Fotagliptin monotherapy with alogliptin as an active comparator in patients with uncontrolled type 2 diabetes mellitus: a randomized, multicenter, double-blind, placebo-controlled, phase 3 trial.

BACKGROUND: Dipeptidyl peptidase-4 inhibitors (DPP-4i) have become firmly established in treatment algorithms and national guidelines for improving glycemic control in type 2 diabetes mellitus (T2DM).To report the findings from a multicenter, randomized, double-blind, placebo-controlled phase 3 clinical trial, which was designed to assess the efficacy and safety of a novel DPP-4 inhibitor fotagliptin in treatment-naive patients with T2DM. METHODS: Patients with T2DM were randomized to receive fotagliptin (n = 230), alogliptin (n = 113) or placebo (n = 115) at a 2:1:1 ratio for 24 weeks of double-blind treatment period, followed by an open-label treatment period, making up a total of 52 weeks. The primary efficacy endpoint was to determine the superiority of fotagliptin over placebo in the change of HbA1c from baseline to Week 24. All serious or significant adverse events were recorded. RESULTS: After 24 weeks, mean decreases in HbA1c from baseline were -0.70% for fotagliptin, -0.72% for alogliptin and -0.26% for placebo. Estimated mean treatment differences in HbA1c were -0.44% (95% confidence interval [CI]: -0.62% to -0.27%) for fotagliptin versus placebo, and -0.46% (95% CI: -0.67% to -0.26%) for alogliptin versus placebo, and 0.02% (95%CI: -0.16% to 0.19%; upper limit of 95%CI < margin of 0.4%) for fotagliptin versus alogliptin. So fotagliptin was non-inferior to alogliptin. Compared with subjects with placebo (15.5%), significantly more patients with fotagliptin (37.0%) and alogliptin (35.5%) achieved HbA1c < 7.0% after 24 weeks of treatment. During the whole 52 weeks of treatment, the overall incidence of hypoglycemia was low for both of the fotagliptin and alogliptin groups (1.0% each). No drug-related serious adverse events were observed in any treatment group. CONCLUSIONS: In summary, the study demonstrated improvement in glycemic control and a favorable safety profile for fotagliptin in treatment-naive patients with T2DM. TRIAL REGISTRATION: ClinicalTrail.gov NCT05782192.

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction.

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis.

Echocardiographic phenotypes of Chinese patients with type 2 diabetes may indicate early diabetic myocardial disease.

AIM: Type 2 diabetes may impair cardiac structure and function at very early stage, other factors, for example, obesity and hypertension, can induce aforementioned abnormalities individually. This study aimed to explore precise prevention and treatment of diabetic cardiomyopathy (DCM) by using cluster analysis of echocardiographic variables. METHODS AND RESULTS: A total of 66 536 inpatients with diabetes from 2013 to 2018 were investigated, and 7112 patients were available for analysis after nadir. The cluster analysis was performed on echocardiographic variables to assess the clinical profiles and risk factors of clusters. Two clusters were identified. Cluster 1 with 3576 patients (50.3%, including 62.5% female) had hypertension in 62.4%, while the lower rate of obesity (13.7%). Ultrasound findings showed that 79.9% of them had left ventricular diastolic dysfunction (LVDD), the most characteristic change in the early stages of DCM. Systolic blood pressure (SBP), uric acid and antithrombin III were independent risk factors for LVDD (P < 0.0001); 64.0% of the 3536 patients in the second group were male, with a high prevalence of obesity (30.1%) and a higher prevalence of hypertension (79.5%), In particular, decreased systolic function and a high rate of LV hypertrophy (46.8%) represented the progressive phase of DCM (P < 0.0001). SBP, diastolic blood pressure, BMI and creatinine were independent correlates of LV mass index (P < 0.05). CONCLUSION: The cluster analysis of echocardiographic variables may improve the identification of groups of patients with similar risks and different disease courses and will facilitate the achievement of targeted early prevention and treatment of DCM.

Free Triiodothyronine Levels are Related to Executive Function and Scene Memory in Type 2 Diabetes Mellitus Patients Without Diagnosed Thyroid Diseases.

Purpose: We aim to determine the role of free triiodothyronine (FT3), the main active ingredient of thyroid hormones (THs), in type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI). Patients and Methods: A total of 255 T2DM patients without diagnosed thyroid diseases were recruited and divided into MCI group and healthy cognition group. Neuropsychological functions were observed by multidimensional cognitive function scales in including MoCA, Digit Span Test (DST), Verbal Fluency Test (VFT), Clock drawing test (CDT), Trail Making Test (TMT) A and B, Instantaneously Recalled Auditory Verbal Learning Test (AVLT-IR), Delayed Recalled Auditory Verbal Learning Test (AVLT-DR) and Logical Memory Test (LMT). Correlation and logistic regression analyses were performed to explore the association between FT3 and diabetic cognitive dysfunction. Results: Compared with 147 normal cognition patients, 108 MCI patients exhibited lower FT3 and higher HOMA-IR. FT3 level was not only positively correlated with MoCA scores, but DST, VFT and LMT, while negatively associated with TMTB. Furthermore, there is a negative association between FT3 and HOMA-IR. Logistic regression showed that decreased FT3 is a risk factor of MCI in T2DM patients. Although FT3 is not the risk factor of MCI after homeostasis model assessment of insulin resistance (HOMA-IR) was entered as an independent variable, lower FT3 is associated with VFT and LMT adjusted by age, education, BMI, DM duration, HBP duration, smoking, HbA1c and HOMA-IR. Conclusion: Lower FT3 levels may involve in MCI, especially for executive function and scene memory in T2DM patients without diagnosed thyroid diseases.

Prevalence of cardiovascular disease risk factors in Chinese patients with type 2 diabetes mellitus, 2013-2018.

OBJECTIVE: Coronary heart disease (CHD) is the most common cause of death in patients with type 2 diabetes (T2DM). We aim to estimate the prevalence of CHD and cardiovascular risk factors in Chinese patients with T2DM. METHODS: A total of 66,536 inpatients with diabetes treated from 2013 to 2018 were investigated, and demographic and clinical data were collected from 30,693 patients with T2DM. Age-standardized prevalence of CHD was calculated on the basis of data from the Chinese population census in 2010. Logistic regression analysis was used to analyze the risk factors. RESULTS: The crude prevalence of CHD was estimated to be 23.5% and a standardized prevalence was 13.9% (16.0% in men and 11.9% in women). More than half of patients with CHD have four or more of the five traditional risk factors, much higher than the 38.96% of patients without CHD (p < .01). Multivariate regression analysis showed that diabetes duration, hypertension, smoking, underweight, overweight, obesity and hypoglycaemia were significantly associated with increased risk of CHD (all p < .05). The odds ratio of CHD in patients with three, four or five defined CHD risk factors (i.e. diabetes, hypertension, dyslipidaemia, overweight or obese, and smoking) were 2.35 (95% CI 1.81-3.04), 2.96 (95% CI 2.28-3.85) or 5.29 (95% CI 4.04-6.93), compared with diabetes patients without any other risk factors. CONCLUSIONS: The prevalence of CHD was rather high in Chinese T2DM inpatients, and the aggregation of CHD risk factors was severe. Thus, hierarchical CHD prevention strategies based on risk factors are necessary.

Assessment of Cardiovascular Risk Factors and Their Interactions in the Risk of Coronary Heart Disease in Patients with Type 2 Diabetes with Different Weight Levels, 2013-2018.

PURPOSE: In addition to hyperglycemia and hyperlipidemia, obesity and hypertension are important cardiovascular risk factors for coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2DM). This study aims to explore the interaction of these risk factors. PATIENTS AND METHODS: Data of hospitalized patients with T2DM from 2013 to 2018 were collected. A multivariate logistic regression model was established. Patients with normal weight and blood pressure were recruited as controls. The interaction on the risk of CHD was evaluated by relative excess risk due to interaction (RERI). RESULTS: Among the 30,693 patients with T2DM, 7202 (23.5%) had CHD. In the low weight group, the prevalence of CHD in patients with hypertension was nearly four times higher than that in patients without hypertension (42.7% vs 11.3%, P < 0.01). The OR value of hypertension alone on CHD increased from 1.29 (95% CI 1.06-1.56) in the body mass index (BMI) 30.0-34.9 kg/m2 group to 1.35 (95% CI 1.11-1.62) in the BMI ≤ 18.5 kg/m2 group. Additive interaction was observed between hypertension and BMI in CHD risk, especially in the low weight group (RERI:2.2, 95% CI 0.9-3.5). BMI and CHD risk showed a smile curve relationship. The attributive proportion in the low weight group was higher than that in the severe obesity group, that is, 0.52 (95% CI 0.35-0.69) vs 0.18 (95% CI -0.59 to 0.95). CONCLUSION: Maintaining normal weight and avoiding low weight are particularly important for patients with co-occurring diabetes and hypertension to prevent the risk of CHD.

Performance of Fasting Plasma Glucose and Postprandial Urine Glucose in Screening for Diabetes in Chinese High-risk Population.

BACKGROUND: The conventional approaches to diabetes screening are potentially limited by poor compliance and laboratory demand. This study aimed to evaluate the performance of fasting plasma glucose (FPG) and postprandial urine glucose (PUG) in screening for diabetes in Chinese high-risk population. METHODS: Nine hundred and nine subjects with high-risk factors of diabetes underwent oral glucose tolerance test after an overnight fast. FPG, hemoglobin A1c, 2-h plasma glucose (2 h-PG), and 2 h-PUG were evaluated. Diabetes and prediabetes were defined by the American Diabetes Association criteria. The area under the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy of 2 h-PUG, and the optimal cut-off determined to provide the largest Youden index. Spearman correlation was used for relationship analysis. RESULTS: Among 909 subjects, 33.4% (304/909) of subjects had prediabetes, and 17.2% (156/909) had diabetes. The 2 h-PUG was positively related to FPG and 2 h-PG (r = 0.428 and 0.551, respectively, both P < 0.001). For estimation of 2 h-PG ≥ 7.8 mmol/L and 2 h-PG ≥ 11.1 mmol/L using 2 h-PUG, the area under the ROC curve were 0.772 (95% confidence interval [CI ]: 0.738-0.806) and 0.885 (95% CI: 0.850-0.921), respectively. The corresponding optimal cut-offs for 2 h-PUG were 5.6 mmol/L and 7.5 mmol/L, respectively. Compared with FPG alone, FPG combined with 2 h-PUG had a higher sensitivity for detecting glucose abnormalities (84.1% vs. 73.7%, P < 0.001) and diabetes (82.7% vs. 48.1%, P < 0.001). CONCLUSION: FPG combined with 2 h-PUG substantially improves the sensitivity in detecting prediabetes and diabetes relative to FPG alone, and may represent an efficient layperson-oriented diabetes screening method.

Formononetin attenuates IL-1ß-induced apoptosis and NF-κB activation in INS-1 cells.

Several studies suggest that the inflammation plays a role in the pathogenesis of some glucose disorders in adults. Exposure of pancreatic ß-cells to cytokines, such as interleukin-1ß (IL-1ß), is thought to contribute to ß-cell apoptosis. One important event triggered by IL-1ß is induction of nitric oxide synthase (iNOS), an enzyme that catalyzes intracellular generation of the cytotoxic free radical NO. Recent work have suggested that formononetin, as an O-methylated isoflavone found in a number of plants and herbs like Astragalus membranaceus, inhibited some pro-inflammatory cytokine production in macrophages. However, the roles of formononetin in pancreatic beta cells have not been fully established. The aim of the present study was to assess possible in vitro effects of formononetin on cell apoptosis induced by IL-1ß in the rat insulinoma cell line, INS-1. Our results demonstrate that formononetin significantly prevents IL-1ß-increased INS-1 cell death and blocks cytokine-induced apoptotic signaling (the reduction of Bax/Bcl-2 ratio and caspase-3 activity). Formononetin also inhibited the activation of nuclear factor-kappaB (NF-κB), which is a significant transcription factor for iNOS, so as to decease nitric oxide (NO) formation in a dose dependent manner in vitro. Our observations indicated that formononetin could protect against pancreatic ß-cell apoptosis caused by IL-1ß and therefore could be used in the future as a new drug improving diabetes mellitus.

[Value of serum advanced glycation end products-peptide in the screening of diabetes mellitus in a community-based population of high-risk diabetics].

OBJECTIVE: To explore the value of serum advanced glycation end products-peptide (AGE-P) in the screening of diabetes mellitus in a community-based population of high-risk diabetics. METHODS: A total number of 857 adult high-risk diabetics from a community-based population underwent 75 g oral glucose tolerance test (OGTT). Blood samples were drawn to measure the levels of fasting blood glucose (FBG), postprandial blood glucose (2 hPG) and glycosylated hemoglobin A1c (HbA1c). And blood samples were also collected to determine the serum level of AGE-P with the technique of flow injection analysis. Receiver operating characteristic (ROC) curve was plotted to assess the screening value of serum AGE-P in diabetes mellitus. Pearson correlation analysis was conducted to evaluate the association between serum AGE-P and FBG, 2 hPG, HbA1c, body mass index (BMI), waist-to-hip ratio (WHR) and age. RESULTS: Among them, 218 adults were diagnosed with diabetes based on the 2010 American Diabetes Association (ADA) criteria. According to the ROC curve, the optimal cut-point of serum AGE-P for diagnosing diabetes was 10.22 mg/L (a peak height of 25.39 mm) with sensitivity of 84.1%, specificity of 88.3% and positive predictive value of 71%. The area under curve (AUC) of serum AGE-P, FBG, 2 hPG and HbA1c for diagnosing diabetes was 0.924, 0.905, 0.951 and 0.874 respectively. When comparing AUC between serum AGE-P and HbA1c, FBG and 2 hPG, statistical significance was only found in the comparisons between serum AGE-P and HbA1c (P<0.025). Pearson correlation analysis showed that serum AGE-P was highly positively correlated with HbA1c, significantly positively correlated with FBG and 2 hPG and slightly positively correlated with WHR and age (all P<0.05). But there was no correlation with BMI. CONCLUSIONS: Serum AGE-P may be used for the screening of diabetes in the community-based population of high-risk diabetics. And it is even superior to HbA1c.