Real-world effectiveness of early insulin therapy on the incidence of cardiovascular events in newly diagnosed type 2 diabetes.

Early insulin therapy is capable to achieve glycemic control and restore ß-cell function in newly diagnosed type 2 diabetes (T2D), but its effect on cardiovascular outcomes in these patients remains unclear. In this nationwide real-world study, we analyzed electronic health record data from 19 medical centers across China between 1 January 2000, and 26 May 2022. We included 5424 eligible patients (mean age 56 years, 2176 women/3248 men) who were diagnosed T2D within six months and did not have prior cardiovascular disease. Multivariable Cox regression models were used to estimate the associations of early insulin therapy (defined as the first-line therapy for at least two weeks in newly diagnosed T2D patients) with the incidence of major cardiovascular events including coronary heart disease (CHD), stroke, and hospitalization for heart failure (HF). During 17,158 persons years of observation, we documented 834 incident CHD cases, 719 stroke cases, and 230 hospitalized cases for HF. Newly diagnosed T2D patients who received early insulin therapy, compared with those who did not receive such treatment, had 31% lower risk of incident stroke, and 28% lower risk of hospitalization for HF. No significant difference in the risk of CHD was observed. We found similar results when repeating the aforesaid analysis in a propensity-score matched population of 4578 patients and with inverse probability of treatment weighting models. These findings suggest that early insulin therapy in newly diagnosed T2D may have cardiovascular benefits by reducing the risk of incident stroke and hospitalization for HF.

Microbial and metabolomic profiles of type 1 diabetes with depression: A case-control study.

BACKGROUND: Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression. METHODS: A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model. RESULTS: In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone. CONCLUSIONS: T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Basal Hyperglycemia Contributes More Than Fifty Percent to Time in Range in Pregnant Women with Type 1 Diabetes.

AIMS: To evaluate the relative contribution of basal hyperglycemia (BHG) and postprandial hyperglycemia (PHG) to the time in range (TIR) categories and adverse pregnancy outcomes in pregnant women with type 1 diabetes mellitus (T1DM). MATERIALS AND METHODS: This observational study included 112 pregnancies with T1DM from the CARNATION study who wore continuous glucose monitoring (CGM) devices during pregnancy. The data from CGM were analyzed for TIR (range, 3.5-7.8 mmol/L), areas under the curve (AUC) of PHG, AUC of BHG, basal and postprandial hyperglycemia contribution rates. The contribution rates of BHG and PHG to the different levels of TIR(＜60%, 60-78%, ≥78%) and adverse pregnancy outcomes were analyzed. RESULTS: The participants' average age was 28.8±3.9 years with a diabetes duration of 8.4±6.2 years. All women experienced a mean TIR of 75.6±19.0% and a mean HbA1c of 6.2±1.1% during pregnancy. The BHG contribution accounted for 74.9(36.8, 100)%, 69.2(13.4, 100)%, and 66.5(10.0, 100)% (P＜0.001) and PHG accounted for 25.1(0, 63.2)% and 30.8(0, 86.6)% and 33.5(0, 90.0)% (P＜0.001) when participants experienced the TIR＜60%, 60-78%, ≥78%, respectively. Participants with higher BHG contribution rates tended to have more adverse pregnancy outcomes. CONCLUSIONS: Basal hyperglycemia was the major contributor to TIR during pregnancy. Along with controlling the postprandial hyperglycemia, pregnant women with T1DM who did not reach the target of TIR may benefit more from the optimization of insulin regimens focusing on reducing basal glucose.

Continuous subcutaneous insulin infusion versus multiple daily injection therapy in pregnant women with type 1 diabetes.

INTRODUCTION: The study aimed to compare glycemic control and pregnancy outcomes in women with type 1 diabetes mellitus (T1DM) using multiple daily injection therapy (MDI) and continuous subcutaneous insulin infusion (CSII) and to compare outcomes of women treated with long-acting insulin or neutral protamine Hagedorn (NPH). METHODS: This multicenter prospective cohort study involved women with pregestational T1DM treated with MDI and CSII. Primary outcome was glycated hemoglobin (HbA1c) before and during pregnancy. Secondary outcomes included maternal and neonatal outcomes and quality of life. RESULTS: Of the 121 studied women, the average age was 28.48 years, and the average body mass index was 21.29 kg/m2 at conception and 26.32 kg/m2 at delivery. Of the studied women, 78.51% had planned pregnancy. Women treated with MDI and CSII had comparable HbA1c before pregnancy or in the first and second trimesters. In the third trimester, women on CSII therapy had significantly lower HbA1c (6.07 ± 0.62 vs 6.20 ± 0.88%, p = .017), higher HbA1c on-target rate (71.43% vs 64.62%, p = .030), and greater decline of HbA1c from preconception to the third trimester (-0.65 vs -0.30%, p = .047). Fewer daily insulin requirements were observed in those used CSII compared with MDI-treated women (0.60 ± 0.22 vs 0.73 ± 0.25 U/kg/day, p = .004). Newborns born of mothers treated with the CSII method were more likely to have neonatal jaundice (adjusted odds ratio [OR] 2.76, 95% confidence interval [CI] 1.16-6.57) and neonatal intensive care unit (adjusted OR 3.73, 95%CI 1.24-11.16), and women on CSII had lower scores in patient-reported quality of life (p = .045). In the MDI group, those receiving long-acting insulin had nonsignificant lower HbA1c and higher HbA1c on-target rate in the second and third trimesters, compared with those treated with NPH. CONCLUSIONS: Insulin pump users may achieve better glycemic control than multiple daily insulin injections, which did not substantially improve pregnancy outcome.

Association between perioperative glucose profiles assessed by the continuous glucose monitoring (CGM) system and prognosis in patients with ST-segment elevation myocardial infarction (STEMI): protocol for a cohort study.

INTRODUCTION: ST-segment elevation myocardial infarction (STEMI) presents a serious cardiovascular condition requiring prompt intervention. Dysglycaemia has been identified as a significant risk factor impacting STEMI prognosis. However, limited research has focused on comprehensively examining the association between glucose dynamics during the perioperative period and patient outcomes. This study aims to address this gap by leveraging continuous glucose monitoring (CGM) technology to gain real-time insights into glucose fluctuations and their potential impact on STEMI prognosis. METHODS AND ANALYSIS: This is a multicentre, prospective, 3-year follow-up cohort study. Between May 2023 and May 2024, 550 eligible STEM patients who underwent percutaneous coronary intervention are expected to be recruited. Using the CGM system, continuous glucose levels will be collected throughout the perioperative phase. Key clinical parameters, including cardiac biomarkers, angiographic findings and major adverse cardiovascular events, will be assessed in relation to glucose profile. ETHICS AND DISSEMINATION: The study was approved by the Medical Research Ethics Committee of The First Affiliated Hospital of University of Science and Technology of China and will be conducted in accordance with the moral, ethical and scientific principles of the Declaration of Helsinki. Written informed consent will be obtained from all participants before any study-related procedures are implemented. Study results will be disseminated through conferences and peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: ChiCTR2300069662.

TRIM56 protects against nonalcoholic fatty liver disease by promoting the degradation of fatty acid synthase.

Nonalcoholic fatty liver disease (NAFLD) encompasses a disease continuum from simple steatosis to nonalcoholic steatohepatitis (NASH). However, there are currently no approved pharmacotherapies for NAFLD, although several drugs are in advanced stages of clinical development. Because of the complex pathophysiology and heterogeneity of NAFLD, the identification of potential therapeutic targets is clinically important. Here, we demonstrated that tripartite motif 56 (TRIM56) protein abundance was markedly downregulated in the livers of individuals with NAFLD and of mice fed a high-fat diet. Hepatocyte-specific ablation of TRIM56 exacerbated the progression of NAFLD, while hepatic TRIM56 overexpression suppressed it. Integrative analyses of interactome and transcriptome profiling revealed a pivotal role of TRIM56 in lipid metabolism and identified the lipogenesis factor fatty acid synthase (FASN) as a direct binding partner of TRIM56. TRIM56 directly interacted with FASN and triggered its K48-linked ubiquitination-dependent degradation. Finally, using artificial intelligence-based virtual screening, we discovered an orally bioavailable small-molecule inhibitor of FASN (named FASstatin) that potentiates TRIM56-mediated FASN ubiquitination. Therapeutic administration of FASstatin improved NAFLD and NASH pathologies in mice with an optimal safety, tolerability, and pharmacokinetics profile. Our findings provide proof of concept that targeting the TRIM56/FASN axis in hepatocytes may offer potential therapeutic avenues to treat NAFLD.

Nanotechnology translation in vascular diseases: From design to the bench.

Atherosclerosis is a systemic pathophysiological condition contributing to the development of majority of polyvascular diseases. Nanomedicine is a novel and rapidly developing science. Due to their small size, nanoparticles are freely transported in vasculature, and have been widely employed as tools in analytical imaging techniques. Furthermore, the application of nanoparticles also allows target intervention, such as drug delivery and tissue engineering regenerative methods, in the management of major vascular diseases. Therefore, by summarizing the physical and chemical characteristics of common nanoparticles used in diagnosis and treatment of vascular diseases, we discuss the details of these applications from cellular, molecular, and in vivo perspectives in this review. Furthermore, we also summarize the status and challenges of the application of nanoparticles in clinical translation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Cardiovascular Disease Implantable Materials and Surgical Technologies > Nanomaterials and Implants Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Preconception episodes of severe hypoglycemia and risk of adverse pregnancy outcomes in pregnant women with type 1 diabetes mellitus.

AIMS: To explore the relationship between preconception severe hypoglycemia (PSH) and pregnancy outcomes in pregnancies complicated with type 1 diabetes mellitus (T1DM). MATERIALS AND METHODS: In this multicenter prospective cohort study, women with pregestational T1DM were stratified by episodes of severe hypoglycemia within 1 year before conception: No PSH, sporadic PSH (1-6 times/year), and recurrent PSH (>6 times/year). We analysed the predictive ability of PSH for maternal and neonatal outcomes using log-binomial regression models and receiver operating characteristic (ROC) curve. RESULTS: Of the 124 women studied, 37.1% experienced at least one episode of severe hypoglycemia preconception. In the multiple adjusted regression models, recurrent PSH was significantly associated with increased incidence of preeclampsia (RR 17.59, 95% CI: 2.89-150.62, p for trend = 0.007), preterm birth (RR 6.34, 95% CI: 1.22-40.63, p for trend = 0.027), neonatal hypoglycemia (RR 4.52, 95% CI: 1.14-17.16, p for trend = 0.017), neonatal hyperbilirubinemia (RR 4.12, 95% CI: 1.11-15.56, p for trend = 0.004), and composite neonatal outcome (RR 3.85, 95% CI: 1.01-19.61, p for trend = 0.003). In the ROC analysis, PSH predicted preeclampsia, preterm birth, neonatal hypoglycemia, neonatal hyperbilirubinemia, and composite neonatal outcome with areas under the ROC curve all ≥0.6. CONCLUSIONS: Recurrent preconception severe hypoglycemia is associated with increased risks of adverse outcomes in pregnant women with T1DM.

Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.

INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophysiological mechanisms of T1D. OBJECTIVES: We aimed to explore the gut microbiota, serum metabolite, and serum lipid signatures in T1D patients by machine learning. METHODS: We evaluated 137 individuals in a cross-sectional cohort involving 38 T1D patients, 38 healthy controls, and 61 T1D patients for validation. We characterized gut microbiome, serum metabolite, and serum lipid profiles with machine learning approaches (logistic regression, support vector machine, Gaussian naive Bayes, and random forest). RESULTS: The machine learning approaches using the microbiota composition did not accurately diagnose T1D (model accuracy = 0.7555), while the accuracy of the model using the metabolite composition was 0.9333. Based on the metabolite composition, 3-hydroxybutyric acid and 9-oxo-ode (area under curve = 0.70 and 0.67, respectively, both increased in T1D) were meaningful overlap metabolites screened by multiple bioinformatics methods. We confirmed the biological relevance of the microbiome, metabolome, and lipidome features in the validation group. CONCLUSION: By using machine learning algorithms and multi-omics, we demonstrated that T1D patients are associated with altered microbiota, metabolite, and lipidomic signatures or functions.

A pregnancy-specific Glucose management indicator derived from continuous glucose monitoring in pregnant women with type 1 diabetes.

OBJECTIVE: Glucose management indicator (GMI) is a core metric derived from continuous glucose monitoring (CGM) and is widely used to evaluate glucose control in patients with diabetes. No study has explored the pregnancy-specific GMI. This study aimed to derive a best-fitting model to calculate GMI from mean blood glucose (MBG) obtained from CGM among pregnant women with type 1 diabetes mellitus (T1DM). METHODS: A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analysed in this study. Continuous glucose monitoring data were collected to calculate MBG, time-in-range (TIR), and glycaemic variability parameters. The relationships between the MBG and HbA1c during pregnancy and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM. RESULTS: The pregnant women had a mean age of 28.9 ± 3.8 years, with a diabetes duration of 8.8 ± 6.2 years and a mean body mass index (BMI) of 21.1 ± 2.5 kg/m2 . The HbA1c levels were 6.1 ± 1.0% and 6.4 ± 1.0% during pregnancy and at postpartum (p = 0.024). The MBG levels were lower during pregnancy than those at postpartum (6.5 ± 1.1 mmol/L vs. 7.1 ± 1.5 mmol/L, p = 0.008). After adjusting the confounders of haemoglobin (Hb), BMI, trimesters, disease duration, mean amplitude of glycaemic excursions and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* [Trimester] + 0.08 * [ BMI in kg/m2 ] + 0.01 * [Hb in g/mL] + 0.50 * [MBG in mmol/L]. CONCLUSIONS: We derived a pregnancy-specific GMI equation, which should be recommended for antenatal clinical care. CLINICAL TRIAL REGISTRY NUMBER: ChiCTR1900025955.

Relative contributions of fasting and postprandial glucose increments, glycemic variability, and non-glycemic factors to HbA1c in individuals with type 1 diabetes.

AIM: Evidence for contribution of basal and postprandial glucose increment, and glycemic variability to glycated hemoglobin (HbA1c) among adults with type 1 diabetes (T1D) is limited. This study aimed to capture glycemic fluctuation patterns and quantify contributions of these factors to HbA1c levels among adults with T1D. METHODS: HbA1c, continuous glucose monitoring (CGM), and diet diaries were collected and pooled from two clinical trials. Available data sets were divided into HbA1c quartiles: group 1 (≤6.7%), group 2 (6.7%-7.3%), group 3 (7.3%-7.8%), and group 4 (≥7.8%). Area under curve above 110 mg/dL (AUC>110mg/dL ) in 24-h profile was defined as overall hyperglycemia and stratified with postprandial hyperglycemia (PHG, AUC>110mg/dL in 3-h period after meals) and basal hyperglycemia (BHG, AUC>110mg/dL in remaining period). Linear regression analysis was used to estimate the proportion of variance in HbA1c explained by BHG, preprandial glucose, PHG, glycemic variability, and non-glycemic factors (age, body mass index, hemoglobin, and duration). RESULTS: A total of 169 550 glucose data in 2409 meals recorded from 102 patients (male/female, 34/68) were included. Age and duration were 35.2 ± 12.6 and 8.9 (2.9, 13.0) years, with 51.0% using pumps. Overall, BHG was four times higher than PHG (p all <.05) and between-group comparisons showed BHG exhibited a progressive increase (group 1 vs. 2, 3, 4, p = .053, .086, .006) with fasting contribution of 76.1%, 82.6%, 81.5%, and 84.3% from group 1 to 4. The increment was not significant among groups 2, 3, and 4 (p > .05). Factors included in analysis explained a total of 74% of the variance in HbA1c, in which BHG accounted for 32.1% of variance whereas PHG accounted for 24.4%. In group with HbA1c >7.3%, BHG accounted for a higher percentage with 33.8% of the variance in HbA1c. CONCLUSIONS: In our study, basal hyperglycemia better predicts overall glycemic control than postprandial hyperglycemia among adults with T1D. The relative contribution of basal hyperglycemia increased gradually with HbA1c increasing and predominant strategy for insulin titration among T1D is different among different levels of glycemic control.

Adaptive control for downhole nuclear magnetic resonance excitation.

Nuclear magnetic resonance (NMR) measurements are performed with the pulse sequence and acquisition parameters set by the operator, which cannot be adjusted in real time according to sample characteristics. In one acquisition cycle, usually thousands of high-power pulses are transmitted and thousands of echo points are acquired. The power consumption cause the RF amplifier to overheat, and large amounts of acquired data may be invalid. Therefore, the optimization of excitation and acquisition processes is necessary to improve measurement efficiency. We explore a scheme for the real-time measurement of the samples by adaptively regulating the pulse sequence, which adapts the variable TE pulse sequence as the reconnaissance mode. The appropriate pulse sequence and reasonable parameters (NE, TE) can be selected according to the relaxation characteristics of the samples.This adaptive control strategy has great significance in guiding both dynamic and static measurements, and it is especially suitable for occasions where low magnetic field gradients and diffusion terms can be ignored. We also design a test circuit for adaptive control, which has the function of automatic parameter adjustment. By adjusting parameters such as the number of refocusing pulses, echo spacing, etc., the effective measurement of the samples can be achieved in practice.

Effects of intermittently scanned continuous glucose monitoring in adult type 1 diabetes patients with suboptimal glycaemic control: A multi-centre randomized controlled trial.

AIMS: To investigate whether intermittently scanned continuous glucose monitoring without alarms (intermittently scanned CGM (isCGM)) improves glycaemic control over capillary blood glucose monitoring (BGM) among adult type 1 diabetes mellitus (T1DM) patients with suboptimal control. MATERIALS AND METHODS: Adults with T1DM and HbA1c between 7% and 10% were 1:1 randomized to use isCGM or BGM for 24 weeks. The primary outcome was the change in HbA1c levels after intervention. The secondary outcomes were the changes in sensor-derived metrics. RESULTS: A total of 104 adults with T1DM (34.2 ± 12.2 years; M/F, 38/66) were randomized to the isCGM group (n = 54) and the BGM group (n = 50). After 24 weeks, HbA1c significantly decreased in the isCGM group (8.1 ± 0.7% to 7.5 ± 1.0%) and the BGM group (8.0 ± 0.8% to 7.7 ± 1.0%) with between-group differences of 0.3% (95% coefficient intervals, 0.0%-0.6%; P = 0.04). The percentage of HbA1c reduction over 1.0% and 1.5% was significantly higher in the isCGM group with adjusted odds ratios of 2.5 (95% CI: 1.1-5.5; P = 0.03) and 3.2 (95% CI: 1.1-9.0; P = 0.03). Mean time-in-range 70-180 mg/dl (TIR) in the isCGM group significantly increased (from 58.5 ± 13.0% to 63.0 ± 12.6%), whereas mean TIR was similar in the BGM group (from 58.0 ± 14.6% to 57.5 ± 14.5%). Time spent in hyperglycemia reduced more in the isCGM group and time spent in hypoglycemia did not change significantly in both groups. CONCLUSIONS: Among adult T1DM patients with suboptimal glycaemic control, compared with BGM, isCGM use resulted in a statistically significant improvement in glycaemic control after 24-week intervention. TRIAL REGISTRATION: Clinicaltrials.gov Identifier (NCT03522870).

A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning.

Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The inversion of the measured relaxation data is an ill-posed problem and may lead to deviations of inversion results, which may degrade the accuracy of further data analysis and evaluation. This paper proposes a deep learning method for multi-exponential inversion of NMR relaxation data to improve accuracy. Simulated NMR data are first constructed using a priori knowledge based on the signal parameters and Gaussian distribution. These data are then used to train the neural network designed to consider noise characteristics, signal decay characteristics, signal energy variations, and non-negative features of the T2 spectra. With the validation from simulated data, the models introduced by multi-scale convolutional neural network (CNN) and attention mechanism outperform other approaches in terms of denoising and T2 inversion. Finally, NMR measurements of rock cores are used to compare the effectiveness of the attention multi-scale convolutional neural network (ATT-CNN) model in practical applications. The results demonstrate that the proposed method based on deep learning has better performance than the regularization method.

Deterioration in glycemic control on schooldays among children and adolescents with type 1 diabetes: A continuous glucose monitoring-based study.

Background: To investigate the effect of school life by comparing the glycemic control between holidays and schooldays in children and adolescents with type 1 diabetes (T1D). Methods: This observational study enrolled school-aged students with T1D (aged 6-19) from September 2019 to July 2021. Continuous glucose monitoring (CGM) records were processed and divided into holidays and schooldays. Other information was collected via questionnaires. We compared the results using paired T-test, Wilcoxon paired test and logistic regression analysis. Results: 78 paticipants were included (40 boys, mean age 9.95 years). A total of 142,945 h of CGM data were analyzed. Overall, TIR (3.9-7.8 mmol/L) during holidays was better than schooldays [56.97 (SD 15.03) vs. 55.87 (15.06), %, p = 0.039]. On nocturnal (0-6 am) glycemic fluctuation, TIR was longer in children aged 6-10 [60.54 (17.40) vs. 56.98 (SD 16.32), %, p = 0.012] during holiday and TAR (7.8 mmol/L) was shorter [31.54 (17.54) vs. 35.54 (16.95), %, p = 0.013], compared with schooldays. In adolescents aged 10-19 years, TAR was also significantly shorter during holidays. Stratified analysis showed that girls, patients with longer duration, and insulin pump users had more pronounced worsening of nighttime glycemia on schooldays. Logistic regression analysis showed that girls had higher risk of worse nocturnal glycemic control [3.26, 95% CI: (1.17, 9.72), p = 0.027] and nocturnal hyperglycemia [OR = 2.95, 95% CI: (1.08, 8.56), p = 0.039], compared to boys. Conclusions: Children and adolescents with T1D were found to have worse glycemic control in nighttime during schooldays.

The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications.

Diabetic retinopathy is one of the most common complications of diabetes mellitus and the leading cause of low vision and blindness worldwide. Mounting evidence demonstrates that inflammation is a key mechanism driving diabetes-associated retinal disturbance, yet the pathophysiological process and molecular mechanisms of inflammation underlying diabetic retinopathy are not fully understood. Cytokines, chemokines, and adhesion molecules interact with each other to form a complex molecular network that propagates the inflammatory and pathological cascade of diabetic retinopathy. Therefore, it is important to understand and elucidate inflammation-related mechanisms behind diabetic retinopathy progression. Here, we review the current understanding of the pathology and pathogenesis of inflammation in diabetic retinopathy. In addition, we also summarize the relevant clinical trials to further suggest inflammation-targeted therapeutics for prevention and management of diabetic retinopathy.

High-risk genotypes for type 1 diabetes are associated with the imbalance of gut microbiome and serum metabolites.

Background: The profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes. Methods: We did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry. Results: Study individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways. Conclusions: The present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.

Intrapartum and early postpartum glycemic profiles in women with gestational diabetes mellitus: an observational study.

BACKGROUND: Data on the glycemic profile of pregnant women with gestational diabetes mellitus (GDM) during the perinatal period are sparse. This study described the intrapartum and early postpartum glucose profiles among pregnant women with GDM, and analyzed factors potentially affecting glycemic parameters during the period. METHODS: This was a prospective observational study conducted from March 2020 to November 2021. Pregnant women with GDM receiving lifestyle interventions alone during pregnancy and matched women with non-diabetic pregnancies (NDPs) were enrolled from among patients admitted to the obstetrics department for childbirth. Glucose monitoring was performed via a flash glucose monitoring (FGM) system on admission, and glucose readings during labor and early postpartum were analyzed. The clinical characteristics and FGM-based parameters of participants in the two groups were compared. RESULTS: A total of 124 participants (mean age: 29.5 ±â€Š3.5 years, 92 [74.2%] primipara) were included in the final analysis. A total of 17,571 glucose readings were retrieved. There were no significant differences in clinical characteristics between the GDM (n = 60) and NDP (n = 64) groups. The average glucose level was 92.2 mg/dL, and the level was higher in the GDM group (95.5 ±â€Š12.1 mg/dL vs. 89.1 ±â€Š13.4 mg/dL, P = 0.008) during the intrapartum and early postpartum periods. The data were split into the intrapartum period (from the start of labor to delivery of the placenta) and the early postpartum period (within 24 h after placental delivery) for analysis. During intrapartum, women with GDM exhibited glycemic profiles and fluctuations similar to those in the NDP group. However, women with GDM had higher postpartum glucose levels (97.7 ±â€Š13.4 mg/dL vs. 90.8 ±â€Š15.3 mg/dL, P = 0.009), a longer time spent >140 mg/dL (8.7 ±â€Š9.3% vs. 5.9 ±â€Š10.3%, P = 0.011), and greater glycemic fluctuations than those with NDP. Postpartum hyperglycemia in GDM might be associated with high parity and postprandial glucose abnormalities in GDM screening tests. CONCLUSION: Compared to those with normoglycemia, pregnant women with GDM receiving lifestyle interventions alone had similar intrapartum glucose profiles but higher early postpartum glucose levels and greater glucose variability, providing evidence for modification of the current perinatal glucose monitoring strategy for GDM. TRIAL REGISTRATION: ChiCTR.org.cn, ChiCTR2000030972.

Current practice, attitude and views of providing pregnancy care for women with type 1 diabetes in China: a qualitative study.

OBJECTIVE: Data are sparse on healthcare needs related to pregnancy among Chinese women with type 1 diabetes (T1D) or the gap between the needs and healthcare provision in China. We aimed to identify their needs and the gaps in pregnancy care provision. DESIGN: This is a qualitative, face-to-face, one-to-one in-depth interview study. We recruited our participants using a purposive sampling strategy. Semistructural outlines were used to guide the interviews. The interviews were digitally recorded, transcribed and analysed using a thematic framework method with NVivo V.10.0. SETTING: Guangdong Province in China. PARTICIPANTS: This study involved three key stakeholders of pregnancy care for women with T1D: 29 women with T1D of childbearing age (aged 18-50 years), 16 family members (husbands, parents and parents-in-law of women with T1D) and 35 relevant healthcare providers (HCPs). RESULTS: We found that women with T1D and the family members had a more pessimistic attitude towards pregnancy outcomes, which was different from the more positive view of HCPs. However, all three stakeholders shared the following perspectives regarding pregnancy-related care for women with T1D: (1) lack of knowledge and access to education, (2) lack of multidisciplinary cooperation, (3) education should be started earlier in adulthood, (3) positive role of peer support, and (4) hope for future training of HCPs for relevant knowledge and skills specified for T1D and pregnancy with T1D. CONCLUSIONS: An immense gap was identified between the needs of women with T1D regarding pregnancy-related care and current care provision in China. These findings suggest that education be provided to patients and HCPs, and the role of professional and multidisciplinary support should be enhanced to optimise pregnancy care for women with T1D in China.

Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.

BACKGROUND: The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce. METHODS: Serum samples were obtained from aged mice (18-month-old) and young mice (3-month-old). LC-MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging-related biomarkers. RESULTS: In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, q-value < 0.05, and Fold-Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D-glutamine and D-glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging-related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging-related indicators, including oleic acid, citric acid, D-glutamine, trypophol, and L-methionine. CONCLUSIONS: Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.