Developing and validating a predictive model of delivering large-forgestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Analysis of the Application Effect of Family Collaborative Care Model on Elderly Patients with Type 2 Diabetes Mellitus and the Situation of Self-Care Ability

Objective: To study the application effect of the family collaborative care model on elderly patients with type 2 diabetes mellitus and its influence on self-care ability. Methods: The elderly type 2 diabetes mellitus patients (400 cases) treated in our hospital between March 2020 and July 2023 were divided into two groups by randomized grouping method;the control group received the conventional nursing program, while the observation group received the family collaborative nursing model. Blood glucose level, self-care ability, and quality of life were compared between the groups. Results: The blood glucose level of the observation group was lower than that of the control group (P < 0.05). The self- care ability and quality of life scores of the observation group were higher than those of the control group (P < 0.05). Conclusion: The family collaborative care model for elderly patients with type 2 diabetes mellitus can promote their self- care ability, improve the effect of glycemic control, and improve their quality of life, and is suitable for further promotion and application.

Analysis of the Effect of Integrated Extended Care Model in Improving the Quality of Life of Elderly Patients with Type 2 Diabetes Mellitus

Objective: To analyze the impact of an integrated extended care model on improving the quality of life of elderly patients with Type 2 Diabetes Mellitus (T2DM). Methods: A total of 176 patients admitted to the hospital from March 2015 to February 2018 were selected and randomly assigned to an observation group and a control group, with 88 patients each. The control group implemented conventional nursing interventions, and the observation group carried out an integrated extended-care model. The level of glycemic control, quality of life, and daily medication adherence between both groups were compared. Results: The observation group showed significant improvement in the level of glycemic control, and their fasting blood glucose, 2-hour postprandial blood glucose, and glycated hemoglobin levels were significantly lower as compared with those in the study group (P < 0.05). The quality of life of the patients in the observation group was higher than that of the control group (P < 0.05). The observation group had a higher compliance score (95.48 ± 7.45) than the control group (81.31 ± 8.72) (t = 8.909, P < 0.05). Conclusion: The integrated extended care model allows patients to receive comprehensive and individualized nursing services after discharge, which improves the effect of drug therapy and the quality of life of patients.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

BACKGROUND Among older adults,type 2 diabetes mellitus(T2DM)is widely recognized as one of the most prevalent diseases.Diabetic nephropathy(DN)is a frequent com-plication of DM,mainly characterized by renal microvascular damage.Early detection,aggressive prevention,and cure of DN are key to improving prognosis.Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.AIM To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.METHODS The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed.According to whether the patients had DN,they were divided into the DN group(complicated with DN)and the non-DN group(without DN).Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM.The data were randomly split into a training set(n=147)and a test set(n=63)in a 7:3 ratio using a random function.The training set was used to construct the nomogram,decision tree,and random forest models,and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity,specificity,accuracy,recall,precision,and area under the receiver operating characteristic curve.RESULTS Among the 210 patients with T2DM,74(35.34%)had DN.The validation dataset showed that the accuracies of the nomogram,decision tree,and random forest models in predicting DN in patients with T2DM were 0.746,0.714,and 0.730,respectively.The sensitivities were 0.710,0.710,and 0.806,respectively;the specificities were 0.844,0.875,and 0.844,respectively;the area under the receiver operating characteristic curve(AUC)of the patients were 0.811,0.735,and 0.850,respectively.The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models(P<0.05),whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant(P>0.05).CONCLUSION Among the three prediction models,random forest performs best and can help identify patients with T2DM at high risk of DN.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND Type 2 diabetes mellitus(T2DM)is associated with periodontitis.Currently,there are few studies proposing predictive models for periodontitis in patients with T2DM.AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.METHODS In this a retrospective study,300 patients with T2DM who were hospitalized at the First People’s Hospital of Wenling from January 2022 to June 2022 were selected for inclusion,and their data were collected from hospital records.We used logistic regression to analyze factors associated with periodontitis in patients with T2DM,and random forest and logistic regression prediction models were established.The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve(AUC).RESULTS Of 300 patients with T2DM,224 had periodontitis,with an incidence of 74.67%.Logistic regression analysis showed that age[odds ratio(OR)=1.047,95%confidence interval(CI):1.017-1.078],teeth brushing frequency(OR=4.303,95%CI:2.154-8.599),education level(OR=0.528,95%CI:0.348-0.800),glycosylated hemoglobin(HbA1c)(OR=2.545,95%CI:1.770-3.661),total cholesterol(TC)(OR=2.872,95%CI:1.725-4.781),and triglyceride(TG)(OR=3.306,95%CI:1.019-10.723)influenced the occurrence of periodontitis(P<0.05).The random forest model showed that the most influential variable was HbA1c followed by age,TC,TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showedthat in the training dataset, the AUC of the random forest model was higher than that of the logistic regressionmodel (AUC = 1.000 vs AUC = 0.851;P < 0.05). In the validation dataset, there was no significant difference in AUCbetween the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915;P > 0.05).CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the riskof periodontitis in patients with T2DM.

Insulin Sensitivity and Insulin Secretion Estimated by Homeostatic Model Assessment (HOMA) in Gestational Diabetes Mellitus

Background: Progressive insulin resistance (IR) is an important pathophysiologic mechanism of gestational diabetes mellitus (GDM). Homeostatic model assessment (HOMA) is commonly used as a parameter of the severity of insulin resistance. Aims: To determine indices of insulin resistance (IR) and β-cell function in gestational diabetes mellitus (GDM). Methods: This cross sectional study was conducted from March 2017 to September 2018 at Department of Endocrinology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. The study was performed with 41 GDM and equal number of pregnant women with normal glucose tolerance (NGT) diagnosed on basis of WHO criterion-2013 during 24 - 40 weeks of gestation. Serum glucose was measured by glucose oxidase method and fasting serum insulin was measured by chemiluminescent immunoassay. Equations of homeostatic model assessment (HOMA) were used to calculate insulin indices like-insulin resistance (HOMA-IR), β-cell function (HOMA-B) and insulin sensitivity (HOMA-%S). Data were analyzed and compared by statistical tests. Results: A total of eighty-two (82) subjects [41 women with GDM (age: 28.29 ± 3.79 years, BMI: 27.16 ± 4.13 kg/m2) and 41 women with NGT (age: 26.22 ± 5.13 years, BMI: 25.27 ± 3.01 kg/m2)] were included in this study. It was observed that GDM women were significantly older (p = 0.041) and had significantly higher BMI (p = 0.020) than pregnant women with NGT. The GDM group had significantly higher IR as indicated by higher fasting insulin value [GDM vs. NGT;10.19 (7.71 - 13.34) vs. 6.88 (5.88 - 8.47) μIU/ml, median (IQR);p = 0.001] and HOMA-IR [GDM vs. NGT;2.31 (1.73 - 3.15) vs. 1.42 (1.15 - 1.76), median (IQR);p < 0.001], poor β-cell secretory capacity [GDM vs. NGT;HOMA-B: 112.63 (83.52 - 143.93) vs. 128.60 (108.77 - 157.58), median (IQR);p = 0.04] and low insulin sensitivity [GDM vs. NGT;HOMA-%S: 43.29 (31.77 - 57.98) vs. 70.42 (56.86 - 86.59), median (IQR);p < 0.001]. Conclusions: GDM is associated with both insulin resistance and inadequate insulin secretion.

Cognitive deficits and Alzheimer-like neuropathological impairments during adolescence in a rat model of type 2 diabetes mellitus

Numerous studies have shown that many patients who suffer from type 2 diabetes mellitus exhibit cognitive dysfunction and neuronal synaptic impairments. Therefore, growing evidence suggests that type 2 diabetes mellitus has a close relationship with occurrence and progression of neurodegeneration and neural impairment in Alzheimer＇s disease. However, the relationship between metabolic disorders caused by type 2 diabetes mellitus and neurodegeneration and neural impairments in Alzheimer＇s disease is still not fully determined. Thus, in this study, we replicated a type 2 diabetic animal model by subcutaneous injection of newborn Sprague-Dawley rats with monosodium glutamate during the neonatal period. At 3 months old, the Barnes maze assay was performed to evaluate spatial memory function. Microelectrodes were used to measure electrophysiological function in the hippocampal CA1 region. Western blot assay was used to determine expression levels of glutamate ionotropic receptor NMDA type subunit 2 A（GluN2A） and GluN2B in the hippocampus. Enzyme-linked immunosorbent assay was used to determine levels of interleukin-1β, tumor necrosis factor α, and interleukin-6 in the hippocampus and cerebral cortex, as well as hippocampal amyloid beta（Aβ）1-40 and Aβ_（1-42） levels. Our results showed that in the rat model of type 2 diabetes mellitus caused by monosodium glutamate exposure during the neonatal period, latency was prolonged and the number of errors increased in the Barnes maze. Further, latency was increased and time in the escape platform quadrant shortened. Number of times crossing the platform was also reduced in the Morris water maze. After high frequency stimulation of the hippocampus, synaptic transmission was inhibited, expression of GluN2A and GluN2B were decreased in the hippocampus, expression of interleukin 1β, interleukin 6, and tumor necrosis factor α was increased in the hippocampus and cortex, and levels of Aβ_（1-40） and Aβ_（1-42） were increased in the hippocampus. These findings confirm that type 2 diabetes mellitus induced by neonatal monosodium glutamate exposure results in Alzheimer-like neuropathological changes and further causes cognitive deficits and neurodegeneration in young adulthood.

Sericin can reduce hippocampal neuronal apoptosis by activating the Akt signal transduction pathway in a rat model of diabetes mellitus

In the present study, a rat model of type 2 diabetes mellitus was established by continuous peritoneal injection of streptozotocin. Following intragastric perfusion of sericin for 35 days, blood glucose levels significantly reduced, neuronal apoptosis in the hippocampal CA1 region decreased, hippocampal phosphorylated Akt and nuclear factor kappa B expression were enhanced, but Bcl-xL/Bcl-2 associated death promoter expression decreased. Results demonstrated that sericin can reduce hippocampal neuronal apoptosis in a rat model of diabetes mellitus by regulating abnormal changes in the Akt signal transduction pathway.

Study on the Effect of Web-Based Real-Time Interactive Intervention Teaching Model on Self-Efficacy of Gestational Diabetes Mellitus Patients

Object: To explore the effect of web-based real-time interactive intervention teaching model on self-efficacy of Gestational Diabetes Mellitus (GDM) patients. Method: Based on the hospital’s antenatal check-up archives from June 2018 to January 2019, patients diagnosed with GDM in the second trimester were randomly divided into the control group (100 cases) and the experimental group (121 cases). Patients in the control group received routine care following the diabetes mellitus one-day outpatient guidance, while patients in the experimental group received social media real-time interactive teaching intervention based on routine care, and accepted a nursing intervention scheme based on knowledge-attitude-practice mode. The knowledge of GDM, self-efficacy and self-management behavior indicators were compared between the two groups. Results: After the intervention, the self-efficacy scores, the blood glucose monitoring times and the blood glucose compliance rates of the experimental group were significantly higher than those of the control group (P < 0.05). The post-intervention GDM knowledge scores of the experimental group were higher than those of the control group, and the difference was not statistically significant (P = 0.072). Conclusion: Web-based real-time interactive intervention teaching model can effectively improve the self-efficacy of GDM patients and promote the formation of healthy behaviors.

The role of glycogen synthase kinase 3 beta in brain injury induced by myocardial ischemia/reperfusion injury in a rat model of diabetes mellitus

Myocardial ischemia/reperfusion injury can lead to severe brain injury.Glycogen synthase kinase 3 beta is known to be involved in myocardial ischemia/reperfusion injury and diabetes mellitus.However,the precise role of glycogen synthase kinase 3 beta in myocardial ischemia/reperfusion injury-induced brain injury is unclear.In this study,we observed the effects of glycogen synthase kinase 3 beta on brain injury induced by myocardial ischemia/reperfusion injury in diabetic rats.Rat models of diabetes mellitus were generated via intraperitoneal injection of streptozotocin.Models of myocardial ischemia/reperfusion injury were generated by occluding the anterior descending branch of the left coronary artery.Post-conditioning comprised three cycles of ischemia/reperfusion.Immunohistochemical staining and western blot assays demonstrated that after 48 hours of reperfusion,the structure of the brain was seriously damaged in the experimental rats compared with normal controls.Expression of Bax,interleukin-6,interleukin-8,terminal deoxynucleotidyl transferase d UTP nick end labeling,and cleaved caspase-3 in the brain was significantly increased,while expression of Bcl-2,interleukin-10,and phospho-glycogen synthase kinase 3 beta was decreased.Diabetes mellitus can aggravate inflammatory reactions and apoptosis.Ischemic post-conditioning with glycogen synthase kinase 3 beta inhibitor lithium chloride can effectively reverse these changes.Our results showed that myocardial ischemic post-conditioning attenuated myocardial ischemia/reperfusion injury-induced brain injury by activating glycogen synthase kinase 3 beta.According to these results,glycogen synthase kinase 3 beta appears to be an important factor in brain injury induced by myocardial ischemia/reperfusion injury.

Comparative evaluation in treating qi-yin deficiency and phlegm stasis syndrome of type 2 diabetes mellitus in a rat model

Objective:To compare the efficacy of traditional Chinese medicine (TCM),western medicine and integrative medicine in treating type 2 diabetes mellitus (T2DM) in a rat model.Methods:The T2DM rat model was established with a high-fat diet (HFD) for 35 days and a single injection of streptozotocin (STZ,30 mg/kg).The T2DM-induced rats were divided into three groups,and treated with Yiqi Yangyin Huatan (YQYYHT) granules (3.84 g/kg per day),pioglitazone (1.35 mg/kg per day) or YQYYHT granules + pioglitazone (3.84 g/kg per day+1.35 mg/kg per day) respectively for 14 days.Clinical features and behavioral changes,as well as T2DM indicators,were recorded to evaluate therapeutics effects in each treatment group.Results:The T2DM rat model expressed insulin resistance (IR),with features similar to qi-yin deficiency and phlegm stasis syndrome,including decreased cyclic adenosine monophosphate/cyclic guanosine monophosphate (cAMP/cGMP) ratio,decreased levels of Na+-K+-ATPase,superoxide dismutase (SOD) and high density lipoprotein-cholesterol (HDL-C),and increased levels of serum triglyceride (TG),total cholesterol (TC) and low density lipoprotein (LDL-C).All three treatment groups showed significant decreases in fasting blood glucose (FBG) and fasting insulin (Fins),and improvement of TCM syndrome to different degrees.Importantly,YQYYHT improved the most of the indicators of T2DM,followed by integrative medicine and pioglitazone alone.Conclusion:Compared with western medicine or integrative medicine,prescription of TCM based on syndrome differentiation may offer more advantages in the prevention and treatment of T2DM.

A Competing Risk Hazard Model for Complications of Diabetes Mellitus

Diabetes mellitus is a major public health problem globally. It is the cause of serious and sometimes life-threatening complications leading to death. It is one of the most common non communicable diseases and leading cause of death in most developed countries including Bangladesh. It is associated with the increased risk of microvascular (nephropathy and retinopathy), macrovascular (cardiovascular diseases, stroke) and other mixed (cataract, pulmonary tuberculosis, foot ulcer etc.) complications. The aim of this study was to determine the risk factors related to diabetes complications and to propose competing risk models for analyzing complications of diabetes mellitus. We study 2887 diabetic patients from the BIRDEM registry record books for the period from 1984 to 1997 who have at least two follow-up visits and who are free from complications at the first visit. The data on variables such as 2-hBG, age, sex, family history of diabetes, area of residence, educational level, BMI, SBP, DBP and treatment status are obtained. Also, the time from the diagnosis of type 2 diabetes mellitus (T2DM) to the occurrence of complications was recorded. We use the competing risk hazard model and examined the potential risk factors for determining the development of such complications. This study reveals that increase in blood pressure is a potential risk factor for CHD and nephropathy in T2DM. We also found that male and illiterate patients are more affected by nephropathy. In our study, we also revealed that female and illiterate patients are also more influenced by cataract.

Poorly controlled type II diabetes mellitus significantly enhances postoperative chemoresistance in patients with stage III colon cancer

BACKGROUND Type II diabetes mellitus(T2DM)has been associated with increased risk of colon cancer(CC)and worse prognosis in patients with metastases.The effects of T2DM on postoperative chemoresistance rate(CRR)and long-term disease-free survival(DFS)and overall survival(OS)in patients with stage III CC who receive curative resection remain controversial.AIM To investigate whether T2DM or glycemic control is associated with worse postoperative survival outcomes in stage III CC.METHODS This retrospective cohort study included 278 patients aged 40-75 years who underwent surgery for stage III CC from 2018 to 2021.Based on preoperative T2DM history,the patients were categorized into non-DM(n=160)and DM groups(n=118).The latter was further divided into well-controlled(n=73)and poorly controlled(n=45)groups depending on the status of glycemic control.DFS,OS,and CRR were compared between the groups and Cox regression analysis was used to identify risk factors.RESULTS Patients in the DM and non-DM groups demonstrated similar DFS,OS,and CRR(DFS:72.03%vs 78.75%,P=0.178;OS:81.36%vs 83.12%,P=0.638;CRR:14.41%vs 7.5%,P=0.063).Poorly controlled DM was associated with a significantly worse prognosis and higher CRR than well-controlled DM(DFS:62.22%vs 78.07%,P=0.021;OS:71.11%vs 87.67%,P=0.011;CRR:24.40%vs 8.22%,P=0.015).High preoperative fasting plasma glucose[DFS:Hazard ratio(HR)=2.684,P<0.001;OS:HR=2.105,P=0.019;CRR:HR=2.214,P=0.005]and glycosylated hemoglobin levels(DFS:HR=2.344,P=0.006;OS:HR=2.119,P=0.021;CRR:HR=2.449,P=0.009)indicated significantly poor prognosis and high CRR,while T2DM history did not(DFS:HR=1.178,P=0.327;OS:HR=0.933,P=0.739;CRR:HR=0.997,P=0.581).CONCLUSION Increased preoperative fasting plasma glucose and glycosylated hemoglobin levels,but not T2DM history,were identified as risk factors associated with poor postoperative outcomes and high CRR in patients with stage III CC.

Multilevel Modeling of PM2.5 and Risk of Cardiovascular Disease and Diabetes Mellitus in Adults Aged 65 and Older

We aimed to test a hypothesis that elevated ambient particulate matter (PM) 2.5 concentrations are significantly associated with risk of coronary heart disease (CHD), stroke, and diabetes mellitus (DM) in adults aged 65 and older. We analyzed data (2010-2013) from U.S. 1118 counties to examine the association between PM2.5 concentrations and risk of prevalent CHD, stroke, and DM, and mortality from cardiovascular disease (CVD), CHD, stroke, and DM in adults aged ≥ 65. Multilevel regression analysis technique was applied to test these associations. The results show that the annual mean of PM2.5 concentration was 8.7 μg/m3 in the total study sample. Significant differences in mean PM2.5 concentrations were observed across counties and states in the U.S. Multilevel regression analysis indicates that an average annual concentration of 1 μg/m3 increase in PM2.5 concentration was significantly associated with an increased prevalence of CHD, stroke, and DM by 4.9‰ (95% CI: 3.1‰ - 6.7‰), 0.8‰ (0.5‰ - 1.1‰), and 3.3‰ (2.9‰ - 4.4‰), respectively. State-level correlation analyses indicate that increased PM2.5 concentrations were significantly associated with increased age-adjusted mortality from CVD (r = 0.76, p < 0.001), CHD (r = 0.0.40, p = 0.004), stroke (r = 0.60, p p = 0.02). In conclusion, Elevated PM2.5 concentrations were significantly associated with an increased risk of the prevalence and mortality from CVD, CHD, stroke, and DM. Continued effort to control ambient PM2.5 concentrations could play an important role in risk reduction of cardiovascular disease and diabetes in the elderly.

Intestinal glucagon-like peptide-1:A new regulator of impaired counterregulatory responses to hypoglycemia in type 1 diabetes mellitus

In this article,we review the study by Jin et al,which examined the role of intestinal glucagon-like peptide-1(GLP-1)in counterregulatory responses to hypoglycemia in patients with type 1 diabetes mellitus(T1DM).With the global rise of T1DM,there is an increased burden on society and healthcare systems.Due to insulin therapy and islet dysfunction,T1DM patients are highly vulnerable to severe hypoglycemia,a leading cause of mortality.In healthy individuals,counterregulatory mechanisms restore blood glucose during hypoglycemia,but repeated episodes impair these responses.Jin et al demonstrated that overexpression of GLP-1 attenuates the sympathetic-adrenal reflex and disrupts the secretion of counterregulatory hormones such as glucagon during hypoglycemia,leading to counterregulatory dysfunction.These findings highlight the critical role of GLP-1 in the impaired counterregulatory response to hypoglycemia in T1DM patients and provide new insights into the potential application of GLP-1-related therapies in T1DM patients.

Application of Dorzagliatin in peritoneal dialysis patients with type 2 diabetes mellitus:A case report

BACKGROUND Treating diabetes in dialysis patients remains a challenge,with many hypoglycemic drugs requiring dose adjustments or avoidance in these patients.CASE SUMMARY This report describes an 83-year-old female patient with a 30-year history of type 2 diabetes(T2DM)who had struggled to control her blood sugar for more than a year.She had a history of high blood pressure for 30 years,had undergone continuous ambulatory peritoneal dialysis for more than two years,was 163 cm tall,weighed 77 kg,and had a body mass index of 28.98 kg/m2.Despite intensive insulin therapy at a daily dose of 150 units,adding Dorzagliatin at a dosage of 75 mg orally twice daily led to immediate blood sugar improvement and a gradual reduction in insulin dosage.After one month of follow-up,the fasting plasma glucose was 6-8 mmol/L,and the 2-hour postprandial glucose was 8-12 mmol/L.CONCLUSION To our knowledge,this report is the first to use Dorzagliatin to treat type 2 diabetes peritoneal dialysis patients with challenging glucose control.Dorzagliatin,a novel glucokinase activator primarily metabolized by the liver,exhibits no pharmacokinetic differences among patients with varying degrees of chronic kidney disease.It has a high plasma protein binding rate and may not be cleared by peritoneal dialysis,potentially offering a new glycemic control option for Type 2 diabetic patients on peritoneal dialysis.

Unveiling mitochondrial mysteries:Exploring novel tRNA variants in type 2 diabetes mellitus

The recent study of Ding et al provides valuable insights into the functional implications of novel mitochondrial tRNATrp and tRNASer(AGY)variants in type 2 diabetes mellitus(T2DM).This editorial explores their findings,highlighting the role of mitochondrial dysfunction in the pathogenesis of T2DM.By examining the molecular mechanisms through which these tRNA variants contribute to disease progression,the study introduces new targets for therapeutic strategies.We discuss the broader implications of these results,emphasizing the importance of understanding mitochondrial genetics in addressing T2DM.

Intricacies during pregnancy with gestational diabetes mellitus

The study by Cao et al aimed to identify early second-trimester biomarkers that could predict gestational diabetes mellitus(GDM)development using advanced proteomic techniques,such as Isobaric tags for relative and absolute quantitation isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry liquid chromatography-mass spectrometry.Their analysis revealed 47 differentially expressed proteins in the GDM group,with retinol-binding protein 4 and angiopoietin-like 8 showing significantly elevated serum levels compared to controls.Although these findings are promising,the study is limited by its small sample size(n=4 per group)and lacks essential details on the reproducibility and reliability of the protein quantification methods used.Furthermore,the absence of experimental validation weakens the interpretation of the protein-protein interaction network identified through bioinformatics analysis.The study's focus on second-trimester biomarkers raises concerns about whether this is a sufficiently early period to implement preventive interventions for GDM.Predicting GDM risk during the first trimester or pre-conceptional period may offer more clinical relevance.Despite its limitations,the study presents valuable insights into potential GDM biomarkers,but larger,well-validated studies are needed to establish their predictive utility and generalizability.

Genetic and epigenetic alterations associated with gestational diabetes mellitus and adverse neonatal outcomes

Gestational diabetes mellitus(GDM)is a metabolic disorder,recognised during 24-28 weeks of pregnancy.GDM is linked with adverse newborn outcomes such as macrosomia,premature delivery,metabolic disorder,cardiovascular,and neurological disorders.Recent investigations have focused on the correlation of genetic factors such asβ-cell function and insulin secretary genes(transcription factor 7 like 2,potassium voltage-gated channel subfamily q member 1,adipo-nectin etc.)on maternal metabolism during gestation leading to GDM.Epigenetic alterations like DNA methylation,histone modification,and miRNA expression can influence gene expression and play a dominant role in feto-maternal meta-bolic pathways.Interactions between genes and environment,resulting in differ-ential gene expression patterns may lead to GDM.Researchers suggested that GDM women are more susceptible to insulin resistance,which alters intrauterine surroundings,resulting hyperglycemia and hyperinsulinemia.Epigenetic modi-fications in genes affecting neuroendocrine activities,and metabolism,increase the risk of obesity and type 2 diabetes in offspring.There is currently no treatment or effective preventive method for GDM,since the molecular processes of insulin resistance are not well understood.The present review was undertaken to un-derstand the pathophysiology of GDM and its effects on adverse neonatal out-comes.In addition,the study of genetic and epigenetic alterations will provide lead to researchers in the search for predictive molecular biomarkers.