Type 2 Diabetes Research Articles

Amino acids and CART distinguish A-β+ Ketosis-Prone Diabetes from type 1 and type 2 diabetes during hyperglycemic crises

Abstract Context When clinically stable, patients with A-β+ Ketosis-Prone Diabetes (KPD) manifest unique markers of amino acid metabolism. Biomarkers differentiating KPD from type 1 (T1D) and type 2 diabetes (T2D) during hyperglycemic crises would accelerate diagnosis and management. Objective Compare serum metabolomics of KPD, T1D and T2D patients during hyperglycemic crises, and utilize Classification and Regression Tree (CART) modeling to distinguish these forms of diabetes. Setting Urban hospital emergency center. Participants Adults with KPD, T1D and T2D during hyperglycemic crises (with or without diabetic ketoacidosis (DKA), and healthy controls. Interventions Comparisons of serum metabolite and hormonal profiles, and CART analysis. Main Outcome Measures Group differences in concentrations of amino acids, their metabolites and relationship to glucose counterregulatory hormones; C-peptide cutoffs and analytes to distinguish KPD, T1D and T2D. Results Concentrations of most amino acids were similar in KPD and T1D and lower compared to T2D (P<0.05). Glucagon and cortisol concentrations were correlated with 3-methylhistidine and blood urea nitrogen in KPD but not in T1D. A C-peptide cutoff of 0.496 ng/mL differentiated T1D from KPD during DKA. CART revealed that a regression model based on the concentrations of β-hydroxybutyrate, C-peptide, glucagon, alpha-keto-β-methylvalerate, cystine and myristoyl-L-carnitine distinguished KPD from T1D and T2D. Conclusions During DKA, KPD and T1D patients have similarly altered amino acid profiles that differentiate them from T2D patients. Elevated protein catabolic hormones drive altered amino acid metabolism in KPD, rather than insulin deficiency as with T1D. A combination of 6 analytes differentiates KPD from T1D and T2D during hyperglycemic crises.

Multidimensional Sleep Health, Glycemic Control, and Self-Reported Outcomes in Type 1 Diabetes: A Cross-Sectional Study

Abstract Context Sleep health is multidimensional. While studies have shown associations between certain sleep dimensions and health in type 1 diabetes (T1D), global sleep health has rarely been considered. Objective To examine the associations between individual sleep dimensions and multidimensional sleep health (MSH) on glycemic control and self-reported outcomes in T1D. Methods Data from 116 adults with T1D participating in a sleep study (NCT04506151) were analyzed. Sleep satisfaction and alertness were assessed by questionnaires. Sleep timing, efficiency, duration, and regularity were derived from 7-day actigraphy. A composite MSH score was created by counting “healthy sleep” across these six measures. Glycemic control was assessed by 7-day continuous glucose monitoring and A1C. Self-reported outcomes were collected through questionnaires. Results After adjusting for covariates, greater sleep irregularity was associated with higher glycemic variability (b=5.048, p<0.01), less time in range (TIR) (b=-10.806, p<0.01), higher time above range (TAR) (b=7.40, p<0.05), and higher A1C (b=0.365, p<0.05)]. Poor sleep satisfaction was associated with higher diabetes distress and depression (b=0.29, p<0.05, b=3.59, p<0.05), respectively. Later sleep timing was associated with higher depression (b=1.545, p<0.05), while lower sleep efficiency was associated with higher depression (b=1.545, p<0.01). Worse MSH was significantly associated with lower TIR (b=2.376, p<0.05), higher TAR, A1C, and depression (b=-2.38, p<0.05; b=-0.177, p<0.01; b=-1.275, p<0.05, respectively). Conclusions Sleep irregularity likely drives the association between MSH and glycemic control, while poor sleep satisfaction, lower efficiency, and later timing contribute to the association between MSH and depression. These results highlight the importance of comprehensive sleep health evaluation in T1D.

Effect of Chrysin, a Flavonoid Present in Food, on the Skeletal System in Rats with Experimental Type 1 Diabetes

Background: It seems that some substances of plant origin may exert health-promoting activities in diabetes and its complications, including those concerning bones. Chrysin (5,7-dihydroxyflavone), present in honey, some plants, and food of plant origin, has been reported to exert, among others, antioxidative, anti-inflammatory and antidiabetic effects. The aim of this study was to investigate the effects of chrysin on the skeletal system of rats with experimental type 1 diabetes (T1D). Methods: The experiments were carried out on mature male Wistar rats. T1D was induced by a single streptozotocin injection. Administration of chrysin (50 or 100 mg/kg p.o., once daily) began two weeks later and lasted four weeks. Serum bone turnover markers, bone mass, density and mineralization, mechanical properties and histomorphometric parameters of cancellous and compact bone were examined. Results: T1D profoundly affected bone metabolism, leading to worsening of bone strength in comparison with the healthy controls. After administration of chrysin, slight improvement of only some parameters was demonstrated in relation to the diabetic controls. Conclusions: Results of the present study indicate that chrysin may exert some very limited favorable effects on the skeletal system in diabetic conditions.

A Randomized, Placebo-Controlled, Single-Center, Crossover Study to Evaluate the Effects of Pre-Meal Whey Protein Microgel on Post-Prandial Glucometabolic and Amino Acid Response in People with Type 2 Diabetes and Overweight or Obesity

Purpose: Whey protein (WP) consumption prior to a meal curbs appetite and reduces postprandial glucose (PPG) through stimulating endogenous GLP-1 secretion and insulin. Methods: We assessed the metabolic effects of a concentrated WP, using a new micelle-technology (WPM), in people with type 2 diabetes (T2D) and overweight or obesity (NCT04639726). In a randomized-crossover design, participants performed two 240 min lunch meal (622 kcal) tests 7 ± 4 days apart. After an overnight fast and a standardized breakfast, 10g (125 mL) WPM (40kcal) or placebo (125 mL water, 0 kcal) was consumed 15 min ahead of the mixed-nutrient meal. Effects on PPG (primary endpoint), insulin, GLP-1, and branched-chain amino acids (BCAAs) were evaluated with frequent blood sampling. Changes in incremental areas under the concentration curve (iAUC) were compared using a mixed model. Results: Twenty-six individuals (14 females, mean ± SD age 62.0 ± 8.3 years, HbA1c 58 ± 12 mmol/mol/7.5 ± 1.1%, BMI 29.2 ± 4.8 kg/m2) completed both tests. WPM significantly reduced PPG iAUC0–2h by 22% (p = 0.028), and iAUC0–3h numerically by −18% (p = 0.090) vs. placebo. WPM also increased insulin iAUC0–1h by 61% (p < 0.001), and iAUC0–3h by 30% (p = 0.004), respectively. Total GLP-1 iAUC0–2h was enhanced by 66% (p < 0.001). Postprandial plasma BCAA patterns were characterized by a rapid increase and larger iAUC0–2h (all p < 0.001) after WPM. No adverse events were ascribed to consuming WPM. Conclusion: A 125 mL pre-meal drink containing just 10g WPM before a mixed meal reduced PPG and increased insulin, GLP-1, and BCAAs. WPM may therefore serve as a metabolic modulator in people with T2D living with overweight or obesity.

Experiences and opinions of adults with type 1 diabetes on the android-based open-source closed-loop system in China: a qualitative study

ObjectiveAs an emerging technology, Android-based open-source closed-loop system also called Android Artificial Pancreas System (AAPS), has been increasingly validated by evidence for its effectiveness in improving glycaemic outcomes, positioning it...

The association between type 2 diabetes and asthma incidence: a longitudinal analysis considering genetic susceptibility

BackgroundThe prevalence of type 2 diabetes (T2D) and asthma is rising, yet evidence regarding the relationship between T2D and asthma, particularly in the context of genetic predispositions, remains limited.MethodsThis study utilized data from the UK Biobank longitudinal cohort, involving 388,775 participants. A polygenic risk score (PRS) for asthma was derived from genome-wide association studies summary. Cox regression models were used to assess the association between T2D and asthma, incorporating the asthma PRS.ResultsOver a median follow-up of 13.62 years, 10,211 asthma cases were documented. After adjusting for age, sex, current smoking status, and other confounding variables, T2D was significantly associated with an increased risk of developing asthma (Hazard Ratios [HR] 1.16, 95% confidence interval [CI] 1.06–1.26). This association remained significant after further adjustments for genetic susceptibility to asthma. Furthermore, T2D increased the risk of developing asthma across both high and low genetic risk groups.ConclusionsT2D is associated with an increased risk of developing asthma, irrespective of genetic susceptibility. These findings underscore the importance of incorporating glucose regulation strategies into asthma prevention efforts.

Associations between the Global Diet Quality Score and risk of type 2 diabetes: Tehran lipid and glucose study

Background Previous studies reported that focusing on healthy lifestyle, especially high diet quality is necessary for preventing type 2 diabetes (T2D). This study investigated the association between the innovative index, the Global Diet Quality Score (GDQS), and the risk of Type 2 Diabetes incidence. Methods In this secondary analysis, we included elective adult participants (n = 5948) from the third and fourth survey of the Tehran Lipid and Glucose Study. Participants checked out until the sixth phase with an average follow-up of 6.65 years. Expert nutritionists collected dietary data using a valid and reliable semi-quantitative food frequency questionnaire. The GDQS were calculated, including healthy and unhealthy food group scores. Biochemical and anthropometric characteristics were assessed during the first and follow-up surveys. Multivariable Cox proportional hazards regression models were used to estimate the progression of T2D in association with the GDQS. Results This study was implemented on 2,688 men and 3,260 women, respectively with the mean (SD) age of 41.5(14.1) and 39.3(13.02) years. A total of 524 subjects were found to have had T2D incidence. The healthy component of GDQS was conversely associated with T2D incidence [HR: 1, 0.91 (0.84–0.98), 0.91 (0.84–0.98), 0.84 (0.77–0.92) P trend = <0.001] in an adjusted model. The unhealthy component of GDQS was conversely associated with T2D incidence in an adjusted model [HR: 1, 0.86 (0.80–0.92), 0.93 (0.86–1.01), 0.89 (0.81–0.98) P trend = 0.009]. Conclusion The results of this study suggested that higher adherence to the healthy component of GDQS and lower intake of the unhealthy component decreased the risk of T2D incidence.

Changes in 90-Day Time in Range Among Youth with Type 1 Diabetes Initiating Different Automated Insulin Delivery Systems

Abstract Objective Glycemic outcomes in youth with type 1 diabetes (T1D) in the United States using the two most common automated insulin delivery (AID) systems, Insulet Omnipod 5 (OP5) and Tandem Control IQ (CIQ), have not been compared. We performed the first head-to-head analysis of changes in glycemic metrics among youth initiating AID. Methods This single center, retrospective study included youth <21 years with T1D, who started OP5 or CIQ between 1/2020 and 12/2023, and had ≥70% CGM active time. 14-day baseline and 90-day CGM and AID data were obtained. A multiple linear regression model assessed for changes in 90-day time in range (TIR) according to AID system, adjusting for covariates. Sub-analyses were conducted according to baseline TIR categories. Results Among the 428 youth included, there were 214 (50%) in each AID group. OP5 users had a shorter T1D duration (1.6 vs 5.5 years, p<0.001) and were more likely to have transitioned from multiple daily injections (76.1% vs 20.1%, p<0.001). Baseline TIR was similar between groups (OP5 51.6% vs CIQ 53.1%, p=0.70). 90-day TIR increased in both groups (p<0.001), rising by 11.8%-points (95% CI[10.4,13.3]) in OP5 users and 9.8%-points (95% CI[8.3,11.2]) in CIQ users, without any significant between group differences (p=0.08). There were no between group differences in 90-day TIR according to categorical baseline TIR. Conclusions There are no clinically significant differences in 90-day TIR among youth with T1D initiating the two most commonly used AID systems. Patient preference and shared decision making should continue to guide the selection of AID systems.

Genetic Predisposition to Low-Density Lipoprotein Cholesterol and Incident Type 2 Diabetes.

Treatment to lower high levels of low-density lipoprotein cholesterol (LDL-C) reduces incident coronary artery disease (CAD) risk but modestly increases the risk for incident type 2 diabetes (T2D). The extent to which genetic factors across the cholesterol spectrum are associated with incident T2D is not well understood. To investigate the association of genetic predisposition to increased LDL-C levels with incident T2D risk. In this large prospective, population-based cohort study, UK Biobank participants who underwent whole-exome sequencing and genome-wide genotyping were included. Participants were separated into 7 groups with familial hypercholesterolemia (FH), predicted loss of function (pLOF) in APOB or PCSK9 variants, and LDL-C polygenic risk score (PRS) quintiles. Data were collected between 2006 and 2010, with a median follow-up of 13.7 (IQR, 12.9-14.5) years. Data were analyzed from March 1 to November 1, 2024. LDL-C level, LDL-C PRS, FH, or pLOF variant status. Cox proportional hazards regression models adjusted for age, sex, genotyping array, lipid-lowering medication use, and the first 10 genetic principal components were fitted to assess the association between LDL-C genetic factors and incident T2D and CAD risks. Among the 361 082 participants, mean (SD) age was 56.8 (8.0) years, 194 751 (53.9%) were female, and mean (SD) baseline LDL-C level was 138.0 (33.6) mg/dL. During the follow-up period, 22 619 (6.3%) participants developed incident T2D and 17 966 (5.0%) developed incident CAD. The hazard ratio for incident T2D was lowest in the FH group (0.65; 95% CI, 0.54-0.77), while the highest risk was in the pLOF group (1.48; 95% CI, 1.18-1.86). The association between LDL-C PRS and incident T2D was 0.72 (95% CI, 0.66-0.79) for very high LDL-C PRS, 0.87 (95% CI, 0.84-0.90) for high LDL-C PRS, 1.13 (95% CI, 1.09-1.17) for low LDL-C PRS, and 1.26 (95% CI, 1.15-1.38) for very low LDL-C PRS. CAD risk increased directly with the LDL-C PRS. In this cohort study, LDL-C and T2D risks were inversely associated across genetic mechanisms for LDL-C variation. Further elucidation of the mechanisms associating low LDL-C risk with increased risk of T2D is warranted.

Predicting rapid decline in kidney function among type 2 diabetes patients: a machine learning approach

Diabetic kidney disease (DKD) is one of the typical complications of type 2 diabetes (T2D), with approximately 10% of DKD patients experiencing a Rapid decline (RD) in kidney function. RD leads to an increased risk of poor outcomes such as the need for dialysis. Albuminuria is a known kidney damage biomarker for DKD, yet RD cases do not always show changes in albuminuria, and the exact mechanism of RD remains unclear. Previous studies focused on a limited number of laboratory tests, no comprehensive study targeting a wide range of laboratory tests has been done. We target to develop a model that predicts RD among T2D and points to key laboratory tests of interest in understanding RD from various laboratory tests. Our machine learning model predicts whether RD, as represented via eGFR, will happen within 1 year. Additionally, the model uses Recursive feature elimination with cross-validation (RFECV) to eliminate the features that do not contribute to the prediction. We trained and assessed the model using 1202 types of laboratory tests from 3438 diabetes patients at the University of Tokyo Hospital. The means (95% confidence interval) of the receiver operating characteristic area under the curve (ROC-AUC), precision-recall area under the curve, accuracy rate, and F1-score of an 8-feature-model were 0.820 (0.811, 0.829), 0.430 (0.410, 0.451), 0.754 (0.747, 0.761), and 0.500 (0.485, 0.515), respectively. The RFECV revealed that 7 test types (MCH, γ-GTP, Cre, HbA1c, HDL-C, eGFR, and Hct) contributed to RD prediction. The model's ROC-AUC of 0.820 improves on the ROC-AUC of 0.775 seen in previous studies. The proposed model accurately predicts RD among diabetes patients and helps physicians focus on inhibiting progression of kidney damage. The contributing laboratory tests may serve as alternative biomarkers for DKD.

Research on the application of CRISPR technology in the treatment of type 1 diabetes (T1D)

Diabetes is a chronic disease with a rapid increase in incidence, which is a general problem around the world. Two main types of diabetes are included. Polygenic diabetes includes type 1 diabetes (T1D) and type 2 diabetes (T2D), and their monogenic forms are juvenile mature diabetes (MODY) and neonatal diabetes (NDM). CRISPR technology is a gene therapy technology with low cost but very high feasibility. At present, researchers have envisioned the use of CRISPR technology in the treatment of T1D, but this technology has a high off-target rate, immune rejection and no abundant clinical trials in the human body to verify this technology, resulting in the fact that this technology is not really used in clinical treatment. In the middle. This article mainly analyzes and summarizes the problems and solutions encountered by CRISPR-cas9 technology from basic theory to idealized models. This article provides new solutions for the treatment of T1D with CRISPR technology and also provides references for this technology. In addition, problems such as immune rejection in the human body have not been solved. Future research can focus on the solution to the problem of the suppression of cellular immune rejection after treatment and the impact of re-mutation.

The Association between Serum Polychlorinated Biphenyls and Incident Cardiovascular Disease among Type 2 Diabetes

Abstract Background Polychlorinated biphenyls (PCBs) were associated with cardiovascular disease (CVD) in the general population. However, it is unclear whether PCBs exposure increases the additional risk of CVD among type 2 diabetes (T2D) cases. This study aims to investigate the associations between serum concentrations of PCBs and incident CVD among T2D cases. Methods The study population was derived from Dongfeng-Tongji cohort in 2008 and followed up until December 31, 2018, with a total of 2,806 participants with type 2 diabetes included and 1180 of them developed CVD during the follow-up. Cox proportional hazard regression models and quantile g-computation method were conducted for the associations of serum PCB levels with incident CVD risk. Results Compared with the first quartile, the risk of incident CVD was increased by 25%, 30%, and 28% in the fourth quartile of serum concentrations of PCB28, PCB52, and PCB101, respectively. Similar results were obtained for lower-chlorinated PCBs (PCB28 + PCB52 + PCB101) with HR (95% CI) of 1.257 (1.063, 1.486), and 1.346 (1.139, 1.589) in the third and fourth quartiles, respectively (P trend = 0.001). Quantile g-computation indicated that mixed exposure to PCBs increased the risk of CVD and the top three weights of PCB congeners were PCB101, PCB52, and PCB28. Conclusions Serum PCB independently increased the risk of incident CVD among T2D cases, in which lower-chlorinated PCBs played a dominant role.

Circulating Cell-Free DNA in Metabolic Diseases

Abstract Metabolic diseases affect a consistent part of the human population, leading to rising mortality rates. This raises the need for diagnostic tools to monitor the progress of these diseases. Lately, circulating cell-free DNA (cfDNA) has emerged as promising biomarker for various metabolic diseases, including obesity, type 2 diabetes (T2D), and metabolic associated fatty liver disease (MAFLD). CfDNA is released from apoptotic and necrotic cells into the bloodstream and other body fluids, and it retains various molecular signatures of its tissue of origin. Thus, cfDNA load and composition can reflect tissue pathologies and systemic metabolic dysfunctions. In addition to its potential as diagnostic biomarker, interest in cfDNA derives from its recently discovered role in adipose tissue inflammation in obesity. This review discusses detection methods and clinical significance of cfDNA in metabolic diseases.

LCN2 blockade mitigating metabolic dysregulation and redefining appetite control in type 2 diabetes

LCN2 has an osteokine important for appetite regulation; in type 2 diabetes (T2D) it is not known whether appetite regulation mediated by LCN2 in the brain is altered. In this work, we focus on exploring the role of blocking LCN2 in metabolic health and appetite regulation within the central nervous system of mice with T2D. Material and methods: 4-week-old male C57BL/6 mice were used, divided into four experimental groups: intact, T2D, TD2/anti-LCN2, and T2D/IgG as isotype control. T2D was induced by low doses of streptozotocin and a high-carbohydrate diet. LCN2 blockade was performed by intraperitoneal administration of a polyclonal anti-LCN2 antibody. We analyzed metabolic parameters, food intake, feeding patterns, and serum LCN2 and leptin concentrations. In another group of intact or T2D mice, we analyzed the effect of blocking LCN2 and recombinant LCN2 on food consumption in a fasting-refeeding test and, the expression of cFOS and LCN2 in brain sections, specifically in the hypothalamus, piriform cortex, visceral area, arcuate nucleus and caudate-putamen. Results: T2D caused an increase in serum LCN2, without alterations in Ad libitum feeding, but with changes in the feeding pattern associated with alterations in LCN2-cFOS signalling in hypothalamic and non-hypothalamic brain regions. Blocking LCN2 improved metabolic parameters, increased Ad libitum feeding, and restored the feeding pattern after fasting, which is associated with enhanced LCN2 signalling in the brain. Conclusions: Blocking LCN2 restores metabolic health and normalizes the pattern of food consumption by normalizing LCN2 signalling in different brain regions.Graphical

Characterizing Circulating microRNA Signatures of Type 2 Diabetes Subtypes

Type 2 diabetes (T2D) is a heterogeneous disease influenced by both genetic and environmental factors. Recent studies suggest that T2D subtypes may exhibit distinct gene expression profiles. In this study, we aimed to identify T2D cluster-specific miRNA expression signatures for the previously reported five clinical subtypes that characterize the underlying pathophysiology of long-standing T2D: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and mild early-onset diabetes (MEOD). We analyzed the circulating microRNAs (miRNAs) in 45 subjects representing the five T2D clusters and 7 non-T2D healthy controls by single-end small RNA sequencing. Bioinformatic analyses identified a total of 430 known circulating miRNAs and 13 previously unreported novel miRNAs. Of these, 71 were upregulated and 37 were downregulated in either controls or individual clusters. Each T2D subtype was associated with a specific dysregulated miRNA profile, distinct from that of healthy controls. Specifically, 3 upregulated miRNAs were unique to SIRD, 1 to MARD, 9 to MOD, and 18 to MEOD. Among the downregulated miRNAs, 11 were specific to SIRD, 9 to SIDD, 2 to MARD, and 1 to MEOD. Our study confirms the heterogeneity of T2D, represented by distinguishable subtypes both clinically and epigenetically and highlights the potential of miRNAs as markers for distinguishing the pathophysiology of T2D subtypes.

Characterization of Individuals Achieving Type 2 Diabetes Remission in Real-World Settings: Bridging Clinical Evidence and Patient Experiences.

The objectives of the study were to: 1) Describe characteristics and lifestyle factors of individuals who have achieved type 2 diabetes (T2D) remission (sub-diabetes glucose levels without glucose-lowering medications for ≥3 months) through changes to diet and exercise behaviour in real-world settings; 2) Investigate continuous glucose monitoring (CGM) profiles of these individuals and explore how dietary pattern may influence glucose regulation metrics. This cross-sectional study recruited individuals living with T2D who achieved remission via changes to diet or exercise behaviours. Various questionnaires were used to assess overall health and participants wore a blinded CGM for 14 days to assess glucose profiles and filled out three-day food records. A total of 21 adults (57 ± 8 years of age) who were recently diagnosed with T2D (4±3 years) with a A1c of 5.7±0.4% volunteered to participate. Participants achieved remission through various means (e.g., combination of diet and exercise/physical activity) and self-reported following different diets, including 52% following a low-carbohydrate or very low-carbohydrate diet, 14% following a "ketovore/carnivore" diet, 10% using a meal replacement diet, 5% following Weight Watcher's diet, and 19% no defined dietary pattern. The 24-hour average CGM glucose value was 5.0 [4.8-5.6] mmol/L (median [IQR]) with 92 [85-97]% of time spent in range (between 4.0-9.9 mmol/). 24-hour average CGM glucose (r=0.692; P=0.001), as well as A1c (r=0.470; P=0.049), were correlated with the daily percentage of energy intake from carbohydrate. Remission of T2D appears achievable through various means, including adoption of different dietary approaches and a more active lifestyle underpinning the importance of a patient-centred care.

Insights into the molecular underpinning of type 2 diabetes complications.

Type 2 diabetes (T2D) complications pose a significant global health challenge. Omics technologies have been employed to investigate these complications and identify the biological pathways involved. In this review, we focus on four major T2D complications: diabetic kidney disease, diabetic retinopathy, diabetic neuropathy, and cardiovascular complications. We discuss advancements in omics research, summarizing findings from genetic, epigenomic, transcriptomic, proteomic, and metabolomic studies across different ancestries and disease-relevant tissues. We stress the importance of integrating multi-omics techniques to elucidate the biological mechanisms underlying T2D complications and advocate for ancestrally diverse studies. Ultimately, these insights will improve risk prediction for T2D complications and inform translation strategies.

Citrobacter rodentium promotes brain cognitive dysfunction of type 2 diabetes mice by activating FXR mediated gut barrier damage.

Type 2 diabetes (T2D) is an important risk factor for brain cognitive impairment, but the specific mechanism is still unclear. The imbalance of gut microbiota under pathological conditions (such as an increase in pathogenic bacteria) may be involved in the occurrence of various diseases. The purpose of this study is to investigate the effect of increased abundance of gut Citrobacter rodentium on cognitive function in T2D mice. Our results indicate that an increase in the abundance of Citrobacter rodentium leads to impaired intestinal barrier, elevated expression of inflammatory factors in blood and brain tissue, and promotes cognitive impairment in T2D mice. The specific pathway involves activation of farnesol X receptor (FXR) expression-mediated intestinal barrier dysfunction. The use of intestinal mucosal protectants and FXR inhibitors improved intestinal barrier function and brain cognitive function. Therefore, the research results provide a mechanistic link between the increased abundance of Citrobacter in the gut of T2D mice and brain cognitive function, and provide a reference for the occurrence of brain cognitive dysfunction in T2D.

Budget impact analysis of insulin glargine vs. isophane protamine insulin treatment for patients with type-2 diabetes and severe hypoglycemia in Thailand

ObjectiveThis study aimed to assess the financial impact of different adoption rates of insulin glargine (IGlar) treatment compared to isophane protamine (neutral protamine hagedorn [NPH]) insulin treatment for patients with type-2 diabetes (T2D) and severe hypoglycemia in Thailand from the payer’s perspective.MethodsThe budget impact analysis (BIA) model over a period of 5 years was used to estimate the net budget impact (NBI) of IGlar treatment by comparing the total budget expenditures under two scenarios: scenario 1 involved only NPH insulin and scenario 2 included the introduction of IGlar. The total budget included either the cost of insulin or a combination of the costs of insulin and the expense related to severe hypoglycemia. Scenario 2 started at 20% uptake of IGlar and a yearly increase of 20%. NBI was calculated as the difference between the total budgets of scenarios 1 and 2. NBI and one-way sensitivity analyses were conducted for evaluation.ResultsConsidering only the cost of insulin, the use of IGlar for patients with T2D and severe hypoglycemia resulted in a yearly average NBI of 174.9 million Thai baht (THB) (5.1 million USD). However, when the cost related to severe hypoglycemia was included, the total budget incurred from scenario 2 was less than that of scenario 1, leading to a negative NBI or cost savings.ConclusionThe NBI of IGlar adoption would be substantial when considering only the cost of insulin; however, the significant benefit of IGlar in terms of a lower rate of severe hypoglycemia compared with NPH insulin would clearly offset the additional cost of IGlar.

A comparative analysis of current С-peptide assays compared to a reference method: can we overcome inertia to standardization?

C-peptide is an equimolar by-product of insulin biosynthesis. It is used clinically to assess insulin secretion and differentiate types of diabetes. However, the lack of standardization across assays limits its broader application. This study aimed to examine discrepancies between the leading C-peptide measurement methods used in clinical laboratories and propose a solution to reduce them based on a complete traceability chain. Two sets of serum samples were distributed to 10 manufacturers of C-peptide assays. The first set (A, n=20) was analyzed independently by each manufacturer, who then returned their results to us. Subsequently, we sent out the second set (B, n=20) along with the reference values for set A. For set B, each manufacturer provided both non-calibrated and recalibrated values for each sample. The recalibration was performed according to each manufacturer's internal standard protocols. We assessed how recalibration affected agreement between methods and alignment with the reference method. Non-parametric statistical approaches, including Passing-Bablok regression, level of agreement, and standard deviation analysis, were applied to compare data from multiple perspectives. Despite most manufacturers using the same WHO C-peptide calibrator material, significant disagreement was observed between methods prior to recalibration. Recalibration with matrix-appropriate serum samples reduced the discordance among assays, bringing them closer to the reference method. Overall, recalibration reduced both systematic bias and individual assay disagreement. These findings underscore the importance of appropriate calibration schemes to improve agreement across C-peptide assays, enhancing the accuracy of C-peptide testing for clinical practice.