To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.
Photoplethysmography/methods* ; Diabetes Mellitus, Type 2/diagnosis* ; Humans ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Prediction Algorithms

OBJECTIVES: To study the clinical manifestations and genetic characteristics of children with maturity-onset diabetes of the young type 2 (MODY2), aiming to enhance the recognition of MODY2 in clinical practice. METHODS: A retrospective analysis was conducted on the clinical data of 13 children diagnosed with MODY2 at the Department of Pediatrics of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology from August 2017 to July 2023. RESULTS: All 13 MODY2 children had a positive family history of diabetes and were found to have mild fasting hyperglycemia [(6.4±0.5) mmol/L] during health examinations or due to infectious diseases. In the oral glucose tolerance test, two cases met the diagnostic criteria for diabetes with fasting blood glucose, while the others exhibited impaired fasting glucose or impaired glucose tolerance. The one-hour post-glucose load (1-hPG) fluctuated between 8.31 and 13.06 mmol/L, meeting the diagnostic criteria for diabetes recommended by the International Diabetes Federation. All 13 MODY2 children had heterozygous variants in the glucokinase (GCK) gene, with Cases 6 (GCK c.1047C>A, p.Y349X), 11 (GCK c.1146_1147ins GCAGAGCGTGTCTACGCGCGCTGCGCACATGTGC, p.S383Alafs*87), and 13 (GCK c.784_785insC, p.D262Alafs*13) presenting variants that had not been previously reported. CONCLUSIONS This study enriches the spectrum of genetic variations associated with MODY2. Clinically, children with a family history of diabetes, incidental findings of mild fasting hyperglycemia, and negative diabetes-related antibodies should be considered for the possibility of MODY2.
Humans ; Diabetes Mellitus, Type 2/diagnosis* ; Male ; Female ; Child ; Retrospective Studies ; Glucokinase/genetics* ; Adolescent ; Child, Preschool ; Glucose Tolerance Test

The prevalence of type 1 diabetes (T1DM) is increasing annually, and its complications seriously impair the quality of life of affected children. Early screening for T1DM helps reduce the occurrence of diabetic ketoacidosis, protect β-cell function, and delay disease onset in high-risk populations. This article summarizes current domestic and international screening technologies for T1DM. Screening methods remain centered on detection of diabetes-related antibodies and glycometabolic markers, while factors related to disease pathogenesis hold promise as sensitive screening markers. Expanding T1DM screening in China is expected to improve early diagnosis and treatment.
Diabetes Mellitus, Type 1/diagnosis* ; Humans ; Early Diagnosis ; Autoantibodies/blood* ; Mass Screening/methods*

Saliva is an important body fluid in the oral cavity containing lots of biomarkers, whose inherent micro-ecosystem holds significant value for early diagnosis and monitoring of oral diseases. Simultaneously, saliva has particular advantages, such as ease of sampling, painless and non-invasive collection, and suitability for repeated sampling, making it highly appropriate for surveillance and follow-up of diseases. In a series of studies conducted by the research group for preventive dentistry in Peking University School and Hospital of Stomatology, we compared different segments of saliva and those samples collected via different sampling methods using proteomic/peptidomic and microbiomic technologies to explore the stability of saliva samples. Besides, the significance of applying representative salivary biomarkers in early prevention and control of representative oral diseases (e.g. dental caries, periodontal diseases) and systemic conditions (e.g. type 2 diabetes mellitus, chronic kidney disease) was confirmed as well.
Humans ; Saliva/chemistry* ; Dental Caries/diagnosis* ; Biomarkers/analysis* ; Periodontal Diseases/diagnosis* ; Mouth Diseases/diagnosis* ; Proteomics/methods* ; Diabetes Mellitus, Type 2/diagnosis* ; Microbiota ; Renal Insufficiency, Chronic/prevention & control*

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

Diabetic Ketoacidosis/diagnosis* ; Humans ; Child ; Diabetes Mellitus, Type 1/diagnosis* ; China ; Insulin/administration & dosage* ; Hypoglycemic Agents/therapeutic use*

Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
Humans ; Glucose ; Blood Glucose ; Insulin Resistance/physiology* ; Diabetes Mellitus, Type 2/diagnosis* ; Blood Glucose Self-Monitoring ; Time Factors ; Insulin

Objective To investigate the cardiac structural and functional characteristics in the patients with heart failure with preserved ejection fraction (HFpEF) and type 2 diabetes mellitus (T2DM),and predict the factors influencing the characteristics. Methods A total of 783 HFpEF patients diagnosed in the Department of Geriatric Cardiology,the First Hospital of Lanzhou University from April 2009 to December 2020 were enrolled in this study.Echocardiography and tissue Doppler technique were employed to evaluate cardiac structure and function.According to the occurrence of T2DM,the patients were assigned into a HFpEF+T2DM group (n=332) and a HFpEF group (n=451).Propensity score matching (PSM)(in a 1∶1 ratio) was adopted to minimize confounding effect.According to urinary albumin excretion rate (UAER),the HFpEF+T2DM group was further divided into three subgroups with UAER<20 μg/min,of 20-200 μg/min,and>200 μg/min,respectively.The comorbidities,symptoms and signs,and cardiac structure and function were compared among the groups to clarify the features of diabetes related HFpEF.Multivariate linear regression was conducted to probe the relationship of systolic blood pressure,blood glucose,glycosylated hemoglobin,and UARE with cardiac structural and functional impairment. Results The HFpEF+T2DM group had higher prevalence of hypertension (P=0.001) and coronary heart disease (P=0.036),younger age (P=0.020),and larger body mass index (P=0.005) than the HFpEF group,with the median diabetic course of 10 (3,17) years.After PSM,the prevalence of hypertension and coronary heart disease,body mass index,and age had no significant differences between the two groups(all P>0.05).In addition,the HFpEF+T2DM group had higher interventricular septal thickness (P=0.015),left ventricular posterior wall thickness (P=0.040),and left ventricular mass (P=0.012) and lower early diastole velocity of mitral annular septum (P=0.030) and lateral wall (P=0.011) than the HFpEF group.Compared with the HFpEF group,the HFpEF+T2DM group showed increased ratio of early diastolic mitral filling velocity to early diastolic mitral annular velocity (E/e') (P=0.036).Glycosylated hemoglobin was correlated with left ventricular mass (P=0.011),and the natural logarithm of UAER with interventricular septal thickness (P=0.004),left ventricular posterior wall thickness (P=0.006),left ventricular mass (P<0.001),and E/e' ratio (P=0.049). Conclusion The patients with both T2DM and HFpEF have thicker left ventricular wall,larger left ventricular mass,more advanced left ventricular remodeling,severer impaired left ventricular diastolic function,and higher left ventricular filling pressure than the HFpEF patients without T2DM.Elevated blood glucose and diabetic microvascular diseases might play a role in the development of the detrimental structural and functional changes of the heart.
Humans ; Aged ; Heart Failure/diagnosis* ; Diabetes Mellitus, Type 2 ; Stroke Volume ; Glycated Hemoglobin ; Blood Glucose ; Propensity Score ; Ventricular Function, Left ; Hypertension

Blood glucose monitoring has become the weakest point in the overall management of diabetes in China. Long-term monitoring of blood glucose levels in diabetic patients has become an important means of controlling the development of diabetes and its complications, so that technological innovations in blood glucose testing methods have far-reaching implications for accurate blood glucose testing. This article discusses the basic principles of minimally invasive and non-invasive blood glucose testing assays, including urine glucose assays, tear assays, methods of extravasation of tissue fluid, and optical detection methods, etc., focuses on the advantages of minimally invasive and non-invasive blood glucose testing methods and the latest relevant results, and summarizes the current problems of various testing methods and prospects for future development trends.
Humans ; Blood Glucose ; Blood Glucose Self-Monitoring/methods* ; Diabetes Mellitus/diagnosis* ; Monitoring, Physiologic/methods* ; Tears

BACKGROUND: The hemoglobin glycation index (HGI) was developed to quantify glucose metabolism and individual differences and proved to be a robust measure of individual glycosylated hemoglobin (HbA1c) bias. Here, we aimed to explore the relationship between different HGIs and the risk of 5-year major adverse cardiovascular events (MACEs) by performing a large multicenter cohort study in China. METHODS: A total of 9791 subjects from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a Longitudinal Study (the REACTION study) were divided into five subgroups (Q1-Q5) with the HGI quantiles (≤5th, >5th and ≤33.3th, >33.3th and ≤66.7th, >66.7th and ≤95th, and >95th percentile). A multivariate logistic regression model constructed by the restricted cubic spline method was used to evaluate the relationship between the HGI and the 5-year MACE risk. Subgroup analysis between the HGI and covariates were explored to detect differences among the five subgroups. RESULTS: The total 5-year MACE rate in the nationwide cohort was 6.87% (673/9791). Restricted cubic spline analysis suggested a U-shaped correlation between the HGI values and MACE risk after adjustment for cardiovascular risk factors ( χ2 = 29.5, P <0.001). After adjustment for potential confounders, subjects with HGIs ≤-0.75 or >0.82 showed odds ratios (ORs) for MACE of 1.471 (95% confidence interval [CI], 1.027-2.069) and 2.222 (95% CI, 1.641-3.026) compared to subjects with HGIs of >-0.75 and ≤-0.20. In the subgroup with non-coronary heart disease, the risk of MACE was significantly higher in subjects with HGIs ≤-0.75 (OR, 1.540 [1.039-2.234]; P = 0.027) and >0.82 (OR, 2.022 [1.392-2.890]; P <0.001) compared to those with HGIs of ≤-0.75 or >0.82 after adjustment for potential confounders. CONCLUSIONS We found a U-shaped correlation between the HGI values and the risk of 5-year MACE. Both low and high HGIs were associated with an increased risk of MACE. Therefore, the HGI may predict the 5-year MACE risk.
Humans ; Cohort Studies ; Longitudinal Studies ; Diabetes Mellitus, Type 2/diagnosis* ; Maillard Reaction ; Glycated Hemoglobin ; Cardiovascular Diseases