U-shaped relationship between fasting blood glucose and urinary albumin-to-creatinine ratio in the general United States population.

Objective: The current controversy surrounding the association between fasting blood glucose (FBG) and albuminuria necessitates further investigation. Hence, the primary objective of this study was to examine the relationship between FBG and urinary albumin-to-creatinine ratio (UACR). Methods: A cohort of complete data from National Health and Nutrition Examination Survey (NHANES) participants (1999-2020) was analyzed. Linear regression analyses and a generalized additive model explored the association between FBG and UACR. Furthermore, the stability of this relationship across different populations was assessed. Results: The study involved a total of 20,264 participants who were identified as U.S. citizens. By employing linear regression analysis, a statistically significant relationship was observed between elevated FBG levels and an increase in UACR (P<0.0001). Additionally, using a generalized additive model analysis, a U-shaped correlation between FBG and UACR was identified. Further examination using threshold effect analysis indicated a turning point for FBG at 5.44 mmol/L. A noteworthy finding in multiple populations is the consistent U-shaped association between FBG and UACR, except for individuals with serum uric acid levels ≥420 µmol/L and those who refrain from alcohol consumption. Conclusion: The general U.S. population has a U-shaped nonlinear relationship between FBG and UACR.

A Single-group, Clinical Interventional Study on the Effect of Iron Deficiency Anemia on Blood Glucose and Insulin Resistance in Women with Type II Diabetes.

AIMS: Iron deficiency anemia (IDA) is one of the disorders recently associated with an increase in insulin resistance (IR) and, consequently, diabetes mellitus (DM) affection by causing oxidative stress. In this study, we look at how IDA may contribute to developing type II diabetes mellitus (T2DM), controlling diabetes, and reducing IR in women with T2DM. METHODS: In this single group, clinical interventional study, we enrolled 40 women with T2DM and IDA. Before and after intervention with ferrous sulfate tablets, their blood glucose (BG) levels and IR levels were evaluated. This study was approved by the Ethics Committee of Qom University of Medical Sciences (ethics code: IR.MUQ.REC.1397.031) and registered at the Iranian Center for Clinical Trials (No. IRCT20170215032587N3). A significant level was considered p <0.05. RESULT: The mean age of patients was 48.18 ± 4.6 years, with 5.3-5.8 years duration of T2DM. After the intervention, the mean fasting blood glucose (FBG) level reached 198.53 ± 48.11 to 170.93 ± 37.41, which was significant (p <0.0001). Also, hemoglobin A1C level reached from 8.49 ± 0.9 to 7.96 ± 0.58, which was significant (p <0.0001). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) demonstrating a significant reduction of IR levels after intervention with ferrous sulfate tablets (p <0.018). CONCLUSIONS: IDA treatment in patients with T2DM can significantly reduce the BG and IR levels. To better control BG, checking iron status and its correction may provide better clinical outcomes in these patients. CLINICAL TRIAL REGISTRATION NUMBER: IRCT20170215032587N3.

Daily fluctuations in blood glucose with normal aging are inversely related to hippocampal synaptic mitochondrial proteins.

Defective brain glucose utilization is a hallmark of Alzheimer's disease (AD) while Type II diabetes and elevated blood glucose escalate the risk for AD in later life. Isolating contributions of normal aging from coincident metabolic or brain diseases could lead to refined approaches to manage specific health risks and optimize treatments targeted to susceptible older individuals. We evaluated metabolic, neuroendocrine, and neurobiological differences between young adult (6 months) and aged (24 months) male rats. Compared to young adults, blood glucose was significantly greater in aged rats at the start of the dark phase of the day but not during the light phase. When challenged with physical restraint, a potent stressor, aged rats effected no change in blood glucose whereas blood glucose increased in young adults. Tissues were evaluated for markers of oxidative phosphorylation (OXPHOS), neuronal glucose transport, and synapses. Outright differences in protein levels between age groups were not evident, but circadian blood glucose was inversely related to OXPHOS proteins in hippocampal synaptosomes, independent of age. The neuronal glucose transporter, GLUT3, was positively associated with circadian blood glucose in young adults whereas aged rats tended to show the opposite trend. Our data demonstrate aging increases daily fluctuations in blood glucose and, at the level of individual differences, negatively associates with proteins related to synaptic OXPHOS. Our findings imply that glucose dyshomeostasis may exacerbate metabolic aspects of synaptic dysfunction that contribute to risk for age-related brain disorders.

Rapid Correction of the Hypoglycemia State in Nonhuman Primates Using a Glucagon Long-Dissolving Microneedle Patch.

The timely administration of glucagon is a standard clinical practice for the treatment of severe hypoglycemia. However, the process involves cumbersome steps, including the reconstitution of labile glucagon and filling of the syringe, which cause considerable delays in emergency situations. Moreover, multiple dosages are often required to prevent the recurrence of the hypoglycemic episode because of the short half-life of glucagon in plasma. Herein, we develop a glucagon-loaded long-dissolving microneedle (GLMN) patch that exhibits the properties of fast onset and sustained activity for the effective treatment of severe hypoglycemia. Three types of MN patches were fabricated with different dimensions (long, medium, and short). The longer MN patch packaged a higher dosage of glucagon and exhibited supreme mechanical strength compared to the shorter one. Additionally, the longer MN patch could insert more deeply into the skin, resulting in higher permeability of glucagon across the skin tissue and more rapid systemic absorption as compared with the shorter MN patch. The GLMN patch was observed to reverse the effects of hypoglycemia within 15 min of application in animal models (specifically, rat and rhesus monkey models) and maintained long-term glycemic control, owing to highly efficient drug permeation and the drug reservoir effect of the MN base. The current study presents a promising strategy for the rapid reversal of severe hypoglycemia that exhibits the desirable properties of easy use, high efficiency, and sustained action.

Exploring Genetic Associations of 3 Types of Risk Factors With Ischemic Stroke: An Integrated Bioinformatics Study.

BACKGROUND: Ischemic stroke (IS) is a major cause of disability and mortality worldwide. Increasing evidence suggests a strong association between blood pressure, blood glucose, circulating lipids, and IS. Nonetheless, the genetic association of these 3 risk factors with IS remains elusive. METHODS: We screened genetic instruments related to blood pressure, blood glucose, and circulating lipids and paired them with IS genome-wide association study data to conduct Mendelian randomization analysis. Positive Mendelian randomization findings were then subjected to colocalization analysis. Subsequently, we utilized the Gene Expression Omnibus data set to perform differential expression analysis, aiming to identify differentially expressed associated genes. We determined the importance scores of these differentially expressed associated genes through 4 machine learning models and constructed a nomogram based on these findings. RESULTS: The combined results of the Mendelian randomization analysis indicate that blood pressure (systolic blood pressure: odds ratio [OR], 1.02 [95% CI, 1.01-1.02]; diastolic blood pressure: OR, 1.03 [95% CI, 1.03-1.04]) and some circulating lipids (low-density lipoprotein cholesterol: OR, 1.06 [95% CI, 1.01-1.12]; apoA1: OR, 0.95 [95% CI, 0.92-0.98]; apoB: OR, 1.05 [95% CI, 1.01-1.09]; eicosapentaenoic acid: OR, 2.36 [95% CI, 1.41-3.96]) have causal relationships with the risk of IS onset. We identified 73 genes that are linked to blood pressure and circulating lipids in the context of IS, and 16 are differentially expressed associated genes. FURIN, MAN2A2, HDDC3, ALDH2, and TOMM40 were identified as feature genes for constructing the nomogram that provides a quantitative prediction of the risk of IS onset. CONCLUSIONS: This study indicates that there are causal links between blood pressure, certain circulating lipids, and the development of IS. The potential mechanisms underlying these causal relationships involve the regulation of lipid metabolism, blood pressure, DNA repair and methylation, cell apoptosis and autophagy, immune inflammation, and neuronal protection, among others.

Using nonlinear auto-regressive with exogenous input neural network (NNARX) in blood glucose prediction.

BACKGROUND: Predicting of future blood glucose (BG) concentration is important for diabetes control. Many automatic BG monitoring or controlling systems use BG predictors. The accuracy of the prediction for long prediction time is a major factor affecting the performance of the control system. The predicted BG can be used for glycemia management in the form of early hypoglycemic/hyperglycemic alarms or adjusting insulin injections. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. Many of those systems need prediction for long prediction horizons to avoid going through hypo or hyperglycemia. METHODS: In this article a nonlinear autoregressive exogenous input neural network (NNARX) is proposed to predict the glucose concentration for longer prediction horizons (PHs) than that was obtained previously with an established recurrent neural network (RNN). The proposed NNARX is a modified version from our previously published RNN with different initialization and building technique but has the same architecture. The modification is based on starting with building nonlinear autoregressive exogenous input model using MATLAB and train it, then close the loop to get NNARX network. RESULTS: The results of using the proposed NNARX indicate that the proposed NNARX is better in prediction and stability than unmodified RNN as PH becomes higher than 45 minutes. CONCLUSIONS: Modification in RNN building extends the ability of the prediction till 100 minutes. It performs statistically significant improvements in the FIT and RMSE values for 100 minutes prediction. It also decreases root mean squared error (RMSE) for both 45 and 60 minutes of prediction.

A Comprehensive Review on GLP-1 Signaling Pathways in the Management of Diabetes Mellitus - Focus on the Potential Role of GLP-1 Receptors Agonists and Selenium Among Various Organ Systems.

Diabetes Mellitus develops when the body becomes unable to fuel its cells with glucose, which results in the accumulation of sugar excess in the bloodstream. Because it has diverse pathophysiological impacts on the body, diabetes mellitus represents a significant issue of concern in an attempt to find suitable treatment modalities and medications for afflicted diabetic patients. Glucagon-like peptide 1 (GLP-1) plays a pivotal role in the incretin effect, emerging as a prospective treatment for diabetes mellitus and a promising means of regenerating pancreatic cells, whether directly or through its receptor agonists. It has been shown that GLP-1 efficiently increases insulin production, lowers blood sugar levels in patients with type 2 diabetes mellitus, and decreases appetite, craving, and hunger, therefore amplifying the sensation of fullness and satiety. Moreover, since they are all dependent on GLP-1 effect, intricate signaling pathways share some similarities during specific phases, although the pathways continue to exhibit significant divergence engendered by specific reactions and effects in each organ, which encompasses the rationale behind observed differences. This triggers an expanding range of GLP-1 R agonists, creating new unforeseen research and therapeutic application prospects. This review aims to explain the incretin effect, discuss how GLP-1 regulates blood glucose levels, and how it affects different body organs, as well as how it transmits signals, before introducing selenium's role in the incretin impact.

The optimal glycemic target in critically ill patients: an updated network meta-analysis.

Acute glycemic control significantly affects the clinical outcomes of critically ill patients. This updated network meta-analysis examines the benefits and harms of four target blood glucose levels (< 110, 110-144, 144-180, and > 180 mg/dL). Analyzing data of 27,541 patients from 37 trials, the surface under the cumulative ranking curve for mortality and hypoglycemia was highest at a target blood glucose level of 144-180 mg/dL, while for infection and acute kidney injury at 110-144 mg/dL. Further evidence is needed to determine whether 110-144 or 144-180 mg/dL is superior as an optimal glucose target, considering prioritized outcomes.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

OBJECTIVE: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning. METHOD: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes. RESULTS: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94). CONCLUSIONS: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

Prevalence of metabolic disorders among women in reproductive age group in hilly rural area of North India.

Background: Metabolic syndrome consists of cardiometabolic risk factors that promote the development of atherosclerotic cardiovascular disease, type 2 'DM' and obesity. These are associated with increased cardiovascular mortality and morbidity. Metabolic disorders (MD) are becoming more prevalent both in developing countries and developed countries and are now considered as lifestyle diseases. In women of reproductive age group, especially pregnancy, the blood glucose level is increasing which adversely affects the health of mother and child. Similarly, high blood pressure also precipitates the problems. This study was carried out to find the prevalence of hypertension, diabetes mellitus, obesity and 'MD' among the women living in remote rural set-up. Materials and Methods: This cross-sectional study was done among women of reproductive age group in 15 villages from 5 panchayats of field practice area of Maharishi Markandeshwar Medical College and Hospital, Kumarhatti, Solan. They were screened for 'MD' through investigative procedures (weight, height, BMR, abdominal girth, blood pressure through sphygmomanometer, blood glucose through the glucometer method), serum HDL and triglycerides. Respondents from the family were asked about the common/general information of house. The tool used for collecting general and relevant information from the respondent was a questionnaire, which was pretested for validity before being used in the field. Results: Four-hundred and sixty-seven women of reproductive age group participated in the study. Half of the participants were with qualification of matriculate and 9.2% participants were illiterate. Three-fourths of the participants were married women and 89% were vegetarian. Sixty-four per cent of participants were housewives. Half of the participants had a normal BMI, whereas 28.9% were overweight and 10% were obese. The prevalence of hypertension and diabetes among the participants were 12.5% and 9.8%, respectively. Forty-seven per cent participants had a waist circumference above 80 cm. Conclusion: The level of non-communicable diseases is related with the MD which has the adverse effect on the various systems and organs of the subjects. The MD can be controlled with the certain changes in the life style pattern. The GOI is also concerned with such scenarios in the country. It is recommended that women of reproductive age group undergo regular blood pressure and blood sugar screenings to detect hypertension and diabetes early and take appropriate measures to manage them.

Morin-Based Nanoparticles for Regulation of Blood Glucose.

Morin, a naturally occurring bioactive compound shows great potential as an antioxidant, anti-inflammatory agent, and regulator of blood glucose levels. However, its low water solubility, poor lipid solubility, limited bioavailability, and rapid clearance in vivo hinder its application in blood glucose regulation. To address these limitations, we report an enzymatically synthesized nanosized morin particle (MNs) encapsulated in sodium alginate microgels (M@SA). This approach significantly enhances morin's delivery efficiency and therapeutic efficacy in blood glucose regulation. Utilizing horseradish peroxidase, we synthesized MNs averaging 305.7 ± 88.7 nm in size. These MNs were then encapsulated via electrohydrodynamic microdroplet spraying to form M@SA microgels. In vivo studies revealed that M@SA microgels demonstrated prolonged intestinal retention and superior efficacy compared with unmodified morin and MNs alone. Moreover, MNs notably improved glucose uptake in HepG2 cells. Furthermore, M@SA microgels effectively regulated blood glucose, lipid profiles, and oxidative stress in diabetic mice while mitigating liver, kidney, and pancreatic damage and enhancing anti-inflammatory responses. Our findings propose a promising strategy for the oral administration of natural compounds for blood glucose regulation, with implications for broader therapeutic applications.

Association between extrapolated time in range and large for gestational age infants in pregnant women with type 1 diabetes.

AIMS: To evaluate the association between extrapolated time in range (eTIR), measured by self-monitoring of blood glucose (SMBG), and large-for-gestational-age (LGA) infants in pregnancies with type 1 diabetes (T1D). METHODS: Retrospective cohort analysis including singleton pregnancies with T1D who started antenatal care before 20 gestational weeks and delivered live newborns at a Brazilian hospital between 2010 and 2019, with LGA fetuses as the main outcome. Glycemic records acquired using SMBG were categorized as eTIR, extrapolated time below range (eTBR), and extrapolated time above range (eTAR). Women were divided into two groups (LGA and adequate for gestational age [AGA]) and compared regarding clinical characteristics, obstetric outcomes, and frequencies of eTIR, eTBR, and eTAR. Logistic regression analysis verified the independent predictive variables for LGA infants. RESULTS: Data from 125 pregnancies were analyzed. For the first, second and third trimesters, each 1 % increase in eTIR was associated with a decreased risk of LGA by 2.9 % (OR: 0.971; 95%CI: 0.945-0.998), 2.5 % (OR: 0.975; 95%CI: 0.951-0.999) and 2.3 % (OR: 0.977; 95%CI: 0.955-0.998) and each 1 % increase in eTAR was associated with an increased risk of LGA by 2.7 % (OR: 1.027; 95%CI: 1.005-1.050), 3.9 % (OR: 1.039; 95%CI: 1.014-1.063) and 4.6 % (OR: 1.046; 95%CI: 1.018-1.075), respectively. CONCLUSION: The concept of TIR can be extrapolated to patients undergoing SMBG to assess the risk of LGA infants in pregnant women with T1D.

Intermittently scanned continuous glucose monitoring provides no benefit over structured self-monitoring of blood glucose in type 2 diabetes not on prandial insulin, in the context of diabetes self-management education: GLucose monitoring programme SingaporE (GLiMPSE).

OBJECTIVE: We evaluated the impact of intermittently scanned continuous glucose monitoring(is-CGM)over self-monitoring of blood glucose(SMBG) in the context of diabetes self-management education (DSME) in sub-optimally controlled type 2 diabetes(T2D) in a multi-ethnicsetting. RESEARCH DESIGN AND METHOD: Randomized-controlled, open-label trial (NCT04564911), of T2D with HbA1c ≥ 7.5-≤10 %, on oral agents with/without basal insulin was carried out. Intervention arm received 6 weeks(w) continuous is-CGM, followed by one is-CGM/month till 24w. Control arm was advised to perform 4 SMBG/day. Educationwas delivered at weeks 0, 2, 8, 16. PRIMARY OUTCOME: Change in HbA1c from baseline at 24w. Modified intention-to-treat (mITT) analysis with linear mixed-effect model for repeated measurementswas performed. RESULTS: 176 subjects, age 55 ± 10.7 years(y), DM duration 11 ± 7.3y, BMI 27.8 ± 5.9 kg/m2, 58 % Male, 29.5 % basal insulin users were analysed. Within each arm,from baseline to 24w, mean HbA1c decreasedby -0.6 % (-6.6.mmol/mol, p-value < 0.01)and weight decreased(isCGM: -1.44 kg; SMBG: -1.25 kg, both p < 0.01). These changes were sustained to one year. However, there wasno significant difference in these parameters between arms (p-value > 0.05). CONCLUSION: In the context of DSME, use of either SMBG or is-CGM led to improved glycaemia and reduced weight over a period of 24 weeks, sustained to one year.

Correlation between centre size, metabolic variation and mean HbA1c in major paediatric diabetes centres.

AIM: To exploit a relatively homogeneous national health care context and a national diabetes database to address the questions: Is there an optimal clinic/centre size in determining outcomes?; and Can improvement in median centre outcomes be driven by reducing variability in outcome? METHODS: Using the Australasian Diabetes Database Network, data from seven tertiary hospital paediatric diabetes clinics for patients with type one diabetes from Australia were recorded from 6-month uploads: September 2017, March 2018, September 2018 and March 2019. Data from 25 244 patient visits included demographic variables, HbA1C, number of patient visits and insulin regimens. RESULTS: There was no association between centre size and median HbA1C. On the other hand, there was a significant association between or median absolute deviation of HbA1C outcomes and the median HbA1C result between centres. On average every two thirds of a median absolute deviation increase in clinic HbA1C was associated with a 1.0% (10.9 mmol/mol) increase in median clinic HbA1C. CONCLUSIONS: Our data have shown that it is likely difficult for centres to have a low median HbA1C if there is high variance of HbA1C's within centres or within centre treatment groups. This appears to be true regardless of centre size. These findings need to be carefully considered by teams who wish to lower their clinic median HbA1C.

Effects of Dexamethasone on Postoperative Glycemic Control in Patients After Primary Total Joint Arthroplasty: A Randomized Double-Blind Controlled Trial.

BACKGROUND: In patients undergoing total joint arthroplasty (TJA), the use of dexamethasone (DEX) may cause perioperative blood glucose (BG) disorders, leading to complications even in patients who do not have diabetes. We aimed to evaluate the effects of different DEX doses on perioperative BG levels. METHODS: A total of 135 patients who do not have diabetes were randomized into three groups: preoperative intravenous injection of normal saline (Group A, the placebo group), preoperative intravenous injection of 10 mg DEX (Group B), and preoperative intravenous injection of 20 mg DEX (Group C). Postoperative fasting blood glucose (FBG) levels were designated as the primary outcome, while postoperative postprandial blood glucose (PBG) levels were assigned as the secondary outcome. The incidence of complications was recorded. We also investigated the risk factors for FBG ≥ 140 mg/dl and PBG ≥ 180 mg/dl. RESULTS: The FBG levels were higher in Groups B and C than in Group A on postoperative days (POD) 0 and 1. The PBG levels were lower for Groups A and B compared to Group C on POD 1. No differences in FBG or PBG were detected beyond POD 1. Elevated preoperative glycosylated hemoglobin A1c (HbA1c) levels increased the risk of FBG ≥ 140 mg/dl and PBG ≥ 180 mg/dl, respectively. However, preoperative intravenous injection of DEX was not associated with FBG ≥ 140 mg/dl or PBG ≥ 180 mg/dl. No differences were found in postoperative complications among the three groups. CONCLUSION: The preoperative intravenous administration of 10 or 20 mg DEX in patients who do not have diabetes showed transient effects on postoperative BG after TJA. The preoperative HbA1c level threshold (regardless of the administration or dosage of DEX) that increased the risk for the occurrence of FBG ≥ 140 mg/dl and PBG ≥ 180 mg/dl was 5.75% and 5.85%, respectively.

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients.

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application.

A low-glucose eating pattern is associated with improvements in glycemic variability among women at risk for postmenopausal breast cancer: an exploratory analysis.

Background: High glycemic variability (GV) is a biomarker of cancer risk, even in the absence of diabetes. The emerging concept of chrononutrition suggests that modifying meal timing can favorably impact metabolic risk factors linked to diet-related chronic disease, including breast cancer. Here, we examined the potential of eating when glucose levels are near personalized fasting thresholds (low-glucose eating, LGE), a novel form of timed-eating, to reduce GV in women without diabetes, who are at risk for postmenopausal breast cancer. Methods: In this exploratory analysis of our 16-week weight loss randomized controlled trial, we included 17 non-Hispanic, white, postmenopausal women (average age = 60.7 ± 5.8 years, BMI = 34.5 ± 6.1 kg/m2, HbA1c = 5.7 ± 0.3%). Participants were those who, as part of the parent study, provided 3-7 days of blinded, continuous glucose monitoring data and image-assisted, timestamped food records at weeks 0 and 16. Pearson's correlation and multivariate regression were used to assess associations between LGE and GV, controlling for concurrent weight changes. Results: Increases in LGE were associated with multiple unfavorable measures of GV including reductions in CGM glucose mean, CONGA, LI, J-Index, HBGI, ADDR, and time spent in a severe GV pattern (r = -0.81 to -0.49; ps < 0.044) and with increases in favorable measures of GV including M-value and LBGI (r = 0.59, 0.62; ps < 0.013). These associations remained significant after adjusting for weight changes. Conclusion: Low-glucose eating is associated with improvements in glycemic variability, independent of concurrent weight reductions, suggesting it may be beneficial for GV-related disease prevention. Further research in a larger, more diverse sample with poor metabolic health is warranted.Clinical trial registration: ClinicalTrials.gov, NCT03546972.

Refined protocol for newly onset identification in non-obese diabetic mice: an animal-friendly, cost-effective, and efficient alternative.

BACKGROUND: Therapeutic interventions for diabetes are most effective when administered in the newly onset phase, yet determining the exact onset moment can be elusive in practice. Spontaneous autoimmune diabetes among NOD mice appears randomly between 12 and 32 weeks of age with an incidence range from 60 to 90%. Furthermore, the disease often progresses rapidly to severe diabetes within days, resulting in a very short window of newly onset phase, that poses significant challenge in early diagnosis. Conventionally, extensive blood glucose (BG) testing is typically required on large cohorts throughout several months to conduct prospective survey. We incorporated ultrasensitive urine glucose (UG) testing into an ordinary BG survey process, initially aiming to elucidate the lag period required for excessive glucose leaking from blood to urine during diabetes progression in the mouse model. RESULTS: The observations unexpectedly revealed that small amounts of glucose detected in the urine often coincide with, sometimes even a couple days prior than elevated BG is diagnosed. Accordingly, we conducted the UG-based survey protocol in another cohort that was validated to accurately identified every individual near onset, who could then be confirmed by following few BG tests to fulfill the consecutive BG + criteria. This approach required fewer than 95 BG tests, compared to over 700 tests with traditional BG survey, to diagnose all the 37-38 diabetic mice out of total 60. The average BG level at diagnosis was slightly below 350 mg/dl, lower than the approximately 400 mg/dl observed with conventional BG monitoring. CONCLUSIONS: We demonstrated a near perfect correlation between BG + and ultrasensitive UG + results in prospective survey with no lag period detected under twice weekly of testing frequency. This led to the refined protocol based on surveying with noninvasive UG testing, allowing for the early identification of newly onset diabetic mice with only a few BG tests required per mouse. This protocol significantly reduces the need for extensive blood sampling, lancet usage, labor, and animal distress, aligning with the 3Rs principle. It presents a convenient, accurate, and animal-friendly alternative for early diabetes diagnosis, facilitating research on diagnosis, pathogenesis, prevention, and treatment.

Frequency of diabetic remission, predictors of remission and survival in cats using a low-cost, moderate-intensity, home-monitoring protocol and twice-daily glargine.

OBJECTIVES: The aim of the present study was to retrospectively assess remission rates and survival in diabetic cats managed using a moderate-intensity, low-cost protocol of home blood glucose measurements and insulin adjustment by clients of a cat-only practice, and to determine if predictors of remission, relapse or survival could be identified. METHODS: The records of a cat-only practice were used to identify 174 cats with newly diagnosed diabetes managed using only pre-insulin home blood glucose measurements for insulin dose adjustments based on a protocol provided to clients aimed at maintaining pre-insulin blood glucose in the range of 6.5-11.9 mmol/l (117-214 mg/dl). Cats were excluded for the following reasons: insufficient follow-up in the records; a lack of owner compliance was recorded; they were receiving ongoing corticosteroids for the management of other conditions; they were euthanased at the time of diagnosis; or they were diagnosed with acromegaly or hyperadrenocorticism. RESULTS: Using only pre-insulin blood glucose measurements at home to adjust the insulin dose to maintain glucose in the range of 6.5-11.9 mmol/l, 47% of cats achieved remission, but 40% of those cats relapsed. A minority (16%) of cats were hospitalised for hypoglycaemia. The survival time was significantly longer in cats in remission and Burmese cats. CONCLUSIONS AND RELEVANCE: The cost and time burden of treating diabetic cats may cause some clients to choose euthanasia over treatment. While the highest rates of diabetic remission have been reported in studies of newly diagnosed cats treated with intensive long-acting insulin protocols and low carbohydrate diets, these protocols may not be suitable for all clients. Nearly 50% of cats with newly diagnosed diabetes achieved remission with this low-cost, moderate-intensity, insulin dosing protocol. As remission was significantly associated with survival time, discussing factors in treatment to optimise remission is important, but it is also important to offer clients a spectrum of options. No cats that started treatment in this study were euthanased because the owner did not wish to continue the diabetes treatment.

After-meal blood glucose level prediction for type-2 diabetic patients.

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well. In this paper, we propose a data-driven approach to predict the 2 h after meal blood glucose level from the previous discrete blood glucose readings, meal, exercise, medication, & profile information of Type 2 diabetes patients. To the best of our knowledge, this is the first attempt to use discrete blood glucose readings for 2 h after meal blood glucose level prediction using data-driven models. In this study, we have collected data from five prediabetic and diabetic patients in free living conditions for six months. We have presented comparative experimental study using different popular machine learning models including support vector regression, random forest, and extreme gradient boosting, and two deep layer techniques: multilayer perceptron, and convolutional neural network. We present also the impact of different features in blood glucose level prediction, where we observe that meal has some modest and medication has a good influence on blood glucose level.