AIM:This study aims to identify oral microbial signatures associated with prediabetes in young adults and to investigate potential oral risk factors for early-onset diabetes, as well as to pinpoint targets for monitoring and intervention. METHODS:The study involved a large cross-sectional analysis of 3,142 participants from two independent cohorts. The discovery cohort consisted of 334 prediabetes cases and 1,266 controls, while the validation cohort had 325 prediabetes cases and 1,217 controls. We compared the basic and clinical characteristics of the different groups. Additionally, 16S rRNA gene sequencing was conducted on oral rinse samples. RESULTS:Prediabetes-enriched taxa comprised Bacteroidetes, Prevotella_7, and Veillonella. In contrast, normoglycemic controls showed a higher presence of Firmicutes and Streptococcus. The combined models, constructed from indicators identified by LASSO regression, including BMI, HOMA-IR, and specific microbiota (Prevotella_7 or Veillonella), demonstrated discriminatory performance. In the discovery set, the AUC values were 0.761 and 0.758, respectively, whereas in the validation set, the AUC values were 0.693 and 0.696, respectively. CONCLUSION:Reproducible alterations and enrichment of Prevotella_7 and Veillonella are linked to prediabetes in young adults. Furthermore, the combined interaction between specific bacterial genera and core clinical indicators may be crucial in the development of prediabetes in young individuals.

AIMS:The aims of the present study were to assess the effects of lipid-lowering drugs [HMG-CoA reductase inhibitors, proprotein convertase subtilisin/kexin type 9 inhibitors, and Niemann-Pick C1-Like 1 (NPC1L1) inhibitors] on novel subtypes of adult-onset diabetes through a Mendelian randomisation study. MATERIALS AND METHODS:We first inferred causal associations between lipid-related traits [including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoproteins A-I, and apolipoproteins B] and novel subtypes of adult-onset diabetes. The expression quantitative trait loci of drug target genes for three classes of lipid-lowering drugs, as well as genetic variants within or nearby drug target genes associated with LDL-C, were then utilised as proxies for the exposure of lipid-lowering drugs. Mendelian randomisation analysis was performed using summary data from genome-wide association studies of LDL-C, severe autoimmune diabetes, severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes. RESULTS:There was an association between HMGCR-mediated LDL-C and the risk of SIRD [odds ratio (OR) = 0.305, 95% confidence interval (CI) = 0.129-0.723; p = 0.007], and there was an association of PCSK9-mediated LDL-C with the risk of SIDD (OR = 0.253, 95% CI = 0.120-0.532; p < 0.001) and MOD (OR = 0.345, 95% CI = 0.171-0.696; p = 0.003). Moreover, NPC1L1-mediated LDL-C (OR = 0.109, 95% CI = 0.019-0.613; p = 0.012) and the increased expression of NPC1L1 gene in blood (OR = 0.727, 95% CI = 0.541-0.977; p = 0.034) both showed a significant association with SIRD. These results were further confirmed by sensitivity analyses. CONCLUSIONS:In summary, the different lipid-lowering medications have a specific effect on the increased risk of different novel subtypes of adult-onset diabetes.

AIMS To conduct the first nationally representative study on epidemiological data of metabolically unhealthy normal weight (MUNW) focused only on non-diabetic subjects and determine the predictive effect on diabetes in China. MATERIALS AND METHODS A longitudinal study was conducted using data from the Rich Healthcare Group in China. The metabolic status was determined by the revised NCEP ATP III criteria, and individuals with two or more criteria were categorized as MUNW and diagnosed with metabolic syndrome (MetS) if they met three or more. RESULTS Of a total of 63,830 non-diabetic normal-weight individuals, 8,935 (14.0%) were classified as MUNW and 1,916 (3.00%) were diagnosed with MetS. After adjusting for potential confounders, individuals with MUNW had a greater diabetes risk (4.234 [95% CI: 3.089,5.803]) than those without MUNW during an average of 3.10 years of follow-up. Also, the multivariable-adjusted HRs for developing diabetes were 3.069 (95% CI: 1.790,5.263), 7.990 (95% CI: 4.668,13.677), 11.950 (95% CI: 6.618,21.579) for participants with one, two, and three or more components, respectively, compared to those without any components. Further analyses suggested that the number of MetS components present is associated with the risk of diabetes, especially in metabolically unhealthy normal-weight young male adults. Multivariable-adjusted hazard ratios (95% CI) for incident diabetes among individuals with one, two, and at least three components were 4.45 (1.45, 13.72), 9.82 (3.05, 31.64), and 15.13 (3.70, 61.84) for participants aged ≤44 years, 3.55 (1.81, 6.97), 8.52 (4.34, 16.73), and 13.69 (6.51, 28.77) for male participants, respectively. CONCLUSIONS The prevalence of MUNW is 14% in Chinese normal-weight non-diabetic individuals, and active intervention is necessary for this category of people. The presence of MUNW significantly increases the risk of diabetes, and the risk of diabetes is associated with the number of MetS components present in the patient.

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

AimsThe comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.MethodsThis was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.ResultsThe results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.ConclusionsT1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.

AIMS:To investigate glycaemic variability (GV) patterns in patients with type 1 diabetes (T1D), type 2 diabetes (T2D), and latent autoimmune diabetes in adults (LADA). MATERIALS AND METHODS:A total of 842 subjects (510 T1D, 105 LADA, 227 T2D) were enrolled and underwent 1 week of continuous glucose monitoring (CGM). Clinical characteristics and CGM parameters were compared among T1D, LADA, and T2D. LADA patients were divided into two subgroups based on glutamic acid decarboxylase autoantibody titres (≥180 U/mL [LADA-1], <180 U/mL [LADA-2]) and compared. The C-peptide cut-offs for predicting a coefficient of variation (CV) of glucose ≥36% and a time in range (TIR) > 70% were determined using receiver operating characteristic analysis. RESULTS:Twenty-seven patients (9 T1D, 18 T2D) were excluded due to insufficient CGM data. Sex, diabetes duration and HbA1c were comparable among the three groups. Fasting and 2-h postprandial C-peptide (FCP, 2hCP) increased sequentially across T1D, LADA, and T2D. T1D and LADA patients had comparable TIR and GV, whereas those with T2D had much higher TIR and lower GV (p < 0.001). The GV of LADA-1 was close to that of T1D, while the GV of LADA-2 was close to that of T2D. CP exhibited the strongest negative correlation with GV. The cut-offs of FCP/2hCP for predicting a CV ≥ 36% and TIR >70% were 121.6/243.1 and 128.9/252.8 pmol/L, respectively. CONCLUSIONS:GV presented a continuous spectrum across T1D, LADA-1, LADA-2, and T2D. More frequent glucose monitoring is suggested for patients with impaired insulin secretion. CLINICAL TRAIL REGISTRATION:Chinese Clinical Trial Registration (ChiCTR) website approved by WHO; http://www.chictr.org.cn/ - ChiCTR2200065036.

OBJECTIVES:Patients with classical type 1 diabetes mellitus (T1DM) require lifelong dependence on exogenous insulin therapy due to pancreatic beta-cell destruction and absolute insulin deficiency. T1DM accounts for about 90% of children with diabetes in China, with a rapid increase in incidence and a younger-age trend. Epidemiological studies have shown that the overall glycated haemoglobin (HbA1c) and compliance rate are low in Chinese children with T1DM. Optimal glucose control is the key for diabetes treatment, and maintaining blood glucose within the target range can prevent or delay chronic vascular complications in patients with T1DM. Therefore, this study aims to investigate the glycemic control of children with T1DM from Hunan and Henan Province with flash glucose monitoring system (FGMS), and to explore factors associated with glycemic variability. METHODS:A total of 215 children with T1DM under 14 years old were enrolled continuously in 16 hospitals from August 2017 to August 2020. All subjects wore a FGMS device to collect glucose data. Correlation of HbA1c, duration of diabetes, or glucose scan rates with glycemic variability was analyzed. Glucose variability was compared according to the duration of diabetes, HbA1c, glucose scan rates and insulin schema. RESULTS:HbA1c and duration of diabetes were positively correlated with mean blood glucose, standard deviation of glucose, mean amplitude of glucose excursions (MAGE), and coefficient of variation (CV) of glucose (all P<0.01). The glucose scan rates during FGMS wearing was significantly positively correlated with time in range (TIR) (P=0.001) and negatively correlated with MAGE and mean duration of hypoglycemia (all P<0.01). Children with duration ≤1 year had lower time below range (TBR) and MAGE when compared with those with duration >1 year (all P<0.05). TIR and TBR in patients with HbA1c ≤7.5% were higher (TIR: 65% vs 45%, TBR: 5% vs 4%, P<0.05), MAGE was lower (7.0 mmol/L vs 9.4 mmol/L, P<0.001) than those in HbA1c >7.5% group. Compared to the multiple daily insulin injections group, TIR was higher (60% vs 52%, P=0.006), MAGE was lower (P=0.006) in the continuous subcutaneous insulin infusion group. HbA1c was lower in the high scan rates (≥14 times/d) group (7.4% vs 8.0%, P=0.046), TIR was significantly higher (58% vs 47%, P<0.001), and MAGE was lower (P<0.001) than those in the low scan rate (<14 times/d) group. CONCLUSIONS:The overall glycemic control of T1DM patients under 14 years old in Hunan and Henan Province is under a high risk of hypoglycemia and great glycemic variability. Shorter duration of diabetes, targeted HbA1c, higher glucose scan rates, and CSII are associated with less glycemic variability.

Background: Predicting hypoglycemia while maintaining low false alarm rate is a bottleneck for wide adoption of continuous glucose monitoring (CGM) in diabetes management. One small study suggested the long short-term memory (LSTM) network deep learning model had better performance of hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training consideration, whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes are unknown.Methods: We assembled two large datasets of patients with both type 1 diabetes and type 2 diabetes. The primary dataset containing 192 patients from Chinese were used to develop the LSTM, support vector machine (SVM) and random forest (RF) models for hypoglycemia prediction at the prediction horizon of 30 minutes. Hypoglycemia was defined as the mild (54mg/dl <= glucose < 70mg/dl) and severe (< 54mg/dl) hypoglycemic level separately. The validation dataset of 427 patients from European-Americans was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated by sensitivity, specificity and area under the operating curve (AUC).Results: For the difficulty to predict mild hypoglycemia events, the LSTM model always achieved AUC greater than 97.22% in the primary dataset, with less than 3% AUC reduction in the validation dataset, indicating the model was robust and generalizable across populations. AUC higher than 93.49% was also achieved when LSTM was applied to both type 1 diabetes and type 2 diabetes in the validation dataset, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms.Conclusions: Our results demonstrated that the LSTM model was robust for hypoglycemia prediction and generalizable across populations or diabetes subtypes. Given its extra advantage on false alarm reduction, the LSTM model was a strong candidate to be widely implemented by future CGM devices for hypoglycemia prediction.

[This corrects the article DOI: 10.3389/fendo.2022.915482.].

The generalizability of numerous tacrolimus population pharmacokinetic (popPK) models constructed to promote optimal tacrolimus dosing in patients with primary nephrotic syndrome (PNS) is unclear. This study aimed to evaluate the predictive performance of published tacrolimus popPK models for PNS patients with an external data set. We prospectively collected 223 concentrations from 50 Chinese adult patients with PNS who were undergoing tacrolimus treatment. Data on published tacrolimus popPK models for adults and children with PNS were extracted from the literature. Model predictability was evaluated with prediction-based and simulation-based diagnostics and Bayesian forecasting. In prediction-based evaluation, none of the 11 identified published popPK models of tacrolimus had met a predefined criteria of a mean prediction error ≤  ± 20%, and the prediction error within ± 30% of the identified models didn’t exceed 50%. Simulation-based diagnostics also indicated unsatisfactory predictability. Bayesian forecasting demonstrated amelioration in the model predictability with the inclusion of 2–3 prior observations. Moreover, the predictive performance of nonlinear models was not better than that of one-compartment models. The prediction of tacrolimus concentrations for patients with PNS remains challenging; published models are not applicable for extrapolation to other hospitals. Bayesian forecasting significantly improved model predictability and thereby helped to individualize tacrolimus dosing.

Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet (3-cell function, which leads to high glucose variability in patients. However, alpha-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

*These authors contributed equally to this work Abstract: Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet β-cell function, which leads to high glucose variability in patients. However, α-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

Abstract Context The long-term effects of dipeptidyl peptidase-4 inhibitors on β-cell function and insulin sensitivity in latent autoimmune diabetes in adults (LADA) are unclear. Objective To investigate the effects of sitagliptin on β-cell function and insulin sensitivity in LADA patients receiving insulin. Design and Setting A randomized controlled trial at the Second Xiangya Hospital. Methods Fifty-one patients with LADA were randomized to sitagliptin + insulin (SITA) group or insulin alone (CONT) group for 24 months. Main Outcome Measures Fasting C-peptide (FCP), 2-hour postprandial C-peptide (2hCP) during mixed-meal tolerance test, △CP (2hCP – FCP), and updated homeostatic model assessment of β-cell function (HOMA2-B) were determined every 6 months. In 12 subjects, hyperglycemic clamp and hyperinsulinemic euglycemic clamp (HEC) tests were further conducted at 12-month intervals. Results During the 24-month follow-up, there were no significant changes in β-cell function in the SITA group, whereas the levels of 2hCP and △CP in the CONT group were reduced at 24 months. Meanwhile, the changes in HOMA2-B from baseline were larger in the SITA group than in the CONT group. At 24 months, first-phase insulin secretion was improved in the SITA group by hyperglycemia clamp, which was higher than in the CONT group (P < .001), while glucose metabolized (M), insulin sensitivity index, and M over logarithmical insulin ratio in HEC were increased in the SITA group (all P < .01 vs baseline), which were higher than in the CONT group. Conclusion Compared with insulin intervention alone, sitagliptin plus insulin treatment appeared to maintain β-cell function and improve insulin sensitivity in LADA to some extent.

Purpose: Postoperative thirst is a common clinical issue. The discomfort caused by thirst during the perioperative period is strong and significant. Postoperative thirst is associated with emotional changes, giving rise to a series of adverse psychological and physical problems to patients. This study aimed to explore the effect of 0.75% citric acid spray on thirst relief during the anesthesia recovery period in China. Design: A randomized controlled trial was conducted on subjects immediately after the removal of the endotracheal tube in a postanesthesia care unit. Methods: A total of 112 patients with TI scores >3 on 0-10 numeric rating scale were randomized to the intervention group (0.75% citric acid spray group; n = 56) or control group (cool water spray; n = 56) by computerized randomization. Thirst assessment was performed before and 5 minutes after the intervention. Five minutes after the intervention, if the TI score was still >3 points, the spray would be added and the thirst assessment would be performed again until the TI score was <3 points. The onset time, duration time, and the number of additional sprays within 20 minutes was recorded. Findings: Five minutes after the intervention, the thirst intensity score of the 0.75% citric acid spray group decreased from 5.57 +/- 1.35 to 3.09 +/- 1.20. The onset and duration times were 0.77 +/- 0.47 min and 4.41 +/- 2.59 min, respectively, and the number of spray additions in 20 min was 1.09 +/- 0.92. The thirst intensity score of the cool water spray group decreased from 5.29 +/- 1.52 to 3.73 +/- 1.54. The onset and duration time were 0.84 +/- 0.42 min and 2.77 +/- 1.80 min, respectively, and the number of spray additions was 1.91 +/- 1.24. No incidence of adverse events, including choking, aspiration, and allergies occurred. Conclusion: For thirsty patients during the anesthesia recovery period, the spray method is safe and has fewer side effects, including choking, aspiration, and allergies. Thus, 0.75% citric acid spray and cool water spray are both safe and effective; however, the 0.75% citric acid spray has a better thirst relief effect that lasts longer than the cool water spray. (c) 2021 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

To explore the effect and magnitude of effect of sodium-glucose cotransporter-2 (SGLT2) inhibitors on haematocrit and haemoglobin and the related cardiorenal benefits in patients with type 2 diabetes mellitus (T2DM), PubMed, Web of Science, CENTRAL and EMBASE were searched to identify eligible trials. Weighted mean differences (WMDs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Seventy-eight studies were included in the meta-analysis. SGLT2 inhibitors significantly increased haematocrit and haemoglobin levels compared with control (total WMD 2.27% [95% CI 2.08, 2.47] and 6.20 g/L [95% CI 5.68, 6.73], respectively). Except for dapagliflozin (p = 0.000), no notable dose-dependent relationship was revealed for other SGLT2 inhibitors. The effect could be sustained or even slightly increased with long-term therapy (coef. =0.009, 95% CI [0.005, 0.013], p = 0.000). In subgroup analyses, haematocrit elevation increased with higher body mass index (BMI). A greater haematocrit elevation could be observed in white patients or when compared with active controls. In conclusion, SGLT2 inhibitors increased haematocrit and haemoglobin levels in T2DM patients. Changes in haematocrit and haemoglobin seem to be surrogate markers of improvement in renal metabolic stress, and important mediators involved in cardiorenal protection.

AbstractAims/IntroductionWe aimed to explore the clinical factors associated with glycemic variability (GV) assessed with flash glucose monitoring (FGM), and investigate the impact of FGM on glycemic control among Chinese type 1 diabetes mellitus patients in a real‐life clinical setting.Materials and MethodsA total of 171 patients were included. GV was assessed from FGM data. A total of 110 patients wore FGM continuously for 6 months (longitudinal cohort). Hemoglobin A1c (HbA1c), fasting and 2‐h postprandial C‐peptide, and glucose profiles were collected. Changes in HbA1c and glycemic parameters were assessed during a 6‐month FGM period.ResultsIndividuals with high residual C‐peptide (HRCP; 2‐h postprandial C‐peptide >200 pmol/L) had less GV than patients with low residual C‐peptide ( 2‐h postprandial C‐peptide ≤200 pmol/L; P < 0.001). In the longitudinal cohort (n = 110), HbA1c and mean glucose decreased, time in range (TIR) increased during the follow‐up period (P < 0.05). The 110 patients were further divided into age and residual C‐peptide subgroups: (i) HbA1c and mean glucose were reduced significantly only in the subgroup aged ≤14 years during the follow‐up period, whereas time below range also increased in this subgroup at 3 months (P = 0.047); and (ii) HbA1c improved in the HRCP subgroup at 3 and 6 months (P < 0.05). The mean glucose decreased and TIR improved significantly in the low residual C‐peptide subgroup; however, TIR was still lower and time below range was higher than those of the HRCP subgroup at all time points (P < 0.05).ConclusionsHRCP was associated with less GV. FGM wearing significantly reduced HbA1c, especially in pediatric patients and those with HRCP. Additionally, the mean glucose and TIR were also found to improve.

AbstractAims/IntroductionType 1 diabetes mellitus is a T cell‐mediated autoimmune disease. However, the determination of the autoimmune status of type 1 diabetes mellitus relies on islet autoantibodies (Abs), as T‐cell assay is not routinely carried out. This study aimed to investigate the diagnostic value of combined assay of islet antigen‐specific T cells and Abs in type 1 diabetes mellitus patients.Materials and MethodsA total of 54 patients with type 1 diabetes mellitus and 56 healthy controls were enrolled. Abs against glutamic acid decarboxylase (GAD), islet antigen‐2 and zinc transporter 8 were detected by radioligand assay. Interferon‐γ‐secreting T cells responding to glutamic acid decarboxylase 65 and C‐peptide (CP) were measured by enzyme‐linked immunospot.ResultsThe positive rate for T‐cell responses was significantly higher in patients with type 1 diabetes mellitus than that in controls (P < 0.001). The combined positive rate of Abs and T‐cell assay was significantly higher than that of Abs assay alone (85.2% vs 64.8%, P = 0.015). A significant difference in fasting CP level was found between the T+ and T– groups (0.07 ± 0.05 vs 0.11 ± 0.09 nmol/L, P = 0.033). Furthermore, levels of fasting CP and postprandial CP were both lower in the Ab−T+ group than the Ab−T− group (fasting CP 0.06 ± 0.05 vs 0.16 ± 0.12 nmol/L, P = 0.041; postprandial CP 0.12 ± 0.13 vs 0.27 ± 0.12 nmol/L, P = 0.024).ConclusionsEnzyme‐linked immunospot assays in combination with Abs detection could improve the diagnostic sensitivity of autoimmune diabetes.

Background Latent autoimmune diabetes in adults (LADA) is characterized by autoimmunity, late-onset and intermediate beta-cell deprivation rate between type 2 diabetes mellitus (T2DM) and type 1 diabetes mellitus (T1DM). Herein, we investigated proinsulin (PI) secretion patterns and the endoplasmic reticulum (ER) dysfunction biomarker, PI-to-C-peptide (PI:CP) ratio, to elucidate beta-cell intrinsic pathogenesis mechanisms in different types of diabetes. Methods Total serum fasting PI (FPI) were measured in adult-onset and newly-diagnosed diabetes patients, including 60 T1DM, 60 LADA and 60 T2DM. Thirty of each type underwent mixed meal tolerance tests (MMTTs), and hence 120 min postprandial PI (PPI) were detected. PI:CP ratio = PI (pmol/L) ÷ CP (pmol/L) × 100%. PI-related measurements among types of diabetes were compared. Correlation between PI-related measurements and beta-cell autoimmunity were analyzed. The possibility of discriminating LADA from T1DM and T2DM with PI-related measurements were tested. Results FPI and PPI were significantly higher in LADA than T1DM (P<0.001 for both comparisons), but lower than those in T2DM (P<0.001 and P=0.026, respectively). Fasting PI:CP ratio was significantly higher in T1DM than both LADA and T2DM (median 3.25% vs. 2.13% and 2.32%, P=0.011 and P=0.017, respectively). In LADA, positive autoantibody numbers increased by both fasting and postprandial PI:CP ratio (P=0.007 and P=0.034, respectively). Areas under receiver operation characteristic curves (AUCROC) of FPI and PPI for discriminating LADA from adult-onset T1DM were 0.751 (P<0.001) and 0.838 (P<0.001), respectively. Between LADA and T2DM, AUCROC of FPI and PPI were 0.685 (P<0.001) and 0.741 (P=0.001), respectively. Conclusions In the development of autoimmune diabetes, interplays between ER stress and beta-cell autoimmunity are potentially responsible for severer beta-cell destruction. PI-related measurements could help in differentiating LADA from adult-onset T1DM and T2DM.

OBJECTIVE:To explore the clinical features and complications of 545 hospitalized type 1 diabetic patients.
 Methods: All data of 545 patients with typical type 1 diabetes (T1DM) who were hospitalized in the Department of Endocrinology, the Second Xiangya Hospital, Central South University were collected. The data were analyzed retrospectively to explore the clinical features and complications. Clinical and biochemical characteristics were analyzed through comparison between different subgroups according to the onset age (≤13 years old, 14-29 years old, ≥30 years old).
 Results: The median onset age of T1DM patients was 27.0 (15.0, 40.0) years, and the middle-onset was 42.1%. Among the 3 groups, the proportion of female (58.0%) was the highest in the ≤13 years old group, concomitant with the lowest SBP and serum creatinine levels as well as the lowest incidence of all microvascular complications (21.0% of diabetic nephropathy, 23.3% of diabetic retinopathy, 34.1% of diabetic peripheral neuropathy; all P<0.05). Moreover, the fasting C peptide and peak C peptide levels were the lowest in ≥30 years old group compared with the other two groups, and the incidence of ketosis (33.5%) and all macrovascular complications were the highest among the three groups (all P<0.05).
 Conclusion: There are about half of the hospitalized patients with T1DM whose onset ages are ≥30 years. The incidence of ketosis at the onset and the risk for various microvascular and macrovascular complications after onset are higher than those with the onset age <30 years.