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Abstract Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.

Abstract Background and objectives Research suggests that diabetic peripheral neuropathy (DPN) is related to plasma fibrinogen (Fib) concentrations, although its correlation with Fib function has not been reported. Here, the k value and angle α, reflecting the plasma Fib function, were used to analyse its correlation with DPN, and their potential as biological indicators for diagnosing DPN was explored. Subjects and methods This prospective observational clinical study enrolled 561 type 2 diabetes mellitus (T2DM) patients, who were divided into the diabetes with symptomatic neuropathy (161 cases), diabetes with asymptomatic neuropathy (132 cases) and diabetes with no neuropathy (268 cases) groups. Meanwhile, 160 healthy unrelated subjects were recruited as controls. Results Fib levels increased slightly in diabetic subjects with neuropathy compared with those without. The angle α levels increased slightly in subjects with asymptomatic DPN compared with those with no neuropathy and increased greatly in subjects with symptomatic DPN compared with those without. The k value levels slightly decreased in subjects with asymptomatic DPN compared with those with no neuropathy and greatly decreased in subjects with symptomatic DPN compared with those without. The association of the k value and angle α with diabetic neuropathy was independent of the hyperglycaemic state and other potential confounders (odds ratio 0.080 [0.051–0.124], P

ObjectiveTo investigate the value of the retinal nerve fiber layer (RNFL) thickness in the optic disc and the cross-sectional area (CSA) of lower limb nerves in the diagnosis of diabetic peripheral neuropathy (DPN) separately and in combination.MethodsA total of 140 patients with type 2 diabetes were enrolled, including 51 patients with DPN (DPN group) and 89 patients without DPN (NDPN group). Clinical data and biochemical parameters were collected. Electromyography/evoked potential instrument was performed for nerve conduction study. Optical coherence tomography was performed to measure the RNFL thickness of the optic disc. Color Doppler ultrasound was performed to measure CSA of lower limb nerves.ResultsThe RNFL thickness was lower and the CSA of the tibial nerve (TN) in the DPN group was larger than that in the NDPN group. The album/urine creatinine ratio, diabetic retinopathy, and CSA of TN at 3 cm were positively correlated with DPN. The RNFL thickness in the superior quadrant of the optic disc was negatively correlated with DPN. For RNFL thickness to diagnose DPN, the area under the curve (AUC) of the superior quadrant was the largest, which was 0.723 (95% confidence interval [CI]: 0.645–0.805), and the best cutoff value was 127.5 μm (70.5% sensitivity, 72.1% specificity). For CSA of TN to diagnose DPN, the AUC of the distance of 5 cm was the largest, which was 0.660 (95% CI: 0.575–0.739), and the best cutoff value was 13.50 mm2 (82.0% sensitivity, 41.6% specificity). For the combined index, the AUC was greater than that of the above two indicators, which was 0.755 (95% CI: 0.664–0.846), and the best cutoff value was 0.376 (64.3% sensitivity, 83.0% specificity).ConclusionsPatients with DPN have a reduction of the RNFL thickness and an increase in the CSA of TN, and these two changes are related to DPN. The RNFL thickness of the optic disc and the CSA of TN can be used as diagnostic indicators of DPN, and the combination of the two indicators has a higher diagnostic value.

Through the past decade of research, the pathogenic mechanisms underlying metabolic syndrome have been suggested to involve not only the peripheral tissues, but also central metabolic regulation imbalances. The hypothalamus, and the arcuate nucleus in particular, is the control center for metabolic homeostasis and energy balance. Neuropeptide Y neurons are particularly abundantly expressed in the arcuate of the hypothalamus, where the blood-brain barrier is weak, such as to critically integrate peripheral metabolic signals with the brain center. Herein, focusing on metabolic syndrome, this manuscript aims to provide an overview of the regulatory effects of Neuropeptide Y on metabolic syndrome and discuss clinical intervention strategy perspectives for neurometabolic disease.

Acute kidney injury (AKI) is one of the most severe consequences of kidney injury, and it will also cause or aggravate the complications by the fast decline of kidney excretory function. Accurate AKI prediction, including the AKI case, AKI stage, and AKI onset time interval, can provide adequate support for effective interventions. Besides, discovering how the medical features affect the AKI result may also provide supporting information for disease treatment. An attention-based temporal neural network approach was employed in this study for AKI prediction and for the analysis of the impact of medical features from temporal electronic health record (EHR) data of patients before AKI diagnosis. We used the publicly available dataset provided by the Medical Information Mart for Intensive Care (MIMIC) for model training, validation, and testing, and then the model was applied in clinical practice. The improvement of AKI case prediction is around 5% AUC (area under the receiver operating characteristic curve), and the AUC value of AKI stage prediction on AKI stage 3 is over 82%. We also analyzed the data by two steps: the associations between the medical features and the AKI case (positive or inverse) and the extent of the impact of medical features on AKI prediction result. It shows that features, such as lactate, glucose, creatinine, blood urea nitrogen (BUN), prothrombin time (PT), and partial thromboplastin time (PTT), are positively associated with the AKI case, while there are inverse associations between the AKI case and features such as platelet, hemoglobin, hematocrit, urine, and international normalized ratio (INR). The laboratory test features such as urine, glucose, creatinine, sodium, and blood urea nitrogen and the medication features such as nonsteroidal anti-inflammatory drugs, agents acting on the renin–angiotensin system, and lipid-lowering medication were detected to have higher weights than other features in the proposed model, which may imply that these features have a great impact on the AKI case.

Microglia are critical in neuroinflammation. M1/M2 polarization transitions of microglial phenotypes determine the states of neuroinflammation and are regulated by multiple pathways, including miRNAs and other epigenetic regulations. This study investigated the polarization transitions of microglia induced by high glucose and glucose fluctuations, and the role of miR-146a in regulating M1/M2 polarization transitions of microglia. BV-2 cells were cultured with 25 mmol/L glucose, 75 mmol/L glucose, and 25 mmol/L−75 mmol/L glucose fluctuation for 48 h. BV-2 cells overexpressing miR-146a were generated using a lentiviral vector. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure mRNA expression of miR-146a, CD11b, iNOS, Arg-1, IRAK1, TRAF6, and NF-κB. Immunofluorescence was used to measure CD11b expression. Western blot was used to measure protein expression of CD11b, iNOS, and Arg-1. Compared with those in the 25 mmol/L glucose control group, expression of CD11b, iNOS, TNF-α, and IL-6 in the 75 mmol/L glucose or glucose fluctuation groups of cultured BV-2 cells were significantly increased, while Arg-1 and IL-10 was significantly decreased. These effects were reversed by overexpression of miR-146a. Furthermore, expression of IRAK1, TRAF6, and NF-κB was significantly increased in the high glucose and glucose fluctuation groups; this was reduced after miR-146a overexpression. In sum, high glucose and glucose fluctuations induced polarization transitions from M1 to M2 phenotype in BV-2 cells. Overexpression of miR-146a might protect BV-2 cells from high glucose and glucose fluctuation associated with M1/M2 polarization transitions by downregulating the expression of IRAK1, TRAF6, and NF-κB.

Background: Diabetic nephropathy (DN) is the leading cause of end-stage failure of the kidneys; however, its pathogenesis remains unknown. This study assessed the expression of complement components in the kidneys of rats with type 2 DN to investigate their role in DN.Methods: A rat model of type 2 DN was induced by a high-fat diet combined with low-dose streptozotocin. Blood glucose, fasting insulin levels, insulin resistance index, and 24-h urinary albumin excretion (UAE) were measured. Renal tissue morphological features were observed. The mesangial index and arteriosclerosis index were calculated. Immunohistochemistry and western blot were used to measure the expression of complement components in the kidneys.Results: The kidney weight: body weight (mg/g) ratio in the DN group was significantly greater than those in the control and diabetes mellitus (DM) groups. The arteriosclerosis index, mesangial index, and tube area percentage in the DN group were significantly higher than those in the control and DM groups, but these parameters did not significantly differ between the control and DM groups. The expression of the complement components C1q, mannose-binding lectin (MBL), mannan-binding lectin-associated serine protease (MASP)-2, B factor, C3, and C5b-9 in the DN group was significantly higher than that in the control and DM groups but did not significantly differ between the control and DM groups. Most of the complement components were mainly expressed at the renal tubular site. Correlation analysis showed that 24-h UAE were positively correlated with C1q, MBL, MASP-2, B factor, and C5b-9 expression. MI was positively correlated with MBL, B factor, C3, and C5b-9 expression. AI was positively correlated with C1q, MBL, MASP-2, and B factor expression.Conclusion: Complement components including C1q, MBL, MASP-2, B factor, C3, and C5b-9, were highly expressed in the kidneys of type 2 diabetic rats with DN. Most of the complement components were mainly expressed in the renal tubules. High expression of complement components was found to be associated with the progress of DN. Our study suggests that complement system activation is a progressive factor in type 2 diabetic nephropathy. Inhibition of pathological complement activation may be a promising therapeutic strategy for DN.

Objective: Our study aimed to identify and characterize thyroid dysfunctions associated with immune checkpoint inhibitors (ICIs). Design: Data were obtained from VigiBase, between January 1, 2011 to March 6, 2019. Methods: All thyroid drug-adverse events are classified by group queries according to the Medical Dictionary for Regulatory Activities. Information component (IC) and reporting odds ratio (ROR) were considered as measures of disproportionality for the assessment of association between ICIs and thyroid dysfunctions. We used IC to identify meaningful drug-adverse events while using ROR to compare differences in the reporting of drug-adverse events caused by different ICI subgroups. Positive IC values are deemed significant. Results: Compared with the full database, the following ICI-associated thyroid dysfunctions were over-reported: hypothyroidism (1125 reports for ICIs vs 12495 for all drugs; Information Component 4.28 (95% CI: 4.18-4.35)), hyperthyroidism (926 vs 7538; 4.66 (95% CI: 4.55-4.74)), thyroiditis (294 vs 1237; 5.40 (95% CI: 5.21-5.54)), thyrotoxic crisis (11 vs 288; 3.55 (95% CI: 2.61-4.20)). Hypothyroidism was over-reported for patients treated with ICI combination therapy versus those treated with ICI monotherapy (ROR 1.3 (95% CI: 1.1-1.7)), and the same was observed for hyperthyroidism (ROR: 1.9 (95% CI: 1.5-2.4)), thyroiditis (ROR: 3.3 (95% CI: 2.3-4.8)), thyrotoxic crisis (ROR: 11.5 (95% CI: 2.4-53.8)). All 11 thyrotoxic crisis cases were malignant melanoma patients, of which seven occurred under ICI combination therapy. Conclusions: Thyroid dysfunction may occur after ICI therapies, and severe thyrotoxic crisis may even occur. Raising awareness of ICI-associated thyroid dysfunction can improve the detection and treatment of these diseases. [ABSTRACT FROM AUTHOR]