Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study

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Shaikh" }, { "@type": "Person", "name": "Himanshi Yadav" }, { "@type": "Person", "name": "Lakshya Kumar" }, { "@type": "Person", "name": "Gurnoor Gill" }, { "@type": "Person", "name": "Samarth Shah" }, { "@type": "Person", "name": "Jay Patel" }, { "@type": "Person", "name": "Harshal A. Sanghvi" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Atrial fibrillation (AF) is a leading cause of cardioembolic stroke, yet the independent contribution of prediabetes to recurrent ischemic stroke risk in AF patients with prior cerebrovascular events remains poorly defined. Prior cross-sectional studies have yielded conflicting results, and no large propensity-matched investigation has examined longitudinal time-to-event outcomes in this population. Methods Using the TriNetX US Collaborative Network (67 healthcare organizations), adults with AF and prior ischemic stroke or transient ischemic attack were classified as having prediabetes (hemoglobin A1c 5.7%–6.4% or ICD-10 R73.03) or normoglycemia (hemoglobin A1c ≤5.6%). Patients with any form of diabetes mellitus were excluded. One-to-one propensity score matching balanced demographics and comorbidities. The primary outcome was recurrent ischemic stroke. Secondary outcomes included all-cause mortality, gastrointestinal (GI) bleeding, and intracranial hemorrhage (ICH). Cumulative risk and Kaplan-Meier survival analyses were performed. Results After matching, 80,335 patients per cohort were analyzed (mean age 70 years; 54.7% male; all standardized mean differences <0.02). Cumulative recurrent stroke risk did not differ between groups (60.0% vs 60.4%; risk ratio 0.993, 95% CI 0.982–1.005; P = 0.264). Time-to-recurrence was significantly longer in the prediabetes cohort (median 1,794 vs 1,695 days; hazard ratio [HR] 0.960, 95% CI 0.943–0.978; P < 0.001). Prediabetes was associated with lower all-cause mortality (HR 0.891, 95% CI 0.873–0.910; P < 0.001) and lower GI bleeding (HR 0.918, 95% CI 0.889–0.947; P = 0.003). ICH did not differ significantly (HR 0.961, 95% CI 0.917–1.007; P = 0.065). Conclusions Prediabetes was not associated with increased recurrent stroke risk in AF patients with prior cerebrovascular events. The observed longer time-to-recurrence and lower mortality may reflect a metabolic surveillance effect warranting prospective confirmation. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/15-701/v1", "name": "Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation:..." } } ] } Home Browse Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Kachhadia MP, Puri P, Patel K et al. Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :701 ( https://doi.org/10.12688/f1000research.180154.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Brief Report Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study [version 1; peer review: awaiting peer review] Meet Popatbhai Kachhadia https://orcid.org/0009-0001-3123-8850 1 , Piyush Puri 2 , Krina Patel 3 , [...] Usmaan Topiwala 4 , Juber D. Shaikh https://orcid.org/0000-0003-0756-1479 5 , Himanshi Yadav 6 , Lakshya Kumar 7 , Gurnoor Gill 8 , Samarth Shah 9 , Jay Patel 10 , Harshal A. Sanghvi https://orcid.org/0000-0001-6952-0504 11 Meet Popatbhai Kachhadia https://orcid.org/0009-0001-3123-8850 1 , Piyush Puri 2 , [...] Krina Patel 3 , Usmaan Topiwala 4 , Juber D. Shaikh https://orcid.org/0000-0003-0756-1479 5 , Himanshi Yadav 6 , Lakshya Kumar 7 , Gurnoor Gill 8 , Samarth Shah 9 , Jay Patel 10 , Harshal A. Sanghvi https://orcid.org/0000-0001-6952-0504 11 PUBLISHED 11 May 2026 Author details Author details 1 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, Florida, USA 2 Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA 3 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, Florida, USA 4 Smt NHL Municipal Medical College, Ahmedabad, Gujarat, India 5 Henry Ford Hospital, Detroit, Michigan, USA 6 Lady Hardinge Medical College, New Delhi, Delhi, India 7 St Joseph Hospital, Bethpage, NY, USA 8 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, FL, USA 9 Department of Technology and Clinical Trials, Advanced Research LLC, fort lauderdale, FL, USA 10 Department of Technology and Clinical Trials, Advanced Research LLC FL, USA, Fort lauderdale, USA 11 Department of Information Technology and Operations Management (ITOM), College of Business, Florida Atlantic University, Florida, Boca Raton, USA Meet Popatbhai Kachhadia Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Piyush Puri Roles: Conceptualization, Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Krina Patel Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Usmaan Topiwala Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Juber D. Shaikh Roles: Conceptualization, Data Curation, Methodology, Software, Writing – Original Draft Preparation Himanshi Yadav Roles: Conceptualization, Data Curation, Methodology, Writing – Original Draft Preparation Lakshya Kumar Roles: Investigation, Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Gurnoor Gill Roles: Conceptualization, Data Curation, Software, Writing – Original Draft Preparation Samarth Shah Roles: Conceptualization, Data Curation, Methodology, Writing – Original Draft Preparation Jay Patel Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation Harshal A. Sanghvi Roles: Conceptualization, Data Curation, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background Atrial fibrillation (AF) is a leading cause of cardioembolic stroke, yet the independent contribution of prediabetes to recurrent ischemic stroke risk in AF patients with prior cerebrovascular events remains poorly defined. Prior cross-sectional studies have yielded conflicting results, and no large propensity-matched investigation has examined longitudinal time-to-event outcomes in this population. Methods Using the TriNetX US Collaborative Network (67 healthcare organizations), adults with AF and prior ischemic stroke or transient ischemic attack were classified as having prediabetes (hemoglobin A1c 5.7%–6.4% or ICD-10 R73.03) or normoglycemia (hemoglobin A1c ≤5.6%). Patients with any form of diabetes mellitus were excluded. One-to-one propensity score matching balanced demographics and comorbidities. The primary outcome was recurrent ischemic stroke. Secondary outcomes included all-cause mortality, gastrointestinal (GI) bleeding, and intracranial hemorrhage (ICH). Cumulative risk and Kaplan-Meier survival analyses were performed. Results After matching, 80,335 patients per cohort were analyzed (mean age 70 years; 54.7% male; all standardized mean differences <0.02). Cumulative recurrent stroke risk did not differ between groups (60.0% vs 60.4%; risk ratio 0.993, 95% CI 0.982–1.005; P = 0.264). Time-to-recurrence was significantly longer in the prediabetes cohort (median 1,794 vs 1,695 days; hazard ratio [HR] 0.960, 95% CI 0.943–0.978; P < 0.001). Prediabetes was associated with lower all-cause mortality (HR 0.891, 95% CI 0.873–0.910; P < 0.001) and lower GI bleeding (HR 0.918, 95% CI 0.889–0.947; P = 0.003). ICH did not differ significantly (HR 0.961, 95% CI 0.917–1.007; P = 0.065). Conclusions Prediabetes was not associated with increased recurrent stroke risk in AF patients with prior cerebrovascular events. The observed longer time-to-recurrence and lower mortality may reflect a metabolic surveillance effect warranting prospective confirmation. READ ALL READ LESS Keywords Atrial fibrillation, Prediabetes, Recurrent ischemic stroke Corresponding Author(s) Harshal A. Sanghvi ( [email protected] ) Close Corresponding author: Harshal A. Sanghvi Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Kachhadia MP et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Kachhadia MP, Puri P, Patel K et al. Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :701 ( https://doi.org/10.12688/f1000research.180154.1 ) First published: 11 May 2026, 15 :701 ( https://doi.org/10.12688/f1000research.180154.1 ) Latest published: 11 May 2026, 15 :701 ( https://doi.org/10.12688/f1000research.180154.1 ) Introduction Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with an estimated global prevalence exceeding 52 million individuals as of 2021. 1 , 2 AF confers a well-established independent risk for ischemic stroke, and AF-related strokes are characteristically more severe, carry higher disability rates, and are associated with greater mortality compared with strokes of other etiologies. 3 , 4 Despite widespread adoption of oral anticoagulation, the residual risk of stroke recurrence in AF patients remains clinically significant; a recent systematic review reported pooled recurrence rates of 3.75% per year overall and 7.20% per year among anticoagulated patients. 5 Prediabetes, defined by hemoglobin A1c (HbA1c) levels of 5.7% to 6.4%, impaired fasting glucose, or impaired glucose tolerance, affects hundreds of millions of adults worldwide and is associated with endothelial dysfunction, chronic systemic inflammation, and a prothrombotic state. 6 – 9 While overt diabetes mellitus is incorporated into the CHA 2 DS 2 -VASc scoring system, the independent contribution of prediabetes to stroke recurrence in AF patients has not been adequately characterized. 10 , 11 Evidence to date has been conflicting. Desai et al. reported a nearly two-fold higher risk of recurrent stroke among prediabetic AF patients using the National Inpatient Sample (NIS), a cross-sectional database limited to in-hospital events during single encounters (N = 480 prediabetes cases). 12 Conversely, a large meta-analysis by Cai et al. found no significant association between prediabetes and stroke recurrence in patients with established atherosclerotic cardiovascular disease (relative risk 1.05, 95% CI 0.81–1.36). 13 No large-scale, propensity-matched study has examined longitudinal time-to-event outcomes for recurrent stroke in AF patients stratified by prediabetes status. Accordingly, we investigated the association of prediabetes with recurrent ischemic stroke, all-cause mortality, gastrointestinal (GI) bleeding, and intracranial hemorrhage (ICH) using the TriNetX US Collaborative Network. 14 Methods Study design and data source This retrospective propensity-matched cohort study used the TriNetX US Collaborative Network, a federated platform aggregating de-identified electronic health record (EHR) data from 67 healthcare organizations across the United States. 14 , 15 All data are de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The study was granted an institutional review board (IRB) exemption by the Western IRB (WCG IRB), as all data were de-identified and the study did not constitute human subjects research under 45 CFR 46. A formal IRB reference number was not assigned, as the study qualified for exemption rather than full board review. The study was conducted in accordance with the principles of the Declaration of Helsinki ( https://www.wma.net/policies-post/wma-declaration-of-helsinki/ ). Individual informed consent (written or verbal) was not required and was waived by the IRB, as the study exclusively utilized de-identified electronic health records with no direct patient contact or access to identifiable information. This study adhered to the STROBE guidelines for observational research. Study population Adults aged 18 years or older with documented AF (ICD-10 code I48) and prior ischemic stroke (ICD-10 I63) or transient ischemic attack (TIA; ICD-10 G45) were included. The prediabetes cohort comprised patients with a diagnosis of prediabetes (ICD-10 R73.03) or a documented HbA1c between 5.7% and 6.4%. The normoglycemic control cohort included patients with a documented HbA1c of 5.6% or less, without a prediabetes diagnosis. Both cohorts excluded patients with type 1, type 2, or other specified diabetes mellitus (ICD-10 E10, E11, E13) or an HbA1c of 6.5% or greater. Before matching, 84,011 patients met criteria for the prediabetes cohort and 95,352 for the control cohort. Propensity score matching One-to-one propensity score matching was performed using a greedy nearest-neighbor algorithm. Covariates included age at index event, sex, race, ethnicity, and ICD-10 diagnosis chapter categories (including circulatory, endocrine, nervous system, digestive, respiratory, genitourinary, and mental health disorders). A standardized mean difference (SMD) threshold below 0.1 was prespecified for adequate balance. Index event and outcomes The index event was the date on which each patient first met all cohort criteria. Outcomes were assessed starting 1 day after the index event with no prespecified end date. Patients with the outcome of interest prior to the observation window were excluded from the respective analysis. The primary outcome was recurrent ischemic stroke (ICD-10 I63). Secondary outcomes were all-cause mortality, GI bleeding (ICD-10 K92, including K92.1 melena), and ICH (ICD-10 I61). Statistical analysis Cumulative risk was compared using risk differences, risk ratios (RR), and odds ratios with 95% confidence intervals (CI). Kaplan-Meier survival estimation with log-rank tests and Cox proportional hazards regression (hazard ratios [HR]) were used for time-to-event analyses. A two-sided P value below 0.05 was considered significant. All analyses were performed using the TriNetX analytics platform. 14 , 15 Results After propensity score matching, 80,335 patients remained in each cohort with excellent covariate balance (all SMDs <0.02; Table 1 ). The mean age at index was 69.9 ± 10.6 years in the prediabetes cohort and 70.0 ± 11.2 years in the control cohort. Male sex was equally represented at 54.7% in both groups. Comorbidity burden was well balanced across all ICD-10 diagnosis chapter categories. Table 1. Baseline characteristics after propensity score matching. Variable Prediabetes (n = 80,335) Control (n = 80,335) SMD Age at index, y, mean ± SD 69.9 ± 10.6 70.0 ± 11.2 0.015 Male sex, % 54.7 54.7 <0.001 White race, % 80.2 80.5 0.008 Black race, % 12.3 12.2 0.004 Hispanic ethnicity, % 2.3 2.3 0.006 Circulatory disease, % 83.1 83.0 0.002 Endocrine/metabolic, % 71.1 71.3 0.004 Nervous system, % 60.4 60.5 0.004 Mental/behavioral, % 43.5 43.8 0.007 Respiratory, % 51.0 51.2 0.004 Digestive system, % 52.5 52.8 0.006 Primary outcome After excluding patients with the outcome prior to the observation window, recurrent ischemic stroke occurred in 23,375 of 38,943 patients (60.0%) in the prediabetes cohort and 23,068 of 38,181 patients (60.4%) in the control group. Cumulative risk did not differ significantly (RR 0.993, 95% CI 0.982–1.005; P = 0.264). However, Kaplan-Meier analysis demonstrated significantly longer time-to-recurrence in the prediabetes cohort (median 1,794 vs 1,695 days; log-rank P < 0.001). The hazard ratio for recurrent stroke was 0.960 (95% CI 0.943–0.978; P < 0.001), indicating a 4% lower hazard of recurrence at any given time point in the prediabetes group. Secondary outcomes All-cause mortality was significantly lower in the prediabetes cohort (21.6% vs 23.6%; HR 0.891, 95% CI 0.873–0.910; P < 0.001), with a median survival difference of approximately 380 days (5,427 vs 5,047 days). GI bleeding was also lower in the prediabetes group (10.3% vs 10.9%; HR 0.918, 95% CI 0.889–0.947; P = 0.003). ICH rates were numerically lower but did not reach statistical significance (4.6% vs 4.7%; HR 0.961, 95% CI 0.917–1.007; P = 0.065). Table 2 summarizes all outcomes. Table 2. Summary of primary and secondary outcomes after propensity score matching. Outcome Prediabetes, n/N (%) Control, n/N (%) RR (95% CI) HR (95% CI) P Recurrent stroke 23,375/38,943 (60.0) 23,068/38,181 (60.4) 0.993 (0.982–1.005) 0.960 (0.943–0.978) <0.001 All-cause mortality 17,260/79,959 (21.6) 18,880/79,945 (23.6) 0.914 (0.898–0.931) 0.891 (0.873–0.910) <0.001 GI bleeding 7,670/74,509 (10.3) 8,098/74,003 (10.9) 0.941 (0.913–0.969) 0.918 (0.889–0.947) 0.003 ICH 3,481/76,087 (4.6) 3,529/75,717 (4.7) 0.982 (0.938–1.028) 0.961 (0.917–1.007) 0.065 Discussion In this propensity-matched analysis of over 160,000 AF patients with prior ischemic stroke from 67 US healthcare organizations, prediabetes was not associated with increased cumulative recurrent stroke risk. Time-to-recurrence was significantly longer in the prediabetes cohort, and prediabetes was associated with lower all-cause mortality and lower GI bleeding. These findings diverge from prior work and warrant consideration of the methodological factors that may account for the discrepancy. The contrast with the results of Desai et al. 12 is likely attributable to several differences in study design. Their analysis used the NIS, an administrative database capturing events during single hospitalizations without longitudinal follow-up. The prediabetes sample was small (N = 480), and the outcome metric (in-hospital stroke recurrence) is susceptible to length-of-stay bias. Our study, by contrast, leveraged a federated EHR platform with longitudinal patient-level data, rigorous one-to-one propensity score matching on demographic and clinical covariates, and Kaplan-Meier survival analysis across a substantially larger sample (80,335 matched pairs). The findings align more closely with the meta-analysis by Cai et al., 13 which found no significant increase in stroke recurrence among patients with established cardiovascular disease and prediabetes. A key observation from this analysis is the discrepancy between cumulative risk and time-to-event results for recurrent stroke. While overall proportions of patients experiencing recurrence were similar between groups, the Kaplan-Meier analysis showed that patients with prediabetes reached this threshold at a measurably slower rate (median difference of approximately 99 days). These two analytic approaches provide complementary information: cumulative risk reflects the proportion who ultimately experience the event, whereas survival analysis captures the tempo of events. The clinical significance of a three-month delay in stroke recurrence, in terms of preserved neurological function and quality-adjusted life years, should not be dismissed. 5 , 16 We propose a metabolic surveillance effect as one potential explanation for these findings. Patients diagnosed with prediabetes have, by definition, undergone glycemic screening and entered a framework of heightened medical attention. This surveillance may lead to earlier identification and optimization of modifiable risk factors, including hypertension, dyslipidemia, and anticoagulation management, all of which independently influence stroke recurrence. 13 , 17 This hypothesis is consistent with the differential effect of prediabetes by baseline cardiovascular risk reported by Cai et al., in which prediabetes conferred excess risk in general populations but not in those with established cardiovascular disease already under active management. 13 The lower all-cause mortality observed in the prediabetes cohort (HR 0.891; P < 0.001; median survival difference approximately 380 days) may similarly reflect the downstream benefits of increased clinical contact and preventive care. The lower rate of GI bleeding (HR 0.918; P = 0.003) could stem from more careful anticoagulant selection and dose adjustment in patients under closer metabolic surveillance. The absence of a significant difference in ICH provides reassurance that prediabetes does not confer additional hemorrhagic risk in this population. Limitations Several limitations apply. The retrospective design precludes causal inference. EHR-based coding may introduce misclassification of both exposure and outcomes. Detailed anticoagulation data (agent, dose, adherence) were unavailable. Stroke severity (NIHSS) was not captured. Data were restricted to US healthcare organizations, limiting generalizability. Prediabetes may progress to overt diabetes during follow-up, and this trajectory could not be adjusted for. Residual confounding from unmeasured variables (lifestyle, socioeconomic status, medication adherence) cannot be excluded. Despite these constraints, the study benefits from a large sample size, longitudinal EHR data, rigorous propensity score matching (all post-matching SMDs <0.02), and a multidimensional outcome assessment spanning stroke recurrence, mortality, and bleeding events. Conclusions In AF patients with prior ischemic stroke, prediabetes was not associated with increased cumulative recurrent stroke risk and was associated with longer time-to-recurrence, lower all-cause mortality, and lower GI bleeding. These results suggest that prediabetes alone should not be considered an additional high-risk marker for stroke recurrence in this population. The observed associations may partly reflect a metabolic surveillance benefit. Prospective studies should evaluate whether structured glycemic monitoring programs can improve long-term cerebrovascular outcomes in AF patients. Ethics and consent This study was granted an institutional review board (IRB) exemption by the Western IRB (WCG IRB), as all data were de-identified electronic health records and the study did not constitute human subjects research under 45 CFR 46. A formal IRB reference or permit number was not assigned, as the study qualified for exemption rather than full board review. The study was performed in accordance with the principles stated in the Declaration of Helsinki ( https://www.wma.net/policies-post/wma-declaration-of-helsinki/ ). Individual informed consent (written or verbal) was not required and was waived by the IRB, as the study exclusively utilized de-identified electronic health records with no direct patient contact, no access to identifiable patient information, and no prospective enrollment of human participants. Declaration of Generative AI Use During the preparation of this work, the authors used AI-assisted tools for manuscript formatting and language editing. The authors reviewed and edited all content and take full responsibility for the content of the published article. Data availability Underlying data The data underlying this study were accessed through the TriNetX research network, a federated platform that provides access to de-identified electronic health records. Due to the platform’s data use agreement and governance policies, patient-level datasets cannot be extracted, downloaded, or shared publicly. TriNetX restricts data access to authorized users through its secure, cloud-based analytics environment to protect patient privacy and comply with HIPAA regulations. The Western IRB (WCG IRB) granted an exemption for this study, as it involves secondary analysis of de-identified data and does not constitute human subjects research under 45 CFR 46; no specific guidance on data sharing was required by the IRB given the nature of the data access. Researchers wishing to replicate or verify the findings may apply for access to the TriNetX platform through their institution at https://trinetx.com . Access is granted to institutions that execute a data use agreement with TriNetX. All analytic queries, cohort definitions, and statistical parameters used in this study are fully described in the Methods section to enable replication. Underlying data Figshare. Underlying data for: Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study. DOI: https://doi.org/10.6084/m9.figshare.32024610 . 18 Data are available under the terms of the CC BY 4.0 license . Reporting guidelines This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting cohort studies. A completed STROBE checklist is available as extended data. Extended data The following extended data are available for this study: STROBE Checklist: A completed STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cohort studies, indicating where each recommended item is reported in the manuscript. No questionnaires, participant information sheets, interview guides, or consent forms were used in this study, as it was a retrospective analysis of de-identified electronic health records. No additional large tables beyond those presented in the manuscript were generated. The extended data (STROBE checklist) should be uploaded to the repository alongside the manuscript. Acknowledgments None. References 1. Cheng X, Hu Y, Zhang S, et al. : Global, regional, and national burden of atrial fibrillation and atrial flutter, 1990–2021: results from the Global Burden of Disease Study 2021. Arrhythmia Electrophysiol. Rev. 2025; 14 : e15. Publisher Full Text 2. Linz D, Gawalko M, Betz K, et al. : Atrial fibrillation: epidemiology, screening and digital health. Lancet Reg Health Eur. 2024; 37 : 100786. PubMed Abstract | Publisher Full Text | Free Full Text 3. Jensen M, Thomalla G, Saarinen J, et al. : Recurrent ischemic stroke risk and mortality in atrial fibrillation. Stroke. 2024; 55 (11): 2684–2693. Publisher Full Text 4. Sposato LA, Cameron AC, Johansen MC, et al. : Ischemic stroke prevention in patients with atrial fibrillation and a recent ischemic stroke, TIA, or intracranial hemorrhage: a World Stroke Organization scientific statement. Int. J. Stroke. 2025; 20 (2): 140–163. Publisher Full Text 5. Palaiodimou L, Stefanou MI, Katsanos AH, et al. : Residual risk of recurrent stroke despite anticoagulation in patients with atrial fibrillation: a systematic review and meta-analysis. JAMA Neurol. 2025; 82 (9): 925–933. Publisher Full Text 6. Magliano DJ, Boyko EJ, Balkau B, et al. : Global and regional prediabetes prevalence: updates for 2024 and projections to 2050 from the International Diabetes Federation Diabetes Atlas, 11th edition. Diabetes Care. 2025; 48 (10): 1773–1781. Publisher Full Text 7. International Diabetes Federation: IDF Diabetes Atlas. Brussels, Belgium: International Diabetes Federation; 11th ed2025. 8. DeFronzo RA, Abdul-Ghani MA: Pathobiology of prediabetes: understanding and interrupting the borderland of diabetes. Diabetes. 2025; 74 (12): 2207–2210. PubMed Abstract | Publisher Full Text | Free Full Text 9. Altintas E, Erdem Y, Sahin M: Endothelial dysfunction in prediabetes and diabetes mellitus patients with normal coronary angiograms. BMC Cardiovasc. Disord. 2024; 24 (1): 621. Publisher Full Text 10. Hsu JC, Yang YY, Chuang SL, et al. : Prediabetes as a risk factor for new-onset atrial fibrillation: the propensity-score matching cohort. Cardiovasc. Diabetol. 2023; 22 (1): 35. PubMed Abstract | Publisher Full Text | Free Full Text 11. Gong D, Xu J, Sun Y, et al. : Impact of preexisting diabetes mellitus on cardiovascular and all-cause mortality in patients with atrial fibrillation: a meta-analysis. Front. Endocrinol. (Lausanne). 2022; 13 : 921159. PubMed Abstract | Publisher Full Text | Free Full Text 12. Desai R, Vasavada A, Patel BA, et al. : Association of prediabetes and recurrent stroke in atrial fibrillation patients: a population-based analysis of hospitalizations and outcomes. J. Clin. Med. 2024; 13 (2): 573. PubMed Abstract | Publisher Full Text | Free Full Text 13. Cai X, Zhang Y, Li M, et al. : Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 2020; 370 : m2297. PubMed Abstract | Publisher Full Text | Free Full Text 14. Forrest IS, Chaudhary K, Giannakoulas G, et al. : A comprehensive review of methodologies and application to use the TriNetX platform for clinical and epidemiological research. Front. Pharmacol. 2025; 16 : 1535427. PubMed Abstract | Publisher Full Text | Free Full Text 15. TriNetX: Publication guidelines. Accessed March 25, 2026. Reference Source 16. Dondorp AM, Bos S, Greijdanus C, et al. : Prediabetes and diabetes mellitus type II after ischemic stroke. Eur. Stroke J. 2025; 10 (1): 89–97. Publisher Full Text 17. Xu C, Gao Y, Wang Y, et al. : Diabetes mellitus and risk of future stroke: evidence from a population-based study. Brain Behav. 2024; 14 (11): e70151. PubMed Abstract | Publisher Full Text | Free Full Text 18. Kachhadia MP, Puri P, Patel K, et al. : Underlying data for: Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study. Dataset. figshare. 2026. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 11 May 2026 ADD YOUR COMMENT Comment Author details Author details 1 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, Florida, USA 2 Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA 3 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, Florida, USA 4 Smt NHL Municipal Medical College, Ahmedabad, Gujarat, India 5 Henry Ford Hospital, Detroit, Michigan, USA 6 Lady Hardinge Medical College, New Delhi, Delhi, India 7 St Joseph Hospital, Bethpage, NY, USA 8 Florida Atlantic University Charles E Schmidt College of Medicine, Boca Raton, FL, USA 9 Department of Technology and Clinical Trials, Advanced Research LLC, fort lauderdale, FL, USA 10 Department of Technology and Clinical Trials, Advanced Research LLC FL, USA, Fort lauderdale, USA 11 Department of Information Technology and Operations Management (ITOM), College of Business, Florida Atlantic University, Florida, Boca Raton, USA Meet Popatbhai Kachhadia Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Piyush Puri Roles: Conceptualization, Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Krina Patel Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Usmaan Topiwala Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Juber D. Shaikh Roles: Conceptualization, Data Curation, Methodology, Software, Writing – Original Draft Preparation Himanshi Yadav Roles: Conceptualization, Data Curation, Methodology, Writing – Original Draft Preparation Lakshya Kumar Roles: Investigation, Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Gurnoor Gill Roles: Conceptualization, Data Curation, Software, Writing – Original Draft Preparation Samarth Shah Roles: Conceptualization, Data Curation, Methodology, Writing – Original Draft Preparation Jay Patel Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation Harshal A. Sanghvi Roles: Conceptualization, Data Curation, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 11 May 2026, 15:701 https://doi.org/10.12688/f1000research.180154.1 Copyright © 2026 Kachhadia MP et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Kachhadia MP, Puri P, Patel K et al. Prediabetes and Risk of Recurrent Ischemic Stroke in Atrial Fibrillation: A Propensity-Matched Cohort Study [version 1; peer review: awaiting peer review] . 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