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Immunologic clustering of donor-specific antibodies and clinical outcomes in kidney transplant recipients
Salem H Al-Qurashi, Muhammad Abdul Mabood Khalil, Hinda Hassan Khideer Mahmood, Maram Majid Alsharif, Yara Faisal Alqurashi, Lama Alghamdi, Rawan A Al-Ghamdi, Zeyad Adel Alsaedi, Aileen Jean Dela Cruz, Ghaleb A Aboasamh, Nihal Mohammed Sadagah
Salem H Al-Qurashi, Muhammad Abdul Mabood Khalil, Hinda Hassan Khideer Mahmood, Aileen Jean Dela Cruz, Ghaleb A Aboasamh, Nihal Mohammed Sadagah, Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia
Maram Majid Alsharif, Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah 21955, Makkah al Mukarramah, Saudi Arabia
Yara Faisal Alqurashi, Lama Alghamdi, Rawan A Al-Ghamdi, Zeyad Adel Alsaedi, Department of Medicine, King Fahad Armed Forces Hospital, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia
Author contributions: Khalil MAM, Sadagah NM, and Al-Qurashi SH planned and designed the outline of the manuscript; Mahmood HHK, Alsharif MM, Alqurashi YF, Alghamdi L, Al-Ghamdi RA, Alsaedi ZA, Cruz AJD, and Aboasamh GA collected data, helped in data analysis, and helped in the literature search; Khalil MAM wrote the manuscript; Sadagah NM, and Al-Qurashi SH provided funding; all authors read and agreed to the final manuscript.
AI contribution statement: ChatGPT was used only to improve the readability and language clarity of the manuscript. It did not contribute to study design, data analysis, interpretation of results, writing of scientific content, or generation of images.
Institutional review board statement: This study was approved by the Research Ethics Committee of King Fahad Armed Forces Hospital, Jeddah (No. REC 890).
Informed consent statement: Informed consent was obtained from all participants or, when applicable, from their next of kin.
Conflict-of-interest statement: We declare no conflict of interest.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Access to patient-level data is restricted to protect privacy and confidentiality.
Corresponding author: Muhammad Abdul Mabood Khalil, MD, FRCP, Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital, Al Kurnaysh Br Road, Al Andalus, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia.
doctorkhalil1975@hotmail.com
Received: January 6, 2026
Revised: February 28, 2026
Accepted: April 1, 2026
Published online: June 18, 2026
Processing time: 148 Days and 1.3 Hours
BACKGROUND
Donor-specific antibodies (DSA) against human leukocyte antigens (HLA) are associated with increased immunologic risk in kidney transplant recipients (KTR). However, outcomes among DSA-positive patients are highly variable. Traditional markers, such as DSA class and mean fluorescence intensity (MFI), often fail to capture the multidimensional nature of immunologic risk.
AIM
This retrospective study aims to stratify DSA-positive KTR into immunologic risk groups using unsupervised machine learning and to assess the relationship between antibody profiles and early post-transplant outcomes.
METHODS
During the study period, 260 kidney transplants were performed at King Fahad Armed Forces Hospital in Saudi Arabia, of which 151 recipients were DSA-positive. Clustering analysis was performed in 112 DSA-positive recipients with complete immunologic and follow-up data. Key variables used for clustering included DSA class, number of DSA, cumulative MFI, early graft function (estimated glomerular filtration rate at 4 months), and HLA mismatch. K-means clustering was applied to identify distinct immunologic risk groups. Clinical outcomes, including acute rejection, graft loss, delayed graft function, and mortality, were compared across clusters.
RESULTS
Three distinct clusters were identified: High-risk (n = 58) with both Class I and II DSA, high DSA numbers and MFI, and the highest rates of acute rejection (6.9%), graft loss (6.9%), and mortality (5.2%); intermediate-risk (n = 34) with predominantly Class II DSA, moderate MFI, and mild graft loss and mortality; and low-risk (n = 20) with isolated Class I DSA, low DSA numbers and MFI, and excellent early graft function with minimal complications. Clusters demonstrated a clear gradation of immunologic burden, which corresponded with early post-transplant outcomes. Principal component analysis showed clear separation among the three groups.
CONCLUSION
Unsupervised clustering effectively stratifies DSA-positive KTR into clinically meaningful risk groups. High-risk recipients with broad and strong DSA profiles are more likely to experience early adverse outcomes. Low-risk recipients have favorable early graft function. This data-driven approach may help guide individualized monitoring, donor selection, and immunosuppression strategies for DSA-positive KTR.
Core Tip: Donor-specific antibodies (DSA) in kidney transplant recipients (KTR) are associated with variable immunologic risk, which is not always well defined by DSA class or mean fluorescence intensity alone. In this study, an unsupervised machine learning approach was used to group DSA-positive recipients based on antibody characteristics and transplant-related factors. These groups showed apparent differences in early post-transplant risk profiles. Patients with broader and stronger antibody responses were more likely to experience adverse early outcomes, whereas those with a limited antibody burden had favorable graft function. This approach may help refine risk assessment and support more individualized management of DSA-positive KTR.
