Circulating microRNAs as biomarkers for diabetic retinopathy stage identification: A DTA systematic review and meta-analysis

PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 1 / 20 OPEN ACCESS Citation: Martínez-Santos M, Ybarra M, Pires ME, Ceresoni C, Martínez-López E, Sancho-Pelluz J, et al. (2025) Circulating microRNAs as biomarkers for diabetic retinopathy stage identification: A DTA systematic review and meta-analysis. PLoS One 20(11): e0335434. https://doi.org/10.1371/ journal.pone.0335434 Editor: Yalong Dang, Sanmenxia Central Hospital, Henan University of Science and Technilogy, CHINA Received: May 20, 2025 Accepted: October 12, 2025 Published: November 21, 2025 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal. pone.0335434 Copyright: © 2025 Martínez-Santos et al . This is an open access article distributed under the RESEARCH ARTICLE Circulating microRNAs as biomarkers for diabetic retinopathy stage identification: A DTA systematic review and meta-analysis Miriam Martínez-Santos1,2,3, María Ybarra1,2,3, Maria E. Pires1,2,3, Chiara Ceresoni1,2,3, Elías Martínez-López4, Javier Sancho-Pelluz2,3, Maria Oltra 2,3*, Jorge M. Barcia1,2,3 1 Escuela de Doctorado Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain, 2 Facultad de Medicina y Ciencias de la Salud, Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain, 3 Centro de Investigación Traslacional San Alberto Magno, Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain, 4 Department of General and Digestive Surgery, Hospital Universitario Doctor Peset, Valencia, Spain * [email protected] Abstract Purpose To evaluate the diagnostic accuracy of circulating miRNAs in distinguishing between different diabetic retinopathy (DR) stages in type 2 diabetes mellitus (T2DM). Methods We conducted a systematic review and meta-analysis in accordance with PRISMADTA and Cochrane guidelines. The protocol was not registeres and no external funding was received. A comprehensive search was performed in PubMed, CENTRAL, Scopus, Web of Science, ScienceDirect, and ClinicalTrials (up to January 2025) to identify diagnostic test accuracy studies on circulating miRNAs for DR. Eligible studies included three predefined comparisons: healthy controls versus DR (CTL vs DR), T2DM without DR versus DR (T2DM vs DR), and non-proliferative versus proliferative DR (NPDR vs PDR). DR diagnosis was confirmed using fundus fluorescein angiography and/or fundus examination. Two reviewers independently conducted study selection, data extraction, and risk of bias assessment with QUADAS-2; certainty of evidence was assessed using GRADE. Data were synthesized using a bivariate random-effects meta-analysis, with subgroup analyses, meta-regression, and sensitivity analyses to explore heterogeneity. Data were synthesized via a bivariate random-effects meta-analysis, with subgroup analyses, meta-regression, and sensitivity tests to explore heterogeneity. PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 2 / 20 Results Sixteen studies (1849 participants; 21 miRNAs) were included. For CTL vs DR (7 studies), pooled sensitivity was 77% (70–82) and specificity 84% (77–89), AUC 0.86 (0.82–0.89). For T2DM vs DR (9 studies), sensitivity was 81% (75–86) and specificity 80% (71–87), AUC 0.88 (0.84–0.91). For NPDR vs PDR (12 studies), sensitivity was 84% (79–87) and specificity 82% (76–88), AUC 0.90 (0.87–0.93). Heterogeneity arose chiefly from sample matrix, normalization strategies and inter-study expression trends. Patient selection posed the greatest bias risk. Conclusions Circulating miRNAs exhibit promising diagnostic accuracy for differentiating among various stages of DR. However, future large, prospective studies in diverse populations and standardized pre-analytical protocols are required to confirm and translate these findings. Introduction Diabetic retinopathy (DR) is one of the leading causes of vision loss among working-age adults worldwide [1]. As the global prevalence of type 2 diabetes mellitus (T2DM) continues to rise, so does the incidence of DR, posing a growing public health concern [2]. The condition progresses through well-defined stages, starting with non-proliferative diabetic retinopathy (NPDR) and potentially advancing to proliferative diabetic retinopathy (PDR), characterized by neovascularization and an increased risk of retinal detachment or hemorrhage [3]. The cumulative occurrence of progression from NPDR to vision-threatening complications has been estimated at approximately 14–16%. with the risk of progression to vision loss rising significantly, reaching nearly 58% [4,5]. Accurate detection and staging of DR are therefore essential for timely clinical decision-making and for preventing irreversible vision loss [4]. Current diagnostic methods for DR include fundus examination and fluorescein angiography (FA) among others, FA is the gold standard for staging due to its high sensitivity in detecting microvascular damage [6,7]. Fundus examination is more accessible but less sensitive, particularly in early disease [8]. FA, while accurate, is invasive and resource-intensive, limiting its routine use [9]. These limitations highlight the need for non-invasive, accessible biomarkers that could support early detection and improve screening and risk stratification in broader clinical settings [10,11]. Circulating microRNAs (miRNAs) have emerged as promising candidates for this role. MiRNAs are small, non-coding RNAs involved in the post-transcriptional regulation of gene expression and are detectable in various biological fluids, including serum, plasma, aqueous humor, and extracellular vesicles [12,13]. Their high stability in circulation, disease-specific expression patterns, and accessibility through noninvasive sampling make them attractive tools for biomarker discovery [11]. Although numerous studies have investigated the diagnostic potential of circulating miRNAs in terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The present work received internal funds from Centro de Investigación Traslacional SanAlberto Magno (CITSAM, UCV) and external funds from Agencia Estatal de Investigación Española (PID2020-117875GB-10), Instituto de Salud Carlos III (ISCIII, PI21/00083) and the European Union research fund, HORIZON MSCA 2021-DN-01-01_RETORNA 101073316 and Generalitat Valenciana ACIF 2023-128-001. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 3 / 20 DR, marked heterogeneity in sample types, analytical methods (e.g., RT-qPCR, microarrays, NGS), and target miRNAs has limited comparability across studies [14–18]. Notably, one prior meta-analysis has reviewed the use of circulating miRNAs for DR detection [19], it did not perform any stratification by disease stage or type of control group. This represents a notable gap in the current literature, as the ability to distinguish early from advanced stages of DR is critical for clinical decision-making. Therefore, the aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of circulating miRNAs in DR. Specifically, we assess their performance in distinguishing between healthy controls vs DR patients, T2DM vs DR, and NPDR vs PDR stages. This stratified approach addresses a critical unmet need by systematically analyzing the diagnostic value of miRNAs across clinically relevant disease stages and control populations. Methods Search strategy This systematic review and meta-analysis were conducted following the Preferred Reporting Items for PRISMADiagnostic Test Accuracy (PRISMA-DTA) [20] for further information consult (S1 and S2 Tables in S1 File). This systematic review was not registered. We performed extensive search in the following databases: PubMed, CENTRAL, Scopus, Web of Science, Science Direct, and Clinical Trials. The last update of this review was on January 20, 2025. The search strategy was designed to identify relevant studies evaluating the diagnostic accuracy of miRNAs in DR. A combination of Medical Subject Headings (MeSH) terms and free-text keywords was applied, using Boolean operators (AND, OR, NOT) to refine the search. The main topics included: DR, miRNAs, expression profiling, biomarkers, and biological sample types (serum, plasma, aqueous humor, extracellular vesicles). For the complete search strategies please refer to (S1 Text in S1 File). Eligibility criteria This systematic review and meta-analysis follow the Population–Index test–Target condition (PIT) structure, as recommended by the Cochrane. We focused on (P) Population: human participants diagnosed with DR at various stages (NPDR or PDR), as well as individuals without DR, including healthy controls and patients with T2DM without DR; (I) Index test: miRNA expression levels measured in serum, plasma, aqueous humor, or extracellular vesicles, using validated techniques such as quantitative real-time (RT-qPCR), microarrays, or next-generation sequencing (NGS); and (T) Target condition: DR. We included studies published from 2014 onward, as long as they evaluated the diagnostic accuracy of miRNAs across different stages of DR confirmed using FA and fundus examination. To be eligible, studies also needed to report key accuracy metrics such as: sensitivity, specificity, or area under the curve (AUC). On the other hand, we excluded studies that focused on other types of diabetes, in vitro or in animal models. Study selection and data extraction Two independent reviewers (M.M-S and E.M-L.) assessed studies for eligibility based on predefined inclusion and exclusion criteria. Full-text articles of potentially relevant studies were reviewed, and any discrepancies were resolved by consensus; when necessary, a third reviewer (M.O.) provided arbitration. From each included study, we extracted data on study characteristics (author, year, country, design), population details, biological sample type, index test platform, normalization strategy, and diagnostic accuracy metrics such as: sensitivity, specificity and AUC. The complete extraction dataset is provided in S3 Table in S1 File. Information on miRNA expression trends and cut-off values was recorded when available. Duplicate records were identified and removed in EndNote X9, following the methodology described by Kwon et al. [21]. All references were managed using EndNote X9 software. PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 4 / 20 Quality assessment The risk of bias and applicability concerns of the included studies were assessed using the QUADAS-2 tool [22], applied independently by two reviewers across the four standard domains: patient selection, index test, reference standard, and flow/timing. Discrepancies were resolved by consensus. In addition, the GRADE approach [23], adapted for diagnostic test accuracy studies, was used to evaluate the overall strength of evidence and guide recommendations. Statistical analysis We calculated 2 × 2 contingency tables true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN) for each included study. Pooled sensitivity, specificity, likelihood ratios (PLR, NLR), diagnostic odds ratio (DOR), and SROC curve were estimated using a bivariate random-effects model, recommended for diagnostic test accuracy meta-analyses. This model jointly accounts for sensitivity and specificity, including their correlation and between-study variability. Since most studies did not report diagnostic thresholds, a hierarchical HSROC model was not applicable. Instead, we generated empirical SROC curves from bivariate estimates to visualize overall diagnostic performance. Heterogeneity was assessed using Cochran’s Q-test and the I2 statistic derived from the bivariate model. An I2 value above 50% or a p-value < 0.05 was considered indicative of substantial heterogeneity. To investigate its potential sources, pre-specified univariable meta-regressions were performed within the bivariate framework, using variables such as sample size, country of origin, biological specimen type normalization strategy, and miRNA expression pattern. To assess the robustness of our findings, sensitivity analyses were carried out using leave-one-out methods and influence diagnostics. Model assumptions were evaluated via residual deviance plots and bivariate normality tests. Finally, to evaluate potential publication bias, Deeks’ funnel plot asymmetry test was performed. Fagan nomograms were generated to translate likelihood ratios into post-test probabilities, facilitating clinical interpretation. Percentages are reported as whole numbers rounded to the nearest integer; exact estimates and 95% confidence intervals are provided in the corresponding tables and figures. All statistical analyses were performed at 95% CI, using STATA 18 (STATA Corporation, College Station, TX, USA), incorporating the MIDAS package for meta-analysis of DTA. Additional details are provided in S4, S5 and S6 Tables in S1 File. Results Study characteristics and quality assessment A total of 454 articles were identified from various databases, including: CENTRAL (n = 2), Scopus (n = 122), PubMed (n = 77), Clinical Trials (n = 1), Web of Science (n = 125), and Science Direct (n = 127). Additionally, 6 abstracts were retrieved from grey literature (ARVO). After the removal of 270 duplicates, 180 records were screened based on title and abstract, resulting in 60 full-text articles being assessed for eligibility. Among these, 38 studies were excluded for the following reasons: in vitro studies (n = 8), reviews (n = 4), animal studies (n = 6), and studies with microvascular complications other than DR (n = 20). Ultimately, 16 studies met the inclusion criteria and were included in the quantitative and qualitative synthesis meta-analysis (Fig 1). These 16 studies analyzed 1.849 patients and investigated 21 distinct miRNAs. Among these miRNAs, 6 were detected in plasma, 14 in serum, and 1 in exosomes. In terms of study design, 14 studies were case-control [14–18,24–31], while 2 were cross-sectional studies [32,33]. Geographically, the majority of studies were from China (n = 11) [14,17,18,24,25,27,28,30,33,34], followed by Egypt (n = 3) [16,26,31], Italy (n = 1) [29], and Indonesia (n = 1) [32]. Regarding the analytical approach, 3 studies assessed miRNA panels [15,27,29], whereas the remaining 13 studies focused on single miRNA analysis [14,16–18, 24–26,28,30,32–34]. In terms of comparison groups, 9 studies have a single type of comparison, whereas 7 studies had multiple comparisons across different disease stages. The distribution of comparisons was as follows: 6 studies analyzed CTL vs DR [24,25,27,32–34], 7 studies analyzed T2DM vs DR [14,16,24,26,28–30], PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 5 / 20 Fig 1. Flow diagram detailing the selection of studies included in the diagnostic accuracy meta-analysis. https://doi.org/10.1371/journal.pone.0335434.g001 PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 6 / 20 Table 1. Quantitative and qualitative characteristics of included studies. Study (year) Country Study Design Comparation miRNAs Expression Specimen Method Normalization TP TN FP FN Sen % Spe % Total (n) Jiang et al., 2017 China Case control CTL vs DR miR-21 Up Plasma RT-qPCR U6 82 104 11 42 66.1 90.4 239 Qin et al., 2017 China Case control CTL vs DR miR-126 Down Plasma RT-qPCR U6 32 53 6 7 81.25 90.34 98 Wan et al., 2017 China Case control CTL vs DR miR-7 Down Serum RT-qPCR MIR2911 58 54 20 18 76 73 150 Wan et al., 2017 China Case control CTL vs DR miR-7 (exosome) Down Exosome RT-qPCR MIR2911 57 57 17 18 75 77 149 Liu et al., 2018 China Cross Sectional CTL vs DR miR-211 Up Serum RT-qPCR U6 54 29 4 10 85 87 97 Li et al., 2019 China Case control CTL vs DR miR-4448, miR338-3p, miR-190a-5p, mir485-5p, miR-9-5p Up/ Down Serum RNA-Seq DESeq2 9 10 1 1 90 90.9 21 Surasmiati et al., 2023 Indonesia Cross sectional CTL vs DR miR-126 Down Serum RT-qPCR miRNA328-3p 9 8 2 2 75 50 21 Qin et al., 2017 China Case control T2DM vs DR miR-126 Down Plasma RT-qPCR U6 69 42 2 12 84.8 94.9 125 Shaker et al., 2019 Egypt Case control T2DM vs DR miR-20b Down Serum RT-qPCR SNORD68 31 18 12 19 62 60 80 Shaker et al., 2019 Egypt Case control T2DM vs DR miR-17-3p Down Serum RT-qPCR SNORD68 46 17 13 4 92 56.7 80 Yin et al., 2020 China Case control T2DM vs DR miR-210 Up Serum RT-qPCR U6 92 32 8 18 83.6 80 150 Santavito et al., 2021 Italy Case control T2DM vs DR miR-25-3p, miR320b, miR-495-3p Up/ Down Plasma RT-qPCR miR-19-5p, miR125a-5p 17 9 1 3 85 85 30 Wang et al., 2021 China Case control T2DM vs DR miR-374a Up Serum RT-qPCR U6 110 58 12 27 80.3 82.9 207 Saleh et al., 2022 Egypt Case control T2DM vs DR miR-93 Down Serum RT-qPCR miRNA-16 68 69 11 12 85 86 160 Saleh et al., 2022 Egypt Case control T2DM vs DR miR-152 Up Serum RT-qPCR miRNA-16 68 58 22 12 85 72 160 Zhao et al., 2023 China Case control T2DM vs DR miR-221-3p Up Serum RT-qPCR U6 110 84 18 48 69.7 82.3 260 Qing et al., 2014 China Case control NPDR vs PDR miR-21, miR-181c, miR-1179 Up Serum RT-qPCR U6 74 86 4 16 82 95 180 Jiang et al., 2017 China Cross Sectional NPDR vs PDR miR-21 Up Plasma RT-qPCR U6 37 58 15 14 72.5 79.5 124 Shaker et al., 2019 Egypt Case control NPDR vs PDR miR-20b Down Serum RT-qPCR SNORD68 14 23 7 6 70 76.6 50 Shaker et al., 2019 Egypt Case control NPDR vs PDR miR-17-3p Down Serum RT-qPCR SNORD68 10 24 6 10 50 80 50 (Continued) PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 7 / 20 9 studies analyzed NPDR vs PDR [15–18,26,28,30,31,34]. The primary technique employed for miRNA detection was RT-qPCR in 15 studies, while RNA-seq was used in one study [27]. Regarding normalization methods, 9 studies utilized U6 [14,15,18,24,28,30,31,33,34], while the remaining 7 studies applied different miRNA normalization methods [16,17,25–27,29,32] (Table 1). The QUADAS-2 assessment showed that the main sources of bias came from how patients were selected and how the index test was applied. A high risk of bias was mostly linked to patient selection, often due to nonrandom sampling methods and retrospective study designs. In the index test domain, there were some concerns as well, particularly in studies that didn’t clearly define diagnostic thresholds ahead of time. On the other hand, the reference standard and the flow and timing of the studies generally showed a lower risk of bias, with most studies following proper diagnostic procedures and reasonable timelines. When it came to applicability, most studies posed low concern across all domains. However, a few showed minor issues related to the index test, mainly because of differences in how it was applied across studies. A detailed summary of the risk of bias and applicability concerns is shown in (Fig 2). Study (year) Country Study Design Comparation miRNAs Expression Specimen Method Normalization TP TN FP FN Sen % Spe % Total (n) Hui et al, 2019 China Case control NPDR vs PDR miR-126 Down Plasma RT-qPCR cel-miR39-3p 28 31 12 8 78.4 73.2 79 Ma et al., 2019 China Case control NPDR vs PDR miR-93 and miR-21 Up Plasma RT-qPCR U6 39 30 4 3 92 89 76 Ma et al., 2019 China Case control NPDR vs PDR miR-93 Up Plasma RT-qPCR U6 37 28 6 5 89 81 76 Ma et al., 2019 China Case control NPDR vs PDR miR-21 Up Plasma RT-qPCR U6 38 24 10 4 90 71 76 Yin et al., 2020 China Case Control NPDR vs PDR miR-210 Up Serum RT-qPCR U6 41 45 15 9 84.2 78.9 110 Wang et al., 2021 China Case Control NPDR vs PDR miR-374a Up Serum RT-qPCR U6 61 51 13 10 84.2 78.8 135 Saleh et al., 2022 Egypt Case control NPDR vs PDR miR-93 Down Serum RT-qPCR miRNA-16 34 25 15 6 85 63 80 Saleh et al., 2022 Egypt Case control NPDR vs PDR miR-152 Up Serum RT-qPCR miRNA-16 34 32 8 6 85 80 80 Salem et al., 2022 Egypt Case control NPDR vs PDR miR-181c Up Serum RT-qPCR U6 54 60 0 6 90 100 120 Salem et al., 2022 Egypt Case control NPDR vs PDR miR-1179 Up Serum RT-qPCR U6 54 48 12 6 90 80 120 TP: True Positives, TN: True Negatives; FP: False Positives; FN: False Negatives; Sen: Sensitivity. https://doi.org/10.1371/journal.pone.0335434.t001 Table 1. (Continued) PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 8 / 20 Diagnostic accuracy of miRNAs in CTL vs DR, T2DM vs DR, and NPDR vs PDR comparisons A total of 7 studies contributed data to the comparison of CTL vs DR. The pooled estimates from the random‐effects model showed a summary sensitivity of 77% (70–82), with an I2 of 47%, and a summary specificity of 84% (77–89), with an I2 of 62%, indicating moderate heterogeneity (Fig 3A-B). The SROC curve yielded an AUC of 0.86 (0.84–0.92) (Fig 3C), suggesting moderate‐to‐high overall accuracy. To understand the clinical relevance of these findings, a Fagan nomogram was constructed using the actual pre-test probability of DR in the population studied 22% [2]. The plot revealed a positive miRNA result raises the probability of having the disease to 58%, while a negative result reduces it to just 7% (Fig 4A). In parallel, a scatter matrix was used to visualize the relationship between the likelihood ratios across all miRNA tests included in this comparison. The pooled positive likelihood ratio (PLR) was 4.77 (3.19–7.13), indicating that patients with DR are nearly five times more likely to test positive than those without the disease. On the other hand, the pooled negative likelihood ratio (NLR) was 0.29 (0.23–0.37), suggesting a notable reduction in the probability of disease following a negative result (Fig 4A-B). Lastly, Deeks’ test indicated no significant presence of publication bias (p = 0.27) (Fig 5A). Data from 9 studies in T2DM vs DR comparison indicated a summary sensitivity of 81% (75–86), with a specificity of 80% (71–87), with an I2 of 73%, suggesting moderate to high heterogeneity in both measures (Fig 3D-E). The SROC curve reported an AUC of 0.88 (0.80–0.95), indicating strong overall diagnostic accuracy (Fig 3F). Fagan nomogram was constructed using the actual pre-test probability of DR in this population 28% [35]. The plot showed that a positive miRNA result would increase the probability of detecting DR of 61%, while a negative result would lower the likelihood to just 8%. This reflects a meaningful shift in post-test probabilities, reinforcing the role of miRNAs in helping clinicians differentiate between uncomplicated diabetes and the onset of DR (Fig 4C). The scatter matrix illustrated the distribution of diagnostic performance showed the PLR was 4.1 (2.8–6.1), suggesting that patients with DR are approximately four times more likely to test positive compared to those with T2DM alone. Meanwhile, the NLR was 0.23 (0.17–0.32), indicating a substantial decrease in the probability of disease following a negative test (Fig 4D). The Deeks’ funnel plot analysis showed no significant evidence of publication bias (p = 0.81), indicating a low risk that the results are influenced by publication bias (Fig 5B). Twelve studies were included in this analysis comparing NPDR vs PDR. The meta-analysis found a pooled sensitivity of 84% (79–87) with an I2 of 56% and a specificity of 82% (76–88) with an I2 of 77%, showing moderate-to-high heterogeneity, (Fig 3GH). The SROC curve demonstrated an AUC of 0.90 (0.82–0. 91), pointing to high overall diagnostic accuracy (Fig 3I). The Fagan nomogram was constructed using a pre-test probability of 17% [5], based on the actual prevalence observed in this population. Fig 2. Risk of bias and applicability assessment using the QUADAS-2 tool. https://doi.org/10.1371/journal.pone.0335434.g002 PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 9 / 20 Fig 3. Forest plots of sensitivity and specificity and SROC curves showing diagnostic accuracy of miRNAs: CTL vs DR (A-C), T2DM vs DR (DF), and NPDR vs PDR (G-I) comparisons. https://doi.org/10.1371/journal.pone.0335434.g003 PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 16 / 20 Biological insights and candidate miRNAs Among the studies included, several miRNAs appeared recurrently, highlighting theirpotential relevance across different experimental contexts. The most frequently reported was miR-126 [17,24,32], identified in three separate studies. This miRNA has Table 6. Certainty of evidence for the diagnostic accuracy of miRNAs according to the GRADE approach in T2DM vs DR. Pretest probability (global prevalence of DR in T2DM patients): 28.41%$ Pooled sensitivity: 80% IC95% (0.77–0.83) Pooled specificity: 80% IC95% (0.76–0.83) T2DM vs DR Outcome Number of studies Study design Risk of bias Indirect evidence Inconsistency Imprecision Publication bias Effect x1000* Quality True positives 7 (1012 patients) Case-control (n = 7) High Serious2Serious3Not serious Not detected 227 (22.7%) ⊕⊕◯◯ Low True negatives 7 (1012 patients) Case-control (n = 7) High Serious2Serious3Not serious Not detected 570 (57%) ⊕⊕⊕◯ Moderate False Positives 7 (1012 patients) Case-control (n = 7) High Serious2Serious3Not serious Not detected 146 (14.6%) ⊕⊕◯◯ Very low False negatives 7 (1012 patients) Case-control (n = 7) High Serious2Serious3Not serious Not detected 57 (5.7%) ⊕⊕⊕◯ Moderate *Number of patients per 1000 tested for a prevalence of 28.41% 2: The evidence was rated as serious due to variability in sample types (plasma vs. serum) and differences in miRNA expression patterns (overexpression vs. underexpression), which may affect the applicability of the results. 3: Heterogeneity was rated as serious due to high inconsistency, with an I2 of 72.5% for sensitivity and 73.1% for specificity, indicating substantial variability among studies. $: Hashemi, H., Rezvan, F., Pakzad, R., Ansaripour, A., Heydarian, S., Yekta, A., … Khabazkhoob, M. (2021). Global and Regional Prevalence of Diabetic Retinopathy; A Comprehensive Systematic Review and Meta-analysis. Seminars in Ophthalmology, 37(3), 291–306. https://doi.org/10.1371/journal.pone.0335434.t006 Table 7. Certainty of evidence for the diagnostic accuracy of miRNAs according to the GRADE approach in NPDR vs PDR. Pretest probability (global prevalence of PDR): 17%$ Pooled sensitivity: 84% IC95% (0.81–0.86) Pooled specificity: 82% IC95% (0.79–0.85) NPDR vs PDR Outcome Number of studies Study design Risk of bias Indirect evidence Inconsistency Imprecision Publication bias Effect x1000* Quality True positives 9 (954 patients) Case-control (n = 9) High Serious2Serious3Not serious Not detected 142 (14.2%) ⊕⊕◯◯ Low True negatives 9(954 patients) Case-control (n = 9) High Serious2Serious3Not serious Not detected 678 (67.8%) ⊕⊕⊕◯ Moderate False Positives 9 (954 patients) Case-control (n = 9) High Serious2Serious3Not serious Not detected 152 (15.2%) ⊕◯◯◯ Very low False negatives 9 (954 patients) Case-control (n = 9) High Serious2Serious3Not serious Not detected 28 (2.8%) ⊕⊕⊕◯ Moderate *Number of patients per 1000 tested for a prevalence of 17% 2: The evidence was rated as serious due to variability in sample types (plasma vs. serum) and differences in miRNA expression patterns. 3: Heterogeneity was rated as serious due to high inconsistency, with an I2 of 55.6% for sensitivity and 77.1% for specificity, indicating substantial variability among studies. $: Yang QH, Zhang Y, Zhang XM, Li XR. Prevalence of diabetic retinopathy, proliferative diabetic retinopathy and non-proliferative diabetic retinopathy in Asian T2DM patients: a systematic review and Meta-analysis. Int J Ophthalmol. 2019 Feb 18;12(2):302–311 https://doi.org/10.1371/journal.pone.0335434.t007 PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 17 / 20 been linked to DR through its role in vascular integrity and angiogenesis regulation, with decreased levels observed in plasma and vitreous samples of affected patients [37]. miR-21, was also reported in 3 studies [15,18,34] and has been implicated in retinal angiogenesis and inflammation in the diabetic context [38]. Additionally, miR-181c [15,25], miR-1179 [15,31] and miR93 [16,18] were each reported in two studies, miR-93 has been associated with increased DR risk in T2DM [39]. Clinical translation and the need for standardization Our meta-analysis revealed considerable heterogeneity. One major source of variability stems from the type of biological matrix used. Although both plasma and serum were employed, plasma may offer a more reliable profile of circulating miRNAs [40]. Unlike serum, plasma avoids the confounding release of platelet-derived miRNAs during coagulation, which can distort expression profiles and lead to inconsistent results [41]. Another key issue is the lack of a universally accepted internal control for normalization. While U6 small nuclear RNA was the most commonly used reference gene across included studies, it is predominantly nuclear and may degrade in cell-free conditions such as plasma or serum, thus introducing bias [42]. Alternative reference miRNAs like miR-16-5p have demonstrated greater stability and may represent more appropriate normalization candidates in extracellular RNA research [43]. Other methodological factors, including the time elapsed between sample collection and processing [44], the type of miRNA extraction kit, and detection platform used, contribute further to between-study variability [45,46]. This lack of uniformity hinders the comparability of results and reduces their generalizability across different clinical contexts. To overcome these barriers, the field urgently needs standardized pre-analytical protocols, consensus-based reporting guidelines, and the development of validated multi-miRNA diagnostic panels [47]. Furthermore, training and certification of technical personnel involved in miRNA handling and data interpretation would help minimize human error and increase reproducibility [48,49]. Without these improvements, the integration of miRNAs into clinical diagnostic workflows will remain theoretical, regardless of their promising statistical performance [50]. Limitations This meta-analysis has several limitations that must be considered when interpreting the findings. First, the protocol was not registered in Prospero. Second, a predominant number of the included studies were conducted in Chinese populations, which may introduce demographic bias and limit the generalizability of the findings to other ethnic groups, particularly Western cohorts. While this does not compromise internal validity, it underscores the need for broader geographic representation in future studies. Third, the study designs were primarily case-control and cross-sectional, which are inherently more prone to bias than prospective cohort studies [51]. These designs may overestimate diagnostic accuracy due to spectrum bias or inappropriate patient selection [52]. Furthermore, randomized clinical trials are entirely lacking, reflecting both operational and methodological challenges in conducting such studies in this field. Fourth, pre-analytical and analytical heterogeneity was substantial across studies. Variations in the biological sample type, normalization strategies, and miRNA isolation and detection platforms contributed significantly to inconsistency in results. Fifth, most studies did not report explicit diagnostic cut-off values for individual miRNAs. Although bivariate random-effects modeling allows for robust estimation of sensitivity, specificity, and AUC, the absence of threshold values limits the clinical applicability of the findings [53]. Diagnostic thresholds are essential for guiding real-world decisionmaking and should be established and validated in future research. The lack of cut-offs also reflects a broader issue: limited statistical training and methodological standardization among many investigators in the field [54]. Conclusion This meta-analysis demonstrates that circulating miRNAs exhibit promising diagnostic accuracy for distinguishing among various stages of DR, supporting their role as practical, non-invasive biomarkers. By stratifying the analysis into three clinically relevant comparison groups (CTL vs DR; T2DM vs DR; and NPDR vs PDR), we reduced inter-study heterogeneity PLOS One | https://doi.org/10.1371/journal.pone.0335434 November 21, 2025 18 / 20 and generated more precise and clinically meaningful estimates of diagnostic performance. To further enhance their translational potential, miRNA expression profiling should be integrated with established clinical assessments and validated in well-designed prospective cohorts. We call upon the scientific and clinical community to establish international consensus on sample processing, normalization protocols, and diagnostic threshold definition. Only through rigorous standardization and prospective validation can circulating miRNAs be successfully incorporated into routine diagnostic workflows. Supporting information S1 File. S1 Text. Complete search strategy. S1 Table. Prisma DTA abstract checklist. S2 Table. Prisma DTA checklist. S3 Table. Extraction data. S4 Table. Dataset used for STATA meta-analysis and meta-regression (CTL vs DR). S5 Table. Dataset used for STATA meta-analysis and meta-regression (T2DM vs DR). S6 Table. Dataset used for STATA metaanalysis and meta-regression (NPDR vs PDR). (ZIP) Author contributions Conceptualization: Miriam Martínez-Santos, Elías Martínez-López, Jorge M. Barcia. Data curation: María Ybarra, Maria E. Pires, Chiara Ceresoni. Formal analysis: Miriam Martínez-Santos, Elías Martínez-López. Funding acquisition: Jorge M. Barcia, Maria Oltra, Javier Sancho-Pelluz. Investigation: Miriam Martínez-Santos. Methodology: Miriam Martínez-Santos, Elías Martínez-López, Maria Oltra. 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