ABSTRACT Introduction This is the 38th Annual Report of the American Association of Poison Control Centers’ (AAPCC) National Poison Data System (NPDS). As of 1 January, 2020, all 55 of the nation’s poison centers (PCs) uploaded case data automatically to NPDS. The upload interval was 6.15 [4.60, 8.62] (median [25%, 75%]) minutes, effectuating a near real-time national exposure and information database and surveillance system. Methods We analyzed the case data tabulating specific indices from NPDS. The methodology was similar to that of previous years. Where changes were introduced, the differences are identified. Cases with medical outcomes of death were evaluated by a team of medical and clinical toxicologist reviewers using an ordinal scale of 1-6 to assess the Relative Contribution to Fatality (RCF) of the exposure. Results In 2020, 3,316,738 closed encounters were logged by NPDS: 2,128,198 human exposures, 66,745 animal exposures, 1,116,568 information requests, and 5,160 human confirmed nonexposures. Total encounters showed a 28.9% increase from 2019, while health care facility (HCF) human exposure cases decreased by 10.6%. While all information requests increased by 218.0%, medication identification (Drug ID) requests decreased by 31.5%, and human exposure cases decreased by 0.928%. Medical Information requests showed a 32.6-fold increase, reflecting COVID-19 pandemic calls to PCs. Human exposures with less serious outcomes have decreased 1.90% per year since 2008, while those with more serious outcomes (moderate, major or death) have increased 4.59% per year since 2000.Consistent with the previous year, the top 5 substance classes most frequently involved in all human exposures were analgesics (10.3%), household cleaning substances (8.37%), cosmetics/personal care products (6.53%), antidepressants (5.30%), and sedatives/hypnotics/antipsychotics (4.92%). As a class, antidepressant exposures increased most rapidly, by 1,793 cases/year (5.84%/year) over the past 10 years for cases with more serious outcomes.The top 5 most common exposures in children age 5 years or less were cosmetics/personal care products (11.8%), household cleaning substances (11.3%), analgesics (7.57%), foreign bodies/toys/miscellaneous (6.71%), and dietary supplements/herbals/homeopathic (6.44%). Drug identification requests comprised 2.89% of all information contacts. NPDS documented 4,488 human exposures resulting in death; 3,869 (86.2%) of these were judged as related (RCF of 1-Undoubtedly responsible, 2-Probably responsible, or 3-Contributory). Conclusions These data support the continued value of PC expertise and need for specialized medical toxicology information to manage more serious exposures. Unintentional and intentional exposures continue to be a significant cause of morbidity and mortality in the US. The near real-time status of NPDS represents a national public health resource to collect and monitor US exposure cases and information contacts. The continuing mission of NPDS is to provide a nationwide infrastructure for surveillance for all types of exposures (e.g., foreign body, infectious, venomous, chemical agent, or commercial product), and the identification and tracking of significant public health events. NPDS is a model system for the near real-time surveillance of national and global public health.
Abstract Background The gut microecosystem represents the most abundant and complex microbial ecosystem in the human body. Maintaining homeostasis of gut microbiota and their metabolites is essential for human health. As a chronic metabolic disorder, the association between benign prostatic hyperplasia (BPH) and gut microbiota remains unclear. Growing evidence suggests that modulating the composition and function of gut microbiota may influence the gut-prostate axis, thereby affecting the development and progression of prostatic hyperplasia. In this study, we employed network pharmacology to systematically elucidate the complex interactions among gut microbiota, microbial metabolites, and BPH-related therapeutic targets. Methods In this study, we first retrieved information on gut microbial metabolites from the gutMGene database. Subsequently, we identified overlapping targets of these metabolites using the SEA and STP databases. To further clarify targets related to BPH, we integrated data from authoritative databases such as Genecard and OMIM. Based on this information, we constructed a protein-protein interaction (PPI) network to screen for core targets. In addition, we performed systematic GO and KEGG functional enrichment analyses of these targets using the DAVID database. we constructed a network model to illustrate the interactions among microbiota, substrates, metabolites, and targets.Finally, molecular docking validation was performed between the core targets and gut microbiota metabolites. Results We identified 43 overlapping targets between gut microbial metabolites and BPH. Subsequently, we selected AKT1, IL-6, and IL-1B as core therapeutic targets for BPH. By constructing an MSMT comprehensive network, we found that these three core targets exert therapeutic effects on BPH through interactions with 11 metabolites, 2 substrates, and 4 gut microbial species. Furthermore, GO analysis revealed that gut microbial metabolites influence prostatic hyperplasia by regulating inflammation, immune responses, and the activation of oxidoreductase activity. KEGG analysis indicated that the AGE-RAGE signaling pathway, Toll-like receptor signaling pathway, HIF-1 signaling pathway, C-type lectin receptor signaling pathway, and PI3K/Akt signaling pathway are the major pathways involved in BPH.The molecular docking results demonstrated that butyrate may influence prostatic hyperplasia by modulating the AKT1 gene. Discussion This study employs a network pharmacology approach to elucidate the intricate “Microbiota-Substrate-Metabolite-Target” (M-S-M-T) network in Benign Prostatic Hyperplasia (BPH), identifying key hub genes (AKT1, IL-6, IL-1B), signaling pathways (PI3K/Akt, AGE-RAGE, HIF-1), and gut microbiota-derived metabolites (butyrate, propionate, TMAO) as central regulators. It further characterizes the functional significance of the Bifidobacterium-tryptophan and Clostridium sporogenes-tyrosine axes, highlighting their probiotic potential for microbiota-targeted BPH therapy. While demonstrating the therapeutic promise of modulating the gut microbiome, the study underscores the need for future experimental validation to decipher the precise mechanistic links within the M-S-M-T network and its role in BPH pathogenesis Conclusion IL-6, AKT1, and IL-1B serve as the primary targets through which gut microbiota metabolites exert their therapeutic effects on benign prostatic hyperplasia.
Narongsak Chaiyabutr, Taksa Vasaruchapong, Panithi Laoungbua
et al.
Abstract Background: Acute kidney injury (AKI) is a serious complication associated with Daboia siamensis envenomation, primarily due to direct nephrotoxicity. This study aimed to investigate the effects of the phospholipase A2 (RvPLA₂) fraction from D. siamensis venom on renal function and to assess whether pretreatment with ion channel blockers could mitigate these effects using an isolated perfused kidney (IPK) model. Methods: Twenty IPKs were allocated into five groups (n = 4 each): (1) RvPLA₂ in calcium-deficient modified Krebs-Henseleit solution (MKHS), (2) RvPLA₂ in standard MKHS, (3) RvPLA₂ following pretreatment with verapamil (a voltage-gated Ca²⁺ channel blocker), (4) RvPLA₂ following pretreatment with amiloride (a Na⁺ channel blocker), and (5) RvPLA₂ following pretreatment with minoxidil (a KATP channel opener). Renal function parameters were assessed accordingly. Results: Administration of 280 μg of RvPLA₂ in calcium-deficient MKHS caused no significant changes in renal function. In contrast, RvPLA₂ in standard MKHS (1.9 mM Ca²⁺) significantly increased perfusion pressure (PP), renal vascular resistance (RVR), and free water excretion (p < 0.05), while non-significant increases were observed in glomerular filtration rate (GFR), urinary flow rate (UF), osmolar clearance (Cosm), and the fractional excretion of sodium (FENa⁺) and potassium (FEK⁺). Verapamil alone caused significant increases in GFR and Cosm (p < 0.05) and non-significant increases in PP, RVR, UF, FENa⁺, and free water excretion. Amiloride and minoxidil alone did not alter renal function. Pretreatment with verapamil, amiloride, or minoxidil failed to prevent the renal functional changes induced by RvPLA₂. Conclusions: The RvPLA2 activity requires Ca2+ for activation which may target distinct sites on the cell membrane, including ion channel receptors in nephrons. The effects of RvPLA2 on glomerular and renal tubular function are independent and cannot be modified by pretreatment with different ion channel blockers.
Although exposure to metals remains a public health concern, few studies have examined exposure to combinations of metals. This study characterized prevalent combinations of cadmium (Cd), mercury (Hg), and lead (Pb) in women (n = 10,152; aged 20–44 years) who participated in the U.S. National Health and Nutrition Examination Survey (NHANES) 1999–2018. To explore relative metal exposures within this population, Cd, Hg, and Pb blood levels were dichotomized as “high” and “low” categories using median values to represent the center of the metal concentrations in the study population, not thresholds for adverse health effects. The prevalence of the three metal combinations at “high” levels (singular, binary, tertiary combinations) was calculated. Multinomial logistic regression was used to calculate odds ratios for each combination relative to none of these combinations after adjusting for potential confounders. Among the pregnant women (n = 1297), singular Hg was most prevalent (19.2% [95% CI 15.0–23.3]), followed by singular Cd (14.7% [95% CI 11.2–18.2]), tertiary combination Cd/Hg/Pb (11.0% [95% CI 8.7–13.2]), binary combinations Cd/Pb (9.8% [95% CI 7.4–12.2]), Hg/Pb (9.2% [95% CI 6.5–11.8]), Cd/Hg (7.8% [95% CI 6.0–9.6]), and singular Pb (5.5% [95% CI 4.1–6.9]). We found significantly lower odds of having Cd/Hg/Pb (adjusted odds ratio (adjOR) = 0.49: <i>p</i> < 0.001) and Cd/Pb (adjOR = 0.68: <i>p</i> < 0.0364) combinations among pregnant women compared to non-pregnant women. The odds of having higher levels of singular Pb were significantly lower (adjOR = 0.31: <i>p</i> < 0.0001) in women pregnant in their first and second trimesters (n = 563) than in non-pregnant women (n = 6412), whereas, though nonsignificant, the odds were higher for women pregnant in their third trimester (n = 366) (adjOR = 1.25: <i>p</i> = 0.4715). These results indicate the possibility that the fetus might be exposed to higher levels of the metal mixtures due to placental transfer, particularly to Pb, during the early stages of pregnancy. Further research is warranted to understand the relationship between metal combination exposures during pregnancy and maternal and infant health.
The present study describes the “fit for purpose” testing and the independent product-specific GMP validation of the monocyte activation test (MAT) to detect pyrogenic and pro-inflammatory contaminants, MAT Method A, Quantitative Test (European Pharmacopoeia, Ph. Eur. chapter 2.6.30, 2017). A fit for purpose study was carried out to ensure that the chosen MAT set-up (cryopreserved PBMC, IL-6 detection) can reliably discriminate between batches of product containing pyrogenic contaminants below the contaminants limit concentration, CLC, from batches containing pyrogenic contaminants above the CLC. Such testing is carried out once, before the chosen MAT set-up is used for subsequent product testing to show that the incidence of false positives (pyrogen-negative (<CLC) batches testing as pyrogen-positive (>CLC) batches) and – especially – false negatives (pyrogen-positive (>CLC) testing as pyrogen-negative (<CLC)) is low. This study also afforded the opportunity to collect an independent body of validation data for comparison with that obtained previously (Daniels et al., 2022) to evaluate the robustness of MAT Method A and its fitness to replace the rabbit pyrogen test (RPT) where this has not already happened.
Tommaso Pacini, Emanuela Verdini, Serenella Orsini
et al.
The widespread use of agrochemicals raises concerns about environmental impacts, particularly on pollinators, such as bees, which serve as bioindicators of contamination. Developing methods to assess contamination risks in bioindicators supports regulatory frameworks, including EU regulations on the maximum residue limits (MRLs) for pesticides in food and the environment. This study presents the development and validation of two complementary analytical methods (LC–MS/MS and IC–HRMS) for highly polar pesticide (HPP) detection and quantification in bee matrices. Both methods were validated according to document SANTE/11312/2021 v2. LC–MS/MS was validated with a limit of quantification (LOQ) of 0.005 mg/kg for all the analytes. Repeatability at 0.005, 0.010, 0.020, and 0.100 mg/kg showed RSD<sub>r</sub> from 1.6% to 19.7% and recoveries between 70% and 119%. Interlaboratory precision at 0.020 mg/kg across two labs showed RSD<sub>R</sub> from 5.5% to 13.6%, with recoveries between 91% and 103%. The IC–HRMS method achieved LOQs of 0.01 mg/kg (glufosinate, N-acetyl glufosinate, MPPA, glyphosate, N-acetyl glyphosate, N-acetyl AMPA) and 0.1 mg/kg (fosetyl, phosphonic acid, AMPA), with mean recoveries in repeatability conditions from 84% to 114% and RSD<sub>r</sub> from 2% to 14%. Intralaboratory precision showed mean recoveries from 87% to 119%, with RSD<sub>wR</sub> values between 10% and 18%. These methods enable accurate monitoring of HPP contamination, supporting risk assessment and sustainable agriculture.
The study examines the health of petroleum industry employees in Basrah City, southern Iraq, with a focus on their exposure to toxic chemicals, specifically the impact of oxidative stress on their hearts. This study included two groups of men: in the first group, ninety employees were exposed to crude oil well sites in Basrah, and ninety individuals were in the control group. This study evaluated two ultra-fine particles in the participants' blood: the polycyclic aromatic hydrocarbon metabolite [Benzopyrene diol epoxide (BPDE)] level and the toxic cadmium. The study also aimed to evaluate the levels of malondialdehyde (MDA) and superoxide dismutase (SOD) in the serum of the study participants and monitor the lipid profile. The results showed high levels of BPDE, a high concentration of cadmium in the blood, increased lipid peroxidation, and decreased SOD in the exposed group compared to the control group. The results also showed a significant increase in triglycerides. The increase in reactive oxygen species production is a major risk factor for atherosclerosis, and high triglycerides indicate artery wall deposits, leading to cardiovascular disease.
Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly accessible and modifiable. While previous studies have exposed knowledge poisoning risks in RAG systems, existing attack methods suffer from critical limitations: they either require injecting multiple poisoned documents (resulting in poor stealthiness) or can only function effectively on simplistic queries (limiting real-world applicability). This paper reveals a more realistic knowledge poisoning attack against RAG systems that achieves successful attacks by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. Our proposed AuthChain address three challenges to ensure the poisoned documents are reliably retrieved and trusted by the LLM, even against large knowledge bases and LLM's own knowledge. Extensive experiments across six popular LLMs demonstrate that AuthChain achieves significantly higher attack success rates while maintaining superior stealthiness against RAG defense mechanisms compared to state-of-the-art baselines.
Imtiyaz Qayoom, Masood Balkhi, Malik Mukhtar
et al.
Organophosphate insecticide spray poses potential threat of contamination of environmental components their accumulation in aquatic organisms. Although various physiological deficits associated with their exposure in fishes are documented, yet their retention in their edible muscle tissues has been poorly studied. In this context, the study was undertaken to ascertain the bioaccumulation of two organophosphate insecticide compounds (dimethoate and chlorpyrifos) in the muscles of juvenile Cyprinus carpio. The study could provide insight into the risks to human health associated with consuming contaminated fish flesh. The fishes exposed to various concentrations of dimethoate and chlorpyrifos in-vivo for 96 to ascertain the uptake and retention of these insecticides in the muscle. Results indicated that fish muscles accumulated the residues at all the concentrations with the recovery of 2.99% (0.032 ppm) of dimethoate exposed to LC50 concentrations. In contrast, the chlorpyrifos residues were found Below the Detection Level (BDL) in the fishes exposed to LC50 concentrations. The percentage bioaccumulation of dimethoate in fish muscle was 88.10%, and that of chlorpyrifos was BDL. The bio-concentration factor was dose-dependent and increased with increasing doses of both insecticides. The study invites attention to human health risk assessment in the regions where contaminated fish are consumed without scientific supervision.
Brendan Toy, Marcus W. N. Yong, David K. Lempert
et al.
Amygdalin is an aromatic cyanogenic glycoside, and available in tablet form as amygdalin, vitamin B17 or laetrile. We describe an intentional amygdalin drug overdose resulting in fatal cyanide poisoning which eventuated into tissue and organ procurement with successful organ transplantation. Because measured cyanide concentrations were not immediately available, the ICU adopted a 48-hour observation period to monitor organ function and lactate concentrations to determine suitability for organ tissue donation. This led to the acceptance of the heart, liver and one kidney. All three transplant recipients were doing well in the 12 months following donation.
Local differential privacy (LDP), which enables an untrusted server to collect aggregated statistics from distributed users while protecting the privacy of those users, has been widely deployed in practice. However, LDP protocols for frequency estimation are vulnerable to poisoning attacks, in which an attacker can poison the aggregated frequencies by manipulating the data sent from malicious users. Therefore, it is an open challenge to recover the accurate aggregated frequencies from poisoned ones. In this work, we propose LDPRecover, a method that can recover accurate aggregated frequencies from poisoning attacks, even if the server does not learn the details of the attacks. In LDPRecover, we establish a genuine frequency estimator that theoretically guides the server to recover the frequencies aggregated from genuine users' data by eliminating the impact of malicious users' data in poisoned frequencies. Since the server has no idea of the attacks, we propose an adaptive attack to unify existing attacks and learn the statistics of the malicious data within this adaptive attack by exploiting the properties of LDP protocols. By taking the estimator and the learning statistics as constraints, we formulate the problem of recovering aggregated frequencies to approach the genuine ones as a constraint inference (CI) problem. Consequently, the server can obtain accurate aggregated frequencies by solving this problem optimally. Moreover, LDPRecover can serve as a frequency recovery paradigm that recovers more accurate aggregated frequencies by integrating attack details as new constraints in the CI problem. Our evaluation on two real-world datasets, three LDP protocols, and untargeted and targeted poisoning attacks shows that LDPRecover is both accurate and widely applicable against various poisoning attacks.
3D Gaussian splatting (3DGS), known for its groundbreaking performance and efficiency, has become a dominant 3D representation and brought progress to many 3D vision tasks. However, in this work, we reveal a significant security vulnerability that has been largely overlooked in 3DGS: the computation cost of training 3DGS could be maliciously tampered by poisoning the input data. By developing an attack named Poison-splat, we reveal a novel attack surface where the adversary can poison the input images to drastically increase the computation memory and time needed for 3DGS training, pushing the algorithm towards its worst computation complexity. In extreme cases, the attack can even consume all allocable memory, leading to a Denial-of-Service (DoS) that disrupts servers, resulting in practical damages to real-world 3DGS service vendors. Such a computation cost attack is achieved by addressing a bi-level optimization problem through three tailored strategies: attack objective approximation, proxy model rendering, and optional constrained optimization. These strategies not only ensure the effectiveness of our attack but also make it difficult to defend with simple defensive measures. We hope the revelation of this novel attack surface can spark attention to this crucial yet overlooked vulnerability of 3DGS systems. Our code is available at https://github.com/jiahaolu97/poison-splat .
Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed to counteract these attacks, they often come at the cost of the classifier's utility, thus creating a challenging trade-off between privacy protection and model utility. Moreover, most existing defenses require retraining the classifier for enhanced robustness, which is impractical for large-scale, well-established models. This paper introduces a novel defense mechanism to better balance privacy and utility, particularly against adversaries who employ a machine learning model (i.e., inversion model) to reconstruct private data. Drawing inspiration from data poisoning attacks, which can compromise the performance of machine learning models, we propose a strategy that leverages data poisoning to contaminate the training data of inversion models, thereby preventing model inversion attacks. Two defense methods are presented. The first, termed label-preserving poisoning attacks for all output vectors (LPA), involves subtle perturbations to all output vectors while preserving their labels. Our findings demonstrate that these minor perturbations, introduced through a data poisoning approach, significantly increase the difficulty of data reconstruction without compromising the utility of the classifier. Subsequently, we introduce a second method, label-flipping poisoning for partial output vectors (LFP), which selectively perturbs a small subset of output vectors and alters their labels during the process. Empirical results indicate that LPA is notably effective, outperforming the current state-of-the-art defenses. Our data poisoning-based defense provides a new retraining-free defense paradigm that preserves the victim classifier's utility.
Prompt injection attack, where an attacker injects a prompt into the original one, aiming to make an Large Language Model (LLM) follow the injected prompt to perform an attacker-chosen task, represent a critical security threat. Existing attacks primarily focus on crafting these injections at inference time, treating the LLM itself as a static target. Our experiments show that these attacks achieve some success, but there is still significant room for improvement. In this work, we introduces a more foundational attack vector: poisoning the LLM's alignment process to amplify the success of future prompt injection attacks. Specifically, we propose PoisonedAlign, a method that strategically creates poisoned alignment samples to poison an LLM's alignment dataset. Our experiments across five LLMs and two alignment datasets show that when even a small fraction of the alignment data is poisoned, the resulting model becomes substantially more vulnerable to a wide range of prompt injection attacks. Crucially, this vulnerability is instilled while the LLM's performance on standard capability benchmarks remains largely unchanged, making the manipulation difficult to detect through automated, general-purpose performance evaluations. The code for implementing the attack is available at https://github.com/Sadcardation/PoisonedAlign.
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are particularly notable among user-side attacks, as participants upload malicious model updates to deceive the global model, often intending to promote or demote specific targeted items. This study investigates strategies for executing promotion attacks in federated recommender systems. Current poisoning attacks on federated recommender systems often rely on additional information, such as the local training data of genuine users or item popularity. However, such information is challenging for the potential attacker to obtain. Thus, there is a need to develop an attack that requires no extra information apart from item embeddings obtained from the server. In this paper, we introduce a novel fake user based poisoning attack named PoisonFRS to promote the attacker-chosen targeted item in federated recommender systems without requiring knowledge about user-item rating data, user attributes, or the aggregation rule used by the server. Extensive experiments on multiple real-world datasets demonstrate that PoisonFRS can effectively promote the attacker-chosen targeted item to a large portion of genuine users and outperform current benchmarks that rely on additional information about the system. We further observe that the model updates from both genuine and fake users are indistinguishable within the latent space.
Cardinality estimation (CE) plays a crucial role in database optimizer. We have witnessed the emergence of numerous learned CE models recently which can outperform traditional methods such as histograms and samplings. However, learned models also bring many security risks. For example, a query-driven learned CE model learns a query-to-cardinality mapping based on the historical workload. Such a learned model could be attacked by poisoning queries, which are crafted by malicious attackers and woven into the historical workload, leading to performance degradation of CE. In this paper, we explore the potential security risks in learned CE and study a new problem of poisoning attacks on learned CE in a black-box setting. Experiments show that PACE reduces the accuracy of the learned CE models by 178 times, leading to a 10 times decrease in the end-to-end performance of the target database.
Noah S. Ball, Brittany M Knable, Taylor A Relich
et al.
Abstract Purpose Xylazine is an alpha-2-adrenergic agonist used for its sedative and analgesic properties in veterinary medicine. While not approved by the Food and Drug Administration for use in humans, anecdotal evidence suggests that exposures in humans is on the rise. We sought to systematically review and synthesize the evidence on xylazine exposure in humans focusing on the clinical presentation, management, and outcomes. Methods We conducted a systematic review of the literature including PubMed, Embase, and Scopus from their inception to September 9, 2021. We searched abstracts from selected emergency medicine and toxicology conferences from 2011 through 2021. We included clinical reports of xylazine exposure in humans. We excluded animal studies, in vitro studies, laboratory studies, or articles in a language other than English. From each included article, we extracted subjective and objective data that focused on clinical presentation, management, and outcomes of patients exposed to xylazine. Results We evaluated a total of 1409 records, rendering a final set of 17 articles and 2 abstracts meeting inclusion criteria. We identified a total of 98 patients amongst reports ranging from 1979 to 2020 and across nine countries. The most common types of xylazine exposures reported were unintentional exposure and intentional misuse/abuse. Common symptoms on presentation included hypotension, bradycardia, drowsiness, lethargy, while apnea with intubation and death were less frequently reported. Conclusion Human exposure to xylazine appears to be a rising concern within the prehospital and emergency medicine setting. Although a standardized treatment algorithm cannot be recommended at this time, further research is needed to improve the care of patients exposed to xylazine.
Syed Tasleem Raza, Saliha Rizvi, Sheeba Afreen
et al.
Background: Diabetes mellitus (DM) is an array of metabolic diseases, which results from deregulation in insulin secretion or its action leading to abnormally high levels of blood sugar associated with long-term damage, organs failure, especially the eyes, heart, kidneys, blood vessels and nerves whose frequency has increased progressively worldwide. Objectives: The aims of this study were to evaluate the plasma levels of miRNA-126, miRNA-486, miRNA-223 and miRNA-375 in newly diagnosed T2DM susceptible and healthy control individuals. Methods: In this study, we evaluated the T2DM-related miRNAs miRNA-126, miRNA-486, miRNA-223 and miRNA-375 in plasma of three study groups comprising healthy control (N = 36), newly diagnosed T2DM (N = 29) and T2DM susceptible individuals (N = 30) using Real Time polymerase chain reaction (RT-PCR) (Taqman®). All the statistical analysis were done using SPSS version 21software. Results: Significant associations were found between these parameters. The expression levels of miR-126, miR-486, miR-223, and miR-375 were downregulated in T2DM patients and further reduced in T2DM-susceptible individuals compared to healthy controls. Additionally, miR-223 and miR-375 showed an inverse correlation with HbA1c levels, while all four miRs displayed an inverse correlation with FBS levels in the combined group analysis. Conclusion: These observed associations further suggest that miR-126, miR-223, and miR-375 may be involved in the regulation of glucose metabolism and glycemic control. Their down regulation could potentially contribute to impaired insulin signaling, beta-cell dysfunction, inflammation, and disrupted glucose homeostasis, all of which are characteristics of diabetes.