Insulin resistance (IR) plays a crucial role in the development and progression of metabolism-related diseases such as diabetes, hypertension, tumors, and nonalcoholic fatty liver disease, and provides the basis for a common understanding of these chronic diseases. In this study, we provide a systematic review of the causes, mechanisms, and treatments of IR. The pathogenesis of IR depends on genetics, obesity, age, disease, and drug effects. Mechanistically, any factor leading to abnormalities in the insulin signaling pathway leads to the development of IR in the host, including insulin receptor abnormalities, disturbances in the internal environment (regarding inflammation, hypoxia, lipotoxicity, and immunity), metabolic function of the liver and organelles, and other abnormalities. The available therapeutic strategies for IR are mainly exercise and dietary habit improvement, and chemotherapy based on biguanides and glucagon-like peptide-1, and traditional Chinese medicine treatments (e.g., herbs and acupuncture) can also be helpful. Based on the current understanding of IR mechanisms, there are still some vacancies to follow up and consider, and there is also a need to define more precise biomarkers for different chronic diseases and lifestyle interventions, and to explore natural or synthetic drugs targeting IR treatment. This could enable the treatment of patients with multiple combined metabolic diseases, with the aim of treating the disease holistically to reduce healthcare expenditures and to improve the quality of life of patients to some extent.

Background: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. Objective: This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. Methods: We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. Results: The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. Conclusions: The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

Diabetic retinopathy (DR) is a prevalent complication of diabetes, significantly impacting patients’ quality of life due to vision loss. No pharmacological therapies are currently approved for DR, excepted the drugs to treat diabetic macular edema such as the anti-VEGF agents or steroids administered by intraocular route. Advancements in research have highlighted the crucial role of early intervention in DR for halting or delaying disease progression. This holds immense significance in enhancing patients’ quality of life and alleviating the societal burden associated with medical care costs. The non-proliferative stage represents the early phase of DR. In comparison to the proliferative stage, pathological changes primarily manifest as microangiomas and hemorrhages, while at the cellular level, there is a loss of pericytes, neuronal cell death, and disruption of components and functionality within the retinal neuronal vascular unit encompassing pericytes and neurons. Both neurodegenerative and microvascular abnormalities manifest in the early stages of DR. Therefore, our focus lies on the non-proliferative stage of DR and we have initially summarized the mechanisms involved in its development, including pathways such as polyols, that revolve around the pathological changes occurring during this early stage. We also integrate cutting-edge mechanisms, including leukocyte adhesion, neutrophil extracellular traps, multiple RNA regulation, microorganisms, cell death (ferroptosis and pyroptosis), and other related mechanisms. The current status of drug therapy for early-stage DR is also discussed to provide insights for the development of pharmaceutical interventions targeting the early treatment of DR.

Diabetic nephropathy (DN) and diabetic retinopathy (DR), as microvascular complications of diabetes mellitus, are currently the leading causes of end-stage renal disease (ESRD) and blindness, respectively, in the adult working population, and they are major public health problems with social and economic burdens. The parallelism between the two in the process of occurrence and development manifests in the high overlap of disease-causing risk factors and pathogenesis, high rates of comorbidity, mutually predictive effects, and partial concordance in the clinical use of medications. However, since the two organs, the eye and the kidney, have their unique internal environment and physiological processes, each with specific influencing molecules, and the target organs have non-parallelism due to different pathological changes and responses to various influencing factors, this article provides an overview of the parallelism and non-parallelism between DN and DR to further recognize the commonalities and differences between the two diseases and provide references for early diagnosis, clinical guidance on the use of medication, and the development of new drugs.

The escalating incidence and mortality of diabetic kidney disease (DKD) underscore the critical need to elucidate its pathogenesis. Programmed cell death (PCD) plays a dual role in maintaining physiological homeostasis and driving pathological processes in DKD. Accumulating evidence demonstrates that apoptosis, autophagy, pyroptosis, and ferroptosis contribute directly or indirectly to DKD progression via distinct gene-regulated signaling pathways. Recently identified PCD modes (eg, necroptosis, parthanatos) remain poorly characterized in DKD, with emerging evidence suggesting crosstalk between different PCD pathways. This review synthesizes current knowledge on PCD-mediated DKD pathogenesis and PCD-targeted therapies, while highlighting research limitations (eg, unclear PCD interactions, translational gaps). We propose that dissecting the multifaceted roles of PCD in DKD will deepen mechanistic understanding and accelerate the development of novel therapeutics, offering significant scientific and clinical benefits.