Introduction

Prediabetes is an established risk factor for cardiovascular disease (CVD), driven in part by metabolic abnormalities such as dyslipidemia and chronic low-grade inflammation that promote vascular injury and atherosclerosis1. Proinflammatory cytokines, including interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α), play critical roles in endothelial dysfunction, a key event in early atherogenesis2. IL-1β contributes to atherogenesis by enhancing oxidative stress, inducing endothelial apoptosis, and promoting vascular inflammation through multiple mechanisms3,4. Elevated reactive oxygen species (ROS) mediate the oxidation of low-density lipoprotein (ox-LDL), facilitating foam cell formation and macrophage recruitment, thus perpetuating the inflammatory cycle within the vessel wall5. Additionally, IL-1β increases the expression of adhesion molecules such as ICAM-1 and VCAM-1, further compromising vascular integrity6,7. TNF-α signals primarily through tumor necrosis factor receptor 1 (TNFR1), triggering endothelial apoptosis and upregulating adhesion molecules that exacerbate endothelial dysfunction8,9,10.

The Atherogenic Index of Plasma (AIP), calculated as log₁₀[TG (mg/dL)/HDL-C (mg/dL)], integrates atherogenic and protective lipid fractions and is recognized as a robust predictor of cardiovascular risk and subclinical atherosclerosis11. However, few studies have investigated the relationship between inflammatory biomarkers, particularly IL-1β and TNFR1, and AIP in individuals with prediabetes. This study aims to elucidate the association of these proinflammatory cytokines with AIP to improve the prediction of atherogenic risk in prediabetic patients.

Methods and materials

This study adopted a cross-sectional design involving a sample of 56 individuals. The mean age of the patients in the study was 51.5 years, approximately 55.36% were female (n = 31), and 44.64% male (n = 25). The study was conducted in the Department of Endocrine Clinic at National Hospital it was performed by the declaration of Helsinki and adhered to all relevant guidelines for human research and was approved by the Medical Ethics Committee of Damascus University (No. m/471-12/5/2021).This study is registered in the Australian New Zeeland Clinical Registry on 20/09/2024. Trial ID: ACTRN12624001135505 (https://www.anzctr.org.au/ACTRN12624001135505.aspx).

All participants provided written informed consent before enrollment.

It included 56 nonsmoker , newly diagnosed prediabetes patients aged 30–71 years (51.5 ± 9.52), with body mass index,calculated by the following formula: Weight (kg)/height (m)2 BMI (29.08 ± 3.75), diagnosed based on American Diabetes Association 2010 (ADA 2010) criteria.The prediabetes is defined as fasting serum glucose (100–125 mg/dL). or 2 h (Oral Glucose Tolerance Test) OGTT (145 mg/dl–200 mg/dl) or HbA1c% (5.6–6.4%).

Exclusion criteria included

  1. (i)

    Patients who had prior diagnosis with diabetes (defined as FPG ≥ 126 mg/dL or HbA1c ≥ 6.5%) and those who treated previously with metformin.

  2. (ii)

    Patients with a history of cardiovascular disease, renal disease, tumors, or autoimmune disorders.

Inclusion criteria included individuals with prediabetes who had not received any prior pharmacological treatment.

Sample collection and analyses

A total of 5 mL of venous blood was collected from all patients after an overnight fast and placed in tubes containing heparin and EDTA for HbA1c percentage determination. Fasting plasma glucose (FPG) levels were measured in all patients. This was done by centrifuging the plasma for 10 min at 2500 rpm and using an automatic analyzer (AMS, Italy) with a standard glucose oxidase method.

HbA1c levels were measured photometrically using the HemoCue HbA1c 501 analyzer, which is based on the boronate affinity method. Lipid profiles, including total cholesterol, triglycerides, low-density lipoprotein (LDL), and high-density lipoprotein (HDL), were measured photometrically using the ELITech kits (reference # CHSL-M690, reference # TGML-M690, reference # CLDL-M330, reference # CHDL-M330, France) respectively. The AIP index is calculated by the formula log 10[TG (mg/dl)/HDL-C (mg/dl)].

Quantitative determination of serum levels of CRP was measured by turbidometry using the Biorex kit (catalog # BXC0324A, UK).

Insulin levels were measured in plasma samples via ELISA (diametra kit, UK).

TNFR1 and IL-1β were measured by the quantitative sandwich enzyme-linked immunosorbent assay (ELISA) method using RayBiotech ELISA kit, according to the manufacturer’s protocol.Human TNFR1(catalog# ELH-TNFR1,RayBiotech,USA).Human IL-1β (catalog# ELH-IL1b, RayBiotech,USA).

The sensitivity of TNFR1 assay and IL-1β assay, defined as The minimum detectable doses of Human TNFR1 and Human IL-1 beta, was determined to be 1 pg/ml, and 0.3 pg/ml, respectively). The specificity was confirmed by the manufacturer and no cross-reactivity was observed with other cytokines for both kits. The Intra-Assay CV%: < 10% and Inter-Assay CV%: < 12% for both assays.

Blood samples for these measurements were collected in heparin-containing tubes, and the supernatant was separated by centrifugation for 10 min at 3000 rpm, and stored at − 80 °C until analysis.

Sample size calculation

The sample size was calculated using G Power software (GPower 3.1), setting α error at 5%, number of predictors (k = 2), and power (1-β) at 80%. Based on the prevalence of the major outcome AIP due to prior studies related to dyslipidemia and prediabetes with high AIP. It was assumed to be 50%. The minimum sample size required was determined to be 35 participants. We included 56 participants in the study to increase the power of the study.

Statistical analysis

All statistical analyses were performed with SPSS for Windows, version 27 17.0k (SPSS Inc., Chicago, IL, USA). Normality of continuous variables was assessed using Kolmogorov–Smirnov test. Variables with normal distribution are expressed as mean ± SD, whereas non-normally distributed variables are reported as median (IQR).

Spearman’s rank correlation was used to examine the association between AIP and serum levels of TNFR1, IL-1β, and Pearson’s analysis was performed to investigate the association between AIP and CRP, and other continuous variables due to the non-normal distribution of the data.

AIP was dichotomized using a cut-off value of 0.24 to define low and high-risk categories. Given the categorical nature of the outcome variable, binary logistic regression(hierarchical mode) was was constructed to estimate the association between predictive variables and a high risk of atherogenic index of plasma (AIP). A significance level of p < 0.05 was considered.

Variables such as age, BMI, and sex were excluded from the model due to a lack of significant correlation with AIP. Only biomarkers that were significantly associated with AIP in bivariate analyses were included in the regression model.

The inclusion of TNFR1 and IL-1β in logistic regression was based on their potential link to endothelial inflammation as outlined in the article.

Binary multiple logistic regression using a stepwise model was constructed to estimate the association between predictive variables and a high risk of atherogenic index of plasma (AIP). A significance level of p < 0.05 was considered.

Results

A total of 56 non smokers prediabetes patients who had never undergone treatment were recruited. The mean age of the patients in the study was 51.5 years, approximately 55.36% were female (n = 31), and 44.64% male (n = 25). Table 1 presents the demographic and biochemical characteristics of the study participants. Descriptive data are expressed as mean ± standard deviation (SD) or median [interquartile range (IQR)], depending on the distribution of each variable.The mean fasting blood glucose (FBG) level was 112.38 ± 7.58 mg/dL, while the median HbA1c was 6.00% [IQR: 0.38].

Table 1 Baseline demographic and biochemical characteristics of the study participants.
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Table 2 shows the levels of inflammatory biomarkers measured in participants stratified by their atherogenic index of plasma (AIP). Participants in the high AIP group (AIP > 0.24, n = 46) showed higher mean concentrations of TNFR1 (358.07 ± 95.2 pg/mL), IL-1β (1.13 ± 0.86 pg/mL), and CRP (4.99 ± 2.80 mg/dL) compared to those in the low AIP group (AIP ≤ 0.24, n = 10), who had mean levels of TNFR1 (298.68 ± 21.8 pg/mL), IL-1β (0.58 ± 0.13 pg/mL), and CRP (1.89 ± 1.13 mg/dL).

Table 2 Inflammatory biomarkers in study participants stratified by AIP category
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These descriptive findings suggest a potential association between higher AIP values and elevated systemic inflammatory markers.

To investigate the potential relationship between inflammation and atherogenic risk, Spearman’s correlation analysis was conducted. Results showed a significant positive correlation between AIP and IL-1β (r = 0.558, p < 0.001), as well as a moderate positive correlation between AIP and TNFR1 (r = 0.47, p < 0.001),and a significant positive correlation between CRP and AIP (r = 0.57, p < 0.001) These results indicate that elevated levels of IL-1β and TNFR1 are likely linked to increased atherogenic risk in prediabetes.a shown in Table 3.

Table 3 Association between inflammatory biomarkers and AIP.
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Correlational analysis between BMI and variables showed no significant correlation. (BMI versus AIP, r = 0.06, p = 0.65), (BMI versus TNFR1, r = 0.22, p = 0.1), (BMI versus IL-1β, r = 0.28, p = 0.053). This finding suggests that BMI has no influence on AIP and other variables in our study, due to the small sample size.

Patients are categorized according to atherogenic index of plasma (AIP) to low (< 0.24) and high (> 0.24). The proportion of high AIP was 82.1%.

The logistic regression model summary presented in Table 4 shows the results of a hierarchical logistic regression analysis assessing predictors of a high Atherogenic Index of Plasma (AIP). In the first block, IL-1β was entered alone, resulting in a Nagelkerke R2 of 0.507, indicating that IL-1β explained 50.7% of the variance in the likelihood of high AIP.

Table 4 logistic regression model summary for predicting high atherogenic index of plasma (AIP).
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With the addition of TNFR1 in the second block, the Nagelkerke R2 increased slightly to 0.541, suggesting that TNFR1 contributed additional explanatory power to the model beyond IL-1β.

In the third block, CRP was included alongside IL-1β and TNFR1, which marginally decreased the Nagelkerke R2 to 0.514, indicating that while CRP contributes to the model, its incremental predictive value is limited when the other two biomarkers are considered.

The results of the hierarchical binary logistic regression analysis estimating the effect of each predictor on the likelihood of a high Atherogenic Index of Plasma (AIP) are summarized in Table 5. In Step 1, IL-1β demonstrated a significant positive association with high AIP, with a regression coefficient (B) of 9.935 (p = 0.004) and an odds ratio (OR) of 20648.458. This indicates that each unit increase in IL-1β is associated with a markedly increased odds of elevated AIP, underscoring IL-1β as a strong independent risk factor.

Table 5 Logistic regression coefficients and odds ratios for predictors of high atherogenic index of plasma (AIP).
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In Step 2, after adding TNFR1 to the model alongside IL-1β, the coefficient for IL-1β increased to 13.364, approaching significance (p = 0.055) with a very high OR of 636868.535. However, TNFR1 showed a negative but non-significant association with high AIP (B = − 0.025, p = 0.524, OR = 0.975), suggesting that changes in TNFR1 levels do not meaningfully alter the risk for elevated AIP in this model.

In Step 3, CRP was added to the model with IL-1β and TNFR1. IL-1β’s effect size remained large but non-significant (B = 12.949, p = 0.312, OR = 420,242.073). TNFR1 continued to show a non-significant negative association (B = − 0.025, p = 0.521, OR = 0.975), and CRP showed a minimal positive but non-significant effect (B = 0.047, p = 0.969, OR = 1.048). These results indicate that CRP adds little predictive value beyond IL-1β and TNFR1.This indicates that IL-1β is a key risk factor for a higher atherogenic index of plasma (AIP).

Discussion

This study shows that 82.1% of the patients with prediabetes selected based on the ADA 2010 criteria exhibited an atherogenic index of plasma (AIP) exceeding 0.24. This indicates a high proportion at increased risk of cardiovascular disease. Although this percentage does not represent the prevalence of atherogenesis in the prediabetic population, it is rather the proportion of high AIP in our sample. It is important to note that our small sample size limits the generalization of this finding. Further research should confirm whether this proportion can be representative of the prediabetic population. This elevated value underscores the importance of AIP which reflects the underlying metabolic disorders and associated inflammatory states, particularly insulin resistance. Insulin resistance exacerbates dyslipidemia and subsequently impairs beta cell function. These results could serve as a predictive biomarker for atherogenesis risk in prediabetes12. A recent study fosters the significance of AIP as a novel predictor for dyslipidemia in the prediabetic stage, as well as being associated with the increased risk of cardiovascular disease13. This result highlights the necessity of an early intervention targeting dyslipidemia that could mitigate cardiovascular disease morbidity14.

Our study emphasizes the interplay between metabolic dysregulation and inflammatory processes. The oxidative stress enhances macrophage infiltration into the vascular intima and activates The NLRP3 inflammasome. Upon activation, it cleaves pro-IL-1β into its active form, interleukin-1 beta (IL-1β)15. IL-1β is a primordial inflammatory cytokine associated with atherosclerosis through various pathways.

IL-1β activates the renin–angiotensin–aldosterone system that participates in the blood vessel remodeling16. Additionally, IL-1β disrupts vascular integrity by activating vascular cell adhesion molecule 1 (VCAM 1) exacerbating vascular inflammation and increasing plaque instability fostering a chronic atherogenic environment17.

In our hierarchical logistic regression model, IL-1β emerged as a potent independent predictor of elevated AIP (OR = 20648.46; p = 0.004), explaining 50.7% of the variance in atherogenic risk. This supports IL-1β’s pivotal role in driving inflammatory mechanisms that accelerate cardiovascular pathology in prediabetes. Mechanistically, IL-1β promotes atherosclerosis by increasing adhesion molecule expression, inducing monocyte chemoattractant protein-1 (MCP-1) production, and stimulating vascular smooth muscle cell proliferation and migration, all of which compromise vascular integrity18. Additionally, IL-1β inhibits HDL biogenesis via PPARα suppression and enhances LDL oxidation, facilitating foam cell formation and plaque vulnerability19. IL-1β propagates atherogenesis by suppressing HDL biogenesis via PPARα inhibition and inducing LDL oxidation which enhances foam cell formation, contributing to plaque instability19. This finding aligns with clinical trials, CANTOS study, that showed IL-1β targeting can reduce cardiovascular events20.

Conversely, although C-reactive protein (CRP) is widely recognized as a general marker of systemic inflammation, it did not significantly improve the prediction of high AIP in our adjusted models. This finding is consistent with emerging literature suggesting that IL-1β provides superior prognostic value for atherosclerosis risk compared to CRP in prediabetic populations21.

Moreover, the tumor necrosis factor receptor 1 (TNFR1) pathway contributes to endothelial dysfunction and atherogenesis. TNFR1 activation by TNF-α upregulates the expression of adhesion molecules, perpetuating monocyte infiltration and endothelial activation. It also synergizes with IL-1β to amplify inflammatory cascades, further accelerating atherosclerosis. These findings underscore the importance of targeting inflammatory pathways, particularly IL-1β and TNFR1 signaling, to mitigate cardiovascular risk in individuals with prediabetes22.

Summary of strengths, limitations, and future directions

While our research investigates the role of TNFR1 and IL-1β as predictive biomarkers for atherogenesis in prediabetes, therefore enlightening us on inflammatory mechanisms adding to the risk of cardiovascular disease (CVD). The cross-sectional approach, however, restricts causal inferences, and the size of the sample limits the generalization. Although the final hierarchical logistic regression model demonstrated strong predictive value for elevated AIP, with IL-1β alone explaining over 50% of the variance (Nagelkerke R2 = 0.507), the sample size remains relatively small. This may limit the stability and generalizability of the model. A larger cohort would enhance model robustness and allow inclusion of additional covariates to further improve predictive accuracy and external validity.

To validate these conclusions, future investigations using longitudinal designs and more varied samples will be necessary. A substantial imbalance existed between AIP groups, with 46 participants classified as high AIP and 10 as low AIP. This likely reflects the elevated cardiometabolic risk in prediabetes but may limit statistical power to detect differences and inflate odds ratio estimates. Future studies with more balanced group sizes are needed to validate these findings.This study did not include a non-diabetic (healthy) control group, which limits our ability to compare biomarker levels to reference ranges in the general population. However, as prediabetes itself is a pathological condition involving early metabolic and inflammatory changes, our focus was on characterizing associations within this specific group. Future studies including healthy controls are warranted to expand upon these findings.

With new treatments such as IL-1β inhibitors (e.g., canakinumab) showing hope in lowering cardiovascular events, IL-1β and TNFR1 have biological value for prediabetes CVD risk evaluation. Future studies should investigate cause-effect relationships and focused anti-inflammatory therapies using longitudinal studies.

Conclusion

Our results emphasize the importance of TNFR1 and IL-1β for evaluating AIP; IL-1β is especially important for supporting atherogenesis. This data emphasizes the need to focus on IL-1β as a possible plan to reduce cardiovascular risk in individuals with prediabetes. From a clinical standpoint, this points to an optimistic path for early treatment using anti-inflammatory agents. Future investigations should focus on longitudinal studies with larger sample sizes to determine causality and assess the risk stratification for targeted therapies in slowing cardiovascular disease development.