My research area is in Immunology with a special focus on Cytokines' role in Health and Disease. My main interest is in the roles of cytokines in different complications of pregnancy such as Recurrent Spontaneous Miscarriage, Preeclampsia, Intra-uterine Growth Restriction, and Preterm delivery. I am also involved in immunological studies of other conditions such as Multiple Sclerosis, Dengue Fever, Rheumatoid Arthritis, Hemoglobinopathies, and Cytomegalovirus infections. My current research activity is on the cytokine balance in asthma and bone diseases (osteoimmunology).
RESEARCH, TEACHING, or OTHER INTERESTS
Immunology and Microbiology, Rheumatology, Immunology and Allergy, Organizational Behavior and Human Resource Management
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Scopus Publications
Scopus Publications
Immunological Profile of Patients with Rheumatoid Arthritis and Asthma Sana S. Almutairi, Fawaz Y. Azizieh, Adeeba A. Al-Herz, Tahany E. Al-Shemary, Ahmed R. Alsaber, Raj Raghupathy Medical Principles and Practice, 2026 Introduction: The aim of this study was to investigate cytokine profiles in patients with both rheumatoid arthritis (RA) and bronchial asthma (BA). Methods: We studied 25 patients with RA, 25 with BA, and 25 with both RA and BA (BARA). A respiratory questionnaire was completed by all the patients, they underwent spirometry, and their production patterns of selected T helper (Th)1, Th2, and Th17 cytokines were assessed. Results: BA patients had spirometry findings similar to BARA patients, while RA patients had normal spirometry. BA patients produced significantly higher levels of interleukin (IL)-5 (p = 0.03) and IL-10 (p = 0.03) than patients with RA. Median levels of IL-17A and IL-17F and interferon (IFN)-γ were higher (p = 0.001, 0.004, and 0.04) in RA patients than in BA patients. No differences were seen in the levels of cytokines produced by BA patients compared to BARA patients. IL-4 and IL-10 levels were higher (p = 0.04 and 0.03) in BARA patients than in RA patients, while levels of IL-17A and IFN-γ were higher (p = 0.037 and 0.009, respectively) in RA patients than in BARA patients. Ratios of Th1/Th2 and Th17/Th2 cytokines in most combinations were different between RA and BA, and between RA and BARA, but were similar in BA and BARA. Conclusions: Patients with both BA and RA have a Th2-dominant cytokine profile unlike patients with RA alone. These observations contribute to a better understanding of the immunopathogenesis of these diseases and to the management of patients using cytokine-based therapies.
Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables Suhail Al-Shammri, Ahmet Özdil, Amro Aboukoura, Fawaz Azizieh, Bulent Yilmaz, Raj Raghupathy Frontiers in Neurology, 2026 Introduction Accurate prediction of disease progression in multiple sclerosis (MS) remains a critical challenge in clinical management. This study investigates the utility of supervised machine learning (ML) models in predicting clinical disability, as measured by the Expanded Disability Status Scale (EDSS), and radiological activity based on MRI lesion changes in patients with relapsing-remitting MS (RRMS). Methods Using peripheral cytokine profiles (IL-12, TNF-α, IFN-γ, IL-4, IL-10) along with patient metadata (e.g., sex, family history, relapse status), 43 ML classifiers were trained and evaluated for their ability to discriminate between mild and moderate disability (EDSS <1 vs >1, and <2.5 vs >2.5), and to predict new MRI lesions in 15 MS patients. Results Ensemble models consistently outperformed simpler algorithms. For EDSS prediction, Random Forest achieved 90.1% sensitivity and 89.7% specificity, while Simple Logistic Regression reached 92.6% for both metrics when patient ID was included. In predicting new MRI lesions, Random Subspace classifiers performed best, with 82.4% sensitivity and specificity. Discussion These findings suggest that combining cytokine-based immune signatures with machine learning strategies can provide clinically meaningful predictions of both functional disability and radiological progression. Such tools may support more proactive patient monitoring, informed therapeutic decision-making, and risk stratification in the care of RRMS. Further validation in prospective cohorts is warranted to support clinical implementation.
Harnessing Cytokine Signatures and AI for Early Disease Detection and Predictive Medicine Fawaz Azizieh, Bulent Yilmaz Intersecting AI and Medicine for Improved Care and Administrative Efficiency, 2025 Current disease diagnosis and monitoring face several challenges. To address these, there is a growing emphasis on developing more precise and predictive diagnostic tools. Integrating artificial intelligence (AI) into analyzing cytokine signatures represents a transformative approach to understanding and managing complex diseases. Cytokines, as key mediators of immune responses, provide a wealth of information about immune activation, inflammation, and disease progression. However, the complexity and variability of cytokine networks pose significant challenges for traditional analytical methods. AI, with its ability to process large datasets, identify patterns, and make predictions, offers a powerful tool to unlock the potential of cytokine signatures for improved disease prediction, early diagnosis, and personalized interventions. This chapter, as presented by the authors, highlights the potential interdisciplinary significance of integrating AI and cytokine research in advancing our understanding and management of health and disease.
Cytokine production patterns in patients with sickle cell disease and avascular necrosis of the femoral head Fawaz Azizieh, Raj Raghupathy, Renu Gupta, Akmal Zahra, Hanan Al-Abboh, Huda Alsahhaf, Rubina Fatima, Adekunle Adekile Pediatric Hematology Oncology Journal, 2025 Background An imbalance in pro- and anti-inflammatory cytokines has been suggested to contribute to tissue damage in sickle disease (SCD) following recurrent ischemia, which leads to several complications including avascular necrosis (AVN) of the femoral head. This study aimed to investigate the profiles of cytokines produced by mitogen-stimulated peripheral blood mononuclear cells (PBMC) in SCD patients with or without AVN. Methods The patients were recruited from the outpatient hematology clinics of Mubarak al-Kabeer Hospital, Kuwait. They were screened for AVN using magnetic resonance imaging (MRI). Levels of peripheral blood mononuclear cell (PBMC)-secreted cytokines were estimated in 31 AVN-negative and 16 AVN-positive SCD patients. Four pro-inflammatory cytokines (IL-1−β, IL-6, IL-17A, and TNF-α) and three anti-inflammatory cytokines (IL-4, IL-10, and TGF-β) were assayed in a multiplex ELISA. Results Mitogen-activated PBMCs from the patients who were AVN-positive secreted significantly higher levels of the pro-inflammatory cytokines TGF-β, and IL-4 compared to AVN-negative patients. Similarly, three ratios (IL-17A/IL-4, TNF-α/IL-4, and, IL-17/TGF-β) were significantly higher in AVN-negative, compared to AVN-positive patients, thus showing a pro-inflammatory bias in the former. The multivariate pattern plot shows that points of AVN-positive data are clustered closely, separating them from the AVN-negative data. Conclusion Our data suggest that it is worthwhile to explore levels and ratios of pro- to anti-inflammatory cytokines produced by mitogen-stimulated PBMC in patients with SCD. The multivariate pattern analysis of 7 cytokines revealed a pattern that can be used as a predictive tool to delineate those patients that may develop AVN.
High protein diet increases the risk of allergic sensitization but not asthma in mice through modulation of the cytokine milieu toward Th2 bias Waleed Al-Herz, Fawaz Azizieh, Raj Raghupathy World Allergy Organization Journal, 2025 Introduction: The role of different nutrients in allergic sensitization is not clear. In this study we aimed to determine the effect of high protein (HP) diet on allergic sensitization, cytokine profile, and asthma in mice. Methods: Seven- to eight-week old female BALB/c mice were fed either normal (ND) or HP diet and were sensitized with ovalbumin intraperitoneally followed by intranasal challenge. Allergic sensitization was tested by measuring anti-ovalbumin (OVA) IgE, IgG1, and IgG2a antibodies. Cytokine levels were tested by multiplex ELISA in splenocyte supernatants after stimulation. Airway inflammation was tested by measuring total and differential cell counts in bronchoalveolar lavage fluid and by measuring bronchial mucus production, goblet cell hyperplasia and perivascular and peribronchial inflammation severity scores by histologic examination. Results: Mice fed HP diet had a significant increase in weight and higher levels of OVA-specific IgE and IgG1 antibodies compared to the ND group (P-values 0.002, 0.007 and <0.001, respectively). In addition, they showed a selective Th2 bias in cultured splenocyte supernatants compared to the ND group as demonstrated by higher IL-4 and IL-6 levels (P-values <0.001 and 0.011, respectively) and higher ratios of Th2 to Th1 cytokines. However, the level of airway inflammation was comparable between both groups. Conclusions: HP diet increases the risk of allergic sensitization though increase in Th2 cytokines. Efforts should be made to define the upper limit of protein in the diet that does not predispose to allergic sensitization. The effect of diet on health should remain a focus of research for the establishment of optimal health and resilience.
Artificial intelligence predicts pregnancy complications based on cytokine profiles Fawaz Azizieh, Bulent Yilmaz, Raj Raghupathy Journal of Maternal Fetal and Neonatal Medicine, 2025 BACKGROUND Early prediction of pregnancy complications is important for adequate and timely prevention, management, and reducing maternal/fetal pathogenesis. OBJECTIVE To study the prognostic value of cytokines as predictors of pregnancy complications using unbiased artificial intelligence/machine learning (AI/ML) methods. METHODS For this study, we used our previously published data on 127 women with pregnancy complications and 97 women with a history of normal delivery and undergoing a normal delivery. A panel of seven cytokines were analyzed from activated peripheral blood mononuclear cells (PBMC). AI/ML methods such as kNN, SVM, decision tree, and ensemble classification were applied to explore the possible use of AI/ML to compare and predict normal gestation and normal delivery as opposed to different pregnancy complications such as recurrent spontaneous miscarriage (RSM), preterm delivery (PTD), pregnancy-induced hypertension (PIH), and premature rupture of fetal membranes (PROM). RESULTS The study examined cytokine levels in various pregnancy conditions, revealing significant differences, particularly in the levels of IL-2 and IFN-γ, across age-matched comparisons. Additionally, binary classification tasks demonstrated notable accuracies and f-measures for methodologies such as Ensemble (Bagged), QDA, and SVM (Cubic), showcasing their effectiveness in distinguishing between normal delivery and different pregnancy complications. CONCLUSION The study provides a machine learning-based methodology for the prediction of pregnancy complications based on levels of cytokines produced by peripheral blood cells.
Quantitative analyses of cytokine profiles reveal hormone-mediated modulation of cytokine profiles in recurrent spontaneous miscarriage Kamaludin Dingle, Osama M. Kassem, Fawaz Azizieh, Ghadeer AbdulHussain, Raj Raghupathy Cytokine, 2023 PURPOSE: Cytokines play important roles in pregnancy complications. Some hormones such as estrogen, progesterone, and dydrogesterone have been shown to alter cytokine profiles. Understanding how cytokine profiles are affected by these hormones is therefore an important step towards immunomodulatory therapies for pregnancy complications. We analyse previously published data on the effects of estrogen, progesterone, and dydrogesterone on cytokine balances in women having recurrent spontaneous miscarriages. MATERIALS AND METHODS: Levels of eight cytokines (IFN-γ, IL-2, IL-6, IL-10, IL-13, IL-17, IL-23, TNF-α) from n = 22 women presenting unexplained recurrent spontaneous miscarriages were studied. Cytokine values were recorded after in vitro exposure of peripheral blood cells to estrogen, progesterone, and dydrogesterone. We expand on earlier analysis of the dataset by employing different statistical techniques including effect sizes for individual cytokine values, a more powerful statistical test, and adjusting p-values for multiple comparisons. We employ multivariate analysis methods, including to determine the relative magnitude of the effects of the hormone therapies on cytokines. A new statistical method is introduced based on pairwise distances able to accommodate complex relations in cytokine profiles. RESULTS: We report several statistically significant differences in individual cytokine values between the control group and each hormone treated group, with estrogen affecting the fewest cytokines, and progesterone and dydrogesterone both affecting seven out of eight cytokines. Exposure to estrogen produces no large effects sizes however, while IFN-γ and IL-17 show large effect sizes for both progesterone and dydrogesterone, among other cytokines. Our new method for identifying which collections (i.e. subsets) of cytokines best distinguish contrasting groups identifies IFN-γ, IL-10 and IL-23 as especially noteworthy for both progesterone and dydrogesterone treatments. CONCLUSIONS: While some statistically significant differences in cytokine levels after exposure to estrogen are found, these have small effect sizes and are unlikely to be clinically relevant. Progesterone and dydrogesterone both induce statistically significant and large effect-size differences in cytokine levels, hence therapy with these two progestogens is more likely to be clinically relevant. Univariate and multivariate methods for identifying cytokine importances provide insight into which groups of cytokines are most affected and in what ways by therapies.
Cytokineexplore: An online tool for statistical analysis of cytokine concentration datasets Osama Kassem, Abdulwahab Al-Saleh, Fawaz Azizieh, Kamaludin Dingle Journal of Inflammation Research, 2020 Purpose Cytokine data sets are increasing both in the number of different cytokines measured and the number of samples assayed. Further, typically data from different groups may be contrasted, eg, normal vs complication subjects. Many univariate and multivariate statistical techniques exist to study such cytokine datasets, but the ability to implement these techniques may be lacking for some practitioners, or may not be available quickly and conveniently. Here, we introduce CytokineExplore, an online tool for multi-cytokine and multi-group data analysis of user-provided Microsoft Excel data files. Materials and Methods In order to illustrate the tool features, we use data from intrauterine growth retardation (IUGR), a pregnancy complication, and normal healthy subjects as a control. The dataset contains levels for 10 cytokines, namely: IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, IL-18, IL-23, interferon-gamma (IFN-γ) and tumour necrosis-alpha (TNF-α), obtained from 34 women with IUGR (further divided into 17 symmetric and 17 asymmetric cases) and 24 gestationally age-matched normal controls. Results The online tool automatically generates box-plots, histograms, PCA and PLSDA plots, t-tests and Mann–Whitney statistical tests, cytokine importance values for separating two groups, heatmaps for comparing multiple groups, and other functionalities. Figures generated can be directly downloaded for use in presentations or journal articles. Conclusion The tool facilitates quick and easy numerical exploration and multivariate analysis of cytokine datasets, to aid basic understanding and hypothesis generation.
Elevated levels of IL-8 in dengue hemorrhagic fever R. Raghupathy, U. C. Chaturvedi, H. Al-Sayer, E. A. Elbishbishi, R. Agarwal, R. Nagar, S. Kapoor, A. Misra, A. Mathur, H. Nusrat, F. Azizieh, M. A. Y. Khan, A. S. Mustafa Journal of Medical Virology, 1998
