In this paper, we measured B cell function in elderly healthy individuals (EH) and in elderly patients with Type-2 Diabetes Mellitus (T2DM, ET2DM), which are treatment-naive, as compared to healthy young (YH) individuals. Results show a higher serum inflammatory status of elderly versus young individuals, and especially of ET2DM versus EH. This status is associated with a reduced response to the seasonal influenza vaccine and with increased frequencies of the circulating pro-inflammatory B cell subset called Double Negative (DN) B cells. B cells from ET2DM patients are not only more inflammatory but also hyper-metabolic as compared to those from EH controls. The results herein are to our knowledge the first to show that T2DM superimposed on aging further increases systemic and B cell intrinsic inflammation, as well as dysfunctional humoral immunity. Our findings confirm and extend our previously published findings showing that inflammatory B cells are metabolically supported.

UNASSIGNED: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model\'s outcomes with the validated American Diabetes Association\'s (ADA) risk assessment regarding predicting people with high risk for T2DM.
UNASSIGNED: Patients\' age, gender, and risk factors that were obtained from the National Health Information Center\'s dataset were used to build and train the ML model. To evaluate the developed ML model, an external validation study was conducted in three primary health care centers. A random sample (N = 3400) was selected from the non-diabetic individuals.
UNASSIGNED: The results showed the plotted data of sensitivity/100-specificity represented in the Receiver Operating Characteristic (ROC) curve with an AROC value of 0.803, 95% CI: 0.779-0.826.
UNASSIGNED: The current study reveals a new ML model proposed for population-level classification that can be an adequate tool for identifying those at high risk of T2DM or who already have T2DM but have not been diagnosed.

Nonketotic hyperglycemia hemichorea-hemiballismus syndrome (NHH) is an uncommon neurological condition linked to poorly managed diabetes mellitus (DM). It presents with spontaneous, erratic movements that impact just one side of the body. Our case of NHH was of a 76-year-old female with uncontrolled type 2 DM, ischemic heart disease, and dilated cardiomyopathy. Despite previous treatment for similar symptoms, the patient developed left-sided choreo-ballistic movements. Despite difficulties obtaining clear magnetic resonance imaging (MRI) due to involuntary movements, the image revealed T1 hyperintense signals in the right lentiform nucleus and subtle signals in the left lentiform nucleus and external capsule. Management included insulin, tetrabenazine, haloperidol, lorazepam, and other adjunctive therapies, resulting in symptom resolution by the fourth day. This case underscores the importance of considering NHH in patients with uncontrolled DM presenting with abnormal movements, highlighting the challenges in imaging due to involuntary movements and emphasizing the need for aggressive glycemic control and treatment strategies.

BACKGROUND: Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems.
METHODS: This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient\'s phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface.
RESULTS: Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively).
CONCLUSIONS: Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.

UNASSIGNED: To explore the effect of Gegen Qinlian Decoction (GQD) combined with dietary management in the treatment of patients with Type-2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) (T2DM MetS).
UNASSIGNED: This is a retrospective analysis of 102 cases of T2DM in the Brain Hospital of Hunan Province, China from April 2020 to February 2023. Of them, 49 patients received conventional drug treatment (control group), and 53 patients received GQD combined with dietary management on the basis of conventional drugs (observation group). Treatment efficacy was calculated, and blood glucose levels before and after the treatment, blood lipid-related indicators, tumor necrosis factor-α (TNF-α) and adiponectin (ADP) levels, and incidence of adverse reactions were compared between the two groups.
UNASSIGNED: The total efficacy of the observation group (92.45%) was significantly higher than that of the control group (75.51%) (P<0.05). After the treatment, blood glucose and lipid indicators in both groups were significantly improved compared to pretreatment levels, and were significantly better in the observation group than in the control group (P<0.05). After the treatment, TNF-α levels in both groups decreased compared to before the treatment, and were significantly lower in the observation group compared to the control group. Levels of ADP after the treatment increased, and were significantly higher in the observation group compared to the control group (P<0.05).
UNASSIGNED: When taken as an adjunct to the conventional drug regimen, GQD combined with dietary management can effectively regulate blood glucose and lipid metabolism in patients with T2DM and MetS (T2DM MetS), improve TNF-α and ADP levels, and enhance disease treatment effectiveness.

BACKGROUND: Telehealth increases care accessibility to patients with type-2 diabetes mellitus but the duration of its implementation to sustain optimal glycaemic control remains unclear. This study aimed to assess the health outcomes of these patients using the Optimizing care of Patients via Telemedicine In Monitoring and aUgmenting their control of diabetes Mellitus (OPTIMUM) home tele-monitoring (HTM) system 6 months post-intervention, compared to standard care.
METHODS: An open-labelled randomized controlled trial involving 330 participants with type-2 diabetes mellitus, aged 26-65 years, and suboptimal glycaemic control (HbA1c = 7.5%-10%) was conducted. Intervention group received OPTIMUM HTM for 6 months followed by usual care for another 6 months, while control group received usual care for 12 months. OPTIMUM HTM includes in-app video-based tele-education, tele-monitoring of the blood pressure, capillary glucose and weight via Bluetooth devices and mobile applications, followed by algorithm-based tele-management by the OPTIMUM HTM team. Assessments using self-care inventory scale and medication adherence were administered for both groups at baseline, 6-month, and 12-month timepoints.
RESULTS: Complete data from 156 (intervention) and 159 (control) participants, with comparable demographic profiles, were analysed. Both groups showed a significant reduction in HbA1c from baseline (p < 0.001). From 6-month to 12-month time-points, the intervention group was twice as likely to maintain their HbA1c ≤ 8% (adjusted odds ratio = 2.02, 95%CI = 1.18-3.49; p < 0.011). The intervention group demonstrated higher scores for self-care behaviours (adjusted odds ratio = 3.83 [95%CI = 1.68-5.97], p = 0.001) and not skipping medications (adjusted odds ratio = 2.32 [95%CI = 1.09-4.97], p = 0.030) at 12 months.
CONCLUSIONS: The OPTIMUM HTM system enabled patients to maintain their glycaemic control beyond the intervention period. The favourable outcomes could be the effect of telehealth in sustaining self-care behaviour and medication adherence.

BACKGROUND: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40 % of patients do not respond well to this therapy. This study aims to enhance treatment outcomes by using machine learning to predict individual response status to glucagon-like peptide-1 therapy.
METHODS: We analysed a type-2 diabetes mellitus dataset from the Diastrat cohort, recruited at the Northern Ireland Centre for Stratified Medicine. The dataset included individuals prescribed glucagon-like peptide-1 therapy, with response status determined by glycated haemoglobin levels of ≤53 mmol/mol. We identified genomic and proteomic markers and developed machine learning models to predict therapy response.
RESULTS: The study found 5 genomic variants and 45 proteomic markers that help differentiate glucagon-like peptide-1 therapy responders from non-responders, achieving 95 % prediction accuracy with a machine learning model.
CONCLUSIONS: This study demonstrates the potential of machine learning in predicting the response to glucagon-like peptide-1 therapy in individuals with type-2 diabetes mellitus. These findings suggest that integrating genomic and proteomic data can significantly enhance personalized treatment approaches, potentially improving outcomes for patients who might otherwise not respond well to glucagon-like peptide-1 therapy. Further research and validation in larger cohorts are necessary to confirm these results and translate them into clinical practice.

Here we have shown for the first time altered expression of the vascular smooth muscle (VSM) KATP channel subunits in segments of the human internal mammary artery (HIMA) in patients with type-2 diabetes mellitus (T2DM). Functional properties of vascular KATP channels in the presence of T2DM, and the interaction between its subunits and endogenous ligands known to relax this vessel, were tested using the potassium (K) channels opener, pinacidil. HIMA is the most commonly used vascular graft in cardiac surgery. Previously it was shown that pinacidil relaxes HIMA segments through interaction with KATP (SUR2B/Kir6.1) vascular channels, but it is unknown whether pinacidil sensitivity is changed in the presence of T2DM, considering diabetes-induced vascular complications commonly seen in patients undergoing coronary artery bypass graft surgery (CABG). KATP subunits were detected in HIMA segments using Western blot and immunohistochemistry analyses. An organ bath system was used to interrogate endothelium-independent vasorelaxation caused by pinacidil. In pharmacological experiments, pinacidil was able to relax HIMA from patients with T2DM, with sensitivity comparable to our previous results. All three KATP subunits (SUR2B, Kir6.1 and Kir6.2) were observed in HIMA from patients with and without T2DM. There were no differences in the expression of the SUR2B subunit. The expression of the Kir6.1 subunit was lower in HIMA from T2DM patients. In the same group, the expression of the Kir6.2 subunit was higher. Therefore, KATP channels might not be the only method of pinacidil-induced dilatation of T2DM HIMA. T2DM may decrease the level of Kir6.1, a dominant subunit in VSM of HIMA, altering the interaction between pinacidil and those channels.

UNASSIGNED: India is the second-largest country in the world with an estimated 77 million people living with diabetes. Persons with diabetes reported lower quality of life when compared to non-diabetes persons. There are significant associations between poor health-related quality of life (HRQoL) and adverse health outcomes among diabetes people. However, evidence documenting the same in India is scarce.
UNASSIGNED: To assess the health-related quality of life and its determinants among diabetic patients in rural Kerala, India.
UNASSIGNED: We conducted a community-based cross-sectional study among 425 adults type-2 diabetic patients from rural Kerala. The World Health Organization Quality of Life questionnaire was used to measure HRQoL. Multiple logistic regression analysis was used to study the association between HRQoL and independent variables.
UNASSIGNED: More than half of the respondents were men (52%), and 45% were older adults (≥60 years). Poor quality of life was reported by 38% of the respondents. Medium [Odds ratio (OR):4.70, 95%CI: 2.61-8.46] and low socio-economic status (SES) group (OR: 4.59, CI: 2.43-8.66) had poorer HRQoL compared to the higher SES group. Those with multi-morbidity (OR: 2.91, CI: 1.63-5.19), unemployed (OR: 2.54, CI: 1.46-4.42), and less educated (OR: 2.28, CI: 1.34-3.88) and older adults (OR: 2.11, CI: 1.28-3.45) were more likely to have poor HRQoL compared to their counterparts.
UNASSIGNED: More than one-third of the diabetes patients in rural Kerala reported poor HRQoL. The study also identified age, socio-economic status, education, and occupation as the important predictors of HRQoL among diabetes patients. The findings highlighted the need for assessing HRQoL as part of the routine management of diabetes care in similar settings.

This editorial synthesizes insights from a series of studies examining the interplay between metabolic and oxidative stress biomarkers in cardiovascular disease (CVD), focusing particularly on type-2 diabetes mellitus (T2DM) and acute coronary syndrome (ACS). The central piece of this synthesis is a study that investigates the balance between oxidative stress and antioxidant systems in the body through the analysis of serum bilirubin and γ-glutamyltranspeptidase (γ-GGT) levels in T2DM patients with ACS. This study highlights serum bilirubin as a protective antioxidant factor, while elevated γ-GGT levels indicate increased oxidative stress and correlate with major adverse cardiovascular events. Complementary to this, other research contributions reveal γ-GGT\'s role as a risk factor in ACS, its association with cardiovascular mortality in broader populations, and its link to metabolic syndrome, further elucidating the metabolic dysregulation in CVDs. The collective findings from these studies underscore the critical roles of γ-GGT and serum bilirubin in cardiovascular health, especially in the context of T2DM and ACS. By providing a balanced view of the body\'s oxidative and antioxidative mechanisms, these insights suggest potential pathways for targeted interventions and improved prognostic assessments in patients with T2DM and ACS. This synthesis not only corroborates the pivotal role of γ-GGT in cardiovascular pathology but also introduces the protective potential of antioxidants like bilirubin, illuminating the complex interplay between T2DM and heart disease. These studies collectively underscore the critical roles of serum bilirubin and γ-GGT as biomarkers in cardiovascular health, particularly in T2DM and ACS contexts, offering insights into the body\'s oxidative and antioxidative mechanisms. This synthesis of research supports the potential of these biomarkers in guiding therapeutic strategies and improving prognostic assessments for patients with T2DM and some CVD.