Diabetic neuropathy (DN) is a common and serious long-term complication of diabetes, with autonomic neuropathy gaining considerable attention due to its effects on various organ systems. The dysfunction of autonomic nerves is caused by pathological mechanisms such as metabolic imbalances, oxidative stress, and microvascular damage due to high blood sugar levels, leading to clinical symptoms like resting tachycardia, delayed stomach emptying, and bladder issues. The management of diabetic autonomic neuropathy (DAN) involves a foundational approach of stringent glycemic control, complemented by a combination of aldose reductase inhibitors, antioxidants, and neurotrophic agents to synergistically alleviate clinical symptoms. Furthermore, the utilization of neuromodulation techniques and the implementation of personalized treatments enable targeted modulation of the systemic impairments. This article provides a comprehensive review of the pathophysiological mechanisms of DAN, its clinical manifestations across multiple tissues and organs, and treatment strategies based on autonomic nervous system regulation, aiming to establish a theoretical foundation for in-depth analysis of DN pathological mechanisms and optimization of clinical intervention approaches.
Exercise remains a cornerstone in the prevention and management of diabetic complications in patients with type 2 diabetes. However, there have been few discussions about the precautions and implementation key points of exercise intervention for diabetic complications. In this study, we presented some recommendations of exercise intervention, the precautions related to exercise intervention, the selection of exercise timing, and the interactive effects between sports and medications for patients with diabetic complications, based on the latest guidelines on diabetes prevention and management, the expert consensus, and the latest research trials, aiming to provide some practical guidance and evidence-based guidelines for exercise intervention in patients with diabetic complications.
Diabetic kidney disease (DKD) is a typical microvascular complication characterized by insidious onset and poor prognosis. Conventional contrast-enhanced ultrasound (CEUS) can visualize the microvasculature; however, the bolus injection method involves a high contrast agent concentration and rapid infusion rate, which tends to produce a "flooding" effect. In contrast, the intravenous drip "flash-replenishment" CEUS technique can stably and accurately evaluate microcirculatory hemodynamic changes in renal tissue of DKD patients, potentially providing an imaging basis for the early identification of microcirculatory impairment in DKD.
To investigate the value of quantitative parameters derived from intravenous drip "flash-replenishment" CEUS in assessing DKD microcirculation, to construct and validate a CEUS parameter-based risk prediction model for DKD, and to evaluate its clinical value in the early diagnosis of DKD.
A prospective study was conducted enrolling 85 patients with type 2 diabetes mellitus (T2DM) who visited the outpatient clinic or were hospitalized at Dongzhimen Hospital, Beijing University of Chinese Medicine, from November 2024 to September 2025. Based on urinary protein levels, patients were categorized into the DM-only group (n=27), early-stage DKD group (n=38), and clinical-stage DKD group (n=20). Healthy subjects were concurrently recruited as the healthy control group (n=13). Baseline data were collected from all participants, followed by conventional ultrasound, Doppler ultrasound, and CEUS examinations. The LASSO regression was used for variable selection. The dataset was divided into a training set and a validation set at a ratio of 7∶3. Multivariate Logistic regression analysis was performed on the training set to develop a nomogram prediction model. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) goodness-of-fit test, and decision curve analysis (DCA) were applied to the training and validation sets to evaluate the model's discriminative ability, calibration, and clinical utility, respectively.
Significant differences were observed among the healthy control, DM-only, early-stage DKD, and clinical-stage DKD groups in age, systolic blood pressure (SBP) (P<0.05). Doppler ultrasound: statistically significant differences were found in diastolic velocity (Vd) and resistive index (RI) of the renal artery, segmental artery, and interlobar artery among the four groups (P<0.05). CEUS: statistically significant differences were found in cortical time to peak (TTP), cortical wash-in rate (WiR), cortical half WiR, cortical mean transit time (mTT), medullary peak intensity (PKI), medullary WiR, and medullary half WiR among the four groups (P<0.05). LASSO regression analysis identified two predictors associated with DKD risk: duration of diabetes and cortical WiR. Multivariate Logistic regression analysis confirmed that duration of diabetes (OR=1.169, 95%CI=1.069-1.279) and cortical WiR (OR=0.694, 95%CI=0.499-0.964) were independent predictors of DKD (P<0.05). The ROC curves of the nomogram model showed an AUC of 0.880 (95%CI=0.790-0.969) in the training set and 0.838 (95%CI=0.678-0.998) in the validation set. The H-L goodness-of-fit test indicated mild calibration deviation in the training set (P=0.044) and good calibration in the validation set (P=0.209); combined with calibration curve metrics (training set Eavg=0.081, validation set Eavg=0.124), the overall model calibration was acceptable. DCA showed that the model achieved net benefit superior to the "treat-all" and "treat-none" strategies across a wide range of threshold probabilities (training set 0.09-0.99, validation set 0.08-0.83), demonstrating good clinical applicability.
This study found that longer duration of diabetes and lower cortical WiR are independent predictors of DKD. A nomogram prediction model incorporating these risk factors was established, demonstrating satisfactory overall performance and confirming the value of CEUS in the early diagnosis of DKD.
Diabetic foot (DF) is a common and severe chronic complication of diabetes mellitus, characterized by high incidence, high disability rate, and high recurrence rate. It severely affects the quality of life of affected people. Recent studies suggest that dysbiosis of the skin microbiota may play a critical role in the development of DF.
To investigate the differences in plantar skin microbiota composition among patients with varying risk degrees of DF using 16S rRNA gene sequencing, and to explore the association between microbiota imbalance and DF risk, thereby providing a microbial basis for early warning and intervention strategies.
A total of 64 patients with diabetes mellitus treated in the Second Affiliated Hospital of Zunyi Medical University from June 2023 to March 2024, and 16 healthy adults during the same period, were enrolled. According to the International Working Group on the Diabetic Foot (IWGDF) risk classification, participants were divided into five groups: control group (n=16), very low-risk group (VL, n=16), low-risk group (L, n=15), moderate-risk group (M, n=16), and high-risk group (H, n=17). Plantar skin swab samples were collected for DNA extraction. The V3-V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform. Operational taxonomic unit (OTU) clustering and annotation were performed using QIIME 2. Microbiota differences among groups were analyzed using the Linear discriminant analysis Effect Size (LEfSe) and Metastats methods.
The α-diversity indices (Chao1, ACE, Shannon, and Simpson) of plantar skin microbiota were significantly different among the five groups with varying risk levels of DF (P<0.001). Principal coordinate analysis (PCoA) based on Bray-Curtis distances revealed a significant separation of microbiota structures among groups (P=0.001). At the phylum level, the relative abundances of Bacteroidetes and Proteobacteria sequentially increased than the previous low-risk group, whereas those of Firmicutes and Actinobacteria sequentially decreased than the previous low-risk group (P<0.001). At the genus level, the abundances of Corynebacterium, Streptococcus, and Bacteroides significantly sequentially increased than the previous low-risk group, while the abundance of Staphylococcus gradually decreased than the previous low-risk group (P<0.001). LEfSe analysis identified group-specific biomarker genera, namely the Aquabacterium (VL), Bacteroides (L), Gardnerella (M), and Corynebacterium (H)(P<0.05).
The composition of plantar skin microbiota in diabetes mellitus patients is closely associated with the risk degree of DF. With the increasing DF risk, microbiota α-diversity significantly increases and microbial community structure diverges. High-risk patients exhibit elevated levels of Gram-negative bacteria like Bacteroidetes, Proteobacteria, and Bacteroides, along with reduced levels of Gram-positive bacteria like Firmicutes, Actinobacteria, and Staphylococcus, reflecting marked microbiota dysbiosis. Distinct microbial biomarkers are observed across DF risk levels, suggesting that microbial characteristics may serve as potential targets for DF risk assessment and intervention.
