Search Results (123)

Background/Objectives: Drug-coated balloons (DCBs) are emerging as a promising treatment for peripheral artery disease; however, current technologies lack flexibility in customizing drug release profiles and composition, limiting their therapeutic potential. This study aims to develop a Gelatin (Gel) and Sodium Alginate (Alg) bioink loaded with apolipoprotein A-I (apoA-I) for controlled drug delivery by using additive manufacturing technologies. Methods: We developed and printed via rapid freeze prototyping (RFP) a Gel and Alg bioink loaded with different concentrations of apoA-I. Mechanical properties related to compressional and tensile forces have been studied, as well as the structural stability and active release from the 3D structure of apoA-I (cholesterol efflux assays). The biological behavior of HUVEC cells with and without ApoA-I was assessed by proliferation assay, metabolic activity analysis, and fluorescence imaging. Results: The 3D structures presented breakpoint stress values consistent with the mechanical requirements for integration within a DCB, and the ability to effectively promote cholesterol transport in J774 cells. Moreover, in vitro studies on HUVECs revealed that the scaffolds exhibited no cytotoxic effects, leading to increased ATP levels and enhanced metabolic activity over time, confirming the possibility to obtain RFP-printed Alg/Gel scaffolds able to provide a stable structure capable of controlled apoA-I release. Conclusions: These findings support the potential of Alg/Gel+apoA-I scaffolds as biocompatible drug delivery systems for vascular applications. Their ability to maintain structural integrity while enabling controlled biomolecular release positions them as promising candidates for personalized cardiovascular therapy, facilitating the rapid customization of bioprinted therapeutic platforms. Full article

Background/Objectives: Adverse pregnancy outcomes (APOs), which include hypertensive disorders of pregnancy (gestational hypertension, preeclampsia, and related disorders), gestational diabetes, preterm birth, fetal growth restriction, low birth weight, small-for-gestational-age newborn, placental abruption, and stillbirth, are health risks for pregnant women that can have fatal outcomes. This study’s aim is to investigate the usefulness of artificial intelligence (AI) in improving these outcomes and includes changes in the utilization of ultrasound, continuous monitoring, and an earlier prediction of complications, as well as being able to individualize processes and support clinical decision-making. This study evaluates the use of AI in improving at least one APO. Methods: PubMed, Web of Science, and Scopus databases were searched and limited to the English language, humans, and between 2020 and 2024. This scoping review included peer-reviewed articles across any study design. However, systematic reviews, meta-analyses, unpublished studies, and grey literature sources (e.g., reports and conference abstracts) were excluded. Studies were eligible for inclusion if they described the use of AI in improving APOs and the associated ethical issues. Results: Five studies met the inclusion criteria and were included in this scoping review. Although this review initially aimed to evaluate AI’s role across a wide range of APOs, including placental abruption and stillbirth, the five selected studies focused primarily on preterm birth, hypertensive disorders of pregnancy, and gestational diabetes. None of the included studies addressed placental abruption or stillbirth directly. The studies primarily utilized machine-learning models, including extreme gradient boosting (XGBoost) and random forest (RF), showing promising results in enhancing prenatal care and supporting clinical decision-making. Ethical considerations, including algorithmic bias, transparency, and the need for regulatory oversight, were highlighted as critical challenges. Conclusions: The application of these tools can improve prenatal care by predicting obstetric complications, but ethics and transparency are pivotal. Empathy and humanization in healthcare must remain fundamental, and flexible training mechanisms are needed to keep up with rapid innovation. AI offers an opportunity to support, not replace, the doctor–patient relationship and must be subject to strict legislation if it is to be used safely and fairly. Full article

Age-related macular degeneration (AMD) is a multifactorial retinal disease influenced by complex molecular mechanisms, including genetic susceptibility, inflammation, oxidative stress, and metabolic dysregulation. While substantial progress has been made in understanding its pathogenesis, the full molecular underpinnings of AMD remain unclear, impeding the effectiveness of current therapeutic strategies. This study provides an in-depth exploration of the molecular interactions involved in AMD progression, particularly focusing on genetic predispositions (such as CFH, ARMS2/HTRA1, and APOE), inflammatory pathways (including complement system dysregulation and cytokine responses), lipid metabolism (e.g., cholesterol homeostasis and drusen formation), and angiogenesis (VEGF signaling). Through a systematic review and bibliometric analysis of literature published between 2015 and 2025, the study identifies emerging research trends, existing gaps, and promising future therapeutic directions. It further investigates innovative precision medicine approaches, including gene editing (CRISPR), RNA therapeutics (siRNA, antisense oligonucleotides), immunomodulatory therapies, and nanotechnology-based drug delivery systems. Additionally, the study examines the role of metabolic disorders such as diabetes and dyslipidemia in AMD progression, highlighting the influence of systemic health factors on disease onset. Finally, the potential of artificial intelligence (AI) in enhancing AMD management through biomarker-based risk stratification, predictive modeling, and personalized treatment optimization is assessed. By mapping the intricate molecular networks underlying AMD and evaluating novel therapeutic strategies, this research aims to contribute to the development of more effective, individualized treatment protocols for patients with AMD. Full article

Background: Coronary heart disease (CHD) remains a leading cause of death and has been associated with alterations in plasma lipoprotein particles and inflammation markers. This study aimed to evaluate and compare standard and advanced lipid parameters and inflammatory biomarkers in CHD cases and matched control subjects. We hypothesized that incorporating advanced lipid and inflammatory biomarkers into risk models would improve CHD risk prediction beyond the standard lipid measures. Methods: CHD cases (n = 227, mean age 61 years, 47% female) and matched controls (n = 526) underwent fasting blood collection while off lipid-lowering medications. Automated chemistry analyses were performed to measure total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-C (LDL-C), small dense LDL-C (sdLDL-C), apolipoproteins (apos) A-I and B, lipoprotein(a) (Lp(a)), high-sensitivity C-reactive protein (hsCRP), serum amyloid-A (SAA), myeloperoxidase (MPO), and apoA-I in HDL particles (via 2-dimensional electrophoresis and immunoblotting). Univariate, multivariate, and machine learning analyses compared the CHD cases with the controls. Results: The most significant percent differences between male and female cases versus controls were for hsCRP (+78%, +200%), MPO (+109%, +106%), SAA (+84%, +33%), sdLDL-C (+48%; +43%), Lp(a) (+43%,+70%), apoA-I in very large α-1 HDL (−34%, −26%), HDL-C (−24%, −27%), and apoA-I in very small preβ-1 HDL (+17%; +16%). Total C, non-HDL-C, and direct and calculated LDL-C levels were only modestly higher in the cases. Multivariate models incorporating advanced parameters were statistically superior to a standard model (C statistic: men: 0.913 vs. 0.856; women: 0.903 versus 0.838). Machine learning identified apoA-I in preβ-1-HDL, α-2-HDL, α-1-HDL, α-3-HDL, MPO, and sdLDL-C as the top predictors of CHD. Conclusions: This study introduces a novel approach to CHD risk assessment by integrating advanced HDL particle analysis and machine learning. By assessing HDL subpopulations (α-1, α-2, preβ-1 HDL), inflammatory biomarkers (MPO, SAA), and small dense LDL, we provide a more refined stratification model. Notably, preβ-1 HDL, an independent risk factor reflecting impaired cholesterol efflux from the artery wall, is highlighted as a critical marker of CHD risk. Our approach allows for earlier identification of high-risk individuals, particularly those with subtle lipid or inflammatory abnormalities, supporting more personalized interventions. These findings demonstrate the potential of advanced lipid profiling and machine learning to enhance CHD risk prediction. Full article

Background and Objective: Diabetic peripheral neuropathy (DPN) is a prevalent complication of type 2 diabetes mellitus (T2DM), with nerve conduction studies (NCSs) serving as the diagnostic gold standard. Early diagnosis is critical for effective management, yet many cases are detected late due to the gradual onset of symptoms. This study explores the relationship between hematological tests and NCS outcomes in T2DM patients to improve the early detection of DPN. Material and Methods: This retrospective study involved T2DM patients exhibiting neuropathic symptoms, and patients were divided based on NCS findings into groups with normal and abnormal results to assess the diagnostic value of various hematological markers, clinical, and demographic data for DPN. Results: Among 400 participants, 57% (n = 228) had abnormal NCS results indicative of DPN. Significant differences were observed in the abnormal-NCS group, including older age, longer diabetes duration, higher levels of fasting plasma glucose, HbA1c, and apolipoprotein B, along with lower eGFR, HDL-C, and Apo A-I levels. Notably, negative correlations were found between HDL-C, Apo A-I, vitamin B12, and specific NCS measurements, while positive correlations existed with sural sensory nerve amplitudes. Multivariate analysis highlighted the importance of age, diabetes duration, hyperglycemia, and specific hematologic markers in predicting DPN. Conclusions: The findings confirm that NCSs, combined with hematologic testing, can effectively identify DPN in T2DM patients. Consistent with prior research, prolonged hyperglycemia and nephropathy progression are strongly linked to DPN development. Additionally, lower levels of HDL-C, Apo A-I, and vitamin B12 are associated with the condition, suggesting their potential utility in early diagnostic protocols. Full article

High-density lipoprotein (HDL) exhibits multiple metabolic protective functions, such as facilitating cellular cholesterol efflux, antioxidant, anti-inflammatory, anti-apoptotic and anti-thrombotic properties, showing antidiabetic and renoprotective potential. Diabetic kidney disease (DKD) is considered to be associated with high-density lipoprotein cholesterol (HDL-C). The hyperglycemic environment, non-enzymatic glycosylation, carbamylation, oxidative stress and systemic inflammation can cause changes in the quantity and quality of HDL, resulting in reduced HDL levels and abnormal function. Dysfunctional HDL can also have a negative impact on pancreatic β cells and kidney cells, leading to the progression of DKD. Based on these findings, new HDL-related DKD risk predictors have gradually been proposed. Interventions aiming to improve HDL levels and function, such as infusion of recombinant HDL (rHDL) or lipid-poor apolipoprotein A-I (apoA-I), can significantly improve glycemic control and also show renal protective effects. However, recent studies have revealed a U-shaped relationship between HDL-C levels and DKD, and the loss of protective properties of high levels of HDL may be related to changes in composition and the deposition of dysfunctional particles that exacerbate damage. Further research is needed to fully elucidate the complex role of HDL in DKD. Given the important role of HDL in metabolic health, developing HDL-based therapies that augment HDL function, rather than simply increasing its level, is a critical step in managing the development and progression of DKD. Full article

Background and Objectives: Atherosclerosis is an inflammatory condition that results in cholesterol accumulating within vessel wall cells. Atherosclerotic cardiovascular disease is the leading cause of mortality worldwide due to this disease being a major contributor to myocardial infarctions and cerebrovascular accidents. Research suggests that cholesterol accumulation occurring precisely within arterial endothelial cells triggers atherogenesis and exacerbates atherosclerosis. Furthermore, inflamed endothelium acts as a catalyst for atherosclerotic development. Therefore, enhancing cholesterol removal specifically in pro-inflammatory endothelial cells may be a potential treatment option for atherosclerosis. While we have previously shown that inhibiting the microRNA guide strand miR-33a-5p within pro-inflammatory endothelial cells increases both ABCA1 expression and apoAI-mediated cholesterol efflux, it is unknown whether inhibiting the miR-33a-3p passenger strand in pro-inflammatory endothelial cells causes similar atheroprotective effects. In this study, this is what we aimed to test. Materials and Methods: We used plasmid transfection to knockdown miR-33a-3p expression within cultured pro-inflammatory immortalized mouse aortic endothelial cells (iMAECs). We compared ABCA1 expression and apoAI-mediated cholesterol efflux within these cells to cultured pro-inflammatory iMAECs transfected with a control plasmid. Results: The knockdown of miR-33a-3p expression within pro-inflammatory iMAECs resulted in a significant increase in ABCA1 mRNA expression. However, the inhibition of miR-33a-3p did not significantly increase ABCA1 protein expression within pro-inflammatory iMAECs. Moreover, we failed to detect a significant increase in apoAI-mediated cholesterol efflux within pro-inflammatory iMAECs from miR-33a-3p knockdown. Conclusions: Our results indicative that the knockdown of miR-33a-3p alone does not enhance ABCA1-dependent cholesterol efflux within pro-inflammatory endothelial cells. To gain any atheroprotective benefit from inhibiting miR-33a-3p within pro-inflammatory endothelium, additional anti-atherogenic strategies would likely be needed in unison. Full article

The bicuspid aortic valve (BAV) is commonly associated with the early degeneration of the aortic valve. Up to 45% of BAV patients over the age of 50 develop aortic stenosis (AS). Although published data indicate a robust interplay between lipids and calcific AS in tricuspid aortic valve patients, the studies on the BAV population are lacking. We aimed to evaluate the association between selected lipid markers and the occurrence of AS in BAV patients. Methods: The study included 76 adults (21 female) with a BAV diagnosed by echocardiography, divided by age and AS diagnosis. Biochemical parameters concentrations in serum were measured: high density lipoprotein cholesterol (HDL-C) levels by standard enzymatic colorimetric tests, low density lipoprotein cholesterol (LDL-C) levels by the Friedewald formula, apolipoprotein A-I (Apo AI) and apolipoprotein B (Apo B) serum concentration by the nephelometric method, and paraoxonase-1 activity (PON-1 ASE) and arylesterase activity (PON-1 ARE) based on paraoxon and phenyl acetate hydrolysis. Results: A total of 54 patients (15 female) were more than 45 years old and 22 (6 female) were 45 or less years old. BAV patients with AS aged ≤45 had higher levels of Apo B, compared to those without AS [110.5 (102–132) vs. 95.6 (77–101) mg/d; p 0.044]. Similarly, Apo B/Apo AI ratio was higher in BAV patients with AS aged ≤45, compared to those without AS [(0.8 (0.7–1) vs. 0.6 (0.5–0.7); p 0.029]. In the group aged ≤45, Apo B showed a positive correlation with the aortic valve peak transvalvular velocity (AV Vmax) measurement (R Spearman 0.6, p 0.004). We found also that, among young BAV patients, those with AS had a lower level of PON-1 ARE compared to the cohort without AS [63.4 (52–80) vs. 85.3 (70–102); p 0.012]. We did not find any differences in lipid parameters in patients aged >45. Conclusions The metabolic link between Apo B level and Apo B/AI ratio with AS presence in BAV patients under 45 years of age suggests a significant impact of these parameters on the earlier development of AS in the BAV population. Molecules associated with high density lipoprotein and its antioxidant function, such as PON1, are valuable markers for AS development, compared to HDL-C and LDL-C levels. Full article

Insulin resistance (IR) is one of the important causes of metabolic dysfunction-associated steatotic liver disease (MASLD). Apolipoprotein A-I (apoA-I) is secreted primarily by hepatocytes and plays an essential role in reverse cholesterol transport. Our previous studies revealed that apoA-I can mitigate the progression of metabolic dysfunction-associated steatohepatitis (MASH). However, there is no clear evidence to explain the relationship between apoA-I and IR. Here, we investigated the effects of apoA-I overexpression on IR in both HepG2 cells and mice. In vitro experiment results revealed that apoA-I overexpression can promote cellular glucose uptake in oleic acid-induced IR in HepG2 cells. High-fat, high-cholesterol, and high-fructose diets were used to induce IR in mice. The results showed that apoA-I overexpression improved glucose tolerance, reduced serum insulin levels, and ameliorated IR in diet-induced MASLD mice. Moreover, apoA-I promoted the expression of peroxisome proliferator-activated receptor α (PPARα) in the nucleus both in vitro and in vivo. In conclusion, apoA-I could alleviate MASLD by reducing IR in mice and might exert this effect through the PPARα pathway. Full article

Background/Objectives: Familial hypercholesterolemia (FH) is a monogenic dyslipidemia that leads to early cardiovascular events. Subclinical atherosclerosis refers to the formation of atheromatous plaques in arterial beds before any clinical events. In our study, we investigated the presence, extent, and independent predictors of subclinical atherosclerosis among patients diagnosed with FH. Methods: This was a single-center, prospective, and cross-sectional study. This original study included 215 patients diagnosed with FH from a cohort of 1145 individuals assessed according to the Dutch Lipid Clinical Network (DLCN) criteria. Carotid and femoral ultrasonography were performed, and the coronary artery calcium score was measured to screen for subclinical atherosclerosis. Apolipoprotein A-I, apolipoprotein B, and lipoprotein (a) were analyzed using the nephelometric method. Results: The study cohort comprised 136 females (63%) with a mean age of 54 (43–62) years. The stigmata rate was 18%. The rate of statin use during subclinical atherosclerosis screening was 32% and only eight patients (4%) attained LDL-C values < 70 mg/dL. Subclinical atherosclerosis was observed in 148 patients (69%), with rates of 48%, 47.5%, and 40.5% in the coronary arteries, carotid bifurcation, and femoral bifurcation, respectively. Advanced age, male sex, high pretreatment low-density lipoprotein-cholesterol (LDL-C) level, diabetes, and a low Apo A-I/Apo B ratio were identified as independent predictors of subclinical atherosclerosis. Lp(a) levels ≥ 30 mg/dL predicted coronary atherosclerosis, while diabetes and low Apo A-I/Apo B ratios predicted carotid atherosclerosis, and smoking predicted femoral atherosclerosis. Conclusions: Subclinical atherosclerosis is prevalent, and medication adherence remains suboptimal among FH patients. Screening for subclinical atherosclerosis may impact the treatment strategies, via an increase in physician commitment to treatment protocols and improving patient compliance. Full article

Background/Objectives: Autoimmune inflammation enhances the electrical instability of the atrial myocardium in patients with systemic sclerosis (SSc); thus, atrial arrhythmia risk is increased, which might be predicted by evaluating the P wave interval and dispersion of a 12-lead surface electrocardiogram (ECG). Methods: We examined 26 SSc patients and 36 healthy controls and measured the P wave interval and P wave dispersion of the 12-lead surface ECG in each patient. Furthermore, echocardiography and 24-h Holter ECG were performed and levels of inflammatory laboratory parameters, including serum progranulin (PGRN), sVCAM-1, sICAM-1, leptin and C-reactive protein (CRP), were determined. Lipid parameters, such as Apo A-I, LDL-cholesterol (LDL-C), oxidized LDL (oxLDL) and the LDL and HDL subfractions were also evaluated. Results: The P wave interval showed a significant positive correlation with the levels of Apo A-I, LDL-C, CRP, sVCAM-1, sICAM-1 and leptin. The oxLDL level correlated positively with P wave dispersion. Of note, significant positive correlation was also found between the large HDL percentage and the P wave interval. Conclusions: Our results suggest that PGRN, sVCAM-1, sICAM-1, leptin, CRP, LDL-C and oxLDL, along with LDL and HDL subfractions, might have a role in atrial arrhythmogenesis in patients with SSc. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory impairment. Early and accurate detection of AD is crucial for timely intervention and effective treatment. Biomarkers such as amyloid-beta and tau proteins, genetic markers like the APOE genotype, and neuroimaging findings are essential for AD diagnosis and prognosis, but their complex interactions require advanced analytical tools. AI has emerged as a transformative tool in healthcare, offering advanced computational techniques to analyze complex biomarker data with enhanced precision. This review paper explores the advancements in diagnosing Alzheimer’s disease (AD) using artificial intelligence (AI) techniques. In the paper, we discuss the importance of diagnosing AD accurately and the potential benefits of using AI techniques for the early and accurate detection of AD. We emphasize the significance of AI in optimizing biomarker analysis for AD detection, discussing the challenges in their implementation and future implications. AI technologies can transform AD detection by significantly improving diagnostic imaging techniques, identifying key biomarkers, and standardizing the analysis of complex neuroimaging data. In this paper, we also highlight the critical role of AI in addressing challenges associated with integrating new technologies into clinical practice and providing effective solutions for consistent and reliable AD detection techniques. Full article

Chronic kidney disease (CKD) is often associated with dyslipidemia, marked by lipid abnormalities that can worsen kidney function and increase cardiovascular risk. A promising biomarker for evaluating kidney function and metabolic status in chronic kidney disease (CKD) is serum uromodulin (sUmod). This study sought to further investigate the relationship between sUmod levels and metabolic status in non-diabetic CKD patients. A sensitive ELISA method was used to determine sUmod levels in 90 adults with obstructive nephropathy and 30 healthy controls. Kidney function was assessed using the measured glomerular filtration rate (mGFR) through renal clearance of 99mTc-diethylenetriamine penta-acetic acid, along with cystatin C levels. Additionally, glycemic and lipid statuses were evaluated. sUmod concentrations showed a significant association with High-density lipoprotein (HDL) levels. Furthermore, CKD patients with lower sUmod levels had significantly lower Apolipoprotein A-I (Apo A-I) values compared to the control group. Significant predictors of lower sUmod concentrations identified in this study were higher glycemia (B = −15.939; p = 0.003) and lower HDL cholesterol levels (B = 20.588; p = 0.019). We conclude that, in addition to being significantly reduced in CKD patients, sUmod is a potential predictor of metabolic syndrome (MS) in this population. Lower sUmod concentrations, independent of mGFR, predict lower HDL cholesterol levels and higher glycemia values. Full article

Background: Alzheimer’s disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 patients with AD. We applied three ML regression models—XGBoost, random forest (RF), and linear regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models’ performances and analyze the influences of the APOE4 genotype and gender on brain aging. Results: Patients with AD displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. Gender-specific analyses indicated slight enhancements, with the models performing equally well for both genders. Conclusions: This study demonstrates that robust ML models for brain age prediction can play a crucial role in the early detection of AD risk through MRI brain structural imaging. The significant impact of the APOE4 genotype on brain aging and AD risk is also emphasized. These findings highlight the potential of ML models in assessing AD risk and suggest that utilizing AI for AD identification could enable earlier preventative interventions. Full article

CIGB-258 is known to exert anti-inflammatory activity via structural stabilization of apolipoprotein A-I (apoA-I) and functional enhancement of high-density lipoproteins (HDL) against acute toxicity of carboxymethyllysine (CML). The co-presence of CIGB-258 in reconstituted HDL (rHDL) formed larger rHDL particles and enhanced anti-inflammatory activity in a dose-dependent manner of apoA-I:CIGB-258, 1:0, 1:0.1, 1:0.5, and 1:1 of molar ratio, in the synthesis of the rHDL. However, no study has evaluated the enhancement of HDL functionality by the co-presence of lipid-free apoA-I and CIGB-258. The present study was therefore designed to compare the structural stabilization and functional improvement of HDL in the presence of lipid-free apoA-I and CIGB-258 in molar ratios of 1:0, 1:0.1, 1:0.5, and 1:1 within both HDL₂ and HDL₃. As the concentration of CIGB-258 increased, it effectively inhibited the cupric-ion-induced oxidation of HDL, thereby safeguarding apoA-I from proteolytic degradation. Additionally, the wound-healing activity of zebrafish was significantly (p < 0.01) enhanced by the co-addition of apoA-I:CIGB-258 (1:1) up to 1.6-fold higher than apoA-I alone (1:0) under the presence of CML. ApoA-I:CIGB-258 (1:1) treatment exhibited the lowest apoptosis and production of reactive oxygen species against CML-induced damage in the wound site. Also, an increase in wounded tissue granulation and epidermis thickness was observed with increasing concentration of CIGB-258 during 48 h post-treatment via the healing process. Intraperitoneal injection of apoA-I:CIGB-258 mixture remarkably ameliorated the acute paralysis and restored zebrafish swimming ability impaired by the acute toxicity of CML. The increase of CIGB-258 content, especially co-injection of apoA-I:CIGB-258 (1:1), leads to a significant 2.3-fold (p < 0.001) and 4.1-fold (p < 0.001) higher zebrafish survivability and recovery of swimming ability, respectively, than those of CML-control. In the apoA-I:CIGB-258 (1:1) group, neutrophil infiltration and interleukin (IL)-6 production was lowest in the hepatic tissue with the least cellular damage and apoptosis. Additionally, the group treated with apoA-I:CIGB-258 (1:1) demonstrated the lowest plasma levels of total cholesterol (TC) and triglycerides (TG), along with minimal damage to the kidney, ovary, and testicular cells. Conclusively, co-treatment of CIGB-258 with apoA-I effectively mitigated acute inflammation in zebrafish, safeguarded vital organs, structurally stabilized apoA-I, and enhanced HDL functionality. Full article