The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study.

BACKGROUND: The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms. OBJECTIVE: This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations. METHODS: In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG. RESULTS: The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36). CONCLUSIONS: This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

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.

Association of hyperuricemia with metabolic syndrome and its components in an adult population of Faisalabad, Pakistan.

BACKGROUND AND AIMS: The rising prevalence of metabolic syndrome (MetS) is a matter of serious concern worldwide. Hyperuricemia has been observed as an independent risk factor in the development of MetS and each of its individual components in different populations. This study aims to determine the association of hyperuricemia with MetS and its individual components in a Pakistani cohort. METHODS AND RESULTS: A cross-sectional study was performed in a public sector hospital in Faisalabad, Pakistan. Total 204 participants were studied along with their anthropometric measurements and blood sample analysis for clinically important parameters. MetS was defined according to the NCEP-criteria. Independent sample t-test, Binomial logistic regression and Linear regression analyses were used to determine the association between hyperuricemia and metabolic syndrome. The prevalence of MetS and hyperuricemia in our study was 42.6% and 31.9% respectively. As compared to the normo-uricemic group, the hyperuricemic group had a significantly higher systolic blood pressure, BMI and lower HDL-C level (p < 0.05). After adjusting for age, gender, BMI and LDL-C, hyperuricemia was observed to increase the risk of MetS, increased systolic blood pressure and reduce HDL-C respectively by 1.34, 1.23 and 1.20 folds respectively. CONCLUSION: In this study, a significant association between hyperuricemia and metabolic syndrome, systolic hypertension, blood glucose and decreased HDL-C was observed.

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes.

PURPOSE: This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes. METHODS: Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors. RESULTS: The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12. CONCLUSION: Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

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.

Seaweeds as Nutraceutical Elements and Drugs for Diabetes Mellitus: Future Perspectives.

Diabetes mellitus is a chronic metabolic condition marked by high blood glucose levels caused by inadequate insulin synthesis or poor insulin use. This condition affects millions of individuals worldwide and is linked to a variety of consequences, including cardiovascular disease, neuropathy, nephropathy, and retinopathy. Diabetes therapy now focuses on controlling blood glucose levels through lifestyle changes, oral medicines, and insulin injections. However, these therapies have limits and may not successfully prevent or treat diabetic problems. Several marine-derived chemicals have previously demonstrated promising findings as possible antidiabetic medicines in preclinical investigations. Peptides, polyphenols, and polysaccharides extracted from seaweeds, sponges, and other marine species are among them. As a result, marine natural products have the potential to be a rich source of innovative multitargeted medications for diabetes prevention and treatment, as well as associated complications. Future research should focus on the chemical variety of marine creatures as well as the mechanisms of action of marine-derived chemicals in order to find new antidiabetic medicines and maximize their therapeutic potential. Based on preclinical investigations, this review focuses on the next step for seaweed applications as potential multitargeted medicines for diabetes, highlighting the bioactivities of seaweeds in the prevention and treatment of this illness.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Efficacy and safety of basal insulins in people with type 2 diabetes mellitus: a systematic review and network meta-analysis of randomized clinical trials.

Aim: The comparative effectiveness of basal insulins has been examined in several studies. However, current treatment algorithms provide a list of options with no clear differentiation between different basal insulins as the optimal choice for initiation. Methods: A comprehensive search of MEDLINE, Embase, Cochrane Library, ISI, and Scopus, and a reference list of retrieved studies and reviews were performed up to November 2023. We identified phase III randomized controlled trials (RCTs) comparing the efficacy and safety of basal insulin regimens. The primary outcomes evaluated were HbA1c reduction, weight change, and hypoglycemic events. The revised Cochrane ROB-2 tool was used to assess the methodological quality of the included studies. A random-effects frequentist network meta-analysis was used to estimate the pooled weighted mean difference (WMD) and odds ratio (OR) with 95% confidence intervals considering the critical assumptions in the networks. The certainty of the evidence and confidence in the rankings was assessed using the GRADE minimally contextualized approach. Results: Of 20,817 retrieved studies, 44 RCTs (23,699 participants) were eligible for inclusion in our network meta-analysis. We found no significant difference among various basal insulins (including Neutral Protamine Hagedorn (NPH), ILPS, insulin glargine, detemir, and degludec) in reducing HbA1c. Insulin glargine, 300 U/mL (IGlar-300) was significantly associated with less weight gain (mean difference ranged from 2.9 kg to 4.1 kg) compared to other basal insulins, namely thrice-weekly insulin degludec (IDeg-3TW), insulin degludec, 100 U/mL (IDeg-100), insulin degludec, 200 U/mL (IDeg-200), NPH, and insulin detemir (IDet), but with low to very low certainty regarding most comparisons. IDeg-100, IDeg-200, IDet, and IGlar-300 were associated with significantly lower odds of overall, nocturnal, and severe hypoglycemic events than NPH and insulin lispro protamine (ILPS) (moderate to high certainty evidence). NPH was associated with the highest odds of overall and nocturnal hypoglycemia compared to others. Network meta-analysis models were robust, and findings were consistent in sensitivity analyses. Conclusion: The efficacy of various basal insulin regimens is comparable. However, they have different safety profiles. IGlar-300 may be the best choice when weight gain is a concern. In contrast, IDeg-100, IDeg-200, IDet, and IGlar-300 may be preferred when hypoglycemia is the primary concern.