Advanced glycation end products (AGEs) are a group of compounds formed through non-enzymatic reactions between reducing sugars and proteins, lipids, or nucleic acids. AGEs can be generated in the body or introduced through dietary sources and smoking. Recent clinical and animal studies have highlighted the significant role of AGEs in various health conditions. These compounds accumulate in nearly all mammalian tissues and are associated with a range of diseases, including diabetes and its complications, cardiovascular disease, and neurodegeneration. This review summarizes the major diseases linked to AGE accumulation, presenting both clinical and experimental evidence. The pathologies induced by AGEs share common mechanisms across different organs, primarily involving oxidative stress, chronic inflammation, and direct protein cross-linking. Interventions targeting AGE-related diseases focus on inhibiting AGE formation using synthetic or natural antioxidants, as well as reducing dietary AGE intake through lifestyle modifications. AGEs are recognized as significant risk factors that impact health and accelerate aging, particularly in individuals with hyperglycemia. Monitoring AGE level and implementing nutritional interventions can help maintain overall health and reduce the risk of AGE-related complications.

The rapid development of biological and medical examination methods has vastly expanded personal biomedical information, including molecular, cellular, image, and electronic health record datasets. Integrating this wealth of information enables precise disease diagnosis, biomarker identification, and treatment design in clinical settings. Artificial intelligence (AI) techniques, particularly deep learning models, have been extensively employed in biomedical applications, demonstrating increased precision, efficiency, and generalization. The success of the large language and vision models further significantly extends their biomedical applications. However, challenges remain in learning these multimodal biomedical datasets, such as data privacy, fusion, and model interpretation. In this review, we provide a comprehensive overview of various biomedical data modalities, multimodal representation learning methods, and the applications of AI in biomedical data integrative analysis. Additionally, we discuss the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios. We then propose future directions for adapting deep learning methods with model pretraining and knowledge integration to advance biomedical research and benefit their clinical applications.

Background: In recent years, large language models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. Results: We developed the knowledge graph–based thought (KGT) framework, an innovative solution that integrates LLMs with knowledge graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug–cancer associations and can assist in predicting resistance by analyzing relevant biomarkers and genetic mechanisms. To evaluate the knowledge graph question answering task within biomedicine, we utilize a pan-cancer knowledge graph to develop a pan-cancer question answering benchmark, named pan-cancer question answering. Conclusions: The KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field. This study serves as a proof of concept, demonstrating its exceptional performance in biomedical question answering.

Background: Knowledge graphs and large language models (LLMs) are key tools for biomedical knowledge integration and reasoning, facilitating structured organization of scientific articles and discovery of complex semantic relationships. However, current methods face challenges: knowledge graph construction is limited by complex terminology, data heterogeneity, and rapid knowledge evolution, while LLMs show limitations in retrieval and reasoning, making it difficult to uncover cross-document associations and reasoning pathways. Results: We propose a pipeline that uses LLMs to construct a Biomedical Stratified Knowledge Graph (BioStrataKG) from large-scale articles and builds the Biomedical Cross-Document Question Answering Dataset (BioCDQA) to evaluate latent knowledge retrieval and multihop reasoning. We then introduce Integrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) to enhance retrieval accuracy and knowledge reasoning. IP-RAR maximizes information recall through integrated reasoning-based retrieval and refines knowledge via progressive reasoning-based generation, using self-reflection to achieve deep thinking and precise contextual understanding. Experiments show that IP-RAR improves document retrieval F1 score by 20% and answer generation accuracy by 25% over existing methods. Conclusions: The IP-RAR helps doctors efficiently integrate treatment evidence to inform the development of personalized medication plans and enables researchers to analyze advancements and research gaps, accelerating the hypothesis generation phase of scientific discovery and decision-making.

Objective: Previous studies have found that obese people have lower levels of vitamin B, but most have focused on obesity as defined by body mass index (BMI), and its relationship with other types of obesity is unclear. The aim of this study was to explore the relationship between vitamin B levels and obesity assessed by different definitions among Chinese middle-aged and older community-dwelling adults. Methods: This cross-sectional study included 887 participants aged 45 years and older (45–82 years). The concentrations of vitamin B (B₁, B₂, B₆, and B₉) were measured by robotic dry blood spot extraction systems in combination with liquid chromatography–tandem mass spectrometry. BMI, body fat percentage (BF%), visceral fat area (VFA), and waist circumference (WC) were used to diagnose obesity. VFA and BF% were assessed by bioelectrical impedance analysis. The logistic regression model was used to assess the associations between vitamin B levels and the odds of obesity. Results: The average age of all participants was 60.77 (SD 6.33) years. The prevalence of obesity varied from 8.6% to 52.4% depending on different diagnostic criteria. After adjusting for covariates, a negative correlation was observed between vitamin B₁ level and obesity according to the criteria of WC, VFA, and BF%, and the adjusted odds ratio (OR) was 0.47, 0.52, and 0.46, respectively. When using WC and BF% to define obesity, higher quartiles of vitamin B₂ were negatively associated with the odds of obesity (OR: 0.62 and 0.62, respectively). Vitamin B₆ was inversely associated with VFA-defined and BF%-defined obesity (OR: 0.64 and 0.64, respectively). When using VFA and BF% to define obesity, a negative correlation was observed in vitamin B₉ (OR: 0.61 and 0.67, respectively). Conclusions: Vitamin B (B₁, B₂, B₆, and B₉) level was negatively related to obesity (defined by WC, VFA, or BF%) in Chinese middle-aged and older adults.

Diabetes mellitus is one of the most critical global health concerns, with a fast-growing prevalence. The incidence of diabetic vascular complications is also rapidly increasing, exacerbating the burden on individuals with diabetes and the consumption of public medical resources. Despite the overall improvements in the prevention, diagnosis, and treatment of diabetic microvascular complications in recent years, safe and effective alternative or adjunctive therapies are urgently needed. The mechanisms underlying diabetic vascular complications are complex, with hyperglycemia-induced oxidative stress and inflammation being the leading causes. Therefore, glycemic control, antioxidation, and anti-inflammation are considered the main targets for the treatment of diabetes and its vascular comorbidities. Vaccinium L. (Ericaceae) is a genus of plants enriched with polyphenolic compounds in their leaves and fruits. Vaccinium and its extracts have demonstrated good bioactivity in reducing blood glucose, oxidative stress, and inflammation, making them excellent candidates for the management of diabetes and diabetic vascular complications. Here, we review recent preclinical and clinical studies on the potential effect of Vaccinium on ameliorating diabetes and diabetic complications, particularly diabetic kidney disease and diabetic retinopathy.