with established ASCVD biology and enhance clinician trust in the four-variable model. Consequently, the model complements, rather than contradicts, evidence-based lipid-lowering strategies.
Additionally, longitudinal monitoring of inflammatory indices—consistent with Nai et al.’s study (6) that tracked SIRI and carotid plaque over 4.42-year follow-up—will strengthen the model’s ability to identify individuals at persistent plaque progression risk. Unlike single-timepoint measurements, tracking dynamic inflammatory changes and overall burden improves risk stratification. This also helps guide timely interventions to reduce plaque vulnerability.
Addressing these limitations through lipid subfraction analysis, cumulative inflammatory metrics, and prospective data collection will refine the study’s conclusions and better guide clinical practice, bridging the gap between inflammatory biomarker research and evidence-based ASCVD prevention to improve risk identification and management of carotid atherosclerosis.
Author contributions
ZH: Conceptualization, Writing – review & editing, Writing – original draft. HD: Writing – original draft. XH: Writing – original draft, Writing – review & editing, Investigation.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The authors 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.
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References
1. Sun J, Zhang X, Yang M, Yang S, and Zeng H. Exploring the association between immune-inflammation index and carotid plaque formation: a cross-sectional study in a large Chinese health screening population. Front Endocrinol. (2025) 16:1732824. doi: 10.3389/fendo.2025.1732824
2. Mach F, Koskinas KC, Roeters Van Lennep JE, Lennep JER, Tokgözoğlu L, Badimon L, et al. 2025 Focused Update of the 2019 ESC/EAS Guidelines for the management of dyslipidaemias. Eur Heart J. (2025) 46:4359–78. doi: 10.1093/eurheartj/ehaf190
3. Rao SV, O’Donoghue ML, Ruel M, Rab T, Tamis-Holland JT, Alexander JH, et al. 2025 ACC/AHA/ACEP/NAEMSP/SCAI guideline for the management of patients with acute coronary syndromes: A report of the american college of cardiology/american heart association joint committee on clinical practice guidelines. Circulation. (2025) 151:e771–862. doi: 10.1161/CIR.0000000000001309
4. Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. (2017) 38:2459–72. doi: 10.1093/eurheartj/ehx144
5. Krauss RM. Small dense low-density lipoprotein particles: clinically relevant? Curr Opin Lipidol. (2022) 33:160–6. doi: 10.1097/MOL.0000000000000824
6. Ueki Y, Itagaki T, and Kuwahara K. Lipid-lowering therapy and coronary plaque regression. J Atheroscler Thromb. (2024) 31:1479–95. doi: 10.5551/jat.RV22024
7. Nai W, Lei L, Zhang Q, Yan S, Xu J, Lin L, et al. Systemic inflammation response index and carotid atherosclerosis incidence in the Chinese population: A retrospective cohort study. Nutr Metab Cardiovasc Dis. (2025) 35:103787. doi: 10.1016/j.numecd.2024.103787
Keywords: aggregate index of systemic inflammation, atherosclerosis, carotid plaque, systemic immune- inflammation index, systemic inflammation response index
Citation: Hu Z, Dong H and Huang X (2026) Commentary: Exploring the association between immune-inflammation index and carotid plaque formation: a cross-sectional study in a large Chinese health screening population. Front. Endocrinol. 16:1763899. doi: 10.3389/fendo.2025.1763899
Received: 09 December 2025; Accepted: 26 December 2025; Revised: 09 December 2025;
Published: 12 January 2026.
Edited by:
Alexey Sarapultsev, Institute of Immunology and Physiology (RAS), RussiaReviewed by:
Hengyi Xu, The University of Texas at Austin, United StatesGhulam Kawnayn, Bangladesh College of Physicians and Surgeons, Bangladesh
Copyright © 2026 Hu, Dong and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xiaozhu Huang, MTU3MDAwNjAyMDdAMTYzLmNvbQ==