This prospective cross-sectional study included 54 individuals aged between 33 and 58 years, who were evaluated at a Scientific Research Institute - Regional Clinical Hospital No. 1 named after Prof. S.V. Ochapovsky (Krasnodar, Russia) in October 2024. The study cohort comprised two groups:

The sample size was based on convenience, as all eligible patients during the study period were included; no a priori sample size calculation was performed, which we acknowledge as a limitation of the study.

The diagnosis of atherosclerosis was established based on clinical criteria (intermittent claudication, pulse deficit), confirmed by duplex ultrasound and/or catheter-based angiography. The extent and severity of atherosclerotic lesions were graded according to standard international imaging criteria.

Inclusion criteria for the atherosclerosis group were based on WHO recommendations:

Exclusion criteria:

Control subjects were selected based on:

Demographic data, clinical risk factors, and laboratory findings were extracted from electronic health records. Complete blood count and standard biochemical tests were performed at the time of clinical evaluation.

Laboratory research of biomarkers of atherosclerosis was carried out by the method of enzyme immunoassay analysis using the corresponding reagents of the companies "AssayPro" and "RayBiotech", intended only for research purposes and in the absence of reference values usual for routine clinical practice in the kits of the company "RayBiotech", in connection with which the interpretation of absolute values of biomarkers is impossible outside the framework of this research protocol: Lp(a) (EL3001-1, AssayPro", USA), ANGPTL3 (ELH-ANGPTL3, RayBiotech, USA), Lp-PLA2 (ELH-LPPLA2, RayBiotech, USA).

Continuous variables were tested for normality using descriptive statistics and visual inspection of histograms. Due to non-Gaussian distribution and modest sample size, data are presented as median (interquartile range, IQR). Categorical variables are expressed as counts and percentages. Group comparisons were performed using the Mann–Whitney U test for continuous data and Fisher's exact test for categorical data. A logistic regression model was constructed to evaluate the combined predictive value of inflammatory indices and biochemical markers (including lipoprotein(a)). Model performance was assessed using the area under the receiver operating characteristic curve (AUC).

All statistical analyses were performed using Python 3.11 (pandas, scipy, scikit-learn). A two-sided p-value < 0.05 was considered statistically significant.

The study included 54 individuals, of whom 20 were diagnosed with early-onset peripheral atherosclerosis (Group 1), and 34 were age-matched healthy controls (Group 2). Patients with atherosclerosis were generally older, had longer smoking history, and showed a higher prevalence of diabetes mellitus and arterial hypertension compared with healthy controls. These and other baseline characteristics are detailed in Table 1.

Among calculated systemic inflammation indices, SIRI and AISI showed statistically significant elevations in the atherosclerosis group compared to controls:

Fibrinogen levels were markedly elevated in the atherosclerosis group (5.46 g/L vs. 3.71 g/L; p < 0.00001), making it the most significantly different laboratory marker.

Other markers, such as NLR, PLR, and SII, were also elevated in the patient group but did not reach statistical significance. Boxplots comparing these indices are presented in Figure 1 (A–F).

Lipoprotein(a), ANGPTL3, and Lp-PLA₂ were numerically different but not statistically significant between groups. However, lp(a) showed a trend toward higher values in the atherosclerosis group (102.2 vs. 51.4 mg/dL; p = 0.32).

Logistic regression analysis was performed to evaluate the diagnostic utility of these markers. A baseline model including age, NLR, PLR, SII, and low-density lipoprotein (LDL) yielded an AUC of 0.82. The addition of lp(a) improved AUC to 0.85, and further inclusion of fibrinogen significantly increased the model's performance to AUC = 0.91

These results suggest that the combination of systemic inflammatory indices, lp(a), and fibrinogen markedly enhances the ability to distinguish early atherosclerosis from healthy controls.

Figure 2 shows the ROC curves comparing the three models.

SIRI and AISI were significantly elevated in patients with early atherosclerosis, exceeding more commonly reported ratios such as NLR and PLR. SIRI, calculated from neutrophils, monocytes, and lymphocytes, reflects activity of both the innate and adaptive immune systems. It has been proposed as a superior marker of vascular inflammation and has demonstrated prognostic relevance in coronary artery disease and stroke.^9,10

Similarly, AISI — which multiplies neutrophil, monocyte, and platelet counts, then normalizes by lymphocytes — has recently been suggested as a more integrative marker of vascular immune activation. Its predictive value in cardiovascular settings is still being defined, but our findings suggest its relevance in early disease, possibly through better reflection of cellular interplay in atherogenesis.¹⁰ The fact that NLR, PLR, and SII did not reach statistical significance likely reflects the small sample size and the risk of type II error, rather than a lack of pathophysiological relevance. Indeed, prior studies have shown associations of these indices with coronary artery disease, stroke, and mortality.^9,10 Our findings therefore support SIRI and AISI as more sensitive and integrative measures in early PAD, consistent with the emerging literature.

These indices are readily obtainable from routine blood tests, making them attractive tools for risk stratification in primary care or outpatient vascular clinics.

Fibrinogen: a Dual Marker of Inflammation and Thrombosis

Among all individual parameters analyzed, fibrinogen showed the strongest discriminatory power, with a mean value of 5.46 g/L in the atherosclerosis group versus 3.71 g/L in controls (p < 0.00001). Fibrinogen is a well-established acute-phase reactant and a key player in thrombogenesis. Elevated plasma fibrinogen has been repeatedly linked with increased risk of peripheral artery disease (PAD), ischemic stroke, and coronary heart disease.^11,12

The role of fibrinogen in atherogenesis is multifaceted: it promotes platelet aggregation, increases blood viscosity, and supports leukocyte adhesion to the endothelium. Its elevation in our early atherosclerosis cohort suggests that pro-thrombotic and inflammatory pathways may be activated even before overt cardiovascular events occur, supporting its utility as both a biomarker and potential therapeutic target.

While Lp(a) did not reach statistical significance in univariate comparisons, its inclusion in the multivariate model improved AUC from 0.85 to 0.91. This aligns with recent evidence that Lp(a) may act as a conditional risk amplifier, exerting its deleterious effects more prominently in the presence of systemic inflammation or endothelial dysfunction.^13,14

Lp(a) is a genetically determined lipoprotein consisting of an LDL-like core and apolipoprotein(a), which imparts pro-atherogenic and pro-thrombotic properties. Its independent role in atherosclerotic cardiovascular disease (ASCVD) is well established, and recent trials have focused on specific inhibitors (e.g., antisense oligonucleotides) as potential therapeutic strategies. The observed synergy between Lp(a) and inflammatory indices in our cohort supports the idea that multi-pathway interaction may be essential in early atherogenesis, particularly in non-coronary territories such as lower limbs.

Current ESC Dyslipidemia Guidelines (2025) emphasize the importance of measuring Lp(a) at least once in adulthood, with thresholds of >50 mg/dL (>125 nmol/L) considered clinically relevant for elevated cardiovascular risk. In our cohort, the PAD group median exceeded this cut-off, supporting its pathophysiological role despite a lack of univariate significance. This suggests that in smaller cohorts, Lp(a) may not act as an independent discriminator, but in synergy with other pro-atherogenic factors, it strengthens predictive models.

A visual summary of the proposed combined biomarker model is presented in the Central Illustration.

The stepwise improvement of the logistic regression model illustrates the incremental value of combining markers. Age and classical inflammatory indices yielded a baseline AUC of 0.82, which is already clinically relevant. Addition of Lp(a) modestly improved the model (AUC 0.85), while fibrinogen provided a substantial incremental gain (AUC 0.91). Regression coefficients and confidence intervals (Supplementary Table 2) further demonstrate the relative weight of these variables. Such findings emphasize that multi-marker panels may provide a cost-effective and practical tool for early vascular risk assessment, especially in resource-limited settings where advanced imaging is not widely available.

Our findings offer a practical and biologically plausible model for early detection of subclinical atherosclerosis using simple, widely available blood-based parameters. This approach could be particularly valuable in younger patients or those with non-specific vascular symptoms, where standard risk scores may underestimate disease burden.

From a research perspective, future studies should validate these results in larger, ethnically diverse populations, include longitudinal follow-up for clinical outcomes, and integrate additional biomarkers such as hsCRP, IL-6, or oxidized LDL. The potential role of novel therapeutic strategies—such as antisense oligonucleotides targeting Lp(a)—also warrants exploration in PAD cohorts, given that most ongoing trials are focused on coronary artery disease.¹⁵

We propose that fibrinogen and SIRI/AISI be considered in future risk models, possibly complementing or refining existing tools such as the ABI or SCORE2.

Several limitations must be acknowledged: