Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

BACKGROUND: This case report explores the long-term dynamics of insulin secretion and glycemic control in two patients with diabetes mellitus type 2 over 20 years. The observations underscore the impact of lifestyle interventions, including weight loss and calorie restriction, on insulin secretion patterns and glucose levels during 75 g oral glucose tolerance tests. Additionally, the role of hemoglobin A1c fluctuations, influenced by various factors such as body weight, exercise, and pharmacological interventions, is investigated.
METHODS: Case 1 involves a Japanese woman now in her late 70s who successfully maintained her hemoglobin A1c below 7% for over two decades through sustained weight loss and lifestyle changes. Despite a gradual decline in the homeostasis model assessment of β cell function, the patient exhibited remarkable preservation of insulin secretion patterns over the 20-year follow-up. In case 2, a Japanese woman, now in her early 70s, experienced an improvement in hemoglobin A1c to 6.3% after a period of calorie limitation due to a wrist fracture in 2018. This incident seemed to trigger a temporary rescue of pancreatic β cell function, emphasizing the dynamic nature of insulin secretion. Both cases highlight the potential for pancreatic β cell rescue and underscore the persistence of insulin secretion over the 20-year follow-up. Additionally, we have briefly discussed three additional cases with follow-ups ranging from 10 to 17 years, demonstrating similar trends in glucose and insulin ratios.
CONCLUSIONS: Long-term lifestyle interventions, such as weight loss and calorie restriction, can preserve pancreatic β cell function and maintain glycemic control in type 2 diabetes patients over 20 years. Two patients showed stable or improved insulin secretion and favorable hemoglobin A1c levels, challenging the traditional view of irreversible β cell decline. The findings highlight the importance of personalized, nonpharmacological approaches, suggesting that sustained lifestyle changes can significantly impact diabetes management and potentially rescue β cell function.

BACKGROUND: Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort.
METHODS: Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0-100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB).
RESULTS: The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a \'true\' target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%).
CONCLUSIONS: Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.

BACKGROUND: Diabetes disparities exist based on socioeconomic status, race, and ethnicity. The aim of this study is to compare two cohorts with diabetes from California and Florida to better elucidate how health outcomes are stratified within underserved communities according to state location, race, and ethnicity.
METHODS: Two cohorts were recruited for comparison from 20 Federally Qualified Health Centers as part of a larger ECHO Diabetes program. Participant-level data included surveys and HbA1c collection. Center-level data included Healthcare Effectiveness Data and Information Set metrics. Demographic characteristics were summarized overall and stratified by state (frequencies, percentages, means (95% CIs)). Generalized linear mixed models were used to compute and compare model-estimated rates and means.
RESULTS: Participant-level cohort: 582 adults with diabetes were recruited (33.0% type 1 diabetes (T1D), 67.0% type 2 diabetes (T2D)). Mean age was 51.1 years (95% CI 49.5, 52.6); 80.7% publicly insured or uninsured; 43.7% non-Hispanic white (NHW), 31.6% Hispanic, 7.9% non-Hispanic black (NHB) and 16.8% other. Center-level cohort: 32 796 adults with diabetes were represented (3.4% with T1D, 96.6% with T2D; 72.7% publicly insured or uninsured). Florida had higher rates of uninsured (p<0.0001), lower continuous glucose monitor (CGM) use (18.3% Florida; 35.9% California, p<0.0001), and pump use (10.2% Florida; 26.5% California, p<0.0001), and higher proportions of people with T1D/T2D>9% HbA1c (p<0.001). Risk was stratified within states with NHB participants having higher HbA1c (mean 9.5 (95% CI 8.9, 10.0) compared with NHW with a mean of 8.4 (95% CI 7.8, 9.0), p=0.0058), lower pump use (p=0.0426) and CGM use (p=0.0192). People who prefer to speak English were more likely to use a CGM (p=0.0386).
CONCLUSIONS: Characteristics of medically underserved communities with diabetes vary by state and by race and ethnicity. Florida\'s lack of Medicaid expansion could be a factor in worsened risks for vulnerable communities with diabetes.

Background: Identification of the unknown pathogenic factor driving atherosclerosis not only enhances the development of disease biomarkers but also facilitates the discovery of new therapeutic targets, thus contributing to the improved management of coronary artery disease (CAD). We aimed to identify causative protein biomarkers in CAD etiology based on proteomics and 2-sample Mendelian randomization (MR) design. Methods: Serum samples from 33 first-onset CAD patients and 31 non-CAD controls were collected and detected using protein array. Differentially expressed analyses were used to identify candidate proteins for causal inference. We used 2-sample MR to detect the causal associations between the candidate proteins and CAD. Network MR was performed to explore whether metabolic risk factors for CAD mediated the risk of identified protein. Vascular expression of candidate protein in situ was also detected. Results: Among the differentially expressed proteins identified utilizing proteomics, we found that circulating Golgi protein 73 (GP73) was causally associated with incident CAD and other atherosclerotic events sharing similar etiology. Network MR approach showed low-density lipoprotein cholesterol and glycated hemoglobin serve as mediators in the causal pathway, transmitting 42.1% and 8.7% effects from GP73 to CAD, respectively. Apart from the circulating form of GP73, both mouse model and human specimens imply that vascular GP73 expression was also upregulated in atherosclerotic lesions and concomitant with markers of macrophage and phenotypic switching of vascular smooth muscle cells (VSMCs). Conclusions: Our study supported GP73 as a biomarker and causative for CAD. GP73 may involve in CAD pathogenesis mainly via dyslipidemia and hyperglycemia, which may enrich the etiological information and suggest future research direction on CAD.

OBJECTIVE: Glycated hemoglobin (HbA1c) as a glycemic index may have limited value in pediatric patients with acute leukemia as they often present with anemia and/or pancytopenia. To address this issue, we evaluated the usefulness of glycated albumin (GA) as a glycemic monitoring index in pediatric patients with acute leukemia.
METHODS: Medical records of 25 patients with type 2 diabetes mellitus (T2DM), 63 patients with acute leukemia, and 115 healthy children from Seoul St. Mary\'s Hospital, The Catholic University of Korea, were retrospectively investigated for serum GA, HbA1c, and fasting blood glucose (FBG) levels, along with demographic data.
RESULTS: GA, HbA1c, and FBG levels did not differ between the control and acute leukemia groups. In the T2DM group, positive correlations were observed among GA, HbA1c, and FBG (P<0.01). Although GA level was not associated with the HbA1c level in the control group, GA and HbA1c levels showed a positive correlation in the acute leukemia group (P=0.045). Regression analysis revealed GA and HbA1c levels to be positively correlated in the acute leukemia and T2DM groups even after adjusting for age, sex, and body mass index z-score (P=0.007, P<0.01).
CONCLUSIONS: GA may be a useful complementary parameter to HbA1c for glycemic monitoring in pediatric patients with acute leukemia, similar to its use in patients with T2DM.

The burden of diabetes is rising in developing countries, and this is significantly linked to the increasing prevalence of poor glycemic control. The cost of glycated haemoglobin (HbA1c) testing is a barrier to timely glycemic assessments, but newer tests such as glycated albumin may be cheaper and tempting alternatives. Additional research must ascertain if glycated albumin (GA) can act as a viable supplement or alternative to conventional HbA1c measurements for glycemic control in diabetic individuals. GA as a biomarker is an emerging area of interest, particularly for those who display unreliable HbA1c levels or cannot afford the test. This study aims to investigate the prevalence of poor glycemic control in outpatient diabetic patients and the utility of glycated albumin in this population\'s monitoring of glycemic control. Method. A cross-sectional study of 203 diabetic patients will be conducted at the Dodoma Regional Referral Hospital and Benjamin Mkapa Hospital from August 1st, 2023, to August 31st, 2024. Patients diagnosed with diabetes mellitus for over six months will be screened for eligibility. Informed consent, history, clinical examination, and voluntary blood sample collection will be obtained from all eligible patients. Glycated Albumin levels will be obtained from the same blood samples collected. The glycemic status of all patients will be defined as per HbA1c, and a level of greater than 7% will considered as a poor control. The analysis will be computed with SPSS version 28.0, and a predictor variable, P<0.05, will be regarded as statistically significant, with the utility of GA determined by plotting the area under the ROC curve and the confusion matrix.

Background Iron deficiency anemia (IDA) and diabetes are prevalent health concerns, especially in regions like India. While previous studies have explored the relationship between glycated hemoglobin (HbA1c) levels and IDA, there is still inconsistency in the findings, particularly in the Indian population. Understanding this association is crucial for accurate diagnosis and management of both conditions. Materials and methods A case-control study was conducted at the Department of General Medicine at Acharya Vinoba Bhave Rural Hospital (AVBRH), Wardha, India, from May 2022 to October 2022. A total of 141 non-diabetic patients with IDA (study group) and 141 age- and gender-matched non-anemic controls were included. HbA1c levels were measured at baseline and after three months of IDA treatment. Statistical analysis was performed using SPSS software, including the Kolmogorov-Smirnov test, Chi-square test, Mann-Whitney test, and Pearson correlation coefficient. Results In the study group, HbA1c levels significantly increased from a mean of 4.63% at baseline to 5.82% after IDA treatment (p < 0.0001). However, there was no significant correlation between changes in hemoglobin (Hb) levels and HbA1c levels post-correction (r = 0.056, p = 0.510). In addition, all cases and controls were labeled non-diabetic based on a cutoff HbA1c level of 6%. After three months of IDA treatment, 80.85% of cases recovered from IDA. Conclusion The study highlights that HbA1c levels are lower in patients with IDA and may increase with the correction of IDA. However, there is no significant direct correlation between IDA correction and HbA1c increase. Therefore, when interpreting HbA1c levels, clinicians must consider the presence of IDA, especially in regions with high prevalence rates of both IDA and diabetes, like India. This understanding can improve management strategies for both conditions, ensuring better patient health outcomes.

UNASSIGNED: The aim of this research was to ascertain the correlations between alexithymia, social support, depression, and glycemic control in patients diagnosed with type 2 diabetes mellitus. Additionally, this study sought to delve into the potential mediating effects of social support and depression in the relationship between alexithymia and glycemic control.
UNASSIGNED: A purposive sampling methodology was employed to select a cohort of 318 patients afflicted with type 2 diabetes mellitus, hailing from a care establishment situated in Chengdu City. This investigation embraced a cross-sectional framework, wherein instruments such as the General Information Questionnaire, the Toronto Alexithymia Scale 20, the Social Support Rating Scale, and the Hamilton Depression Scale were judiciously administered. The primary objective of this endeavor was to unravel the interplay that exists amongst alexithymia, social support, depression, and glycemic control. The inquiry discerned these interrelationships through both univariate and correlational analyses, subsequently delving into a comprehensive exploration of the mediating ramifications engendered by social support and depression in the nexus between alexithymia and glycemic control.
UNASSIGNED: The HbA1c level of patients diagnosed with type 2 diabetes mellitus was recorded as (8.85 ± 2.107), and their current status with regards to alexithymia, social support, and depression were measured as (58.05 ± 4.382), (34.29 ± 4.420), and (7.17 ± 3.367), respectively. Significant correlations were found between HbA1c and alexithymia (R=0.392, P<0.01), social support (R=-0.338, P<0.01), and depression (R=0.509, P<0.01). Moreover, alexithymia correlation with social support (R=-0.357, P<0.01) and with depression (R=0.345, P<0.01). Regarding the mediation analysis, the direct effect of alexithymia on HbA1c was calculated to be 0.158, while the indirect effect through social support and depression were 0.086 and 0.149, respectively. The total effect value was determined to be 0.382, with the mediating effect accounting for 59.95%, and the direct effect accounting for 40.31%.
UNASSIGNED: Alexithymia exerts both direct and indirect adverse effects on glycemic control, thereby exacerbating disease outcomes. Hence, it is imperative to prioritize the mental health status of individuals with type 2 diabetes to enhance overall well-being, ameliorate diabetes-related outcomes, elevate patients\' quality of life, and alleviate the psychological distress and financial burden associated with the condition.

UNASSIGNED: The study aimed to know the changes in spirometry parameters in chronic smokers and evaluate how the presence of type 2 diabetes mellitus (T2DM) affects their lung function.
UNASSIGNED: A prospective, cross-sectional, observational study was done for 12 months at a tertiary care hospital in the western region of Maharashtra State in India. Two groups of patients and one group of healthy volunteers aged 18 years or more were studied (with 50 in each group, n = 150). Group A consisted of smokers with T2DM, Group B- smokers without T2DM and Group 3- healthy controls who were non-smokers and non-diabetic. Spirometry was done for the following parameters: forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), FEV1/FVC ratio, and peak expiratory flow (PEF) and results compared between the three groups.
UNASSIGNED: The mean age of the participants was 51.13 ± 10.74 years. There were 137 (91.3%) males and 13 (8.6%) females. Among the enrolled subjects, 66% had smoked for more than ten years. All spirometry parameters were significantly different across all three groups. When the spirometry parameters were compared between smokers with and without T2DM, all the parameters were significantly decreased (P < 0.05). FEV1 and FEV1/FVC were significantly decreased in T2DM patients with HbA1c >7%.
UNASSIGNED: The presence of T2DM in smokers significantly affects their pulmonary function tests. Uncontrolled T2DM (HbA1c >7%) can result in increased abnormality in the spirometry parameters studied. Thus, adequate glycemic control and cessation of smoking can be beneficial for the improvement of lung functions in smokers.