Diagnostic Framework Research Articles

Clinical, Diagnostic and Therapeutic Framework of mHSPC and nmCRPC: A Multidisciplinary Consensus Project of the Italian Society for Uro-Oncology (SIUrO).

The recent evidences provided in metastatic hormone sensitive prostate cancer (nmHSPC) and in nonmetastatic castration resistant (nmCRPC) introduced the possibility to adopt Androgen Receptor Signaling inhibitor (ARSi) alone (both settings) or with chemotherapy (in mHSPC). In daily clinical practice there are some opening questions regarding the inclusion of next generation imaging, mainly PSMA-PET, how integrate local treatment as radiotherapy, how to select patients or drugs in a multiple-choice scenario, and how to manage patients with comorbidities and polypharmacy. These issues led the Italian Society for Uro-Oncology (SIUrO) to develop a consensus project involving all of the most important Italian scientific societies engaged in the multidisciplinary and multiprofessional management of the disease. This paper describes the items and statements approved, with the aim to support clinicians in managing metastatic hormone sensitive and nonmetastatic castration resistant prostate cancer patients.

Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters

Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis.

An advanced AI framework for mental health diagnostics using Bidirectional Encoder Representations from Transformers with gated recurrent units and convolutional neural networks

An advanced AI framework for mental health diagnostics using Bidirectional Encoder Representations from Transformers with gated recurrent units and convolutional neural networks

BreastHybridNet: A Hybrid Deep Learning Framework for Breast Cancer Diagnosis Using Mammogram Images

As a common malignancy in females, breast cancer represents one of the most serious threats to a female's life, which is also closely associated with the Sustainable Development Goal 3 (SDG 3) of the United Nations for keeping healthy lives and promoting the well-being of all people. Breast cancer accounts for the highest number of cancer mortality for females, and early diagnosis is key to reducing disease-specific mortality and mortality in general. Current methods struggle to accurately localize important regions, model sequential dependencies, or combine different features despite considerable improvements in artificial intelligence and deep learning domains. They prevent diagnostic frameworks from being reliable and scalable, especially in low-resourced healthcare settings. This study proposes a novel hybrid deep learning framework, BreastHybridNet, using mammogram images to tackle these mutual challenges. The proposed framework combines a pre-trained CNN backbone for feature extraction, a spatial attention mechanism to automatically highlight the image area, which contains signature patterns carrying diagnostic information, a BiLSTM layer to obtain sequential dependencies of diagnostic features, and a feature fusion strategy to process complementarily. Experimental results show that the accuracy of the proposed model is 98.30%, which outperforms the state-of-the-art methods LMHistNet, BreastMultiNet, and DOTNet 2.0 to a considerable extent quantitatively. BreastHybridNet works towards the feasibility of interpretability and scalability on existing systems while contributing to worldwide efforts to alleviate cancer-related mortality using cost-efficient diagnostic lenses. This study highlights the need for AI-enabled solutions to contribute to accessing reliable healthcare technologies for breast cancer screening.

Spectrum of gastric neoplasms in Helicobacter pylori-naïve patients.

Chronic Helicobacter pylori (Hp) infection is the largest etiological factor for gastric cancer, but in recent years the reports of Hp-naïve gastric neoplasms (HpNGNs) have increased as the Hp-infected population in Japan has been declining. The histopathologic spectrum of HpNGNs differs significantly from that of conventional Hp-infected gastric neoplasms. Molecularly, the former harbor considerably fewer genetic and epigenetic abnormalities, reflecting the absence of chronic inflammatory conditions in the gastric mucosa. The majority of HpNGNs fall within several specific histological entities; each arise from particular background mucosa. Most originate from the fundic gland mucosa and have a gastric immunophenotype, as seen in foveolar-type gastric adenoma (FGA), oxyntic gland adenoma (OGA)/gastric adenocarcinoma of fundic gland type (GA-FG), signet-ring cell carcinoma (SRCC), and sporadic fundic gland polyp with dysplasia (FGPD). In contrast, tumors arising from the pyloric or cardiac gland mucosa have a diverse immunophenotype, as seen in intestinal-type gastric dysplasia (IGD) and gastric cardiac carcinoma. FGA, FGPD, SRCC, and IGD are mostly found as small intramucosal lesions. OGA/GA-FG frequently progresses to invasive carcinoma, but only a few have lymph node metastases. Thus, these tumors are regarded as precancerous lesions by Western pathologists, while in Japan they tend to be diagnosed as carcinomas, even in cases of low-grade dysplasia. Gastric cardiac carcinomas, on the other hand, are often found as advanced carcinomas and harbor a high malignant biological potential. A new diagnostic framework for gastric neoplasms is required in the present era of Hp-naïve individuals in Japan.

Sarcopenia and Cardiogeriatrics: The Links Between Skeletal Muscle Decline and Cardiovascular Aging.

Sarcopenia, an age-related decline in skeletal muscle mass, strength, and function, is increasingly recognized as a significant condition in the aging population, particularly among those with cardiovascular diseases (CVD). This review provides a comprehensive synthesis of the interplay between sarcopenia and cardiogeriatrics, emphasizing shared mechanisms such as chronic low-grade inflammation (inflammaging), hormonal dysregulation, oxidative stress, and physical inactivity. Despite advancements in diagnostic frameworks, such as the EWGSOP2 and AWGS definitions, variability in criteria and assessment methods continues to challenge standardization. Key diagnostic tools include dual-energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA) for muscle mass, alongside functional measures such as grip strength and gait speed. The review highlights the bidirectional relationship between sarcopenia and cardiovascular conditions such as heart failure, aortic stenosis, and atherosclerotic cardiovascular disease, which exacerbate each other through complex pathophysiological mechanisms. Emerging therapeutic strategies targeting the mTOR pathway, NAD+ metabolism, and senescence-related processes offer promise in mitigating sarcopenia's progression. Additionally, integrated interventions combining resistance training, nutritional optimization, and novel anti-aging therapies hold significant potential for improving outcomes. This paper underscores critical gaps in the evidence, including the need for longitudinal studies to establish causality and the validation of advanced therapeutic approaches in clinical settings. Future research should leverage multi-omics technologies and machine learning to identify biomarkers and personalize interventions. Addressing these challenges is essential to reducing sarcopenia's burden and enhancing the quality of life for elderly individuals with comorbid cardiovascular conditions. This synthesis aims to guide future research and promote effective, individualized management strategies.

Noninvasive Oral Hyperspectral Imaging-Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study.

Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics. The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms. Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People's Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance. Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model's capacity to accurately identify participants with HFpEF. This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care. China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133.

Particle manipulation under X-force fields.

Particle manipulation is a central technique that enhances numerous scientific and medical applications by exploiting micro- and nanoscale control within fluidic environments. In this review, we systematically explore the multifaceted domain of particle manipulation under the influence of various X-force fields, integral to lab-on-a-chip technologies. We dissect the fundamental mechanisms of hydrodynamic, gravitational, optical, magnetic, electrical, and acoustic forces and detail their individual and synergistic applications. In particular, our discourse extends to advanced multi-modal manipulation strategies that harness the combined power of these forces, revealing their enhanced efficiency and precision in complex assays and diagnostic frameworks. The integration of cutting-edge technologies such as artificial intelligence and autonomous systems further enhances the capabilities of these microfluidic platforms, leading to transformative innovations in personalized medicine and point-of-care diagnostics. This review not only highlights current technological advances, but also forecasts the trajectory of future developments, emphasizing the escalating precision and scalability essential for advancing lab-on-a-chip applications.

The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis.

The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%-87%), specificity of 74% (95% CI=69%-79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82-0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity.This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.

2025 Update Consensus of 99mTc-Pyrophosphate Scintigraphy in the Transthyretin Cardiac Amyloidosis from the Taiwan Society of Cardiology and the Society of Nuclear Medicine of the Republic of China.

This 2025 updated consensus outlines the diagnostic strategy for transthyretin amyloid cardiomyopathy (ATTR-CM). Given that ATTR-CM is a significant contributor to heart failure, this article emphasizes the importance of making an early and precise diagnosis, particularly as new therapeutic options become available. Highlighting the critical importance of an early and accurate diagnosis, particularly in light of emerging therapeutic modalities, this consensus underscores the central role of 99mTc-pyrophosphate (PYP) scintigraphy as a non-invasive diagnostic tool. The consensus calls for the adoption of standardized imaging protocols and interpretation criteria to ensure consistency and reliability across diverse clinical settings. The integration of qualitative and quantitative imaging techniques within a structured diagnostic framework places particular focus on the use of single-photon emission computed tomography/computed tomography (SPECT/CT) imaging to enhance diagnostic precision by minimizing blood pool activity and eliminating overlapping interference. Three-hour imaging is considered to be critical for accurate evaluations and to reduce false-positive findings, and it is recommended for its superior diagnostic accuracy. Moreover, quantitative assessments are also considered to be essential for evaluating myocardial amyloid deposition. This updated consensus provides comprehensive guidelines for clinicians, with the aim of optimizing patient outcomes through precise diagnosis and effective management of ATTR-CM. The consensus concludes by advocating for continued research and refinement of imaging methodologies, particularly to enhance the clinical applicability of 99mTc-PYP scintigraphy and other future developments in nuclear molecular imaging.

Atypical presentation of anorexia nervosa pediatric patient: A multidisciplinary approach to diagnosis and management

ABSTRACT This case report describes the complex presentation, diagnostic problems, and multidisciplinary therapy of a 13-year-old Syrian girl with an unusual form of anorexia nervosa (AN). The goal is to improve understanding of AN’s varied clinical spectrum while emphasizing the significance of a complete diagnostic approach and multidisciplinary therapy in juvenile cases. The patient reported considerable weight loss, amenorrhea, and physical indications of starvation over the previous two months. Notably, she did not exhibit the typical psychological symptoms linked with AN. Extensive examinations, including gastrointestinal, endocrinology, rheumatology, psychology, psychiatry, and neurology, were carried out to determine the underlying reason and develop a personalized care plan. The diagnostic process found unexpected traits that challenged established AN criteria. A multidisciplinary approach aided incorrect diagnosis and guided a treatment strategy that included nutritional rehabilitation, psychosocial support, and medical measures. The patient’s outcomes included weight gain, menstruation resumption, and hormonal and physical parameter normalization. This instance contributes to our growing understanding of AN as a group of illnesses with a variety of clinical manifestations. The patient’s atypical traits highlight the necessity for flexible diagnostic criteria and personalized, multidisciplinary care. The favorable outcomes demonstrate the possibility for positive outcomes with a complete strategy, paving the door for further investigation of diagnostic frameworks and treatment techniques in pediatric AN cases.

An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

Cystic masses of the pediatric lung: update on congenital pulmonary airway malformation and its differential diagnosis.

Localized cystic lung lesions in pediatric patients encompass a spectrum of benign and rare malignant conditions that are quite distinct from cystic lung disease arising in adulthood. The majority have historically fallen under the diagnostic category of "congenital pulmonary airway malformation," a term that has been used to denote a diverse group of diseases ranging in etiology from ectopia to bronchial atresia to mosaic oncogenic mutation or neoplasia. This article reviews the clinical characteristics, gross and histologic features, and pathogenetic underpinnings of congenital pulmonary airway malformation as well as lesions that enter its histologic differential diagnosis. In light of ongoing advances in the field, previously proposed pathology-based classification schemes are critically appraised, and a new diagnostic framework is considered.

MULTI-DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING MODELS

In today's world, a large section of the human population suffers from treatable diseases such as heart problems, diabetes, skin cancer, stroke, liver disease, Parkinson's, malaria, and brain tumors. But due to lack of accessible and affordable healthcare the conditions may not be accurately or timely diagnosed that may lead to severe consequences, including disability or even death. Accuracy, affordability, timeliness and accessibility are critical factors that impact diagnosis quality. To improve diagnosis accuracy, affordability, accessibility and eliminate biases, machine learning and deep learning-based algorithms are gaining attraction in revolutionizing the healthcare industry. The objective of this study is to create a flexible and comprehensive medical diagnostic framework based on machine learning and deep learning models that can predict many diseases based on a patient's health records. The main objective of this study is to prevent problems caused by misdiagnosis and delayed diagnosis. By analyzing multiple diseases using a single platform, the cost of patient treatment can be reduced significantly, making it more accessible for people in underprivileged regions. By increasing the accuracy and speed of disease prediction, the proposed machine learning and deep learning-based diagnosis system has the potential to save lives. Experimental results showed that RandomForest has outperformed competing models on numerical datasets while on image datasets, VGG16 generated best accuracy than ResNet50.

Comprehensive management of complex resorptive lesions: Report of two cases

Abstract In the field of endodontics, the management of complex resorptive lesions demands innovative and precise interventions. This report delineates the comprehensive management of two distinct cases, each presenting unique challenges. The first case involved a 23-year-old male with hypothyroidism, presenting with pain and external cervical resorption with internal communication in his lower left front tooth. The case was diagnosed with pulp necrosis and asymptomatic apical periodontitis. The second case involved a 26-year-old female in good health, dissatisfied with the cosmetic appearance, and experiencing occasional pain from a previously treated upper front tooth with concurrent external apical and internal resorption. It was diagnosed as previously treated, with symptomatic apical periodontitis. These cases were notable for their complexity and the tailored approach to diagnosis and treatment, employing advanced techniques and materials, such as bioceramic materials, for optimal outcomes. The first case was approached through conventional endodontic disinfection, flap reflection, and the meticulous external management of the resorptive defect with bioceramic material, followed by restorative procedures. The second case required retreatment, including wide canal management, apex negotiation, and the establishment of an apical plug with bioceramic material, culminating in backfilling with thermoplasticized gutta-percha and subsequent prosthetic rehabilitation. Remarkably, both cases demonstrated successful healing and patient satisfaction at a 6-month follow-up, with radiographs and photographs evidencing the desired clinical outcomes. This report highlights the necessity of adaptability, precision, and the integration of innovative materials and techniques in endodontic practice to effectively address complex resorptive lesions. It underscores the significance of a comprehensive diagnostic and treatment framework that can guide clinical decision-making and enhance procedural outcomes in similar challenging scenarios. It contributes valuable insights and reinforces the imperative of detailed management in endodontic complexities.

Building a Diagnostic Standard for Alzheimer’s Disease Based on Decision Trees and Analyzing Key Factors with Random Forests

Research Background and Significance: Alzheimer's disease (AD) is a widespread neurodegenerative disorder that poses a significant threat to the health of millions of older adults worldwide. Despite the availability of various methods to assess the severity of dementia, the need for a high-precision diagnostic model remains crucial to enhancing patient outcomes. Accurate diagnosis is essential not only for the well-being of individuals but also for the effective management and treatment of the disease. This study aims to address this critical need by developing a more precise diagnostic framework for AD, utilizing advanced machine learning techniques in combination with comprehensive clinical data. Study Contributions: In this research, a decision tree model was constructed based on the principle of information gain, using a meticulously pre-processed sample of 2,149 patients from a public dataset. The model achieved a diagnostic accuracy of 93.47%, markedly outperforming traditional diagnostic methods. Additionally, a random forest model was employed to identify key risk factors influencing AD, such as age and lifestyle habits. These findings not only equip clinicians with more accurate diagnostic tools but also provide a robust scientific foundation for developing AD prevention and treatment strategies. The study also acknowledges its limitations and suggests directions for future research to further improve the diagnosis and understanding of Alzheimer's disease.

Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection

Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.

XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study

ObjectiveTo use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.MethodsIn this retrospective, population-based cohort study, anonymized questionnaire data was retrieved from the Wu Chuan KOA Study, Inner Mongolia, China. After feature selections, participants were divided in a 7:3 ratio into training and test sets. Class balancing was applied to the training set for data augmentation. Four ML classifiers were compared by cross-validation within the training set and their performance was further analyzed with an unseen test set. Classifications were evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve(AUC), G-means, and F1 scores. The best model was explained using Shapley values to extract highly ranked features.ResultsA total of 1188 participants were investigated in this study, among whom 26.3% were diagnosed with KOA. Comparatively, XGBoost with Boruta exhibited the highest classification performance among the four models, with an AUC of 0.758, G-means of 0.800, and F1 scores of 0.703. The SHAP method reveals the top 17 features of KOA according to the importance ranking, and the average of the experience of joint pain was recognized as the most important features.ConclusionsOur study highlights the usefulness of machine learning in unveiling important factors that influence the diagnosis of KOA to guide new prevention strategies. Further work is needed to validate this approach.

Optimising Outbreak Investigation, Surveillance and Discovery of Pathogens in Aquaculture Using Unbiased Metatranscriptomics

ABSTRACTEmerging infectious diseases are a major threat to global aquaculture production and are projected to increase in frequency due to anthropogenic stressors and climate change. Our ability to screen the global aquatic ‘infectome’ is dramatically improving through unbiased metatranscriptomic (i.e., total RNA) sequencing. Despite recent advances in molecular diagnostics and next‐generation sequencing, outbreak investigation in aquaculture often encounters limitations due to the targeted nature of conventional techniques, which may only capture a small portion of the diverse array of disease‐causing agents present in nature, sometimes requiring years to resolve. Here, we propose that unbiased metatranscriptomic sequencing serves as a cost‐effective and powerful technique for characterising pathogens during disease outbreaks and in surveillance programmes. In doing so, we offer a diagnostic framework to mitigate infectious disease threats to global aquaculture. We emphasise the importance of combining rapid metatranscriptomics with traditional techniques for a holistic examination of the causes of unknown aetiologies in aquatic animals, and how it can be used to examine host responses in parallel with pathogen discovery. We suggest that the integration of metatranscriptomics into aquaculture will require upskilling, proficiency testing, user‐friendly bioinformatic tools and open data sharing. Furthermore, it is essential to establish a reporting framework—with analytical guidelines—that ensures the protection of animal industries while safeguarding trade interests.

Assessment of Biomarkers of Glycation in Type I, Type II and Gestational Diabetes Mellitus

Diabetes Mellitus, a leading global health challenge, necessitates reliable diagnostic and management biomarkers. This study assessed the efficacy of glycation markers in monitoring glycemic status across Type I, Type II, and gestational diabetes in Port Harcourt, Nigeria. A cross-sectional analysis of 120 participants (40 per diabetes type) evaluated fasting blood glucose (FBG), glycated hemoglobin (HbA1c), glycated albumin (GA), fructosamine (FA), and 1,5-anhydroglucitol (1,5-AG), along with antioxidant markers (glutathione and alpha-tocopherol). Findings indicated that HbA1c remains the most reliable indicator of glycemic status, with a strong correlation to FBG (r² = 0.99). GA and FA demonstrated utility as short-term markers but were less robust than HbA1c. Notably, FBG and 1,5-AG showed inverse correlations, suggesting their potential for assessing short-term glycemic fluctuations. Insulin levels were the only statistically significant differential marker among diabetes types. These results support incorporating multiple biomarkers into diagnostic frameworks, particularly where HbA1c reliability may be limited (e.g., anemia or renal dysfunction).