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*

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

Several chronic disorders including type 2 diabetes (T2D), obesity, heart disease and cancer are preceded by a state of chronic low-grade inflammation. Biomarkers for the early assessment of chronic disorders encompass acute phase proteins (APP), cytokines and chemokines, pro-inflammatory enzymes, lipids and oxidative stress mediators. These substances enter saliva through the blood flow and, in some cases, there is a close relation between their salivary and serum concentration. Saliva can be easily collected and stored with non-invasive and cost-saving procedures, and it is emerging the concept to use it for the detection of inflammatory biomarkers. To this purpose, the present review aims to discuss the advantages and challenges of using standard and cutting-edge techniques to discover salivary biomarkers which may be used in diagnosis/therapy of several chronic diseases with inflammatory consequences with the pursuit to possibly replace conventional paths with detectable soluble mediators in saliva. Specifically, the review describes the procedures used for saliva collection, the standard approaches for the measurement of salivary biomarkers and the novel methodological strategies such as biosensors to improve the quality of care for chronically affected patients.
Humans ; Diabetes Mellitus, Type 2/diagnosis* ; Biomarkers ; Cytokines ; Inflammation/diagnosis* ; Oxidative Stress

Objective: Compare and analyze the results of the domestic Lanyi AH600 glycated hemoglobin analyzer and other different detection systems to understand the comparability of the detection results of different detectors, and establish the best cut point of Lanyi AH600 determination of haemoglobin A1c (HbA1c) in the diagnosis of diabetes. Methods: Multi center cohort study was adopted. The clinical laboratory departments of 18 medical institutions independently collected test samples from their respective hospitals from March to April 2022, and independently completed comparative analysis of the evaluated instrument (Lanyi AH600) and the reference instrument HbA1c. The reference instruments include four different brands of glycosylated hemoglobin meters, including Arkray, Bio-Rad, DOSOH, and Huizhong. Scatter plot was used to calculate the correlation between the results of different detection systems, and the regression equation was calculated. The consistency analysis between the results of different detection systems was evaluated by Bland Altman method. Consistency judgment principles: (1) When the 95% limits of agreement (95% LoA) of the measurement difference was within 0.4% HbA1c and the measurement score was≥80 points, the comparison consistency was good; (2) When the measurement difference of 95% LoA exceeded 0.4% HbA1c, and the measurement score was≥80 points, the comparison consistency was relatively good; (3) The measurement score was less than 80 points, the comparison consistency was poor. The difference between the results of different detection systems was tested by paired sample T test or Wilcoxon paired sign rank sum test; The best cut-off point of diabetes was analyzed by receiver operating characteristic curve (ROC). Results: The correlation coefficient R² of results between Lanyi AH600 and the reference instrument in 16 hospitals is≥0.99; The Bland Altman consistency analysis showed that the difference of 95% LoA in Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180) was -0.486%-0.325%, and the measurement score was 94.6 points (473/500); The difference of 95% LoA in the Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant II) was -0.727%-0.612%, and the measurement score was 89.8 points; The difference of 95% LoA in the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT) was -0.231%-0.461%, and the measurement score was 96.6 points; The difference of 95% LoA in the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT) was -0.469%-0.479%, and the measurement score was 91.9 points. The other 14 hospitals, Lanyi AH600, were compared with 4 reference instrument brands, the difference of 95% LoA was less than 0.4% HbA1c, and the scores were all greater than 95 points. The results of paired sample T test or Wilcoxon paired sign rank sum test showed that there was no statistically significant difference between Lanyi AH600 and the reference instrument Arkray HA8180 (Z=1.665,P=0.096), with no statistical difference. The mean difference between the measured values of the two instruments was 0.004%. The comparison data of Lanyi AH600 and the reference instrument of all other institutions had significant differences (all P<0.001), however, it was necessary to consider whether it was within the clinical acceptable range in combination with the results of the Bland-Altman consistency analysis. The ROC curve of HbA1c detected by Lanyi AH600 in 985 patients with diabetes and 3 423 patients with non-diabetes was analyzed, the area under curve (AUC) was 0.877, the standard error was 0.007, and the 95% confidence interval 95%CI was (0.864, 0.891), which was statistically significant (P<0.001). The maximum value of Youden index was 0.634, and the corresponding HbA1c cut point was 6.235%. The sensitivity and specificity of diabetes diagnosis were 76.2% and 87.2%, respectively. Conclusion: Among the hospitals and instruments currently included in this study, among these four hospitals included Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180), Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant Ⅱ), the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT), and the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT), the comparison between Lanyi AH600 and the reference instruments showed relatively good consistency, while the other 14 hospitals involved four different brands of reference instruments: Arkray, Bio-Rad, DOSOH, and Huizhong, Lanyi AH600 had good consistency with its comparison. The best cut point of the domestic Lanyi AH600 for detecting HbA1c in the diagnosis of diabetes is 6.235%.
Pregnancy ; Child ; Humans ; Female ; Glycated Hemoglobin ; Cohort Studies ; Diabetes Mellitus/diagnosis* ; Sensitivity and Specificity ; ROC Curve

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