High-fiber diet reduces bone formation but does not affect bone microarchitecture in type 2 diabetes individuals.

Bone fragility is a recognized complication of type 2 diabetes mellitus (T2DM), increasing patient morbidity. Thus, the development of an effective intervention to prevent diabetic bone fragility is urgently needed. As lifestyle intervention represents an effective option for diabetes management, it may have an impact on bone health. While studies have shown a beneficial effect of dietary fiber in T2DM management, its effect on bone health is still unclear. Thus, we investigated the impact of a high-fiber diet on bone and glucose control in men and women with T2DM. Forty-five T2DM patients (HbA1c: 6.5% ± 0.49%, age: 74 ± 7.29 yr) scheduled for hip arthroplasty were randomly assigned to follow a high-fiber diet (38 g/day) or to make no diet changes for 12 wk. Interestingly, BMI decreased by 4% (p <.0001) and HbA1c by 3.4% (p <.0001) in the high-fiber diet group, but did not decrease in the control group. However, serum concentration of the bone formation marker procollagen type 1 amino-terminal propeptide (P1NP) decreased by 8.6 % in the high-fiber diet group (p =.0004), whereas it remained unchanged in the control group. In contrast, similar to the control group, serum concentration of the bone resorption marker C-terminal telopeptide of type I collagen (CTX) concentrations did not change in the high-fiber diet group. Bone microCT analysis revealed no changes in trabecular and cortical bone parameters between the high-fiber diet and control groups. Similarly, real-time (RT)-PCR analysis in bone tissue showed no changes in the gene expression of Wnt pathway-related genes (Sost, Dkk-1, Wnt10b, and Lef-1), bone formation markers (Runx2, Col1a1, and Ocn), and inflammatory cytokines (IL-6, IL-8, TNF-α, and IL-10) between the two groups. Our findings suggest that 12-wk high-fiber diet intervention improves metabolic outcomes in patients with T2DM. However, it may reduce bone formation without affecting bone microarchitecture or Wnt pathway regulation.

Individualized selection of recent glucose monitoring devices for self-management based on competitive features.

Objective: Goal of the study was to systematically review competitive advantages of medical devices for glucose monitoring in diabetic patients. Method: The review is done systematically according to SALSA criteria and PRISMA guidelines. The search for eligible articles was held from February 16th 2023 to March 1st 2023 in Russian and English languages. The results were synthesized narratively, tabularly, and visually. The search was conducted in the following databases of scientific literature: PubMed, IEEE Xplore, Google Scholar, CyberLeninka, and eLibrary. Results: Twenty-two out of fifty-two manuscripts met the inclusion criteria. The most promising and advantageous characteristics of the evaluated devices, as identified by researchers, include the following: the capability for noninvasive examination; features that facilitate use by patients with fine motor, hearing, and visual impairments; add-ons and software designed to improve patient compliance, including in pediatric populations; and device attributes that enhance the speed and accuracy of analysis while being free of iatrogenic effects. Conclusions: With increasing prevalence of diabetes, glycemic control is crucial for preventing complications. The market offers numerous glucose monitoring devices (GMDs) with varying features, making selection challenging. Our study systematically categorized the strengths of each GMD model for diabetic patients, aiding informed device selection.

Sleep quality and glycemic control in adults with type 2 diabetes mellitus.

Aim: Type 2 diabetes mellitus (T2DM) is a common disease that imposes a substantial burden on the healthcare system and patients. Lifestyle modification such as sleep hygiene plays a crucial role in glycemic control. Sleep disorders impact many aspects of health. In this study, we aimed to investigate the correlation between sleep quality and glycemic control in T2DM. Method: This cross-sectional study was performed on 163 T2DM patients, attending Shahid Beheshti Hospital in Hamadan, Iran from March 2020 to 2021. Besides recording the demographic data and HbA1c level of participants, they were asked the Pittsburgh Sleep Quality Index questionnaire for evaluating sleep quality. We employed SPSS ver. 21 for data analysis and considered 0.05 as a significant level. Results: Among all participants, 62 (38%) were female and 30.7% were illiterate. The mean age was 56.67 ± 12.90 years, and HbA1c was 9.03 ± 1.92 mg/dL. Among sleep metrics, mean waking time was 8.74 ± 1.74 hours, and average sleep time was 12.90 ± 4.90 hours. Overall, 58.2% of the participants had poor glycemic control and 44.8% were suffering from poor sleep quality. We found that patients with poor glycemic control exhibited significantly higher levels of sleep disturbances compared to those with good glycemic control (P < 0.001). Conclusion: Sleep quality is associated with glycemic control in patients with T2DM. Sleep disorders are common among diabetic patients. Thus, healthcare providers need to consider sleep quality improvement in their holistic approach to diabetes management.

The Portfolio Diet and HbA1c in Adults Living with Type 2 Diabetes Mellitus: A Patient-Level Pooled Analysis of Two Randomized Dietary Trials.

(1) Background: The Portfolio Diet, a dietary pattern of cholesterol-lowering foods, is also rich in low glycemic index (GI) foods. While strong evidence supports clinically meaningful reductions in cholesterol, evidence on the relationship between the Portfolio Diet and diabetes management is lacking. (2) Objective: To evaluate the relationship between the Portfolio Diet and glycated hemoglobin (HbA1c) as a determinant of glycemic control among adults living with type 2 diabetes mellitus (T2DM). (3) Methods: Patient-level data was pooled from two randomized dietary trials of low glycemic index interventions compared to high cereal fibre control diets in adults living with T2DM where HbA1c was collected (clinicaltrials.gov identifiers: NCT00438698, NCT00438698). Dietary exposure was assessed using weighed 7-day diet records. Adherence to the Portfolio Diet and its pillars (nuts and seeds, plant protein, viscous fibre, plant sterols, monounsaturated fatty acid [MUFA] oils) was determined using the validated clinical Portfolio Diet Score (c-PDS). Multiple linear regression was used to assess the association between change in the c-PDS and change in HbA1c over 6-months with covariate adjustments. (4) Results: A total of 267 participants, predominantly White (67%) and male (63%), were included, with a mean ± standard error age of 62 ± 0.5 years, baseline BMI of 30.2 ± 0.3 kg/m2, HbA1c of 7.08 ± 0.03%, and a c-PDS of 4.1 ± 0.3 points out of 25. Change in the c-PDS was significantly associated with a change in HbA1c (ß: -0.04% per point, 95% CI: -0.07, -0.02, p = 0.001). A 7.5-point (30%) increase in the c-PDS was associated with a 0.3% reduction in HbA1c. Of the individual pillars, a 1-point change in nut and seeds intake (ß: -0.07%, 95% CI: -0.12, -0.02, p = 0.009) or in plant protein intake (ß: -0.11%, 95% CI: -0.18, -0.03, p = 0.009) was associated with a change in HbA1c. Further analysis of plant protein intake revealed that an increase in dietary pulse intake, a particularly low-GI food, was significantly associated with a reduction in HbA1c (ß: -0.24% per 1-cup points cooked pulses (226 g) or 2 c-PDS points, 95% CI: -0.45, -0.03, p = 0.028). (5) Conclusions: Among adults living with T2DM, the Portfolio Diet was associated with lower HbA1c over a 6-month period, predominantly driven by two pillars: nuts and seeds and plant protein, particularly dietary pulses. These data have implications for including the Portfolio Diet in dietary recommendations for glycemic control in T2DM. A trial demonstrating the direct causal effect of the Portfolio Diet in a diverse group is warranted.

SARS-CoV-2 booster vaccination does not worsen glycemia in people with type 1 diabetes using insulin pumps: an observational study.

BACKGROUND: Despite an increased risk for adverse outcomes from SARS-CoV-2 infection among individuals with type 1 diabetes (T1D), vaccine hesitancy persists due to safety concerns including dysglycemia. The impact of booster vaccination on individuals using automated insulin delivery (AID) systems remains unclear. METHODS: We used continuous glucose monitoring (CGM) data from 53 individuals with T1D using insulin pump therapy who received their third and/or fourth COVID-19 vaccination. CGM data from the 14 days before and 3 and 7 days after each vaccination were compared. The primary outcome was glucose time in range (TIR) (70-180 mg/dL) 3 and 7 days post-vaccination compared with the 14 days prior. Secondary outcomes included other CGM metrics such as time below range (< 70 mg/dL), time above range (> 180 mg/dL), mean glucose, co-efficient of variation and average total daily insulin. RESULTS: The cohort comprised 53 adults (64% women, 64% AID), totaling 74 vaccination periods (84% Pfizer-BioNTech boosters), mean ± SD age 40.0 ± 15.9 years, duration of diabetes 26.0 ± 15.4 years. There was no significant difference between pre-vaccination TIR (61.0%±18.5) versus 3 (60.5%±22.8) and 7 days post-vaccination (60.2%±21.8; p = 0.79). Level 1 hypoglycemia, time in range 54-69 mg/dL, was lower 3 (1.1%±1.7) and 7 days post-vaccination (1.1%±1.6), compared with 14 days pre-vaccination (1.4%±1.4; p = 0.021). CONCLUSION: The study provides evidence that SARS-CoV-2 booster vaccination does not acutely worsen glycemia in people with T1D receiving insulin pump therapy.

Alpha-cyclodextrin increases glucagon-like peptide-1 secretion in multiple models and improves metabolic status in mice.

Alpha-cyclodextrin (α-CD) is a non-absorbable and soluble fiber that causes weight loss. We studied whether this is due to an effect on GLP-1 secretion. In GLUTag cells, α-CD increased GLP-1 secretion up to 170% via adenylyl cyclase, phospholipase C, and L-type calcium channels dependent processes. In rat isolated colon perfusions, luminal α-CD increased GLP-1 secretion with 20%. In lean mice, once daily α-CD versus saline caused weight loss and lowered the peak in glucose after an oral glucose tolerance test (OGTT). In obese mice, α-CD added to high-fat diet caused weight loss similar to the control group (receiving cellulose). However, compared to cellulose, the α-CD group ate less. During an OGTT, no differences were observed in glucose, insulin and GLP-1. Thus, α-CD increases GLP-1 secretion in a dose-dependent manner and could be a safe and easy addition to food products to help reduce body weight.

Effectiveness of Continuous Glucose Monitoring in Glucose Control and Quality of Life Among Type 1 Diabetic Patients in Madina City: A Cross-Sectional Study.

INTRODUCTION: Diabetes is a growing global health problem, affecting millions of people worldwide and in Saudi Arabia in particular. Continuous glucose monitoring (CGM) offers real-time glucose levels, alerts, and 24/7 coverage, making it an affordable treatment option. The study aimed to assess CGM's effect on diabetes control and quality of life among type 1 adult diabetic patients in Saudi Arabia. METHODS: This cross-sectional study enrolled Saudi adults diagnosed with type I diabetes and was conducted from 1 April 2024 to 30 May 2024 in Madinah City, Saudi Arabia. Data was collected from patients' medical records and the diabetes quality of life questionnaire (DQoL). RESULTS: This study enrolled 317 participants, mostly male (52.1%) and a mean age of 34.34±12.28 years. After three months, the HbA1c levels significantly decreased (p<0.001). Older participants reported lower overall quality of life and satisfaction with their level of well-being when using CGM. Univariate analysis found that age had a significant negative association with the total score (B=-0.062, P=0.049) and satisfaction (B=-0.109, P=0.011. Marital status significantly affected the impact score (B=0.567, P=0.024). Education level notably affected satisfaction (B=-0.906, P=0.008) and monthly income influenced satisfaction (B=-1.25, P=0.033). However, multivariate analysis showed that age, education level, and monthly income did not significantly (p>0.005) affect the CGM impact, quality of life, and satisfaction. CONCLUSION: These findings indicate that CGM significantly improved diabetes control, while improved quality of life was not significant. The impact, quality of life, and satisfaction were influenced by age, marital status, education, and income level, though not statistically significant independent predictors. Therefore, we recommend longitudinal studies, controlling for confounders.

From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data.

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

Glycated haemoglobin is a major predictor of disease severity in patients with NAFLD.

OBJECTIVES: Currently, non-invasive scoring systems to stage the severity of non-alcoholic fatty liver disease (NAFLD) do not consider markers of glucose control (glycated haemoglobin, HbA1c); this study aimed to define the relationship between HbA1c and NAFLD severity in patients with and without type 2 diabetes. RESEARCH DESIGN AND METHODS: Data were obtained from 857 patients with liver biopsy staged NAFLD. Generalized-linear models and binomial regression analysis were used to define the relationships between histological NAFLD severity, age, HbA1c, and BMI. Paired biopsies from interventional studies (n = 421) were used to assess the impact of change in weight, HbA1c and active vs. placebo treatment on improvements in steatosis, non-alcoholic steatohepatitis (NASH), and fibrosis. RESULTS: In the discovery cohort (n = 687), risk of severe steatosis, NASH and advanced fibrosis correlated positively with HbA1c, after adjustment for obesity and age. These data were endorsed in a separate validation cohort (n = 170). Predictive modelling using HbA1c and age was non-inferior to the established non-invasive biomarker, Fib-4, and allowed the generation of HbA1c, age, and BMI adjusted risk charts to predict NAFLD severity. Following intervention, reduction in HbA1c was associated with improvements in steatosis and NASH after adjustment for weight change and treatment, whilst fibrosis change was only associated with weight change and treatment. CONCLUSIONS: HbA1c is highly informative in predicting NAFLD severity and contributes more than BMI. Assessments of HbA1c must be a fundamental part of the holistic assessment of patients with NAFLD and, alongside age, can be used to identify patients with highest risk of advanced disease.

The Impact of Dietary Habits on Sleep Deprivation and Glucose Control in School-Aged Children with Type 1 Diabetes: A Cross-Sectional Study.

Diet plays a crucial role in managing type 1 diabetes (T1DM). Background/Objectives:This study aimed to determine the impact of nutritional habits on sleep deprivation and glucose control in school-aged children with T1DM. Methods: In this cross-sectional study, nutritional habits and sleep deprivation were assessed in 100 school-aged children with T1DM, aged 7-13 years. The Dietary Habits Index and the Sleep Deprivation Scale for Children and Adolescents were used to evaluate nutritional habits and the level of sleep deprivation. Patients' sociodemographic and nutritional variables were collected through researcher-composed questionnaires. HbA1c levels over the past 6 months were obtained from the patient data system. Results: The study found a moderately strong positive correlation between the Dietary Habits Index score and HbA1c (p < 0.001), with 28% of the variation in HbA1c explained by changes in the Dietary Habits Index score. However, no correlation was found between the Dietary Habits Index score and the level of sleep deprivation. Conclusions: The nutritional habits of school-aged children with T1DM may affect glucose control and sleep deprivation. Therefore, it is important to educate children with T1DM on making healthy food choices to manage their condition effectively.

Is Renal Denervation Effective in Treating Resistant Hypertension?

Resistant hypertension is diagnosed in patients whose blood pressure target is unmet despite the use of three or more antihypertensive medications. Systemic sympathetic hyperactivation is associated with the development of resistant hypertension. As the kidney is largely pervasive of the sympathetic nervous system renal denervation procedure was developed to control blood pressure by attenuating the renal and systemic sympathetic hyperactivity. Renal denervation is a minimally invasive procedure that uses radiofrequency or ultrasound energy waves to reduce the activity of the renal artery nerves. Previous clinical trials have shown conflicting results regarding the efficacy of the procedure. Symplicity HTN-1 and -2 trials showed effective blood pressure lowering results in the renal denervation group with a good safety profile. However, the Symplicity HTN-3 trial showed no difference in blood pressure lowering effect between the renal denervation and control Sham procedure groups. Notwithstanding, some recent clinical trials with Sham control and meta-analysis showed clinical benefits of renal denervation. Other clinical benefits of renal denervation include glucose control, cardiovascular protective effect, reduction of obstructive sleep apnea, and neuralgia control. A subset of patients with satisfactory blood pressure control response to the procedure may experience improved glucose control due to the overall reduced sympathetic activity and insulin resistance. Sympathetic activity control after renal denervation has cardioprotective effects, especially for those with arrhythmia and left ventricular hypertrophy. Also, renal denervation could be helpful in renalorigin pain control. Renal denervation is an effective, safe, non-invasive procedure with many clinical benefits beyond blood pressure control. Further development in the procedure technique and selection of target patients are needed for wider clinical use of renal denervation in resistant hypertension.

Usefulness of an interprofessional work manual for perioperative glucose control of an artificial pancreas.

BACKGROUND: A closed-loop bedside-type artificial pancreas for perioperative glucose control has previously been introduced. However, artificial pancreas therapy was often interrupted due to continuous blood sampling failure. We developed an interprofessional work manual to reduce the interruption time of artificial pancreatic therapy for perioperative blood glucose control due to continuous blood sampling failure. This study aimed to investigate the usefulness of this manual. METHODS: The manual consisted of the following sections: (1) the roles of the professionals in the preparation and management of the artificial pancreas, (2) how to address continuous blood sampling failure, and (3) checkpoints for interprofessional transfer of the artificial pancreas. We compared the results before the introduction of the manual and 2 years after the introduction of the manual. RESULTS: There were 35 and 37 patients in the Before and After groups, respectively. There were no significant differences in patient backgrounds between the two groups, although there was significantly less blood loss in the After group (1164 vs. 366 mL; p < 0.001). The mean artificial pancreas therapy and artificial pancreas therapy interruption times were 847 min and 20 min, respectively. Artificial pancreas therapy interruption time (34 vs. 8 min; p = 0.078) and time per interruption (24 vs. 4 min; p < 0.001) were significantly shorter in the After group than in the Before group. CONCLUSIONS: The interprofessional working manual was useful in reducing the artificial pancreatic therapy interruption time for perioperative glucose control.

Effect on Diabetes Control in a Resident Clinic Following the Integration of a Clinic Pharmacist.

The impact of a pharmacist has been evaluated within the primary care setting but not within a resident-managed internal medicine clinic. This retrospective study found that the integration of a clinical pharmacist within a resident clinic improved the mean HbA1c of a high-risk patient group by 3% in 3 months and 2.6% in 6 months. None of the residents surveyed reported that the presence of a clinical pharmacist hindered their learning experience. The study also found the residents perceived the clinical pharmacist to be helpful with co-management of diabetes. This data supports the addition of a clinical pharmacist into a resident clinic and continues to support the benefits in the primary care setting.

Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development.

BACKGROUND: Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors. OBJECTIVE: We sought to develop a model that accurately predicts poor glycemic control among patients with T2D receiving usual care. METHODS: Our machine learning model predicts poor glycemic control (HbA1c≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7787 patients with T2D seeing specialist physicians at the University of Tokyo Hospital. The training data include instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate, to that of LightGBM. RESULTS: The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate (95% confidence limits) of the proposed model were 0.925 (95% CI 0.923-0.928), 0.864 (95% CI 0.852-0.875), and 0.864 (95% CI 0.86-0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM's performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control. CONCLUSIONS: The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model's indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.

Improving gestational diabetes care: Mobile glucose monitoring to reduce complications.

BACKGROUND: Gestational diabetes mellitus (GDM) incurs risks for both mother and baby and requires close medical attention throughout pregnancy. This retrospective study examined the impact of myDiabby® software on GDM care and complications. MATERIAL AND METHODS: The study population was divided between a pre-MyDiabby® group, with traditional monitoring before September 2017, and a myDiabby® group, using the myDiabby® app after September 2017. The aim was to compare the main complications of GDM and blood glucose control between the two groups, using Fisher's exact test or bilateral Student t-test as appropriate. Backward logistic regression was used to identify independent factors associated with glycemic control and caesarean section (C-section). RESULTS: There were 622 pre-myDiabby® and 649 myDiabby® patients. The myDiabby® group showed significantly lower risk of C-section (17.2% vs. 11.3%, P=0.004). After adjustment for pre-pregnancy body mass index (BMI), maternal age, prematurity, macrosomia and eclampsia, telemedicine was independently associated with a lower rate of C-section (OR=0.67, 95% CI: 0.51-0.89, P=0.005). Glycemic control improved (66.6% vs. 85.4%, P<0.001), with only a trend for need of insulin treatment. MyDiabby® remained associated with glycemic control (OR=3.15, 95% CI: 2.87-4.33, P<0.001) independently of pre-pregnancy BMI, insulin treatment or personal history of GDM. CONCLUSION: These findings highlight the potential benefits of using telemedicine tools in the management of GDM during pregnancy.

Bruton's tyrosine kinase (BTK) inhibitors alter blood glucose and insulin in obese mice but reduce inflammation independent of BTK.

Obesity is associated with metabolic inflammation, which can contribute to insulin resistance, higher blood glucose, and higher insulin indicative of prediabetes progression. The nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) inflammasome is a metabolic danger sensor implicated in metabolic inflammation. Many features of metabolic disease can activate the NLRP3 inflammasome; however, it is not yet clear which upstream triggers to target, and there are no clinically approved NLRP3 inflammasome inhibitors for metabolic disease. Bruton's tyrosine kinase (BTK) mediates activation of the NLRP3 inflammasome. Ibrutinib is the most-studied pharmacological inhibitor of BTK, and it can improve blood glucose control in obese mice. However, inhibitors of tyrosine kinases are permissive, and it is unknown if BTK inhibitors require BTK to alter endocrine control of metabolism or metabolic inflammation. We tested whether ibrutinib and acalabrutinib, a new generation BTK inhibitor with higher selectivity, require BTK to inhibit the NLRP3 inflammasome, metabolic inflammation, and blood glucose in obese mice. Chronic ibrutinib administration lowered fasting blood glucose and improved glycemia, whereas acalabrutinib increased fasting insulin levels and increased markers of insulin resistance in high-fat diet-fed CBA/J mice with intact Btk. These metabolic effects of BTK inhibitors were absent in CBA/CaHN-Btkxid/J mice with mutant Btk. However, ibrutinib and acalabrutinib reduced NF-κB activity, proinflammatory gene expression, and NLRP3 inflammasome activation in macrophages with and without functional BTK. These data highlight that the BTK inhibitors can have divergent effects on metabolism and separate effects on metabolic inflammation that can occur independently of actions on BTK.NEW & NOTEWORTHY Bruton's tyrosine kinase (BTK) is involved in immune function. It was thought that BTK inhibitors improve characteristics of obesity-related metabolic disease by lowering metabolic inflammation. However, tyrosine kinase inhibitors are permissive, and it was not known if different BTK inhibitors alter host metabolism or immunity through actions on BTK. We found that two BTK inhibitors had divergent effects on blood glucose and insulin via BTK, but inhibition of metabolic inflammation occurred independently of BTK in obese mice.

Assessing outcomes of intravenous insulin therapy in non-intensive care patients.

OBJECTIVES: The aim of this study was to compare outcomes of using intravenous insulin infusion (IVII) therapy for managing hyperglycemia in a non-intensive care unit (ICU) versus an ICU setting. METHODS: We conducted a retrospective analysis on patients who received IVII for hyperglycemia. The analysis compared variables associated with hypoglycemic events while on IVII, and point-of-care blood glucose control and insulin regimens at discharge. Insulin administration errors occurring on IVII were determined. RESULTS: Between November 2020 and August 2022, 881 patients received 1,106 IVIIs (780 in ICU and 326 non-ICU). A cumulative 468 days were spent on IVII in the non-ICU setting and 1564 in the ICU (total 2,032 days). The frequency of hypoglycemia on IVII was higher when provided in the non-ICU vs ICU (1.4% vs 0.7%), p < 0.01). Non-ICU patients had significantly higher average blood glucose during the last 24 h of the hospital stay (185 mg/dL vs 160 mg/dL, non-ICU vs. ICU, Pp < 0.01) and were more likely discharged with basal-bolus insulin therapy (p < 0.01). After adjusting for other variables, the probability of having hypoglycemia (OR 2.35; 95% CI 1.62-3.42; p < 0.001) was higher for the non-ICU cohort. In addition, patients who received IVII in the non-ICU settings had mean glucose levels nearly 26 mg/dL higher (95% CI 19.40-32.9, p < 0.001) at discharge vs. ICU. Seven cases of insulin errors were reported while on IVII in the non-ICU settings, compared to one in the ICU. CONCLUSIONS: A large number (468) of ICU days were avoided by providing IVII in the non-ICU setting. Of the more than 400 days of IVII therapy provided in the non-ICU, only 7 medication errors occurred. Further studies are needed to optimize IVII strategy for non-ICU patients.

Factors associated with glycemic control in patients with T2DM: evidence from a cross-sectional study in China.

OBJECTIVE: This study aimed to analyze the factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM). METHODS: Baseline data, encompassing basic information, lifestyle habits, and treatment of 305 T2DM patients from March 2021 to January 2023, were collected and analyzed using SPSS 26.0 software. RESULTS: Univariate and multivariate logistic regression analyses identified insulin therapy (OR = 2.233; 95%Cl = 1.013-4.520; P = 0.026) and regular clinic visits (OR = 0.567; 95%Cl = 0.330-0.973; P = 0.040) as independent factors influencing glycemic control. No observed interactions between the two variables were noted. CONCLUSION: History of insulin therapy and regular clinic visits were significantly and independently associated with glycated hemoglobin control in T2DM patients. Tailored interventions based on individual circumstances are recommended to optimize glycemic control.